subject:(Model Predictive Control)

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

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APA (6^th 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 (16^th 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 February 27, 2020. 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 (7^th 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. 27 Feb 2020.

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 2020 Feb 27]. 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. Kreuzen, C. 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..

Subjects/Keywords: system & control; model predictive control

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APA (6^th 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 (16^th Edition):

Kreuzen, C. “Cooperative adaptive cruise control: using information from multiple predecessors in combination with MPC:.” 2012. Masters Thesis, Delft University of Technology. Accessed February 27, 2020. http://resolver.tudelft.nl/uuid:dd0c766a-5f16-4799-8c84-39abbddbce9e.

MLA Handbook (7^th Edition):

Kreuzen, C. “Cooperative adaptive cruise control: using information from multiple predecessors in combination with MPC:.” 2012. Web. 27 Feb 2020.

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 2020 Feb 27]. 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

Texas A&M University

3. Zhang, Qingyu. Game Theoretic Model Predictive Control for Autonomous Driving.

Degree: PhD, Mechanical Engineering, 2019, Texas A&M University

URL: http://hdl.handle.net/1969.1/186159

► This study presents two closely-related solutions to autonomous vehicle control problems in highway driving scenario using game theory and model predictive control. We first develop… (more)

▼ This study presents two closely-related solutions to autonomous vehicle control problems in highway driving scenario using game theory and model predictive control. We first develop a game theoretic four-stage model predictive controller (GT4SMPC). The controller is responsible for both longitudinal and lateral movements of Subject Vehicle (SV) . It includes a Stackelberg game as a high level controller and a model predictive controller (MPC) as a low level one. Specifically, GT4SMPC constantly establishes and solves games corresponding to multiple gaps in front of multiple-candidate vehicles (GCV) when SV is interacting with them by signaling a lane change intention through turning light or by a small lateral movement. SV’s payoff is the negative of the MPC’s cost function , which ensures strong connection between the game and that the solution of the game is more likely to be achieved by a hybrid MPC (HMPC). GCV’s payoff is a linear combination of the speed payoff, headway payoff and acceleration payoff. . We use decreasing acceleration model to generate our prediction of TV’s future motion, which is utilized in both defining TV’s payoffs over the prediction horizon in the game and as the reference of the MPC. Solving the games gives the optimal gap and the target vehicle (TV). In the low level , the lane change process are divided into four stages: traveling in the current lane, leaving current lane, crossing lane marking, traveling in the target lane. The division identifies the time that SV should initiate actual lateral movement for the lateral controller and specifies the constraints HMPC should deal at each step of the MPC prediction horizon. Then the four-stage HMPC controls SV’s actual longitudinal motion and execute the lane change at the right moment. Simulations showed the GT4SMPC is able to intelligently drive SV into the selected gap and accomplish both discretionary land change (DLC) and mandatory lane change (MLC) in a dynamic situation. Human-in-the-loop driving simulation indicated that GT4SMPC can decently control the SV to complete lane changes with the presence of human drivers. Second, we propose a differential game theoretic model predictive controller (DGTMPC) to address the drawbacks of GT4SMPC. In GT4SMPC, the games are defined as table game, which indicates each players only have limited amount of choices for a specific game and such choice remain fixed during the prediction horizon. In addition, we assume a known model for traffic vehicles but in reality drivers’ preference is partly unknown. In order to allow the TV to make multiple decisions within the prediction horizon and to measure TV’s driving style on-line, we propose a differential game theoretic model predictive controller (DGTMPC). The high level of the hierarchical DGTMPC is the two-player differential lane-change Stackelberg game. We assume each player uses a MPC to control its motion and the optimal solution of leaders’ MPC depends on the solution of the follower. Therefore, we convert this differential game problem into a… Advisors/Committee Members: Langari, Reza (advisor), Rathinam, Sivakumar (committee member), Talebpour, Alireza (committee member), Suh, Steve (committee member).

Subjects/Keywords: Game theory; Model Predictive Control

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APA (6^th Edition):

Zhang, Q. (2019). Game Theoretic Model Predictive Control for Autonomous Driving. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/186159

Chicago Manual of Style (16^th Edition):

Zhang, Qingyu. “Game Theoretic Model Predictive Control for Autonomous Driving.” 2019. Doctoral Dissertation, Texas A&M University. Accessed February 27, 2020. http://hdl.handle.net/1969.1/186159.

MLA Handbook (7^th Edition):

Zhang, Qingyu. “Game Theoretic Model Predictive Control for Autonomous Driving.” 2019. Web. 27 Feb 2020.

Vancouver:

Zhang Q. Game Theoretic Model Predictive Control for Autonomous Driving. [Internet] [Doctoral dissertation]. Texas A&M University; 2019. [cited 2020 Feb 27]. Available from: http://hdl.handle.net/1969.1/186159.

Council of Science Editors:

Zhang Q. Game Theoretic Model Predictive Control for Autonomous Driving. [Doctoral Dissertation]. Texas A&M University; 2019. Available from: http://hdl.handle.net/1969.1/186159

University of Waterloo

4. 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

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APA (6^th 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 (16^th Edition):

Santander, Omar. “Economic Model Predictive Control of Chemical Processes.” 2015. Thesis, University of Waterloo. Accessed February 27, 2020. 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 (7^th Edition):

Santander, Omar. “Economic Model Predictive Control of Chemical Processes.” 2015. Web. 27 Feb 2020.

