You searched for subject:(Predictive control).
Showing records 1 – 30 of
1165 total matches.
◁ [1] [2] [3] [4] [5] … [39] ▶
University of Waterloo
1.
Tejeda-Iglesias, Manuel.
A Framework for Explicit Model Predictive Control using Adjustable Robust Optimization and Economic Optimization of an Industrial-Scale Sulfuric Acid Plant.
Degree: 2018, University of Waterloo
URL: http://hdl.handle.net/10012/14276 
► Optimization plays an important role in the operation of chemical engineering systems. Due to their typical size, different optimization tools and techniques are required to…
(more)
▼ Optimization plays an important role in the operation of chemical engineering systems. Due to their typical size, different optimization tools and techniques are required to improve the efficiency in process operations. In this thesis, a mathematical tool is developed to address the issue of optimal control for linear systems under uncertainty. Also, a comprehensive plant model describing the behaviour of an industrial-scale Sulphuric Acid plant is developed to assist in identification of the optimal operating conditions under uncertainty
Model predictive control (MPC) is considered an attractive strategy for the optimal control of complex chemical engineering systems. Conventional MPC involves solving an optimization problem online to determine the control actions that minimize a performance criterion function. The high computational expense associated with conventional MPC may make its application challenging for large-scale systems. Explicit MPC has been developed to solve the optimization problem offline. In this work, adjustable robust optimization (ARO) is used to obtain the explicit solution to the MPC optimization problem offline for discrete-time linear time invariant systems with constraints on inputs and states. In the robust model formulation an uncertain additive time-varying error is introduced to account for model uncertainty resulting from plant-model mismatch caused by un-measurable disturbances or process nonlinearities. The explicit solution is an optimal time-varying sequence of feedback control laws for the control inputs parameterized by the system’s states. The control laws are evaluated in a time-varying manner when the process is online using state measurements. This study shows that the resulting control laws ensure the implemented control actions maintain the system states within their feasible region for any realizations of the uncertain parameter within the uncertainty set. Three case studies are presented to demonstrate the proposed approach and to highlight the benefits and limitations of this method.
The optimal operating condition to which an optimal controller will drive a large industrial-scale plant is identified using a different set of tools. In this thesis, an industrial-scale sulfuric acid plant is considered. The production of sulfuric acid is an important process due to its many applications and its use as a mitigation strategy for Sulphur dioxide (SO2). The reactor of the sulfuric acid plant has been the focus of many studies, and thus there has been very limited works in the literature that have analyzed the complete sulfuric acid plant. In this work, the flowsheet for an industrial-scale sulfuric acid plant with scrubbing tower is presented. The model is developed in Aspen Plus V8.8 and it is validated using historical data from an actual industrial plant. A sensitivity analysis was carried out, followed by optimization using two alternative objective functions: maximization of plant profitability or productivity. The optimization was extended to consider uncertainty…
Subjects/Keywords: Model Predictive Control
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Tejeda-Iglesias, M. (2018). A Framework for Explicit Model Predictive Control using Adjustable Robust Optimization and Economic Optimization of an Industrial-Scale Sulfuric Acid Plant. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/14276
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Tejeda-Iglesias, Manuel. “A Framework for Explicit Model Predictive Control using Adjustable Robust Optimization and Economic Optimization of an Industrial-Scale Sulfuric Acid Plant.” 2018. Thesis, University of Waterloo. Accessed January 23, 2021.
http://hdl.handle.net/10012/14276.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Tejeda-Iglesias, Manuel. “A Framework for Explicit Model Predictive Control using Adjustable Robust Optimization and Economic Optimization of an Industrial-Scale Sulfuric Acid Plant.” 2018. Web. 23 Jan 2021.
Vancouver:
Tejeda-Iglesias M. A Framework for Explicit Model Predictive Control using Adjustable Robust Optimization and Economic Optimization of an Industrial-Scale Sulfuric Acid Plant. [Internet] [Thesis]. University of Waterloo; 2018. [cited 2021 Jan 23].
Available from: http://hdl.handle.net/10012/14276.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Tejeda-Iglesias M. A Framework for Explicit Model Predictive Control using Adjustable Robust Optimization and Economic Optimization of an Industrial-Scale Sulfuric Acid Plant. [Thesis]. University of Waterloo; 2018. Available from: http://hdl.handle.net/10012/14276
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Delft University of Technology
2.
de carvalho monteiro, patrik (author).
Model Predictive Control for Reticle Cooling.
Degree: 2019, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:5e2cdcb4-6c86-40a4-8c36-bc2df84cc5b6
► During the exposure process of a wafer scanner, problems occur due to reticle heating. In this project, the consequences of reticle heating in terms of…
(more)
▼ During the exposure process of a wafer scanner, problems occur due to reticle heating. In this project, the consequences of reticle heating in terms of image aberrations are discussed along with how they could be solved with feedback controlled cooling. The problem is defined in terms of a performance index and constraints, which fits the Model Predictive Control (MPC) framework. The principle of MPC is explained and results are compared to the current state of the practice. The results show a significant improvement in performance at the cost of acceptable constraint violations.
Mechanical Engineering
Advisors/Committee Members: Mohajerin Esfahani, Peyman (mentor), van de Wouw, Nathan (mentor), De Schutter, Bart (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: model predictive control
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
de carvalho monteiro, p. (. (2019). Model Predictive Control for Reticle Cooling. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:5e2cdcb4-6c86-40a4-8c36-bc2df84cc5b6
Chicago Manual of Style (16th Edition):
de carvalho monteiro, patrik (author). “Model Predictive Control for Reticle Cooling.” 2019. Masters Thesis, Delft University of Technology. Accessed January 23, 2021.
http://resolver.tudelft.nl/uuid:5e2cdcb4-6c86-40a4-8c36-bc2df84cc5b6.
MLA Handbook (7th Edition):
de carvalho monteiro, patrik (author). “Model Predictive Control for Reticle Cooling.” 2019. Web. 23 Jan 2021.
Vancouver:
de carvalho monteiro p(. Model Predictive Control for Reticle Cooling. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 23].
Available from: http://resolver.tudelft.nl/uuid:5e2cdcb4-6c86-40a4-8c36-bc2df84cc5b6.
Council of Science Editors:
de carvalho monteiro p(. Model Predictive Control for Reticle Cooling. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:5e2cdcb4-6c86-40a4-8c36-bc2df84cc5b6
Penn State University
3.
Fang, Yizhou.
Carleman Linearization-based Nonlinear Model Predictive Control.
Degree: 2016, Penn State University
URL: https://submit-etda.libraries.psu.edu/catalog/27590
► The need of tight operating conditions in chemical, pharmaceutical, and petroleum industries has given rise to the development of advanced process control. Model Predictive Control…
(more)
▼ The need of tight operating conditions in chemical, pharmaceutical, and petroleum industries has given rise to the development of advanced process
control. Model
Predictive Control (MPC) started gaining attention three decades ago for optimal transitions between operating modes. Nonlinear MPC converts a constrained
control problem of a nonlinear system into an optimization problem. This basic architecture makes Nonlinear MPC capable of handling large state-space multi-variable systems with constraints, and dealing with model-mismatches and disturbances readily.
The computation time of
control policy is required to be less than one sampling time for online operation. However, this requirement is most of the times impossible to meet when the system has high nonlinearity. That becomes one of the most significant reasons holding back the application of Nonlinear MPC. As a result, there is strong motivation to develop an advanced formulation of Nonlinear MPC that demands less computational effort and thus decides the
control actions faster.
The primary focus of this thesis is to develop an advanced formulation of Nonlinear MPC that decreases the amount of computational effort in order to circumvent feedback delay, to improve controller performance and to maintain stability of the system. Multiple mathematics tools combined with optimization techniques are implemented for the purpose of accelerated searching algorithms. The optimal
control problem is formulated as a receding horizon one. An optimization problem is solved at each time the finite horizon moves on. Based on Carleman Linearization, the states of the system are extended to higher orders following the Kronecker product rule. The nonlinear dynamic process can thus be modeled with a bilinear representation while keeping nonlinear dynamic information. It enables analytical anticipation of system states and provides the searching algorithm with analytically computed sensitivity of the cost function to the
control signals. The proposed method resembles both collocation and shooting methods for the following reasons. First, the states of the system are discretized explicitly in time while the sensitivity of the
control signals is computed analytically. Second, the states are nonlinear functions of the
control signals, releasing the optimization problem from equality constraints and reducing the number of design variables.
This thesis presents an introduction to MPC, Carleman Linearization and detailed derivations of the proposed method in Chapter 1 and 2. It also provides detailed description of resetting extended states to compensate for the simulation errors caused by Carleman Linearization as an independent Chapter 3. Chapter 4 presents case-study examples to indicate the applications of the proposed method. Chapter 5 concludes the work and future plans.
A part of the work presented in this thesis has been published at American
Control Conference, Chicago, IL on July 1st, 2015.
Advisors/Committee Members: Antonios Armaou, Thesis Advisor/Co-Advisor.
Subjects/Keywords: Process Control; Model Predictive Control
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Fang, Y. (2016). Carleman Linearization-based Nonlinear Model Predictive Control. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/27590
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Fang, Yizhou. “Carleman Linearization-based Nonlinear Model Predictive Control.” 2016. Thesis, Penn State University. Accessed January 23, 2021.
https://submit-etda.libraries.psu.edu/catalog/27590.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Fang, Yizhou. “Carleman Linearization-based Nonlinear Model Predictive Control.” 2016. Web. 23 Jan 2021.
Vancouver:
Fang Y. Carleman Linearization-based Nonlinear Model Predictive Control. [Internet] [Thesis]. Penn State University; 2016. [cited 2021 Jan 23].
Available from: https://submit-etda.libraries.psu.edu/catalog/27590.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Fang Y. Carleman Linearization-based Nonlinear Model Predictive Control. [Thesis]. Penn State University; 2016. Available from: https://submit-etda.libraries.psu.edu/catalog/27590
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Delft University of Technology
4.
Kreuzen, C. (author).
Cooperative adaptive cruise control: Using information from multiple predecessors in combination with MPC.
Degree: 2012, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:dd0c766a-5f16-4799-8c84-39abbddbce9e
► Cooperative adaptive cruise control (CACC) makes the vehicle follow its predecessor at a close but safe distance, and uses information received from other vehicles to…
(more)
▼ Cooperative adaptive cruise
control (CACC) makes the vehicle follow its predecessor at a close but safe distance, and uses information received from other vehicles to accomplish this task. In literature and in practice, the
control method mostly applied for CACC is proportional integral derivative (PID)
control, possibly with some refinement for gear shifting or comfort. The
control method called model
predictive control (MPC) can also be used for CACC, and from literature it appears to be more promising than PID, because of its ability to anticipate future situations and to implement constraints directly into the
control algorithm. MPC applies the first input of a
control input sequence that optimises a performance index calculated from predicted system behaviour, based on a prediction model,
subject to operational constraints, in a receding horizon approach. Furthermore, literature has shown that with PID the use of state information from the second predecessor or the platoon leader, in addition to the direct predecessor’s states, can improve the CACC performance. Therefore, in this thesis the approach of using such additional communicated information from either the second predecessor or the platoon leader is combined with the use of MPC as
control method. The goal is to investigate whether any of these two configurations give an increase in performance compared with similar configurations with PID as
control method, and compared with a more basic configuration that uses just the direct predecessor’s state information with either MPC or PID. Also, the possibly added value of using communicated predicted states, in addition to current states, with MPC is investigated. The CACC controllers are designed to
control the throttle, the brakes, and the gears,
subject to operational constraints on acceleration, velocity, and vehicle-to-vehicle distance. The PID-based CACC controller contains a proportional feedback of the errors in velocity, position, and acceleration, combined with an automatic transmission scheme, and the
control input is restricted at time instants at which a constraint is (almost) violated. The MPC-based CACC controller at each time step minimises the expected errors in position and velocity and the corresponding input variation. The MPC prediction model is obtained by approximating a nonlinear vehicle model by a piecewise affine (PWA) model, and converting the MPC optimisation problem into a mixed integer linear programming (MILP) problem. In this project, tuning is done by applying simulated annealing for a scenario involving four CACC-controlled vehicles following a platoon leader. Then, the tuned controllers are implemented in a validation scenario comprising a larger platoon undergoing a longer simulation. The results from simulating this validation scenario show that the PID-based CACC controller has a low responsiveness, compared with MPC, and lets the first two vehicles crash. With MPC several peaks and oscillations in throttle/brake input and acceleration occur, and it is expected that with…
Advisors/Committee Members: De Schutter, B. (mentor).
Subjects/Keywords: system & control; model predictive control
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Kreuzen, C. (. (2012). Cooperative adaptive cruise control: Using information from multiple predecessors in combination with MPC. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:dd0c766a-5f16-4799-8c84-39abbddbce9e
Chicago Manual of Style (16th Edition):
Kreuzen, C (author). “Cooperative adaptive cruise control: Using information from multiple predecessors in combination with MPC.” 2012. Masters Thesis, Delft University of Technology. Accessed January 23, 2021.
http://resolver.tudelft.nl/uuid:dd0c766a-5f16-4799-8c84-39abbddbce9e.
MLA Handbook (7th Edition):
Kreuzen, C (author). “Cooperative adaptive cruise control: Using information from multiple predecessors in combination with MPC.” 2012. Web. 23 Jan 2021.
Vancouver:
Kreuzen C(. Cooperative adaptive cruise control: Using information from multiple predecessors in combination with MPC. [Internet] [Masters thesis]. Delft University of Technology; 2012. [cited 2021 Jan 23].
Available from: http://resolver.tudelft.nl/uuid:dd0c766a-5f16-4799-8c84-39abbddbce9e.
Council of Science Editors:
Kreuzen C(. Cooperative adaptive cruise control: Using information from multiple predecessors in combination with MPC. [Masters Thesis]. Delft University of Technology; 2012. Available from: http://resolver.tudelft.nl/uuid:dd0c766a-5f16-4799-8c84-39abbddbce9e
Université de Grenoble
5.
Lamoudi, Mohamed Yacine.
Commande prédictive distribuée pour la gestion de l'énergie dans le bâtiment Distributed model predictive control for energy management in building : Distributed Predictive Control for energy management in buildings.
Degree: Docteur es, Sciences et technologie industrielles, 2012, Université de Grenoble
URL: http://www.theses.fr/2012GRENT097
► À l’heure actuelle, les stratégies de gestion de l’énergie pour les bâtiments sontprincipalement basées sur une concaténation de règles logiques. Bien que cette approcheoffre des…
(more)
▼ À l’heure actuelle, les stratégies de gestion de l’énergie pour les bâtiments sontprincipalement basées sur une concaténation de règles logiques. Bien que cette approcheoffre des avantages certains, particulièrement lors de sa mise en oeuvre sur des automatesprogrammables, elle peine à traiter la diversité de situations complexes quipeuvent être rencontrées (prix de l’énergie variable, limitations de puissance, capacitéde stockage d’énergie, bâtiments de grandes dimension).Cette thèse porte sur le développement et l’évaluation d’une commande prédictivepour la gestion de l’énergie dans le bâtiment ainsi que l’étude de l’embarcabilité del’algorithme de contrôle sur une cible temps-réel (Roombox - Schneider-Electric).La commande prédictive est basée sur l’utilisation d’un modèle du bâtiment ainsique des prévisions météorologiques et d’occupation afin de déterminer la séquencede commande optimale à mettre en oeuvre sur un horizon de prédiction glissant.Seul le premier élément de cette séquence est en réalité appliqué au bâtiment. Cetteséquence de commande optimale est obtenue par la résolution en ligne d’un problèmed’optimisation. La capacité de la commande prédictive à gérer des systèmes multivariablescontraints ainsi que des objectifs économiques, la rend particulièrementadaptée à la problématique de la gestion de l’énergie dans le bâtiment.Cette thèse propose l’élaboration d’un schéma de commande distribué pour contrôlerles conditions climatiques dans chaque zone du bâtiment. L’objectif est de contrôlersimultanément: la température intérieure, le taux de CO2 ainsi que le niveaud’éclairement dans chaque zone en agissant sur les équipements présents (CVC, éclairage,volets roulants). Par ailleurs, le cas des bâtiments multi-sources (par exemple:réseau électrique + production locale solaire), dans lequel chaque source d’énergie estcaractérisée par son propre prix et une limitation de puissance, est pris en compte.Dans ce contexte, les décisions relatives à chaque zone ne peuvent plus être effectuéesde façon indépendante. Pour résoudre ce problème, un mécanisme de coordinationbasé sur une décomposition du problème d’optimisation centralisé est proposé. Cettethèse CIFRE 1 a été préparée au sein du laboratoire Gipsa-lab en partenariat avecSchneider-Electric dans le cadre du programme HOMES (www.homesprogramme.com).
Currently, energy management strategies for buildings are mostly based on a concatenationof logical rules. Despite the fact that such rule based strategy can be easilyimplemented, it suffers from some limitations particularly when dealing with complexsituations. This thesis is concerned with the development and assessment ofModel Predictive Control (MPC) algorithms for energy management in buildings. Inthis work, a study of implementability of the control algorithm on a real-time hardwaretarget is conducted beside yearly simulations showing a substantial energy savingpotential. The thesis explores also the ability of MPC to deal with the diversity ofcomplex situations that could be encountered (varying energy…
Advisors/Committee Members: Alamir, Mazen (thesis director).
Subjects/Keywords: Commande predictive; Commande predictive distribué; Énergie; Bâtiment; Predictive control; Distributed model predictive control; Energy; Building
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Lamoudi, M. Y. (2012). Commande prédictive distribuée pour la gestion de l'énergie dans le bâtiment Distributed model predictive control for energy management in building : Distributed Predictive Control for energy management in buildings. (Doctoral Dissertation). Université de Grenoble. Retrieved from http://www.theses.fr/2012GRENT097
Chicago Manual of Style (16th Edition):
Lamoudi, Mohamed Yacine. “Commande prédictive distribuée pour la gestion de l'énergie dans le bâtiment Distributed model predictive control for energy management in building : Distributed Predictive Control for energy management in buildings.” 2012. Doctoral Dissertation, Université de Grenoble. Accessed January 23, 2021.
http://www.theses.fr/2012GRENT097.
MLA Handbook (7th Edition):
Lamoudi, Mohamed Yacine. “Commande prédictive distribuée pour la gestion de l'énergie dans le bâtiment Distributed model predictive control for energy management in building : Distributed Predictive Control for energy management in buildings.” 2012. Web. 23 Jan 2021.
Vancouver:
Lamoudi MY. Commande prédictive distribuée pour la gestion de l'énergie dans le bâtiment Distributed model predictive control for energy management in building : Distributed Predictive Control for energy management in buildings. [Internet] [Doctoral dissertation]. Université de Grenoble; 2012. [cited 2021 Jan 23].
Available from: http://www.theses.fr/2012GRENT097.
Council of Science Editors:
Lamoudi MY. Commande prédictive distribuée pour la gestion de l'énergie dans le bâtiment Distributed model predictive control for energy management in building : Distributed Predictive Control for energy management in buildings. [Doctoral Dissertation]. Université de Grenoble; 2012. Available from: http://www.theses.fr/2012GRENT097
University of Waterloo
6.
Maitland, Anson.
Nonlinear Model Predictive Control Reduction Strategies for Real-time Optimal Control.
Degree: 2019, University of Waterloo
URL: http://hdl.handle.net/10012/14524
► This thesis presents a variety of strategies to accelerate the turnaround times (TATs) of nonlinear and hybrid model predictive controllers (MPCs). These strategies are unified…
(more)
▼ This thesis presents a variety of strategies to accelerate the turnaround times (TATs) of nonlinear and hybrid model predictive controllers (MPCs). These strategies are unified by the themes of symbolic computing, nonlinear model reduction and automotive control.
The first contribution of this thesis is a new MPC problem formulation, called symbolic single shooting (symSS), that leverages the power of symbolic computing to generate an optimization problem of minimal dimension. This formulation is counter to the recent trend of introducing and exploiting sparsity of the MPC optimization problem for tailored solvers to exploit. We make use of this formulation widely in this thesis.
The second contribution of this thesis is a novel application of proper orthogonal decomposition (POD) to MPC. In this strategy we construct a dimensionally-reduced optimization problem by restricting the problem Lagrangian to a subspace. This subspace is found by running simulations offline from which we extract the important solution features. Using this restricted Lagrangian we are able to reduce the problem dimension dramatically, thus simplifying the linear solve. This leads to TAT accelerations of more than two times with minimal controller degradation.
The third contribution of this thesis is an informed move blocking strategy. This strategy exploits the features extracted in the restricted Lagrangian subspace to derive a sequence of increasingly blocked move blocking strategies. These move blocking strategies can then be used to reduce the dimension of the optimization problem in a sparse manner, leading to even greater acceleration of the controller TAT .
The fourth contribution of this thesis is a new quasi-Newton method for MPC. This method utilizes ideas similar to singular perturbation-based model reduction to truncate the expression for the problem Hessian at the symbolic level. For nonlinear systems with a modest Lipschitz constant, we can identify the timestep as a `small' parameter about which we can do a perturbative expansion of the Lagrangian and its derivatives. Truncating to first order in the timestep, we are able to find a good approximation of the Hessian leading to TAT acceleration.
The fifth contribution of this thesis is controller integration strategy based on nested MPCs. Using the symSS formulation we can construct an explicit model of a controlled plant that includes the full model as well as the MPC's action. This form of the controlled plant model allows us to generate exact derivatives so that fast solvers can be used for real time application. We focus here on the problem of planning and motion control integration for autonomous vehicles but this strategy can be extended for other problems that require accurate models of a controlled plant.
The sixth contribution of this thesis is a strategy to handle integer controls in MPC based on a few reasonable assumptions: our predictions over the horizon are almost perfect and the future is inevitable. These assumptions enforce a…
Subjects/Keywords: model predictive control; nonlinear control; hybrid control
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Maitland, A. (2019). Nonlinear Model Predictive Control Reduction Strategies for Real-time Optimal Control. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/14524
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Maitland, Anson. “Nonlinear Model Predictive Control Reduction Strategies for Real-time Optimal Control.” 2019. Thesis, University of Waterloo. Accessed January 23, 2021.
http://hdl.handle.net/10012/14524.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Maitland, Anson. “Nonlinear Model Predictive Control Reduction Strategies for Real-time Optimal Control.” 2019. Web. 23 Jan 2021.
Vancouver:
Maitland A. Nonlinear Model Predictive Control Reduction Strategies for Real-time Optimal Control. [Internet] [Thesis]. University of Waterloo; 2019. [cited 2021 Jan 23].
Available from: http://hdl.handle.net/10012/14524.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Maitland A. Nonlinear Model Predictive Control Reduction Strategies for Real-time Optimal Control. [Thesis]. University of Waterloo; 2019. Available from: http://hdl.handle.net/10012/14524
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Coventry University
7.
Hitzemann, U.
Extensions in non-minimal state-space and state-dependent parameter model based control with application to a DC-DC boost converter.
Degree: PhD, 2013, Coventry University
URL: http://curve.coventry.ac.uk/open/items/ca983ce5-bec4-4598-8ac2-48e7302489f5/1
;
http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.683888
► This Thesis is concerned with model-based control, where models of linear nonminimal state-space (NMSS) and nonlinear state-dependent parameter (SDP) form are considered. In particular, the…
(more)
▼ This Thesis is concerned with model-based control, where models of linear nonminimal state-space (NMSS) and nonlinear state-dependent parameter (SDP) form are considered. In particular, the focus is on model-based predictive control (MPC) in conjunction with the linear NMSS model and on proportional-integralplus (PIP) pole-assignment control in conjunction with the SDP model. The SDP-PIP pole-assignment controller is based on a nonlinear SDP model, however, the approach uses a linear pole-assignment controller design technique. This ‘potential paradox’ is addressed in this Thesis. A conceptual approach to realising the SDP-PIP pole-assignment control is proposed, where an additional conceptual time-shift operator is introduced. This allows the SDPPIP, at each sampling time instance, to be considered as an equivalent linear controller, while operating, in fact, in a nonlinear overall context. Additionally, an attempt to realise SDP-PIP control, where the SDP model exhibits equivalent linear system numerator zeros, is proposed. Regarding the NMSS MPC, emphasis is on square, i.e. equal number of inputs and outputs, multi-input multi-output (MIMO) modelled systems, which exhibit system output cross-coupling effects. Moreover, the NMSS MPC in incremental input form and making use of an integral-of-errors state variable, is considered. A strategy is proposed, that allows decoupling of the system outputs by diagonalising the closed-loop system model via an input transformation. A modification to the NMSS MPC in incremental input form is proposed such that the transformed system input - system output pairs can be considered individually, which allows the control and prediction horizons to be assigned to the individual pairs separately. This modification allows imposed constraints to be accommodated such that the cross-coupling effects do not re-emerge. A practical example is presented, namely, a DC-DC boost converter operating in discontinuous conduction mode (DCM), for which a SDP model is developed. This model is based on measured input-output data rather than on physical relationships. The model incorporates the output current so that the requirements for the load, driven by the converter, is constrained to remain within a predefined output current range. The proposed SDP model is compared to an alternative nonlinear Hammerstein-bilinear structured (HBS) model. The HBS model is, in a similar manner to the SDP model, also based on measured input-output data. Moreover, the differences as well as the similarities of the SDP and HBS model are elaborated. Furthermore, SDP-PIP pole-assignment control, based on the developed SDP model, is applied to the converter and the performance is compared to baseline linear PIP control schemes.