Vancouver:

Santander O. Economic Model Predictive Control of Chemical Processes. [Internet] [Thesis]. University of Waterloo; 2015. [cited 2020 Feb 27]. 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

5. 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

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APA (6^th 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 (16^th Edition):

Maitland, Anson. “Nonlinear Model Predictive Control Reduction Strategies for Real-time Optimal Control.” 2019. Thesis, University of Waterloo. Accessed February 27, 2020. 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 (7^th Edition):

Maitland, Anson. “Nonlinear Model Predictive Control Reduction Strategies for Real-time Optimal Control.” 2019. Web. 27 Feb 2020.

Vancouver:

Maitland A. Nonlinear Model Predictive Control Reduction Strategies for Real-time Optimal Control. [Internet] [Thesis]. University of Waterloo; 2019. [cited 2020 Feb 27]. 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

Université de Grenoble

6. 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

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APA (6^th 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 (16^th 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 February 27, 2020. http://www.theses.fr/2012GRENT097.

MLA Handbook (7^th 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. 27 Feb 2020.

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 2020 Feb 27]. 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

Oregon State University

7. 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)

Subjects/Keywords: model predictive control; Remote submersibles – Automatic control

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APA (6^th 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 (16^th Edition):

Fernández, Daniel C. “Model Predictive Control for Underwater Robots in Ocean Waves.” 2015. Masters Thesis, Oregon State University. Accessed February 27, 2020. http://hdl.handle.net/1957/57267.

MLA Handbook (7^th Edition):

Fernández, Daniel C. “Model Predictive Control for Underwater Robots in Ocean Waves.” 2015. Web. 27 Feb 2020.

Vancouver:

Fernández DC. Model Predictive Control for Underwater Robots in Ocean Waves. [Internet] [Masters thesis]. Oregon State University; 2015. [cited 2020 Feb 27]. 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

University of California – Berkeley

8. 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)

Subjects/Keywords: Mechanical engineering; Control; Model Predictive Control; Symmetry

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APA (6^th 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 (16^th Edition):

Danielson, Claus. “Symmetric Constrained Optimal Control: Theory, Algorithms, and Applications.” 2014. Thesis, University of California – Berkeley. Accessed February 27, 2020. 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 (7^th Edition):

Danielson, Claus. “Symmetric Constrained Optimal Control: Theory, Algorithms, and Applications.” 2014. Web. 27 Feb 2020.

Vancouver:

Danielson C. Symmetric Constrained Optimal Control: Theory, Algorithms, and Applications. [Internet] [Thesis]. University of California – Berkeley; 2014. [cited 2020 Feb 27]. 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

Texas A&M University

9. Vedam, Narayani. Terrain-Adaptive Cruise Control: A Human-Like Approach.

Degree: 2015, Texas A&M University

URL: http://hdl.handle.net/1969.1/156224

► With rapid advancements in the ?eld of autonomous vehicles, intelligent control systems and automated highway systems, the need for GPS based vehicle data has grown… (more)

Subjects/Keywords: Terrain-Adaptive; Cruise Control; Model Predictive Control

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APA (6^th Edition):

Vedam, N. (2015). Terrain-Adaptive Cruise Control: A Human-Like Approach. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/156224

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Vedam, Narayani. “Terrain-Adaptive Cruise Control: A Human-Like Approach.” 2015. Thesis, Texas A&M University. Accessed February 27, 2020. http://hdl.handle.net/1969.1/156224.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Vedam, Narayani. “Terrain-Adaptive Cruise Control: A Human-Like Approach.” 2015. Web. 27 Feb 2020.

Vancouver:

Vedam N. Terrain-Adaptive Cruise Control: A Human-Like Approach. [Internet] [Thesis]. Texas A&M University; 2015. [cited 2020 Feb 27]. Available from: http://hdl.handle.net/1969.1/156224.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Vedam N. Terrain-Adaptive Cruise Control: A Human-Like Approach. [Thesis]. Texas A&M University; 2015. Available from: http://hdl.handle.net/1969.1/156224

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Waterloo

10. Diaz Mendoza, Juan Rosendo. Robust Empirical Model-Based Algorithms for Nonlinear Processes.

Degree: 2010, University of Waterloo

URL: http://hdl.handle.net/10012/5314

► This research work proposes two robust empirical model-based predictive control algorithms for nonlinear processes. Chemical process are generally highly nonlinear thus predictive control algorithms that… (more)

▼ This research work proposes two robust empirical model-based predictive control algorithms for nonlinear processes. Chemical process are generally highly nonlinear thus predictive control algorithms that explicitly account for the nonlinearity of the process are expected to provide better closed-loop performance as compared to algorithms based on linear models. Two types of models can be considered for control: first-principles and empirical. Empirical models were chosen for the proposed algorithms for the following reasons: (i) they are less complex for on-line optimization, (ii) they are easy to identify from input-output data and (iii) their structure is suitable for the formulation of robustness tests. One of the key problems of every model that is used for prediction within a control strategy is that some model parameters cannot be known accurately due to measurement noise and/or error in the structure of the assumed model. In the robust control approach it is assumed that processes can be represented by models with parameters' values that are assumed to lie between a lower and upper bound or equivalently, that these parameters can be represented by a nominal value plus uncertainty. When this uncertainty in control parameters is not considered by the controller the control actions might be insufficient to effectively control the process and in some extreme cases the closed-loop may become unstable. Accordingly, the two robust control algorithms proposed in the current work explicitly account for the effect of uncertainty on stability and closed-loop performance. The first proposed controller is a robust gain-scheduling model predictive controller (MPC). In this case the process is represented within each operating region by a state-affine model obtained from input-output data. The state-affine model matrices are used to obtain a state-space based MPC for every operating region. By combining the state-affine, disturbance and controller equations a closed-loop representation was obtained. Then, the resulting mathematical representation was tested for robustness with linear matrix inequalities (LMI's) based on a test where the vertices of the parameter box were obtained by an iterative procedure. The result of the LMI's test gives a measure of performance referred to as γ that relates the effect of the disturbances on the process outputs. Finally, for the gain-scheduling part of the algorithm a set of rules was proposed to switch between the available controllers according to the current process conditions. Since every combination of the controller tuning parameters results in a different value of γ, an optimization problem was proposed to minimize γ with respect to the tuning parameters. Accordingly, for the proposed controller it was ensured that the effect of the disturbances on the output variables was kept to its minimum. A bioreactor case study was presented to show the benefits of the proposed algorithm. For comparison purposes a non-robust linear MPC was also designed. The results show that the…

Subjects/Keywords: Robust Control; Nonlinear Model Predictive Control

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APA (6^th Edition):

Diaz Mendoza, J. R. (2010). Robust Empirical Model-Based Algorithms for Nonlinear Processes. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/5314

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Diaz Mendoza, Juan Rosendo. “Robust Empirical Model-Based Algorithms for Nonlinear Processes.” 2010. Thesis, University of Waterloo. Accessed February 27, 2020. http://hdl.handle.net/10012/5314.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Diaz Mendoza, Juan Rosendo. “Robust Empirical Model-Based Algorithms for Nonlinear Processes.” 2010. Web. 27 Feb 2020.

Vancouver:

Diaz Mendoza JR. Robust Empirical Model-Based Algorithms for Nonlinear Processes. [Internet] [Thesis]. University of Waterloo; 2010. [cited 2020 Feb 27]. Available from: http://hdl.handle.net/10012/5314.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Diaz Mendoza JR. Robust Empirical Model-Based Algorithms for Nonlinear Processes. [Thesis]. University of Waterloo; 2010. Available from: http://hdl.handle.net/10012/5314

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

UCLA

11. 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)

Subjects/Keywords: Chemical engineering; distributed model predictive control; economic model predictive control; nonlinear systems; process control

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APA (6^th 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 (16^th Edition):

Anderson, Timothy. “Distributed Economic Model Predictive Control of a Catalytic Reactor: Evaluation of Sequential and Iterative Architectures.” 2014. Thesis, UCLA. Accessed February 27, 2020. 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 (7^th Edition):

Anderson, Timothy. “Distributed Economic Model Predictive Control of a Catalytic Reactor: Evaluation of Sequential and Iterative Architectures.” 2014. Web. 27 Feb 2020.

Vancouver:

Anderson T. Distributed Economic Model Predictive Control of a Catalytic Reactor: Evaluation of Sequential and Iterative Architectures. [Internet] [Thesis]. UCLA; 2014. [cited 2020 Feb 27]. 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

Clemson University

12. Koli, Rohit Vishvanath. Model Predictive Control of Modern High-Degree-of-Freedom Turbocharged Spark Ignited Engines with External Cooled EGR.

Degree: PhD, Automotive Engineering, 2018, Clemson University

URL: https://tigerprints.clemson.edu/all_dissertations/2211

► The efficiency of modern downsized SI engines has been significantly improved using cooled Low-Pressure Exhaust Gas Recirculation, Turbocharging and Variable Valve Timing actuation. Control of… (more)

Subjects/Keywords: EGR; Internal Combustion Engine; Model Predictive Control

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APA (6^th Edition):

Koli, R. V. (2018). Model Predictive Control of Modern High-Degree-of-Freedom Turbocharged Spark Ignited Engines with External Cooled EGR. (Doctoral Dissertation). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_dissertations/2211

Chicago Manual of Style (16^th Edition):

Koli, Rohit Vishvanath. “Model Predictive Control of Modern High-Degree-of-Freedom Turbocharged Spark Ignited Engines with External Cooled EGR.” 2018. Doctoral Dissertation, Clemson University. Accessed February 27, 2020. https://tigerprints.clemson.edu/all_dissertations/2211.

MLA Handbook (7^th Edition):

Koli, Rohit Vishvanath. “Model Predictive Control of Modern High-Degree-of-Freedom Turbocharged Spark Ignited Engines with External Cooled EGR.” 2018. Web. 27 Feb 2020.

Vancouver:

Koli RV. Model Predictive Control of Modern High-Degree-of-Freedom Turbocharged Spark Ignited Engines with External Cooled EGR. [Internet] [Doctoral dissertation]. Clemson University; 2018. [cited 2020 Feb 27]. Available from: https://tigerprints.clemson.edu/all_dissertations/2211.

Council of Science Editors:

Koli RV. Model Predictive Control of Modern High-Degree-of-Freedom Turbocharged Spark Ignited Engines with External Cooled EGR. [Doctoral Dissertation]. Clemson University; 2018. Available from: https://tigerprints.clemson.edu/all_dissertations/2211

McMaster University

13. 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)

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

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APA (6^th 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 (16^th Edition):

Kheradmandi, Masoud. “ADVANCES IN MODEL PREDICTIVE CONTROL.” 2018. Doctoral Dissertation, McMaster University. Accessed February 27, 2020. http://hdl.handle.net/11375/23432.

MLA Handbook (7^th Edition):

Kheradmandi, Masoud. “ADVANCES IN MODEL PREDICTIVE CONTROL.” 2018. Web. 27 Feb 2020.