Subjects/Keywords: 629.8; Predictive control; Process control; Automatic control
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Hitzemann, U. (2013). Extensions in non-minimal state-space and state-dependent parameter model based control with application to a DC-DC boost converter. (Doctoral Dissertation). Coventry University. Retrieved from http://curve.coventry.ac.uk/open/items/ca983ce5-bec4-4598-8ac2-48e7302489f5/1 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.683888
Chicago Manual of Style (16th Edition):
Hitzemann, U. “Extensions in non-minimal state-space and state-dependent parameter model based control with application to a DC-DC boost converter.” 2013. Doctoral Dissertation, Coventry University. Accessed January 23, 2021.
http://curve.coventry.ac.uk/open/items/ca983ce5-bec4-4598-8ac2-48e7302489f5/1 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.683888.
MLA Handbook (7th Edition):
Hitzemann, U. “Extensions in non-minimal state-space and state-dependent parameter model based control with application to a DC-DC boost converter.” 2013. Web. 23 Jan 2021.
Vancouver:
Hitzemann U. Extensions in non-minimal state-space and state-dependent parameter model based control with application to a DC-DC boost converter. [Internet] [Doctoral dissertation]. Coventry University; 2013. [cited 2021 Jan 23].
Available from: http://curve.coventry.ac.uk/open/items/ca983ce5-bec4-4598-8ac2-48e7302489f5/1 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.683888.
Council of Science Editors:
Hitzemann U. Extensions in non-minimal state-space and state-dependent parameter model based control with application to a DC-DC boost converter. [Doctoral Dissertation]. Coventry University; 2013. Available from: http://curve.coventry.ac.uk/open/items/ca983ce5-bec4-4598-8ac2-48e7302489f5/1 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.683888
Ryerson University
8.
Jin, Qingxiang.
Synchronous Generator Excitation Control Based on Model Predictive Control.
Degree: 2005, Ryerson University
URL: https://digital.library.ryerson.ca/islandora/object/RULA%3A2136
► This thesis research has designed and developed an optimal predictive excitation control, named the Model Predictive Excitation Control (MPEC), for utility generators. Four significant results…
(more)
▼ This thesis research has designed and developed an optimal
predictive excitation
control, named the Model
Predictive Excitation
Control (MPEC), for utility generators. Four significant results are achieved: First, the MPEC has been designed and has significantly improved the classical model
predictive control and is much simpler and computationally efficient. Second, the MPEC simulation program and results have been accomplished, and study cases have demonstrated the effectiveness of the MPEC. Third, the Modified classical model
predictive control procedure has been formulated to correct a timing error such that the controlling input for the present time is re-written as that for the next step. Fourth, the MPEC optimization formulation and procedure has been developed for the generator
control with only two substation-ready-available measurements which are the generator terminal voltage and speed.
Advisors/Committee Members: Cheung, Richard W.Y. (Thesis advisor), Ryerson University (Degree grantor).
Subjects/Keywords: Predictive control
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Jin, Q. (2005). Synchronous Generator Excitation Control Based on Model Predictive Control. (Thesis). Ryerson University. Retrieved from https://digital.library.ryerson.ca/islandora/object/RULA%3A2136
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Jin, Qingxiang. “Synchronous Generator Excitation Control Based on Model Predictive Control.” 2005. Thesis, Ryerson University. Accessed January 23, 2021.
https://digital.library.ryerson.ca/islandora/object/RULA%3A2136.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Jin, Qingxiang. “Synchronous Generator Excitation Control Based on Model Predictive Control.” 2005. Web. 23 Jan 2021.
Vancouver:
Jin Q. Synchronous Generator Excitation Control Based on Model Predictive Control. [Internet] [Thesis]. Ryerson University; 2005. [cited 2021 Jan 23].
Available from: https://digital.library.ryerson.ca/islandora/object/RULA%3A2136.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Jin Q. Synchronous Generator Excitation Control Based on Model Predictive Control. [Thesis]. Ryerson University; 2005. Available from: https://digital.library.ryerson.ca/islandora/object/RULA%3A2136
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Oregon State University
9.
Barrett, Spencer Brown.
Predictive control using feedback- : a case study of an inverted pendulum.
Degree: MS, Electrical and Computer Engineering, 1995, Oregon State University
URL: http://hdl.handle.net/1957/34657
► Vision is a flexible, non-contact sensor that can be used for position feedback in closed-loop control of dynamic systems. Current vision systems for industrial automation…
(more)
▼ Vision is a flexible, non-contact sensor that can be used for position feedback in
closed-loop
control of dynamic systems. Current vision systems for industrial
automation provide low sample rates and large sample delays relative to other types of
position sensors. Poor sample rates and sample delays are a result of the vast volume of
data that must be collected and processed by the vision system. A
predictive visual
tracker can help compensate for some of the deficiencies of current industrial vision
systems. The objectives of the present research are to demonstrate that vision is a useful
feedback sensor and prediction can be used to improve performance by compensating for
the feedback delay of the vision system.
An inverted pendulum was stabilized using a vision sensor as feedback to a state-feedback
controller. The vision data was run through a d-step ahead predictor to
compensate for the vision system delays. The system was simulated in Mat lab and an
actual physical system was used to test the performance of the
control system.
The inverted pendulum provides a good test-bed for studying
predictive control
using vision feedback. The pendulum will fall without the constant adjustment of the
cart position. The adjustment of the cart by the controller is delayed because of latency
and quantization errors in vision feedback. The better the controller is able to
compensate for delays and quantization errors, the greater its ability to stabilize the
inverted pendulum.
Advisors/Committee Members: Kolodziej, Wojtek (advisor).
Subjects/Keywords: Predictive control
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Barrett, S. B. (1995). Predictive control using feedback- : a case study of an inverted pendulum. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/34657
Chicago Manual of Style (16th Edition):
Barrett, Spencer Brown. “Predictive control using feedback- : a case study of an inverted pendulum.” 1995. Masters Thesis, Oregon State University. Accessed January 23, 2021.
http://hdl.handle.net/1957/34657.
MLA Handbook (7th Edition):
Barrett, Spencer Brown. “Predictive control using feedback- : a case study of an inverted pendulum.” 1995. Web. 23 Jan 2021.
Vancouver:
Barrett SB. Predictive control using feedback- : a case study of an inverted pendulum. [Internet] [Masters thesis]. Oregon State University; 1995. [cited 2021 Jan 23].
Available from: http://hdl.handle.net/1957/34657.
Council of Science Editors:
Barrett SB. Predictive control using feedback- : a case study of an inverted pendulum. [Masters Thesis]. Oregon State University; 1995. Available from: http://hdl.handle.net/1957/34657
Oregon State University
10.
Slegers, Nathan J.
Dynamic modeling, control aspects and model predictive control of a parafoil and payload system.
Degree: PhD, Mechanical Engineering, 2004, Oregon State University
URL: http://hdl.handle.net/1957/17847
► Control issues are investigated for a parafoil and payload system with left and right parafoil brakes used as the control mechanism. It is shown through…
(more)
▼ Control issues are investigated for a parafoil and payload system with left and
right parafoil brakes used as the
control mechanism. It is shown through dynamic
modeling and simulation that parafoil and payload systems can exhibit two basic
modes of lateral
control, namely, roll and skid steering. Using a small parafoil and
payload aircraft, glide rates and turn performance were measured and compared
against a 9 DOF simulation model. This work shows that to properly capture
control
response of parafoil and payload aircraft, tilt of the parafoil canopy must be accounted
for along with left and right parafoil brake deflection. Alternative methods of
controlling a parafoil and payload by tilting the canopy for lateral
control and
changing rigging angle for longitudinal
control are evaluated. A model
predictive
control strategy is developed for an autonomous parafoil and payload system. It is
demonstrated in flight tests that a model
predictive control strategy is a natural and
effective method of achieving trajectory tracking in a parafoil and payload system.
Advisors/Committee Members: Costello, Mark F. (advisor).
Subjects/Keywords: Predictive control
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Slegers, N. J. (2004). Dynamic modeling, control aspects and model predictive control of a parafoil and payload system. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/17847
Chicago Manual of Style (16th Edition):
Slegers, Nathan J. “Dynamic modeling, control aspects and model predictive control of a parafoil and payload system.” 2004. Doctoral Dissertation, Oregon State University. Accessed January 23, 2021.
http://hdl.handle.net/1957/17847.
MLA Handbook (7th Edition):
Slegers, Nathan J. “Dynamic modeling, control aspects and model predictive control of a parafoil and payload system.” 2004. Web. 23 Jan 2021.
Vancouver:
Slegers NJ. Dynamic modeling, control aspects and model predictive control of a parafoil and payload system. [Internet] [Doctoral dissertation]. Oregon State University; 2004. [cited 2021 Jan 23].
Available from: http://hdl.handle.net/1957/17847.
Council of Science Editors:
Slegers NJ. Dynamic modeling, control aspects and model predictive control of a parafoil and payload system. [Doctoral Dissertation]. Oregon State University; 2004. Available from: http://hdl.handle.net/1957/17847
University of Waterloo
11.
Santander, Omar.
Economic Model Predictive Control of Chemical Processes.
Degree: 2015, University of Waterloo
URL: http://hdl.handle.net/10012/9982
► The objective of any chemical process is to transform raw materials into more valuable products subject to not only physical and environmental but also economic…
(more)
▼ The objective of any chemical process is to transform raw materials into more valuable products subject to not only physical and environmental but also economic and safety constraints.
To meet all these constraints in the presence of disturbances the processes must be controlled. Although nowadays there are many available control techniques available Model Predictive Control (MPC) is widely used in industry due to its many advantages such as optimal handling of interactions in multivariable systems and process constraints.
Generally, the MPC strategy is implemented within a hierarchical structure, where it receives set points or targets from the Real Time Optimization (RTO) layer and then maintains the process at these targets by calculating optimal control moves. However, often the set point from the RTO may not be the best optimal operation or it may not be reachable thus motivating the integration of the RTO and MPC calculations into one single computation layer.
This work focuses on this idea of integrating RTO and MPC into one single optimization problem thus resulting in an approach referred in literature as Economic Model Predictive Control (EMPC). The term “Economic” is used to reflect that the objective function used for optimization includes an economic objective generally used in RTO calculations. In this thesis, we propose an EMPC algorithm which calculates manipulated variables values to optimize an objective consisting of a combination of a steady state and a dynamic economic cost. A weight factor is used to balance the contributions of each of these two terms. Also, the cost is defined such as when the best economic steady state is reached the objective is only influenced by the dynamic economic cost.
An additional feature of the proposed algorithm is that the asymptotic stability is satisfied online by enforcing four especial constraints within the optimization problem: 1-positive definiteness of the matrix P defining the Lyapunov function, 2- contraction of the Lyapunov function with respect to set point changes, 3- contraction of the matrix P with respect to time and 4- Lyapunov stability condition. The last constraint both ensures decreasing of the Lyapunov function and also accounts for the robustness of the algorithm with respect to model error (uncertainty).
A particular novelty of this algorithm is that it constantly calculates a best set point with respect to which stability is ensured by the aforementioned constraints. In contrast to other algorithms reported in the literature, the proposed algorithm does not require terminal constraints or terms in the cost that penalize deviations from fixed set points that often lead to conservative closed loop performance.
To account for unmeasured disturbances entering the process, changes in parameters are also explored and the algorithm is devised to compensate for these changes through parameter updating. Accordingly, the parameters are included as additional decision variables within the optimization problem without the need…
Subjects/Keywords: Economic Model Predictive Control
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Santander, O. (2015). Economic Model Predictive Control of Chemical Processes. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/9982
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Santander, Omar. “Economic Model Predictive Control of Chemical Processes.” 2015. Thesis, University of Waterloo. Accessed January 23, 2021.
http://hdl.handle.net/10012/9982.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Santander, Omar. “Economic Model Predictive Control of Chemical Processes.” 2015. Web. 23 Jan 2021.
Vancouver:
Santander O. Economic Model Predictive Control of Chemical Processes. [Internet] [Thesis]. University of Waterloo; 2015. [cited 2021 Jan 23].
Available from: http://hdl.handle.net/10012/9982.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Santander O. Economic Model Predictive Control of Chemical Processes. [Thesis]. University of Waterloo; 2015. Available from: http://hdl.handle.net/10012/9982
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Waterloo
12.