Vancouver:

Kheradmandi M. ADVANCES IN MODEL PREDICTIVE CONTROL. [Internet] [Doctoral dissertation]. McMaster University; 2018. [cited 2020 Feb 27]. 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

California State University – Sacramento

14. Guo, Zhicheng. Model predictive control for two-stage photovoltaic system.

Degree: MS, Electrical and Electronic Engineering, 2017, California State University – Sacramento

URL: http://hdl.handle.net/10211.3/198833

► Energy is a vital component for human economic development and growth. The extensive use of fossil fuels has damaged our living environment. Consequently, the world… (more)

Subjects/Keywords: Model predictive control; Photovoltaic; Two-stage system

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Guo, Z. (2017). Model predictive control for two-stage photovoltaic system. (Masters Thesis). California State University – Sacramento. Retrieved from http://hdl.handle.net/10211.3/198833

Chicago Manual of Style (16^th Edition):

Guo, Zhicheng. “Model predictive control for two-stage photovoltaic system.” 2017. Masters Thesis, California State University – Sacramento. Accessed February 27, 2020. http://hdl.handle.net/10211.3/198833.

MLA Handbook (7^th Edition):

Guo, Zhicheng. “Model predictive control for two-stage photovoltaic system.” 2017. Web. 27 Feb 2020.

Vancouver:

Guo Z. Model predictive control for two-stage photovoltaic system. [Internet] [Masters thesis]. California State University – Sacramento; 2017. [cited 2020 Feb 27]. Available from: http://hdl.handle.net/10211.3/198833.

Council of Science Editors:

Guo Z. Model predictive control for two-stage photovoltaic system. [Masters Thesis]. California State University – Sacramento; 2017. Available from: http://hdl.handle.net/10211.3/198833

University of Illinois – Urbana-Champaign

15. Lee, Seung Ho. A model predictive control approach to a class of multiplayer minmax differential games.

Degree: PhD, Mechanical Engineering, 2016, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/90473

► In this dissertation, we consider a class of two-team adversarial differential games in which there are multiple mobile dynamic agents on each team. We describe… (more)

▼ In this dissertation, we consider a class of two-team adversarial differential games in which there are multiple mobile dynamic agents on each team. We describe such games in terms of semi-infinite minmax Model Predictive Control (MPC) problems, and present a numerical optimization technique for efficiently solving them. We also describe the implementation of the solution method in both indoor and outdoor robotic testbeds. Our solution method requires one to solve a sequence of Quadratic Programs (QPs), which together efficiently solve the original semi-infinite min- max MPC problem. The solution method separates the problem into two subproblems called the inner and outer subproblems, respectively. The inner subproblem is based on a constrained nonlinear numerical optimization technique called the Phase I-Phase II method, and we develop a customized version of this method. The outer subproblem is about judiciously initializing the inner subproblems to achieve overall convergence; our method guarantees exponential convergence. We focus on a specific semi-infinite minmax MPC problem called the harbor defense problem. First, we present foundational work on this problem in a formulation containing a single defender and single intruder. We next extend the basic formulation to various advanced scenarios that include cases in which there are multiple defenders and intruders, and also ones that include varying assumptions about intruder strategies. Another main contribution is that we implemented our solution method for the harbor defense problem on both real-time indoor and outdoor testbeds, and demonstrated its computational effectiveness. The indoor testbed is a custom-built robotic testbed named HoTDeC (Hovercraft Testbed for Decentralized Control). The outdoor testbed involved full-sized US Naval Academy patrol ships, and the experiment was conducted in Chesapeake Bay in collaboration with the US Naval Academy. The scenario used involved one ship (the intruder) being commanded by a human pilot, and the defender ship being controlled automatically by our semi-infinite minmax MPC algorithm.The results of several experiments are presented. Finally, we present an efficient algorithm for solving a class of matrix games, and show how this approach can be directly used to effectively solve our original continuous space semi-infinite minmax problem using an adaptive approximation. Advisors/Committee Members: Dullerud, Geir E (advisor), Dullerud, Geir E (Committee Chair), Basar, Tamer (committee member), Salapaka, Srinivasa (committee member), Mehta, Prashant (committee member).

Subjects/Keywords: model predictive control; min-max; differential games

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Lee, S. H. (2016). A model predictive control approach to a class of multiplayer minmax differential games. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/90473

Chicago Manual of Style (16^th Edition):

Lee, Seung Ho. “A model predictive control approach to a class of multiplayer minmax differential games.” 2016. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed February 27, 2020. http://hdl.handle.net/2142/90473.

MLA Handbook (7^th Edition):

Lee, Seung Ho. “A model predictive control approach to a class of multiplayer minmax differential games.” 2016. Web. 27 Feb 2020.

Vancouver:

Lee SH. A model predictive control approach to a class of multiplayer minmax differential games. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2016. [cited 2020 Feb 27]. Available from: http://hdl.handle.net/2142/90473.

Council of Science Editors:

Lee SH. A model predictive control approach to a class of multiplayer minmax differential games. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/90473

Delft University of Technology

16. Jain, R.P.K. 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. Advisors/Committee Members: Keviczky, T..

Subjects/Keywords: quadrotor; model predictive control; paparazzi; MAV; robotics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th 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 (16^th Edition):

Jain, R P K. “Transportation of Cable Suspended Load using Unmanned Aerial Vehicles: A Real-time Model Predictive Control approach:.” 2015. Masters Thesis, Delft University of Technology. Accessed February 27, 2020. http://resolver.tudelft.nl/uuid:4c6b4a94-4f15-4e67-8c30-eb8156aab406.

MLA Handbook (7^th Edition):

Jain, R P K. “Transportation of Cable Suspended Load using Unmanned Aerial Vehicles: A Real-time Model Predictive Control approach:.” 2015. Web. 27 Feb 2020.

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 2020 Feb 27]. 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

Delft University of Technology

17. Siddaramappa, A. Rigid Formation Control using Hovercrafts: A Spatial Model Predictive Control Approach:.

Degree: 2015, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:3b025de3-52ff-45d3-ab80-05cf2e450ec9

► The steady increase in volume of goods to be transported over land and water has necessitated the development of efficient and alternative methods of transportation.… (more)

▼ The steady increase in volume of goods to be transported over land and water has necessitated the development of efficient and alternative methods of transportation. In current practices considerable amount of transportation delay is caused due to switching between different modes of transportation resulting in economic losses. Another difficulty in the transportation sector arises when handling extremely large objects, such as wind turbines and cargo containers. In order to overcome these difficulties, an alternative method of transportation has been proposed recently. The proposed alternative method consists of the development of a formation control framework using hovercrafts. Formation control is a widely researched topic due its potential benefits in reducing the system cost, structure flexibility and improving the efficiency of the overall system. State-of-the-art methods for formation control are focused towards developing a controller to track a predefined trajectory, and the trajectory generation is an additional problem which is addressed off-line. A path tracking formation control framework is an area which has received less attention in the literature and can have possible improvements. Hence, this thesis work answers the research question of generating a feasible path for a set of initial and final conditions, and tracking the generated path. Model Predictive Controller (MPC) is a promising framework, because of its constraint handling capabilities. Hence, it accounts for the major component of this thesis. A new framework for formation control is proposed which uses spatial-domain parametrization instead of the conventional time-domain parametrization. MPC being a computationally expensive task, the proposed framework reduces the number of decision variables and constraint evaluations which can help to achieve the desired Real Time performance. The specific research question addressed in this thesis work is to develop a path tracking formation control framework while minimising the absolute and relative position error for each vehicle in the formation. A nonlinear dynamic model for the hovercraft is obtained using first principles and its structure is used to design an optimization based controller. An Optimal Control Problem (OCP) is formulated which is solved using direct collocation method. In this method a continuous problem is discretized into a number of collocation points, and the resulting problem is solved using state-of-the-art Nonlinear Program solvers such as, SNOPT and NPSOL. The performance of the proposed framework is illustrated using numerical simulations. Advisors/Committee Members: Keviczky, T..

Subjects/Keywords: Model Predictive Controller; Formation Control; Hovercraft

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Siddaramappa, A. (2015). Rigid Formation Control using Hovercrafts: A Spatial Model Predictive Control Approach:. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:3b025de3-52ff-45d3-ab80-05cf2e450ec9

Chicago Manual of Style (16^th Edition):

Siddaramappa, A. “Rigid Formation Control using Hovercrafts: A Spatial Model Predictive Control Approach:.” 2015. Masters Thesis, Delft University of Technology. Accessed February 27, 2020. http://resolver.tudelft.nl/uuid:3b025de3-52ff-45d3-ab80-05cf2e450ec9.

MLA Handbook (7^th Edition):

Siddaramappa, A. “Rigid Formation Control using Hovercrafts: A Spatial Model Predictive Control Approach:.” 2015. Web. 27 Feb 2020.

Vancouver:

Siddaramappa A. Rigid Formation Control using Hovercrafts: A Spatial Model Predictive Control Approach:. [Internet] [Masters thesis]. Delft University of Technology; 2015. [cited 2020 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:3b025de3-52ff-45d3-ab80-05cf2e450ec9.

Council of Science Editors:

Siddaramappa A. Rigid Formation Control using Hovercrafts: A Spatial Model Predictive Control Approach:. [Masters Thesis]. Delft University of Technology; 2015. Available from: http://resolver.tudelft.nl/uuid:3b025de3-52ff-45d3-ab80-05cf2e450ec9

Delft University of Technology

18. Stive, P.M. Performance assessment of tree-based model predictive control:.

Degree: 2011, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:79a718a3-cd99-4f44-8391-3de5e93c3dcd

► This research focuses on polder-belt canal systems. More is demanded from these systems every day. Man induced changes, like increasing population density and increasing land… (more)

▼ This research focuses on polder-belt canal systems. More is demanded from these systems every day. Man induced changes, like increasing population density and increasing land value on one hand and climate change in the form of longer dry spells and more extreme precipitation events on the other hand are the main sources. The operation of the structures in these systems plays a critical role in successfully dealing with these challenges. To get the most out of the current system and its structures, operation by humans alone is not enough, they need to be aided by computers. A promising technique is Model Predictive Control (MPC). A control algorithm that uses a model of the system and forecasts of the future disturbances to determine the control actions for the structures, whilst adhering to the constraints of the system. Forecasts are uncertain and are therefore provided in the form of ensemble forecasts that consist of multiple scenarios. MPC uses only one scenario and is thus vulnerable to these uncertainties. Treebased Model Predictive Control (TBMPC) considers the complete ensemble to determine control actions. TBMPC has, however, only been tested in theory. Only open loop simulations have been carried out, no continuous closed loop simulations have been done. TBMPC uses the complete ensemble, but to save calculation time reduces it to a tree-shaped representative ensemble with fewer nodes. This means aggregating nodes and scenarios on various points in the ensemble. There are multiple rules that determine which nodes and scenarios are aggregated, however, their optimal setting is not known. Also if TBMPC has more added benefit over MPC on certain system configurations (e.g. configurations with higher discharge or storage capacity) is not known either. A model is developed to simulate the performance of MPC and TBMPC. It can deal with different precipitation series, forecasts, system configurations and control algorithm parameters. All simulations have a duration of one year and a one hour time step. The rules that determine which nodes and scenarios are aggregated are investigated first. Transforming the inflow forecast scenarios to cumulative inflow scenarios before determining which nodes and scenarios to aggregate yields better results. The threshold value is also important as it determines whether or not two scenarios are close enough to each other to be aggregated. Nothing was known about the right value for this parameter. One of the objectives was to be able to fine tune this value to the system con guration. Setting this value as a percentage of the maximum pump capacity of the system works well across di erent system configurations. The optimal value is 100% of the maximum pump capacity. The scenario reduction algorithm (i.e. the algorithm that creates the tree from the original ensemble) has two parts. First it reduces the number of scenarios in the ensemble to a predefined number and secondly it creates a tree out of the reduced ensemble. No information was available about the right amount of… Advisors/Committee Members: Van de Giesen, N.C..