Daoud, Mohamed Ashraf Gameleldin.
Simultaneous Local Motion Planning and Control, Adjustable Driving Behavior, and Obstacle Representation for Autonomous Driving.
Degree: 2020, University of Waterloo
URL: http://hdl.handle.net/10012/16436
► The evolving autonomous driving technology has been attracting significant research efforts in both academia and industry because of its promising potentials. Eliminating the human intervention…
(more)
▼ The evolving autonomous driving technology has been attracting significant research efforts in both academia and industry because of its promising potentials. Eliminating the human intervention in driving will drastically improve the road safety and will create more mobility freedom for the humankind. Decision-making system in autonomy pipeline is the last module that interacts directly with the surrounding environment. Typical decision-making systems perform a variety of tasks including local motion planning, obstacle avoidance, and path-following in a sequential manner. An alternative approach is to perform these tasks simultaneously to obtain faster decision-making actions. This thesis focuses on designing an optimization-based simultaneous controller that performs obstacle avoidance, local motion planning, and vehicle control on roads regardless of their orientation while following a target path, and also incorporates adjustable driving behavior.
Firstly, a decision-making scheme that enables autonomous driving for long trips while expanding the usage of the available computational resources and ensuring obstacle avoidance functionality is proposed. The proposed scheme utilizes a parallel architecture for local motion planning and control layers to increase time efficiency. In addition, a novel feasibility-guaranteed lane change and double lane change planners are introduced for path planning and obstacle avoidance. Finally, an online parameterized curve generator is proposed and integrated with a recently developed path-following controller.
Next, a nonlinear model predictive control (NMPC) scheme is developed for the path-following control of autonomous vehicles. In addition, a dual-objective cost function which is composed of a regulation part and an economic part is introduced. By tuning the weights of this cost, a driving behavior can be implemented; two different driving behaviors are designed, namely, energy-efficient and sport driving modes. Finally, a kinematic bicycle model is used for predicting the vehicle motion while a longitudinal motion dynamic model is used for estimating the energy consumption.
Finally, a novel representation framework for static maps and obstacles based on Fourier Series is proposed. The framework relies on Complex Fourier Series analysis to reduce the computation time and outputs mathematical equations to describe the shape of the considered object. Furthermore, two methods were proposed, namely; offline method and online method. The offline method is used to create accurate representations for static maps and most common obstacles an ego vehicle may encounter. The online method is used to model the free space around the vehicle when dealing with uncertain environments.
The proposed contributions fill significant gaps in the autonomous driving problem. All the proposed work is tested and validated using numerical simulations and some experiments. The results show the effectiveness of the proposed contributions.
Subjects/Keywords: autonomous driving; model predictive control
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Daoud, M. A. G. (2020). Simultaneous Local Motion Planning and Control, Adjustable Driving Behavior, and Obstacle Representation for Autonomous Driving. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/16436
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Daoud, Mohamed Ashraf Gameleldin. “Simultaneous Local Motion Planning and Control, Adjustable Driving Behavior, and Obstacle Representation for Autonomous Driving.” 2020. Thesis, University of Waterloo. Accessed January 23, 2021.
http://hdl.handle.net/10012/16436.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Daoud, Mohamed Ashraf Gameleldin. “Simultaneous Local Motion Planning and Control, Adjustable Driving Behavior, and Obstacle Representation for Autonomous Driving.” 2020. Web. 23 Jan 2021.
Vancouver:
Daoud MAG. Simultaneous Local Motion Planning and Control, Adjustable Driving Behavior, and Obstacle Representation for Autonomous Driving. [Internet] [Thesis]. University of Waterloo; 2020. [cited 2021 Jan 23].
Available from: http://hdl.handle.net/10012/16436.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Daoud MAG. Simultaneous Local Motion Planning and Control, Adjustable Driving Behavior, and Obstacle Representation for Autonomous Driving. [Thesis]. University of Waterloo; 2020. Available from: http://hdl.handle.net/10012/16436
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Oklahoma State University
13.
Govindarajan, Anand.
Accelerating Convergence of Leapfrogging Optimization - Applications to Nonlinear Process Modeling and Nonlinear Model Predictive Control.
Degree: Chemical Engineering, 2014, Oklahoma State University
URL: http://hdl.handle.net/11244/14855
► Conventionally used optimization methods in chemical engineering applications such as linear programming (LP), Levenberg-Marquardt and sequential quadratic programming (SQP) handle nonlinear objective function (OF) surfaces…
(more)
▼ Conventionally used optimization methods in chemical engineering applications such as linear programming (LP), Levenberg-Marquardt and sequential quadratic programming (SQP) handle nonlinear objective function (OF) surfaces by linearizing or assuming quadratic behavior of the surfaces [1]. Process modeling and nonlinear model
predictive control (NMPC) applications, however, present OF surfaces with surface aberrations such as steep slopes, discontinuities, and hard constraints which require a robust and efficient optimization method. Therefore, an optimization method that can handle surface aberrations is required.Leapfrogging (LF) is a recently developed direct search optimization method, potentially best-in-class, which can handle surface aberrations. LF starts with a set of players (trial solutions), randomly placed in the decision variable (DV) space. The worst player (player with the worst OF value) leaps over the best player into a reflected hypervolume [2]. The leapovers continue until all the players converge. LF is robust and efficient - with minimal computation effort (compared to conventional optimization methods), it can handle the challenges posed by nonlinear OF surfaces. LF was demonstrated on over 40 test functions and several modeling and NMPC applications. Rigorous fundamental analysis of LF is required - for a finer understanding of the method, exploring opportunities for improvement and scaling LF applications to large scale systems.This work is focused on exploring and analyzing methods to accelerate convergence of LF, demonstrating application credibility on nonlinear process modeling of steady state binary distillation and NMPC of a binary distillation column. Accelerating convergence opens the doors for using LF in large scale problems that have several hundred variables such as real time optimization and refinery planning where computational effort and time are of essence. Distillation modeling is constrained, nonlinear, and has optimum confined to a narrow region; distillation
control is multivariable, interacting, nonlinear and has severe disturbances.Completion of this work will provide new fundamental understanding of LF which is critical for creating opportunities for algorithm improvement. Demonstrating application to nonlinear process modeling and NMPC will create application credibility, reveal practicality and serve as proof of concept that LF can be an optimizer of choice for use in the process
control community.
Advisors/Committee Members: Rhinehart, R. Russell (advisor), Johannes, Arland H. (committee member), Whiteley, James R. (committee member), Foutch, Gary Lynn (committee member), Vennavelli, Anand N. (committee member).
Subjects/Keywords: leapfrogging; modeling; optimization; predictive control
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Govindarajan, A. (2014). Accelerating Convergence of Leapfrogging Optimization - Applications to Nonlinear Process Modeling and Nonlinear Model Predictive Control. (Thesis). Oklahoma State University. Retrieved from http://hdl.handle.net/11244/14855
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Govindarajan, Anand. “Accelerating Convergence of Leapfrogging Optimization - Applications to Nonlinear Process Modeling and Nonlinear Model Predictive Control.” 2014. Thesis, Oklahoma State University. Accessed January 23, 2021.
http://hdl.handle.net/11244/14855.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Govindarajan, Anand. “Accelerating Convergence of Leapfrogging Optimization - Applications to Nonlinear Process Modeling and Nonlinear Model Predictive Control.” 2014. Web. 23 Jan 2021.
Vancouver:
Govindarajan A. Accelerating Convergence of Leapfrogging Optimization - Applications to Nonlinear Process Modeling and Nonlinear Model Predictive Control. [Internet] [Thesis]. Oklahoma State University; 2014. [cited 2021 Jan 23].
Available from: http://hdl.handle.net/11244/14855.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Govindarajan A. Accelerating Convergence of Leapfrogging Optimization - Applications to Nonlinear Process Modeling and Nonlinear Model Predictive Control. [Thesis]. Oklahoma State University; 2014. Available from: http://hdl.handle.net/11244/14855
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of California – Berkeley
14.
Danielson, Claus.
Symmetric Constrained Optimal Control: Theory, Algorithms, and Applications.
Degree: Mechanical Engineering, 2014, University of California – Berkeley
URL: http://www.escholarship.org/uc/item/7dq9176d
► This dissertation develops the theory of symmetry for constrained linear systems. We use symmetry to design model predictive controllers for constrained linear systems with reduced…
(more)
▼ This dissertation develops the theory of symmetry for constrained linear systems. We use symmetry to design model predictive controllers for constrained linear systems with reduced complexity.The dissertation is divided into three parts. In the first part we review the relevant results from model predictive control and group theory. In particular we present algorithms and data structures from computational group theory used to efficiently search groups.In the second part we develop the theory of symmetry for constrained linear systems and model predictive control problems. Symmetries of constrained linear systems are linear transformations that preserve the dynamics and constraints. A symmetry of a model predictive control problem is a symmetry of the underlying constrained system that also preserves the cost. We use a group theoretic formalism to derive properties of symmetric constrained linear systems and symmetric model predictive control problems. We prove conditions under which the model predictive controller is symmetric and present a procedure for efficiently computing the symmetries of constrained linear systems and model predictive control problems. Our method transforms the problem of finding generators for the symmetry group into a graph automorphism problem. These symmetries are used to design model predictive control algorithms with reduced complexity.We also present two novel explicit model predictive control designs. Both reduce memory requirements by discarding symmetrically redundant pieces of the control-law. The control-law in the eliminated pieces can be reconstructed online using symmetry. We show that storing the symmetries of the problem requires less memory than storing the controller pieces.In the third part of this dissertation we apply our symmetry theory to the battery balancing problem. We use symmetry to reduce the memory requirements for explicit model predictive controllers for seven battery-balancing hardware designs proposed in the literature. This application demonstrates that our symmetric controller designs can significantly reduce the memory requirements of explicit model predictive control. In particular for four out of seven of the designs in our numerical study, the number of pieces in the symmetric controller did not increase as the battery pack-size was increased.
Subjects/Keywords: Mechanical engineering; Control; Model Predictive Control; Symmetry
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Danielson, C. (2014). Symmetric Constrained Optimal Control: Theory, Algorithms, and Applications. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/7dq9176d
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Danielson, Claus. “Symmetric Constrained Optimal Control: Theory, Algorithms, and Applications.” 2014. Thesis, University of California – Berkeley. Accessed January 23, 2021.
http://www.escholarship.org/uc/item/7dq9176d.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Danielson, Claus. “Symmetric Constrained Optimal Control: Theory, Algorithms, and Applications.” 2014. Web. 23 Jan 2021.
Vancouver:
Danielson C. Symmetric Constrained Optimal Control: Theory, Algorithms, and Applications. [Internet] [Thesis]. University of California – Berkeley; 2014. [cited 2021 Jan 23].
Available from: http://www.escholarship.org/uc/item/7dq9176d.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Danielson C. Symmetric Constrained Optimal Control: Theory, Algorithms, and Applications. [Thesis]. University of California – Berkeley; 2014. Available from: http://www.escholarship.org/uc/item/7dq9176d
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Oregon State University
15.
Fernández, Daniel C.
Model Predictive Control for Underwater Robots in Ocean Waves.
Degree: MS, Robotics, 2015, Oregon State University
URL: http://hdl.handle.net/1957/57267
► Underwater robots beneath ocean waves can benefit from feedforward control to reduce position error. This thesis proposes a method using Model Predictive Control (MPC) to…
(more)
▼ Underwater robots beneath ocean waves can benefit from feedforward
control to reduce position error. This thesis proposes a method using Model
Predictive Control (MPC) to predict and counteract future disturbances from an ocean wave field. The MPC state estimator employs a Linear Wave Theory (LWT) solver to approximate the component fluid dynamics under a wave field. Wave data from deployed ocean buoys is used to construct the simulated wave field. The MPC state estimator is used to optimize a set of
control actions by gradient descent along a prediction horizon. The optimized
control input minimizes a global cost function, the squared distance from the target state. The robot then carries out the optimized trajectory with an emphasis on real-time execution. Several prediction horizons are compared, with a horizon of 0.8 seconds selected as having a good balance of low error and fast computation. The controller with the chosen prediction horizon is simulated and found to show a 74% reduction in position error over traditional feedback
control. Additional simulations are run where the MPC takes in noisy measurements of the wave field parameters. The MPC algorithm is shown to be resistant to sensor noise, providing a mean position error 44% lower than the noise-free feedback
control case.
Advisors/Committee Members: Hollinger, Geoffrey A. (advisor), Hatton, Ross L. (committee member).
Subjects/Keywords: model predictive control; Remote submersibles – Automatic control
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Fernández, D. C. (2015). Model Predictive Control for Underwater Robots in Ocean Waves. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/57267
Chicago Manual of Style (16th Edition):
Fernández, Daniel C. “Model Predictive Control for Underwater Robots in Ocean Waves.” 2015. Masters Thesis, Oregon State University. Accessed January 23, 2021.
http://hdl.handle.net/1957/57267.