Subjects/Keywords: tree-based model predictive control; performance assessment

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Stive, P. M. (2011). Performance assessment of tree-based model predictive control:. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:79a718a3-cd99-4f44-8391-3de5e93c3dcd

Chicago Manual of Style (16^th Edition):

Stive, P M. “Performance assessment of tree-based model predictive control:.” 2011. Masters Thesis, Delft University of Technology. Accessed February 27, 2020. http://resolver.tudelft.nl/uuid:79a718a3-cd99-4f44-8391-3de5e93c3dcd.

MLA Handbook (7^th Edition):

Stive, P M. “Performance assessment of tree-based model predictive control:.” 2011. Web. 27 Feb 2020.

Vancouver:

Stive PM. Performance assessment of tree-based model predictive control:. [Internet] [Masters thesis]. Delft University of Technology; 2011. [cited 2020 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:79a718a3-cd99-4f44-8391-3de5e93c3dcd.

Council of Science Editors:

Stive PM. Performance assessment of tree-based model predictive control:. [Masters Thesis]. Delft University of Technology; 2011. Available from: http://resolver.tudelft.nl/uuid:79a718a3-cd99-4f44-8391-3de5e93c3dcd

Cornell University

19. Radecki, Peter. Applied Probabilistic Inference: Model Estimation For Hvac Predictive Controls And All-Weather Perception For Autonomous Vehicles .

Degree: 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: Snavely,Keith Noah (committeeMember), Hencey,Brandon M. (committeeMember).

Subjects/Keywords: Kalman Filter; Probabilistic Inference; Model Predictive Control

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APA (6^th Edition):

Radecki, P. (2016). Applied Probabilistic Inference: Model Estimation For Hvac Predictive Controls And All-Weather Perception For Autonomous Vehicles . (Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/44364

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Radecki, Peter. “Applied Probabilistic Inference: Model Estimation For Hvac Predictive Controls And All-Weather Perception For Autonomous Vehicles .” 2016. Thesis, Cornell University. Accessed February 27, 2020. http://hdl.handle.net/1813/44364.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Radecki, Peter. “Applied Probabilistic Inference: Model Estimation For Hvac Predictive Controls And All-Weather Perception For Autonomous Vehicles .” 2016. Web. 27 Feb 2020.

Vancouver:

Radecki P. Applied Probabilistic Inference: Model Estimation For Hvac Predictive Controls And All-Weather Perception For Autonomous Vehicles . [Internet] [Thesis]. Cornell University; 2016. [cited 2020 Feb 27]. Available from: http://hdl.handle.net/1813/44364.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Radecki P. Applied Probabilistic Inference: Model Estimation For Hvac Predictive Controls And All-Weather Perception For Autonomous Vehicles . [Thesis]. Cornell University; 2016. Available from: http://hdl.handle.net/1813/44364

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Melbourne

20. LAM, DENISE. A model predictive approach to optimal path-following and contouring control.

Degree: 2012, University of Melbourne

URL: http://hdl.handle.net/11343/37555

► In the multi-billion dollar motion control industry, systems are often required to traverse along a geometric path, where the speed at which the path is… (more)

▼ In the multi-billion dollar motion control industry, systems are often required to traverse along a geometric path, where the speed at which the path is traversed is not pre-determined. Such path-following tasks involve competing control objectives of maximising accuracy while minimising traversal time.Existing trajectory planning routines optimise the path speed with respect to some of these objectives. However, trajectory planning is conducted in an open loop fashion, relying on trajectory tracking controllers to achieve the desired motion. Meanwhile, closed-loop path-following control schemes do not explicitly consider the control objectives. This thesis investigates the development of closed-loop path-following control schemes based on minimisation of a cost function within a receding horizon structure. The cost function is selected to reflect the desired objectives such as minimising traversal time, and is minimised subject to the system constraints. The receding horizon formulation allows for feedback to be taken into account at each sample, in contrast to open loop trajectory planning. A receding horizon path-following control scheme is first developed with the intention of achieving time-optimal path-following. The controller is designed to advance along the path as far as possible within a finite horizon while staying exactly on the path and honouring the constraints. The conditions under which the receding horizon approach replicates the time-optimal path-following solution are identified for second order differentially flat systems. In particular, it is shown that minimum-time path-following is achieved when the horizon is sufficiently long. Subsequently, a contouring control scheme for biaxial systems based on a similar framework is developed where the exact path-following constraint is relaxed in favour of penalising contouring error in the finite horizon cost function, which reflects the trade-off between competing control objectives of contouring accuracy, productivity and minimisation of control deviations. The penalty weights may be tuned to favour some objectives over others. A computationally efficient algorithm is proposed, allowing the scheme to be implemented in real time on an X-Y table. Experimental results demonstrate how the behaviour of the controller can be adjusted in a systematic manner by tuning the penalty weights. Results also show that the proposed scheme achieves superior contouring performance compared with tracking controllers implemented with constant path speed. The proposed contouring control approach is then extended to more complicated multi-axis systems involving position and orientation control. A multi-rate control architecture is proposed to facilitate the implementation of the contouring control scheme on industrial hardware. Simulations are conducted with an industrial profile cutting…

Subjects/Keywords: path-following; contouring; model predictive control

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

LAM, D. (2012). A model predictive approach to optimal path-following and contouring control. (Doctoral Dissertation). University of Melbourne. Retrieved from http://hdl.handle.net/11343/37555

Chicago Manual of Style (16^th Edition):

LAM, DENISE. “A model predictive approach to optimal path-following and contouring control.” 2012. Doctoral Dissertation, University of Melbourne. Accessed February 27, 2020. http://hdl.handle.net/11343/37555.

MLA Handbook (7^th Edition):

LAM, DENISE. “A model predictive approach to optimal path-following and contouring control.” 2012. Web. 27 Feb 2020.

Vancouver:

LAM D. A model predictive approach to optimal path-following and contouring control. [Internet] [Doctoral dissertation]. University of Melbourne; 2012. [cited 2020 Feb 27]. Available from: http://hdl.handle.net/11343/37555.

Council of Science Editors:

LAM D. A model predictive approach to optimal path-following and contouring control. [Doctoral Dissertation]. University of Melbourne; 2012. Available from: http://hdl.handle.net/11343/37555

University of New South Wales

21. Rana, Md. Sohel. High Performance Control of an Atomic Force Microscope for Faster Image Scanning.

Degree: Engineering & Information Technology, 2014, University of New South Wales

URL: http://handle.unsw.edu.au/1959.4/54007 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12715/SOURCE02?view=true

► The performance of the atomic force microscope (AFM), a nanoscale imaging tool, is significantly influenced by the dynamics of the piezoelectric tube scanner (PTS). The… (more)

▼ The performance of the atomic force microscope (AFM), a nanoscale imaging tool, is significantly influenced by the dynamics of the piezoelectric tube scanner (PTS). The limitations of the PTS are its nonlinear behavior in the form of hysteresis and creep effects, low mechanical resonance frequency, and cross-coupling between its two axes of motion. The key focus of this dissertation is to mitigate the limitations of the PTS using controllers to increase the scanning speed. Broadly speaking, this thesis reports four significant contributions for improved scanning using AFMs with piezo tube scanners.The first and unique contribution of this thesis is the design and experimental implementation of a model predictive control (MPC) scheme to achieve accurate tracking for improved image quality at high scanning speeds. Close tracking of the reference signal ensures compensation of the creep and hysteresis effects in the PTS. The MPC control scheme is commonly used when tracking a reference trajectory is the primary goal. This capability of MPC controllers has motivated our research to indirectly compensate the creep and hysteresis effects of the PTS to improve the imaging performance of AFMs. The design of this controller is based on an identified single-input single-output (SISO) model of the PTS, and the AFM's improved imaging quality and scanning speed is illustrated for the design using the proposed control scheme. To evaluate the overall performance improvement achieved by the proposed scheme, an experimental comparison of scanned images from the proposed controller and the existing AFM proportional-integral (PI) controller is undertaken, the results of which verify the efficacy of the proposed design.Tube resonance is one of the major limitations for fast image scanning using AFMs. To overcome this, a damping compensator or notch filter introduced with the proposed MPC controller achieves a high closed-loop bandwidth and significant damping, and enables a reference triangular signal to be tracked. This is the second contribution of this dissertation. By using the proposed controller, the vibration effects in scanned images at high scanning speeds are compensated. Comparisons of the performance of the MPC controller and the modified MPC scheme with a damping compensator are presented.Another reason for the poor performance of the PTS for nanopositioning is that any movement in its x- or y-axis direction produces an undesirable motion in the z-direction called the cross-coupling effect. A multi-input multi-output (MIMO) MPC controller is proposed to compensate for the cross-coupling effect and this is the third contribution of this research. The design is based on an identified MIMO model of the PTS that includes the cross-coupling dynamics. Using the proposed controller a significant reduction in the cross-coupling is achieved and the imaging speed is enhanced up to 125 Hz.Also, a barrier in achieving high scanning speeds is the use of the zig-zag raster pattern scanning technique. In this thesis, a high-speed… Advisors/Committee Members: Petersen, Ian, Engineering & Information Technology, UNSW Canberra, UNSW, Pota, Hemanshu, Engineering & Information Technology, UNSW Canberra, UNSW.

Subjects/Keywords: Nanopositioning; Model Predictive Control; Atomic Force Micorscope

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Rana, M. S. (2014). High Performance Control of an Atomic Force Microscope for Faster Image Scanning. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/54007 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12715/SOURCE02?view=true

Chicago Manual of Style (16^th Edition):

Rana, Md Sohel. “High Performance Control of an Atomic Force Microscope for Faster Image Scanning.” 2014. Doctoral Dissertation, University of New South Wales. Accessed February 27, 2020. http://handle.unsw.edu.au/1959.4/54007 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12715/SOURCE02?view=true.