MLA Handbook (7th Edition):
Fernández, Daniel C. “Model Predictive Control for Underwater Robots in Ocean Waves.” 2015. Web. 23 Jan 2021.
Vancouver:
Fernández DC. Model Predictive Control for Underwater Robots in Ocean Waves. [Internet] [Masters thesis]. Oregon State University; 2015. [cited 2021 Jan 23].
Available from: http://hdl.handle.net/1957/57267.
Council of Science Editors:
Fernández DC. Model Predictive Control for Underwater Robots in Ocean Waves. [Masters Thesis]. Oregon State University; 2015. Available from: http://hdl.handle.net/1957/57267
Penn State University
16.
Padghan, Pranjali S.
COMPLEX AERIAL FLIGHT UTILIZING MODEL PREDICTIVE CONTROL.
Degree: 2018, Penn State University
URL: https://submit-etda.libraries.psu.edu/catalog/14828psp5131
► In this dissertation a quadcopter model is developed and Model Predictive Control techniques are implemented for trajectory tracking and obstacle avoidance. The major application that…
(more)
▼ In this dissertation a quadcopter model is developed and Model
Predictive Control techniques are implemented for trajectory tracking and obstacle avoidance. The major application that has been presented in this thesis is choreographed moves for a swarm of quadcopters. First a nonlinear model of the quadcopter is derived from the Newton-Euler’s equations and linearized into state space form. This model was then further used for implementing Model
Predictive Control algorithm for the purpose of trajectory tracking.
The thesis concludes with an investigation of multi-vehicle obstacle avoidance. Model
Predictive Control takes account of the obstacles as constraints as a part of an optimization process. To deal with the obstacles MOANTOOL has been used which serves as an environment for formulating Model
Predictive Control problems while also avoiding obstacles. Matlab simulations demonstrates the system.
Advisors/Committee Members: Alan Richard Wagner, Thesis Advisor/Co-Advisor.
Subjects/Keywords: Model Predictive Control; Quadcopter Dynamics and control
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Padghan, P. S. (2018). COMPLEX AERIAL FLIGHT UTILIZING MODEL PREDICTIVE CONTROL. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/14828psp5131
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Padghan, Pranjali S. “COMPLEX AERIAL FLIGHT UTILIZING MODEL PREDICTIVE CONTROL.” 2018. Thesis, Penn State University. Accessed January 23, 2021.
https://submit-etda.libraries.psu.edu/catalog/14828psp5131.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Padghan, Pranjali S. “COMPLEX AERIAL FLIGHT UTILIZING MODEL PREDICTIVE CONTROL.” 2018. Web. 23 Jan 2021.
Vancouver:
Padghan PS. COMPLEX AERIAL FLIGHT UTILIZING MODEL PREDICTIVE CONTROL. [Internet] [Thesis]. Penn State University; 2018. [cited 2021 Jan 23].
Available from: https://submit-etda.libraries.psu.edu/catalog/14828psp5131.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Padghan PS. COMPLEX AERIAL FLIGHT UTILIZING MODEL PREDICTIVE CONTROL. [Thesis]. Penn State University; 2018. Available from: https://submit-etda.libraries.psu.edu/catalog/14828psp5131
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Texas A&M University
17.
Vedam, Narayani.
Terrain-Adaptive Cruise Control: A Human-Like Approach.
Degree: MS, Electrical Engineering, 2015, Texas A&M University
URL: http://hdl.handle.net/1969.1/156224
► With rapid advancements in the field of autonomous vehicles, intelligent control systems and automated highway systems, the need for GPS based vehicle data has grown…
(more)
▼ With rapid advancements in the field of autonomous vehicles, intelligent
control systems and automated highway systems, the need for GPS based vehicle data has grown in importance. This has provided for a plethora of opportunities to improve upon the existing vehicular systems.
In this study, the use of GPS data for optimal regulation of vehicle speed is explored. A discrete dynamic programming algorithm with a model
predictive control (MPC) scheme is employed. The objective function is formulated in such a way that the weighting gains vary adaptively based on the road slope. Unlike in the prevalent approaches, this eliminates the need for a preprocessing algorithm to ensure tracking along flat stretches of road.
Fuel savings of 0.48% along a downhill have been recorded. Also, the usage of brakes has been considerably reduced due to deceleration prior to descent. This is highly advantageous, particularly in the case of heavy-duty vehicles as they are prone to wearing of brake pad lining. Therefore, this method proves to be a simpler alternative to the existing methods, while incorporating the best attributes of a human driver and the tracking ability of a conventional controller.
Advisors/Committee Members: Bhattacharyya, Shankar P (advisor), Langari, Reza (advisor), Datta, Aniruddha (committee member), Zoghi, Ben (committee member).
Subjects/Keywords: Terrain-Adaptive; Cruise Control; Model Predictive Control
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Vedam, N. (2015). Terrain-Adaptive Cruise Control: A Human-Like Approach. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/156224
Chicago Manual of Style (16th Edition):
Vedam, Narayani. “Terrain-Adaptive Cruise Control: A Human-Like Approach.” 2015. Masters Thesis, Texas A&M University. Accessed January 23, 2021.
http://hdl.handle.net/1969.1/156224.
MLA Handbook (7th Edition):
Vedam, Narayani. “Terrain-Adaptive Cruise Control: A Human-Like Approach.” 2015. Web. 23 Jan 2021.
Vancouver:
Vedam N. Terrain-Adaptive Cruise Control: A Human-Like Approach. [Internet] [Masters thesis]. Texas A&M University; 2015. [cited 2021 Jan 23].
Available from: http://hdl.handle.net/1969.1/156224.
Council of Science Editors:
Vedam N. Terrain-Adaptive Cruise Control: A Human-Like Approach. [Masters Thesis]. Texas A&M University; 2015. Available from: http://hdl.handle.net/1969.1/156224
University of Minnesota
18.
Pate, Joseph.
Improvements in Sparsity Promoting Estimator Design and Network Aware Controller Design for Wireless Structural Control.
Degree: MS, Civil Engineering, 2020, University of Minnesota
URL: http://hdl.handle.net/11299/216749
► Feedback control systems are an attractive approach to limit the structural response to large dynamics loading events such as wind and earthquakes. However, civil structures…
(more)
▼ Feedback control systems are an attractive approach to limit the structural response to large dynamics loading events such as wind and earthquakes. However, civil structures often contain too many degrees of freedom to measure all effectively when controlling the structure’s response. Previous work has shown that the Kalman filter can be used effectively to estimate the state response of structures from a limited set of measurements of the structure. Choosing a limited set of sensors from a larger sensor set poses a combinatorial problem. In light of this, we have devised a systematic method to determine a sparse set of measurements that have the largest impact on estimating the state of the structure. To avoid the use of a combinatorial search, we propose the addition of a sparsity promoting parameter and a penalty function to the optimization problem used to determine the Kalman gain. Promoting sparsity within the Kalman gain provides a method of removing sensors by setting the weight for a given sensor to zero which effectively removes the sensor’s measurements from the Kalman filter estimations. The use of wireless sensors in structural control has recently gained interest. How- ever, wireless sensors have an increased probability of packet error and longer communication delays in comparison with wired sensors. Using a sparse set of sensors has the added benefit of requiring fewer network resources, which allows for more efficient communication over wireless networks. Simulation results of a wireless feedback control of a 9 story structure show improved performance in limiting the structural response when using sensors placed according to the KFADMM in comparison to using the full sensor set. To further address the problem of packet loss and communication delays in wireless networks we examine an integrated model of the controller network and structure. Leveraging the integrated control model, we propose a controller capable of simultaneously controlling the network and structure. Preliminary simulation results indicate that the controller is able to effectively control both the network and structure by switching between sub systems within the controller network.
Subjects/Keywords: model predictive control; sensor selection; structural control
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Pate, J. (2020). Improvements in Sparsity Promoting Estimator Design and Network Aware Controller Design for Wireless Structural Control. (Masters Thesis). University of Minnesota. Retrieved from http://hdl.handle.net/11299/216749
Chicago Manual of Style (16th Edition):
Pate, Joseph. “Improvements in Sparsity Promoting Estimator Design and Network Aware Controller Design for Wireless Structural Control.” 2020. Masters Thesis, University of Minnesota. Accessed January 23, 2021.
http://hdl.handle.net/11299/216749.
MLA Handbook (7th Edition):
Pate, Joseph. “Improvements in Sparsity Promoting Estimator Design and Network Aware Controller Design for Wireless Structural Control.” 2020. Web. 23 Jan 2021.
Vancouver:
Pate J. Improvements in Sparsity Promoting Estimator Design and Network Aware Controller Design for Wireless Structural Control. [Internet] [Masters thesis]. University of Minnesota; 2020. [cited 2021 Jan 23].
Available from: http://hdl.handle.net/11299/216749.
Council of Science Editors:
Pate J. Improvements in Sparsity Promoting Estimator Design and Network Aware Controller Design for Wireless Structural Control. [Masters Thesis]. University of Minnesota; 2020. Available from: http://hdl.handle.net/11299/216749
Delft University of Technology
19.
Khalid, Omer (author).
Centralized nonlinear model predictive control for highway traffic throughput maximization: Case study of on-ramp merging.
Degree: 2020, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:f67cbf26-31ec-4c94-98c7-706548410d15
► Traffic congestion on highways is a multi-sectoral phenomenon affecting society, the economy and the environment. It often takes place at specific locations such as on…
(more)
▼ Traffic congestion on highways is a multi-sectoral phenomenon affecting society, the economy and the environment. It often takes place at specific locations such as on and off-ramps, weaving segments and intersections. The on-ramp merging procedure is considered as one of the main factors that causes traffic congestion on highways. The studies in literature show that the merging procedure can result in adverse traffic scenarios such as the buildup of the vehicles on the ramp which causes a downstream drop in capacity and subsequent blockage of upstream off-ramp traffic flow. Moreover, the on-ramp vehicles need to take the actions of leading and following mainlane vehicles into account during the merging process. On highly congested roads, this merging process becomes even more tedious and undesirable stop-and-go traffic behavior becomes unavoidable. Connected and autonomous vehicles (CAVs) that can provide safe gaps between vehicles along with identifying appropriate merging speed profiles have the potential to reduce traffic accidents and improve traffic efficiency. This thesis introduces a nonlinear model predictive control (NMPC) strategy for autonomous merging control based on a cost function that tracks the desired inter-vehicular gaps for on-ramp and mainlane vehicles, and thus intends to fully exploit the capacity of the road in order to maximize the traffic throughput. The proposed controller aims to optimize both acceleration and steering rate profiles of vehicles, and to guide on-ramp vehicles to merge efficiently, without frequent slowdown or wait for merging gaps at the end of the ramp along with minimal disruption to the mainlane traffic flow. The controller is evaluated under different initial conditions, ranging from low to high traffic conditions. The performance of the controller is compared to that of a baseline scenario, and the results show that the proposed controller increases travel times in the range of 2.46% and 4.17% for different traffic conditions, without disrupting the mainline traffic operation. Additionally, average speed of vehicles is improved in the range of 8.2% and 4.5% under different traffic conditions.
Mechanical Engineering | Systems and Control
Advisors/Committee Members: Steur, E. (mentor), Wang, M. (mentor), De Schutter, B.H.K. (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Model Predictive Control; Ramp Merging; Traffic Control
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Khalid, O. (. (2020). Centralized nonlinear model predictive control for highway traffic throughput maximization: Case study of on-ramp merging. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:f67cbf26-31ec-4c94-98c7-706548410d15
Chicago Manual of Style (16th Edition):
Khalid, Omer (author). “Centralized nonlinear model predictive control for highway traffic throughput maximization: Case study of on-ramp merging.” 2020. Masters Thesis, Delft University of Technology. Accessed January 23, 2021.
http://resolver.tudelft.nl/uuid:f67cbf26-31ec-4c94-98c7-706548410d15.
MLA Handbook (7th Edition):
Khalid, Omer (author). “Centralized nonlinear model predictive control for highway traffic throughput maximization: Case study of on-ramp merging.” 2020. Web. 23 Jan 2021.
Vancouver:
Khalid O(. Centralized nonlinear model predictive control for highway traffic throughput maximization: Case study of on-ramp merging. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Jan 23].
Available from: http://resolver.tudelft.nl/uuid:f67cbf26-31ec-4c94-98c7-706548410d15.
Council of Science Editors:
Khalid O(. Centralized nonlinear model predictive control for highway traffic throughput maximization: Case study of on-ramp merging. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:f67cbf26-31ec-4c94-98c7-706548410d15
University of Oxford
20.
Evans, Martin A.
Multiplicative robust and stochastic MPC with application to wind turbine control.
Degree: PhD, 2014, University of Oxford
URL: http://ora.ox.ac.uk/objects/uuid:0ad9b878-00f3-4cfa-a683-148765e3ae39
;
https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.635233
► A robust model predictive control algorithm is presented that explicitly handles multiplicative, or parametric, uncertainty in linear discrete models over a finite horizon. The uncertainty…
(more)
▼ A robust model predictive control algorithm is presented that explicitly handles multiplicative, or parametric, uncertainty in linear discrete models over a finite horizon. The uncertainty in the predicted future states and inputs is bounded by polytopes. The computational cost of running the controller is reduced by calculating matrices offline that provide a means to construct outer approximations to robust constraints to be applied online. The robust algorithm is extended to problems of uncertain models with an allowed probability of violation of constraints. The probabilistic degrees of satisfaction are approximated by one-step ahead sampling, with a greedy solution to the resulting mixed integer problem. An algorithm is given to enlarge a robustly invariant terminal set to exploit the probabilistic constraints. Exponential basis functions are used to create a Robust MPC algorithm for which the predictions are defined over the infinite horizon. The control degrees of freedom are weights that define the bounds on the state and input uncertainty when multiplied by the basis functions. The controller handles multiplicative and additive uncertainty. Robust MPC is applied to the problem of wind turbine control. Rotor speed and tower oscillations are controlled by a low sample rate robust predictive controller. The prediction model has multiplicative and additive uncertainty due to the uncertainty in short-term future wind speeds and in model linearisation. Robust MPC is compared to nominal MPC by means of a high-fidelity numerical simulation of a wind turbine under the two controllers in a wide range of simulated wind conditions.
Subjects/Keywords: 629.8; Control engineering; predictive control; model predictive control; robust control; stochastic MPC; probabilistic control; control theory; wind power; wind turbine control
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Evans, M. A. (2014). Multiplicative robust and stochastic MPC with application to wind turbine control. (Doctoral Dissertation). University of Oxford. Retrieved from http://ora.ox.ac.uk/objects/uuid:0ad9b878-00f3-4cfa-a683-148765e3ae39 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.635233
Chicago Manual of Style (16th Edition):
Evans, Martin A. “Multiplicative robust and stochastic MPC with application to wind turbine control.” 2014. Doctoral Dissertation, University of Oxford. Accessed January 23, 2021.
http://ora.ox.ac.uk/objects/uuid:0ad9b878-00f3-4cfa-a683-148765e3ae39 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.635233.
MLA Handbook (7th Edition):
Evans, Martin A. “Multiplicative robust and stochastic MPC with application to wind turbine control.” 2014. Web. 23 Jan 2021.
Vancouver:
Evans MA. Multiplicative robust and stochastic MPC with application to wind turbine control. [Internet] [Doctoral dissertation]. University of Oxford; 2014. [cited 2021 Jan 23].
Available from: http://ora.ox.ac.uk/objects/uuid:0ad9b878-00f3-4cfa-a683-148765e3ae39 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.635233.
Council of Science Editors:
Evans MA. Multiplicative robust and stochastic MPC with application to wind turbine control. [Doctoral Dissertation]. University of Oxford; 2014. Available from: http://ora.ox.ac.uk/objects/uuid:0ad9b878-00f3-4cfa-a683-148765e3ae39 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.635233
UCLA
21.
Anderson, Timothy.
Distributed Economic Model Predictive Control of a Catalytic Reactor: Evaluation of Sequential and Iterative Architectures.
Degree: Chemical Engineering, 2014, UCLA
URL: http://www.escholarship.org/uc/item/7qm043kq
► In this work, the development and application of distributed economic model predictive control (DEMPC) methodologies to a catalytic reactor is considered. Specifically, two DEMPC methodologies…
(more)
▼ In this work, the development and application of distributed economic model predictive control (DEMPC) methodologies to a catalytic reactor is considered. Specifically, two DEMPC methodologies are designed for sequential and iterative implementation, respec- tively. The DEMPC architectures are evaluated on the basis of the closed-loop performance and on-line computation time requirements compared to a centralized EMPC approach. For the catalytic reactor considered, DEMPC proves to be a viable option as it is able to give similar closed-loop performance while reducing the on-line computation time requirements relative to a centralized EMPC strategy.
Subjects/Keywords: Chemical engineering; distributed model predictive control; economic model predictive control; nonlinear systems; process control
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Anderson, T. (2014). Distributed Economic Model Predictive Control of a Catalytic Reactor: Evaluation of Sequential and Iterative Architectures. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/7qm043kq
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Anderson, Timothy. “Distributed Economic Model Predictive Control of a Catalytic Reactor: Evaluation of Sequential and Iterative Architectures.” 2014. Thesis, UCLA. Accessed January 23, 2021.
http://www.escholarship.org/uc/item/7qm043kq.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Anderson, Timothy. “Distributed Economic Model Predictive Control of a Catalytic Reactor: Evaluation of Sequential and Iterative Architectures.” 2014. Web. 23 Jan 2021.
Vancouver:
Anderson T. Distributed Economic Model Predictive Control of a Catalytic Reactor: Evaluation of Sequential and Iterative Architectures. [Internet] [Thesis]. UCLA; 2014. [cited 2021 Jan 23].
Available from: http://www.escholarship.org/uc/item/7qm043kq.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Anderson T. Distributed Economic Model Predictive Control of a Catalytic Reactor: Evaluation of Sequential and Iterative Architectures. [Thesis]. UCLA; 2014. Available from: http://www.escholarship.org/uc/item/7qm043kq
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Delft University of Technology
22.
Jeschke, Joost (author).
Parametrized Model Predictive Control in Urban Traffic Networks: Towards real-time implementation.
Degree: 2020, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:c06da4ef-733f-4d78-9d61-7232363af974
► Model Predictive Control (MPC) has shown promising results in the control of urban traffic networks, but has one major drawback. The, often nonlinear, optimization that…
(more)
▼ Model Predictive Control (MPC) has shown promising results in the control of urban traffic networks, but has one major drawback. The, often nonlinear, optimization that has to be performed at every control time step is computationally too complex to use MPC controllers for real-time implementations (i.e. when the online optimization is performed within the control time interval of the controlled system). This thesis proposes a parametrized MPC control approach to lower the computational complexity of the MPC controllers and to strive for real-time implementability. In parametrized MPC, the original decision variables (i.e. the inputs of the system) become a function of a parametrized control law and the parameters of this control law become the new decision variables of the optimization problem. The goal is to lower the computational complexity by reducing the number of decision variables with limited performance decrease. In this thesis, three parametrized control laws are proposed that can be used in the parametrized MPC approach for urban traffic networks. These three control laws are constructed based on the prediction model of the (parametrized) MPC controller, on ART-UTC, an existing control method, and by using supervised learning. The system performance and computational complexity of the different parametrized MPC controllers are compared to that of a conventional MPC controller by performing an extensive simulation-based case study in which different optimization algorithms, emissions, and parameter update time steps are considered. The simulation results show that the control law based on the prediction model results in a parametrized MPC controller which is real-time implementable, uses 2 parameters that are constant over the control horizon per intersection, and has a system performance decrease of less than 3%.
Mechanical Engineering | Systems and Control
Advisors/Committee Members: De Schutter, B. (mentor), Delft University of Technology (degree granting institution).
Subjects/Keywords: Model Predictive Control; Urban Traffic Control; Parametrized Controller; Parametrized Model Predictive Control
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Jeschke, J. (. (2020). Parametrized Model Predictive Control in Urban Traffic Networks: Towards real-time implementation. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:c06da4ef-733f-4d78-9d61-7232363af974
Chicago Manual of Style (16th Edition):
Jeschke, Joost (author). “Parametrized Model Predictive Control in Urban Traffic Networks: Towards real-time implementation.” 2020. Masters Thesis, Delft University of Technology. Accessed January 23, 2021.
http://resolver.tudelft.nl/uuid:c06da4ef-733f-4d78-9d61-7232363af974.
MLA Handbook (7th Edition):
Jeschke, Joost (author). “Parametrized Model Predictive Control in Urban Traffic Networks: Towards real-time implementation.” 2020. Web. 23 Jan 2021.
Vancouver:
Jeschke J(. Parametrized Model Predictive Control in Urban Traffic Networks: Towards real-time implementation. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Jan 23].
Available from: http://resolver.tudelft.nl/uuid:c06da4ef-733f-4d78-9d61-7232363af974.
Council of Science Editors:
Jeschke J(. Parametrized Model Predictive Control in Urban Traffic Networks: Towards real-time implementation. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:c06da4ef-733f-4d78-9d61-7232363af974
University of Newcastle
23.
Kong, He.
A unified approach to linear design and predictive control of constrained systems.
Degree: MPC) for linear systems from a design perspective. Our focus is on establishing a unified approach of linear design methods and MPC so that the benefits of the two can be obtained in a well-defined way for practical applications. For this purpose, we have considered the design question from both the control and communication point of view. On the control side, starting from a basic assumption that an unconstrained pre-stabilizing controller is available, we have considered several closely-related issues. Specifically, we have proposed novel tuning techniques so that an MPC controller can either replace an existing controller or gradually improve the control performance based on the latter. We have presented a detailed stability proof for these techniques. In this thesis we have also discussed the role of the observer in robust MPC. As such, we have considered systems with unstructured uncertainty and presented some design methods of robust policies. We have shown via theoretical analysis and numerical simulation that the choice of the observer makes a key difference in the resulting closed-loop performance. On the communication side, we have presented a sparse communication strategy for networked control systems (NCS) based on the singular value decomposition (SVD, a well-defined way for practical applications. For this purpose, we have considered the design question from both the control and communication point of view. On the control side, starting from a basic assumption that an unconstrained pre-stabilizing controller is available, we have considered several closely-related issues. Specifically, we have proposed novel tuning techniques so that an MPC controller can either replace an existing controller or gradually improve the control performance based on the latter. We have presented a detailed stability proof for these techniques. In this thesis we have also discussed the role of the observer in robust MPC. As such, we have considered systems with unstructured uncertainty and presented some design methods of robust policies. We have shown via theoretical analysis and numerical simulation that the choice of the observer makes a key difference in the resulting closed-loop performance. On the communication side, we have presented a sparse communication strategy for networked control systems (NCS) based on the singular value decomposition (SVD, 2014, University of Newcastle
URL: http://hdl.handle.net/1959.13/1055900
► Research Doctorate - Electrical Engineering
This thesis studies the use of model predictive control (MPC) for linear systems from a design perspective. Our focus is…
(more)
▼ Research Doctorate - Electrical Engineering
This thesis studies the use of model predictive control (MPC) for linear systems from a design perspective. Our focus is on establishing a unified approach of linear design methods and MPC so that the benefits of the two can be obtained in a well-defined way for practical applications. For this purpose, we have considered the design question from both the control and communication point of view. On the control side, starting from a basic assumption that an unconstrained pre-stabilizing controller is available, we have considered several closely-related issues. Specifically, we have proposed novel tuning techniques so that an MPC controller can either replace an existing controller or gradually improve the control performance based on the latter. We have presented a detailed stability proof for these techniques. In this thesis we have also discussed the role of the observer in robust MPC. As such, we have considered systems with unstructured uncertainty and presented some design methods of robust policies. We have shown via theoretical analysis and numerical simulation that the choice of the observer makes a key difference in the resulting closed-loop performance. On the communication side, we have presented a sparse communication strategy for networked control systems (NCS) based on the singular value decomposition (SVD) of the Hessian of the quadratic performance index generally considered in MPC and the unconstrained optimal controller. The singular vectors are employed to generate an orthonormal basis function expansion of the unconstrained solution to the infinite horizon optimal control problem. The proposed control law is deduced from the former unconstrained controller based on cost reduction consideration. We have presented a thorough study of the associated stability analysis and have shown the advantages of the proposed method via simulation studies.
Advisors/Committee Members: University of Newcastle. Faculty of Engineering & Built Environment, School of Electrical Engineering and Computer Science.
Subjects/Keywords: model predictive control; constrained control; robust control; networked control systems
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Kong, H. (2014). A unified approach to linear design and predictive control of constrained systems. (Doctoral Dissertation). University of Newcastle. Retrieved from http://hdl.handle.net/1959.13/1055900
Chicago Manual of Style (16th Edition):
Kong, He. “A unified approach to linear design and predictive control of constrained systems.” 2014. Doctoral Dissertation, University of Newcastle. Accessed January 23, 2021.
http://hdl.handle.net/1959.13/1055900.
MLA Handbook (7th Edition):
Kong, He. “A unified approach to linear design and predictive control of constrained systems.” 2014. Web. 23 Jan 2021.
Vancouver:
Kong H. A unified approach to linear design and predictive control of constrained systems. [Internet] [Doctoral dissertation]. University of Newcastle; 2014. [cited 2021 Jan 23].
Available from: http://hdl.handle.net/1959.13/1055900.
Council of Science Editors:
Kong H. A unified approach to linear design and predictive control of constrained systems. [Doctoral Dissertation]. University of Newcastle; 2014. Available from: http://hdl.handle.net/1959.13/1055900
KTH
24.
Bengtsson, Ivar.
Autonomous Overtaking with Learning Model Predictive Control.
Degree: Optimization and Systems Theory, 2020, KTH
URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276691
► We review recent research into trajectory planning for autonomous overtaking to understand existing challenges. Then, the recently developed framework Learning Model Predictive Control (LMPC)…
(more)
▼ We review recent research into trajectory planning for autonomous overtaking to understand existing challenges. Then, the recently developed framework Learning Model Predictive Control (LMPC) is presented as a suitable method to iteratively improve an overtaking manoeuvre each time it is performed. We present recent extensions to the LMPC framework to make it applicable to overtaking. Furthermore, we also present two alternative modelling approaches with the intention of reducing computational complexity of the optimization problems solved by the controller. All proposed frameworks are built from scratch in Python3 and simulated for evaluation purposes. Optimization problems are modelled and solved using the Gurobi 9.0 Python API gurobipy. The results show that LMPC can be successfully applied to the overtaking problem, with improved performance at each iteration. However, the first proposed alternative modelling approach does not improve computational times as was the intention. The second one does but fails in other areas.
Vi går igenom ny forskning inom trajectory planning för autonom omkörning för att förstå de utmaningar som finns. Därefter föreslås ramverket Learning Model Predictive Control (LMPC) som en lämplig metod för att iterativt förbättra en omkörning vid varje utförande. Vi tar upp utvidgningar av LMPC-ramverket för att göra det applicerbart på omkörningsproblem. Dessutom presenterar vi också två alternativa modelleringar i syfte att minska optimeringsproblemens komplexitet. Alla tre angreppssätt har byggts från grunden i Python3 och simulerats i utvärderingssyfte. Optimeringsproblem har modellerats och lösts med programvaran Gurobi 9.0s python-API gurobipy. Resultaten visar att LMPC kan tillämpas framgångsrikt på omkörningsproblem, med förbättrat utförande vid varje iteration. Den första alternativa modelleringen minskar inte beräkningstiden vilket var dess syfte. Det gör däremot den andra alternativa modelleringen som dock fungerar sämre i andra avseenden.
Subjects/Keywords: Autonomous vehicles; Learning Model Predictive Control; Model Predictive Control; Autonoma fordon; Learning Model Predictive Control; Modellprediktiv reglering; Mathematics; Matematik
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Bengtsson, I. (2020). Autonomous Overtaking with Learning Model Predictive Control. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276691
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Bengtsson, Ivar. “Autonomous Overtaking with Learning Model Predictive Control.” 2020. Thesis, KTH. Accessed January 23, 2021.
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276691.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Bengtsson, Ivar. “Autonomous Overtaking with Learning Model Predictive Control.” 2020. Web. 23 Jan 2021.
Vancouver:
Bengtsson I. Autonomous Overtaking with Learning Model Predictive Control. [Internet] [Thesis]. KTH; 2020. [cited 2021 Jan 23].
Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276691.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Bengtsson I. Autonomous Overtaking with Learning Model Predictive Control. [Thesis]. KTH; 2020. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276691
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Melbourne
25.
Gowri Sankar, Gokul Siva Sankar.
Robust predictive control of diesel airpath.
Degree: 2019, University of Melbourne
URL: http://hdl.handle.net/11343/230607
► In an automotive diesel airpath application, the controller is required to track reference values of intake manifold (boost) pressure and exhaust gas recirculation (EGR) rate.…
(more)
▼ In an automotive diesel airpath application, the controller is required to track reference values of intake manifold (boost) pressure and exhaust gas recirculation (EGR) rate. EGR rate is the ratio of the EGR outflow rate to the combined EGR and compressor outflow rates. The reference values for these output variables are determined by a high level controller based on the current engine operating condition characterised by an engine rotational speed and a fuelling rate. Perfect tracking of these references in a drive cycle ensures optimal driver demand responsiveness, fuel efficiency, and satisfaction of legislated emission limits. However, diesel engine airpath control is challenging owing to its nonlinear multivariable nature.
Conventional approaches use look-up tables and proportional-integral-differential (PID) loops ignoring the cross-sensitivities that can affect the controller performance. Furthermore, these approaches do not guarantee satisfaction of operating and reliability constraints on manifold pressures and physical limitations of the actuators. On the other hand, the constraint handling ability of model predictive control (MPC) makes it an ideal choice of control architecture for constrained multi-input multi-output systems, such as diesel engines. However, efficient calibration of typical implementations of MPC is hindered by the high number of tuning parameters and their non-intuitive correlation with the output response. Additionally, modelling errors might lead to constraint violation and loss of recursive feasibility.
This thesis proposes a robust switched linear time invariant (LTI) MPC architecture for diesel airpath that utilises multiple local controllers that require low calibration effort. In order to reduce the number of effective tuning parameters to aid rapid calibration, first, structural modifications are introduced to the traditional MPC formulation by constraining the outputs within exponentially decaying envelopes and an appropriate cost function parameterisation. Subsequently, a methodology based on sequential convex program (SCP) is proposed to estimate maximal disturbance set that can be handled by each local controller under limited control authority arising due to input saturations typically observed in diesel engine operation. Constraint tightening approach is used to provide robust feasibility guarantees in the face of uncertainties originating from the maximal disturbance set and due to controller switching. In addition, a procedure is proposed for identifying the switched controllers requiring further tuning using feedback from performance over drive cycles. The developed switched LTI-MPC architecture is implemented on rapid prototyping hardware and experimentally demonstrated on a diesel engine test rig. The calibration efficacy of the controller architecture is demonstrated at a steady state condition for fuelling steps and over drive cycles. Furthermore, the control system is shown to track output references while maintaining all input and state constraints in…
Subjects/Keywords: model predictive control; robust model predictive control; model predictive controller calibration; advanced automotive control; diesel engines
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Gowri Sankar, G. S. S. (2019). Robust predictive control of diesel airpath. (Doctoral Dissertation). University of Melbourne. Retrieved from http://hdl.handle.net/11343/230607
Chicago Manual of Style (16th Edition):
Gowri Sankar, Gokul Siva Sankar. “Robust predictive control of diesel airpath.” 2019. Doctoral Dissertation, University of Melbourne. Accessed January 23, 2021.
http://hdl.handle.net/11343/230607.
MLA Handbook (7th Edition):
Gowri Sankar, Gokul Siva Sankar. “Robust predictive control of diesel airpath.” 2019. Web. 23 Jan 2021.
Vancouver:
Gowri Sankar GSS. Robust predictive control of diesel airpath. [Internet] [Doctoral dissertation]. University of Melbourne; 2019. [cited 2021 Jan 23].
Available from: http://hdl.handle.net/11343/230607.
Council of Science Editors:
Gowri Sankar GSS. Robust predictive control of diesel airpath. [Doctoral Dissertation]. University of Melbourne; 2019. Available from: http://hdl.handle.net/11343/230607
Cornell University
26.
Radecki, Peter.
Applied Probabilistic Inference: Model Estimation For Hvac Predictive Controls And All-Weather Perception For Autonomous Vehicles.
Degree: PhD, Mechanical Engineering, 2016, Cornell University
URL: http://hdl.handle.net/1813/44364
► Probabilistic inference and reasoning is applied to two major application areas: HVAC controls in buildings and autonomous vehicle perception. Although the physical domains differ vastly,…
(more)
▼ Probabilistic inference and reasoning is applied to two major application areas: HVAC controls in buildings and autonomous vehicle perception. Although the physical domains differ vastly, across both applications the presented novel contributions share real-time inference of stochastic systems for improved
control capability and performance. Besides performing simple state estimation, Kalman Filters in both applications are extended for model inference-estimating thermal model parameters and disturbances in buildings and dynamic object classification for perception in autonomous vehicles. Part one of this study proposes a general, scalable method to learn controloriented thermal models of buildings that could enable wide-scale deployment of cost-effective
predictive controls. An Unscented Kalman Filter augmented for parameter and disturbance estimation is shown to accurately learn and predict a building's thermal response. By leveraging building topology and measurement data, the filter quickly learns parameters of a thermal network during periods of known or constrained loads and then characterizes unknown loads in order to provide accurate 24+ hour energy predictions. Performance was validated with EnergyPlus simulation data across a year-long study of a passive building. The method is extended to multi-zone actively controlled buildings by using the controller to excite unknown portions of the building's dynamics. A simulation study demonstrates self-excitation improves model estimation. Formalization of parameterization, disturbance estimation, and self-excitation routines is shown with an observability analysis. Comparing against a baseline thermostat controller, a Model
Predictive Control (MPC) framework, which anticipates weather uncertainty and time-varying temperature set-points, is shown to improve energy savings and occupant comfort. Part two of this study presents a novel probabilistic perception algorithm as a real-time joint solution to data association, object tracking, and object classification for an autonomous ground vehicle (AGV) in all-weather conditions. The presented algorithm extends a Rao-Blackwellized Particle Filter originally built for Cornell's AGV for the DARPA Urban Challenge (DUC) to include multiple model tracking for classification. Additionally a state-of-the-art vision detection algorithm that includes heading information for AGV applications was implemented. Cornell's AGV from the DUC was upgraded and used to experimentally examine if and how state-of-the-art vision algorithms can complement or replace lidar and radar sensors. Sensor and algorithm performance in adverse weather and lighting conditions is tested. Experimental evaluation demonstrates that sensor diversity with a joint probabilistic perception algorithm provides robust all-weather data association, tracking, and classification.
Advisors/Committee Members: Campbell,Mark (chair), Snavely,Keith Noah (committee member), Hencey,Brandon M. (committee member).
Subjects/Keywords: Kalman Filter; Probabilistic Inference; Model Predictive Control
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Radecki, P. (2016). Applied Probabilistic Inference: Model Estimation For Hvac Predictive Controls And All-Weather Perception For Autonomous Vehicles. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/44364
Chicago Manual of Style (16th Edition):
Radecki, Peter. “Applied Probabilistic Inference: Model Estimation For Hvac Predictive Controls And All-Weather Perception For Autonomous Vehicles.” 2016. Doctoral Dissertation, Cornell University. Accessed January 23, 2021.
http://hdl.handle.net/1813/44364.
MLA Handbook (7th Edition):
Radecki, Peter. “Applied Probabilistic Inference: Model Estimation For Hvac Predictive Controls And All-Weather Perception For Autonomous Vehicles.” 2016. Web. 23 Jan 2021.
Vancouver:
Radecki P. Applied Probabilistic Inference: Model Estimation For Hvac Predictive Controls And All-Weather Perception For Autonomous Vehicles. [Internet] [Doctoral dissertation]. Cornell University; 2016. [cited 2021 Jan 23].
Available from: http://hdl.handle.net/1813/44364.
Council of Science Editors:
Radecki P. Applied Probabilistic Inference: Model Estimation For Hvac Predictive Controls And All-Weather Perception For Autonomous Vehicles. [Doctoral Dissertation]. Cornell University; 2016. Available from: http://hdl.handle.net/1813/44364
McMaster University
27.
Kheradmandi, Masoud.
ADVANCES IN MODEL PREDICTIVE CONTROL.
Degree: PhD, 2018, McMaster University
URL: http://hdl.handle.net/11375/23432
► In this thesis I propose methods and strategies for the design of advanced model predictive control designs. The contributions are in the areas of data-driven…
(more)
▼ In this thesis I propose methods and strategies for the design of advanced model
predictive control designs. The contributions are in the areas of data-driven model based MPC, model monitoring and explicit incorporation of closed-loop response considerations in the MPC, while handling issues such as plant-model mismatch, constraints and uncertainty.
In the initial phase of this research, I address the problem of handling plant-model mismatch by designing a subspace identification based MPC framework that includes model monitoring and closed-loop identification components.
In contrast to performance monitoring based approaches, the validity of the underlying model is monitored by proposing two indexes that compare model predictions with measured past output. In the event that the model monitoring threshold is breached, a new model is identified using an adapted closed-loop subspace identification method. To retain the knowledge of the nominal system dynamics, the proposed approach uses the past training data and current input, output and set-point as the training data for re-identification.
A model validity mechanism then checks if the new model predictions are better than the existing model, and if they are, then the new model is utilized within the MPC.
Next, the proposed MPC with re-identification method is extended to batch processes. To this end, I first utilize a subspace-based model identification approach for batch processes to be used in model
predictive control. A model performance index is developed for batch process, then in the case of poor prediction, re-identification is triggered to identify a new model. In order to emphasize on the recent batch data, the identification is developed in order to increase the contribution of the current data.
In another direction, the stability of data driven
predictive control is addressed. To this end, first, a data-driven Lyapunov-based MPC is designed, and shown to be capable of stabilizing a system at an unstable equilibrium point. The data driven Lyapunov-based MPC utilizes a linear time invariant (LTI) model cognizant of the fact that the training data, owing to the unstable nature of the equilibrium point, has to be obtained from closed-loop operation or experiments. Simulation results are first presented demonstrating closed-loop stability under the proposed data-driven Lyapunov-based MPC. The underlying data-driven model is then utilized as the basis to design an economic MPC.
Finally, I address the problem of
control of nonlinear systems to deliver a prescribed closed-loop behavior. In particular, the framework allows for the practitioner to first specify the nature and specifics of the desired closed-loop behavior (e.g., first order with smallest time constant, second order with no more than a certain percentage overshoot, etc.). An optimization based formulation then computes the
control action to deliver the best attainable closed loop behavior. To decouple the problems of determining the best attainable behavior and tracking it as closely…
Advisors/Committee Members: Mhaskar, Prashant, Chemical Engineering.
Subjects/Keywords: System identification; Subspace identification; Closed-loop identification; Model predictive control; Re-identification; Lyapunov-based model predictive control; Economic model predictive control; Nonlinear model predictive control
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Kheradmandi, M. (2018). ADVANCES IN MODEL PREDICTIVE CONTROL. (Doctoral Dissertation). McMaster University. Retrieved from http://hdl.handle.net/11375/23432
Chicago Manual of Style (16th Edition):
Kheradmandi, Masoud. “ADVANCES IN MODEL PREDICTIVE CONTROL.” 2018. Doctoral Dissertation, McMaster University. Accessed January 23, 2021.
http://hdl.handle.net/11375/23432.
MLA Handbook (7th Edition):
Kheradmandi, Masoud. “ADVANCES IN MODEL PREDICTIVE CONTROL.” 2018. Web. 23 Jan 2021.
Vancouver:
Kheradmandi M. ADVANCES IN MODEL PREDICTIVE CONTROL. [Internet] [Doctoral dissertation]. McMaster University; 2018. [cited 2021 Jan 23].
Available from: http://hdl.handle.net/11375/23432.
Council of Science Editors:
Kheradmandi M. ADVANCES IN MODEL PREDICTIVE CONTROL. [Doctoral Dissertation]. McMaster University; 2018. Available from: http://hdl.handle.net/11375/23432
Purdue University
28.
Just, Fabian.
A Strategy for Minimizing Parkinsonian Noise from a Joystick Controlled Wheeled Mobile Robot.
Degree: MS, Electrical and Computer Engineering, 2013, Purdue University
URL: http://docs.lib.purdue.edu/open_access_theses/39
► This thesis investigates movement of an electric wheelchair as a wheeled mobile robot (WMR) with a battery rechargeable through regenerative braking.The WMR has two…
(more)
▼ This thesis investigates movement of an electric wheelchair as a wheeled mobile robot (WMR) with a battery rechargeable through regenerative braking.The WMR has two wheels, each of which can propel or brake. This leads to four modes of operation: propel-propel, brake-brake, propel-brake, and brake-propel. Braking can be either done by a propelling wheel using negative torques or by regenerative braking which also applies a negative torque.
The thesis begins with a presentation of the WMR model. Performances Indices (PI) are introduced as metrics for specific driving scenarios. For almost all scenarios, the PI is used in a model
predictive control (MPC) strategy for the following set of scenarios with and without noisy measurements on the distance to a wall which simulate a noisy sensor measurement for:
-a wall following scenario
-a wall cornering scenario
-a combined scenario
Results of a combined scenario with Parkinsonian noise on distance to the wall measurements and velocity with and withoutthe use of a notch filter are presented and interpreted.
Finally Parkinsonian noise is imposed on a joystick wheelchair
control scenario with and without the use of a notch filter.
The central result of this thesis is to erase the Parkinsonian tremor from the input of the joystick of a electric wheelchair to improve the life quality of disabled users.
Advisors/Committee Members: Raymond A. DeCarlo, Michael D. Zoltowski.
Subjects/Keywords: Control; Joystick; Parkinson; Parkinson's Disease; Predictive; Robot
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Just, F. (2013). A Strategy for Minimizing Parkinsonian Noise from a Joystick Controlled Wheeled Mobile Robot. (Thesis). Purdue University. Retrieved from http://docs.lib.purdue.edu/open_access_theses/39
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Just, Fabian. “A Strategy for Minimizing Parkinsonian Noise from a Joystick Controlled Wheeled Mobile Robot.” 2013. Thesis, Purdue University. Accessed January 23, 2021.
http://docs.lib.purdue.edu/open_access_theses/39.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Just, Fabian. “A Strategy for Minimizing Parkinsonian Noise from a Joystick Controlled Wheeled Mobile Robot.” 2013. Web. 23 Jan 2021.
Vancouver:
Just F. A Strategy for Minimizing Parkinsonian Noise from a Joystick Controlled Wheeled Mobile Robot. [Internet] [Thesis]. Purdue University; 2013. [cited 2021 Jan 23].
Available from: http://docs.lib.purdue.edu/open_access_theses/39.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Just F. A Strategy for Minimizing Parkinsonian Noise from a Joystick Controlled Wheeled Mobile Robot. [Thesis]. Purdue University; 2013. Available from: http://docs.lib.purdue.edu/open_access_theses/39
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Southern Mississippi
29.
Sylva Prado, Felipe Vicente.
Model Predictive Control for Temperature Dependent Systems.
Degree: MS, Computing, 2014, University of Southern Mississippi
URL: https://aquila.usm.edu/masters_theses/20
► Manipulating and monitoring the variables of temperature dependent systems can be a very complex task for most industrial facilities since they require either the…
(more)
▼ Manipulating and monitoring the variables of temperature dependent systems can be a very complex task for most industrial facilities since they require either the close attention of experienced engineers or highly expensive
control programs. These systems are often poorly operated, which increases the cost of production and affects the overall performance of the process. This paper aims at proposing a solution to this problem using adaptable Model
Predictive Control (MPC) algorithms for temperature dependent systems and computational methods to optimize their performance, while maintaining a stable temperature within the process. This research investigates and evaluates MPC and compares its performance to manual procedures for controlling temperature dependent systems. The method being investigated approximates future output process values like chemical concentrations in order to determine accurate set point changes to input variables that keep them at their desired targets. In addition, the algorithms in this program match predetermined temperature patterns that indicate if the input variables of the system are correctly balanced for operating at the desired production rate. Balance is achieved by using PID closed-loop
control procedures on the output variables, as well as data storage algorithms to help reduce the error of future set point computation.
Advisors/Committee Members: Dia Ali, Amer Dawoud, Beddhu Murali.
Subjects/Keywords: predictive; control; industrial; engineering; steady; state
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Sylva Prado, F. V. (2014). Model Predictive Control for Temperature Dependent Systems. (Masters Thesis). University of Southern Mississippi. Retrieved from https://aquila.usm.edu/masters_theses/20
Chicago Manual of Style (16th Edition):
Sylva Prado, Felipe Vicente. “Model Predictive Control for Temperature Dependent Systems.” 2014. Masters Thesis, University of Southern Mississippi. Accessed January 23, 2021.
https://aquila.usm.edu/masters_theses/20.
MLA Handbook (7th Edition):
Sylva Prado, Felipe Vicente. “Model Predictive Control for Temperature Dependent Systems.” 2014. Web. 23 Jan 2021.
Vancouver:
Sylva Prado FV. Model Predictive Control for Temperature Dependent Systems. [Internet] [Masters thesis]. University of Southern Mississippi; 2014. [cited 2021 Jan 23].
Available from: https://aquila.usm.edu/masters_theses/20.
Council of Science Editors:
Sylva Prado FV. Model Predictive Control for Temperature Dependent Systems. [Masters Thesis]. University of Southern Mississippi; 2014. Available from: https://aquila.usm.edu/masters_theses/20
Delft University of Technology
30.
Jain, R.P.K. (author).
Transportation of Cable Suspended Load using Unmanned Aerial Vehicles: A Real-time Model Predictive Control approach.
Degree: 2015, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:4c6b4a94-4f15-4e67-8c30-eb8156aab406
► Unmanned Aerial Vehicles (UAV) have received an increasing amount of attention recently with many applications being actively investigated across the globe, and several related open…
(more)
▼ Unmanned Aerial Vehicles (UAV) have received an increasing amount of attention recently with many applications being actively investigated across the globe, and several related open research questions being actively pursued. Possible applications include search and rescue, disaster relief, environmental monitoring and surveillance, transportation, and construction. Transportation of cable suspended payloads using Unmanned Aerial Vehicles is one such application which is the topic of this research. Autonomous transportation of objects using UAV can contribute to the safe and reliable supply of food and medicine in remote or disaster-affected areas and even in commercial delivery of goods. The state-of-the-art approaches towards the slung load transportation either develop non-linear feedback control laws to stabilize the system to a predefined trajectory or employ open loop off-line trajectory planning schemes to generate optimal control inputs to the system. Most of these techniques often rely on availability of an accurate model of the system backed up with simulation results. Very few results exist which target experimental validation of the proposed method. Based on the findings of the previously conducted literature survey, it appears that the application of closed loop on-line trajectory generation and control schemes to transport a slung payload in swing free manner remains unanswered. The work in this thesis sets off to answer the research questions in this direction and address the issues that come along with experimental validation. Model Predictive Control (MPC) is a promising framework, which provides the means to tackle both the trajectory generation problem and the feedback control problem in an unified manner. As a result, it forms the most important component of this thesis. Specific research problem that is addressed in this thesis is to transport a cable suspended load using quadrotor from one point to another, while minimizing the swing through the use of Linear Time Invariant MPC techniques. A non-linear dynamic model for the quadrotor-slung load system is obtained and the structure within the system dynamics is exploited to decide the control strategy. Two different MPC formulations viz. MPC with integral action and MPC with delta-u formulation are simulated and compared to Linear Quadratic control with integral action which acts as a benchmark controller. Backed with simulation results, it is shown through experimental validation that it is possible to control the swing of cable suspended load using linear control techniques. MPC being an computationally expensive task, state-of-the-art fast optimization solvers such as FORCES PRO is used to achieve on-line implementation of MPC for the quadrotor-slung load system. To this end, a new software framework for implementation of MPC is developed which establishes a wireless link with the quadrotor resulting in a real-time networked control loop.
Systems and Control
Delft Center for Systems and Control
Mechanical, Maritime and Materials…
Advisors/Committee Members: Keviczky, T. (mentor).
Subjects/Keywords: quadrotor; model predictive control; paparazzi; MAV; robotics
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Jain, R. P. K. (. (2015). Transportation of Cable Suspended Load using Unmanned Aerial Vehicles: A Real-time Model Predictive Control approach. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:4c6b4a94-4f15-4e67-8c30-eb8156aab406
Chicago Manual of Style (16th Edition):
Jain, R P K (author). “Transportation of Cable Suspended Load using Unmanned Aerial Vehicles: A Real-time Model Predictive Control approach.” 2015. Masters Thesis, Delft University of Technology. Accessed January 23, 2021.
http://resolver.tudelft.nl/uuid:4c6b4a94-4f15-4e67-8c30-eb8156aab406.
MLA Handbook (7th Edition):
Jain, R P K (author). “Transportation of Cable Suspended Load using Unmanned Aerial Vehicles: A Real-time Model Predictive Control approach.” 2015. Web. 23 Jan 2021.
Vancouver:
Jain RPK(. Transportation of Cable Suspended Load using Unmanned Aerial Vehicles: A Real-time Model Predictive Control approach. [Internet] [Masters thesis]. Delft University of Technology; 2015. [cited 2021 Jan 23].
Available from: http://resolver.tudelft.nl/uuid:4c6b4a94-4f15-4e67-8c30-eb8156aab406.
Council of Science Editors:
Jain RPK(. Transportation of Cable Suspended Load using Unmanned Aerial Vehicles: A Real-time Model Predictive Control approach. [Masters Thesis]. Delft University of Technology; 2015. Available from: http://resolver.tudelft.nl/uuid:4c6b4a94-4f15-4e67-8c30-eb8156aab406
◁ [1] [2] [3] [4] [5] … [39] ▶
. 
Open Access Theses and Dissertations