MLA Handbook (7^th Edition):

Rana, Md Sohel. “High Performance Control of an Atomic Force Microscope for Faster Image Scanning.” 2014. Web. 27 Feb 2020.

Vancouver:

Rana MS. High Performance Control of an Atomic Force Microscope for Faster Image Scanning. [Internet] [Doctoral dissertation]. University of New South Wales; 2014. [cited 2020 Feb 27]. Available from: http://handle.unsw.edu.au/1959.4/54007 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12715/SOURCE02?view=true.

Council of Science Editors:

Rana MS. High Performance Control of an Atomic Force Microscope for Faster Image Scanning. [Doctoral Dissertation]. University of New South Wales; 2014. Available from: http://handle.unsw.edu.au/1959.4/54007 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12715/SOURCE02?view=true

University of New South Wales

22. Wang, Xu. Model Predictive Control for UAGV Path Tracking in the Presence of Wheel Slip.

Degree: Mechanical & Manufacturing Engineering, 2017, University of New South Wales

URL: http://handle.unsw.edu.au/1959.4/58474 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:46104/SOURCE02?view=true

► Investigation of Unmanned Agricultural Ground Vehicles (UAGVs) has been on the in- crease in recent decades as UAGVs have great potential in agricultural applications, and… (more)

▼ Investigation of Unmanned Agricultural Ground Vehicles (UAGVs) has been on the in- crease in recent decades as UAGVs have great potential in agricultural applications, and thus UAGVs are expected to rule farms in the future. The use of UAGVs benefits from replacing human operators to do tiresome as well as hazardous work, thus reducing the risk. It can also significantly improve the efficiency to solve food shortages due to the dramatic growth of world. Path Tracking has been an important topic in the development of UAGVs, however the automatic guidance of farm vehicle becomes more difficult and challenging than that of standard mobile robots as farm vehicles are subjected to significant disturbances due to rough terrain and ground engaging operations. The controller of autonomous farm vehicles is required to be sufficiently robust to both guarantee high path tracking accuracy and stability.This thesis mainly researches path tracking control methods for three kinds of UAGVs in the presence of significant wheel slip. In path tracking, UAGVs are guided to follow a de- sired path from an initial position while the controller keeps minimizing offsets with respect to the reference path. To achieve the accuracy required in agriculture, this work utilizes offset models derived based on kinematic models, which take both lateral and longitudinal slips into account. A model predictive controller with receding min-max optimization is then proposed to address the problem of wheel slip in UAGVs. This adaptive min-max model predictive controller provides both robustness and adaptation for path tracking.The superior performance of the proposed controller is verified by kinematic simulation, dynamic simulation as well as field testing, compared to that of the classical model predictive control. Then, the proposed controller is also compared with a well-known robust sliding mode controller and a well-performing backstepping controller, which is carried out by implementing controllers on a UAGV at Elizabeth Macarthur Agricultural Institute. Results from simulations and experiments validate that the proposed adaptive min-max model predictive controller ensures the required accuracy and robustness in the presence of wheel slip without any slip measurement or estimation.Page vii Advisors/Committee Members: Katupitiya, Jay, Mechanical & Manufacturing Engineering, Faculty of Engineering, UNSW.

Subjects/Keywords: Path Tracking; Model Predictive Control; Autonomous Vehicles

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Wang, X. (2017). Model Predictive Control for UAGV Path Tracking in the Presence of Wheel Slip. (Masters Thesis). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/58474 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:46104/SOURCE02?view=true

Chicago Manual of Style (16^th Edition):

Wang, Xu. “Model Predictive Control for UAGV Path Tracking in the Presence of Wheel Slip.” 2017. Masters Thesis, University of New South Wales. Accessed February 27, 2020. http://handle.unsw.edu.au/1959.4/58474 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:46104/SOURCE02?view=true.

MLA Handbook (7^th Edition):

Wang, Xu. “Model Predictive Control for UAGV Path Tracking in the Presence of Wheel Slip.” 2017. Web. 27 Feb 2020.

Vancouver:

Wang X. Model Predictive Control for UAGV Path Tracking in the Presence of Wheel Slip. [Internet] [Masters thesis]. University of New South Wales; 2017. [cited 2020 Feb 27]. Available from: http://handle.unsw.edu.au/1959.4/58474 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:46104/SOURCE02?view=true.

Council of Science Editors:

Wang X. Model Predictive Control for UAGV Path Tracking in the Presence of Wheel Slip. [Masters Thesis]. University of New South Wales; 2017. Available from: http://handle.unsw.edu.au/1959.4/58474 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:46104/SOURCE02?view=true

University of Sydney

23. Lamburn, Darren James. Efficient Constrained Algebraic Model Predictive Control for Aerospace Applications .

Degree: 2016, University of Sydney

URL: http://hdl.handle.net/2123/16331

► The major drawback of MPC is the computational burden associated with the large number of parameters it can be optimised over. One method that improves… (more)

Subjects/Keywords: Explicit model predictive control; efficient model predictive control; constraints; continuous linear systems.

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APA (6^th Edition):

Lamburn, D. J. (2016). Efficient Constrained Algebraic Model Predictive Control for Aerospace Applications . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/16331

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Lamburn, Darren James. “Efficient Constrained Algebraic Model Predictive Control for Aerospace Applications .” 2016. Thesis, University of Sydney. Accessed February 27, 2020. http://hdl.handle.net/2123/16331.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Lamburn, Darren James. “Efficient Constrained Algebraic Model Predictive Control for Aerospace Applications .” 2016. Web. 27 Feb 2020.

Vancouver:

Lamburn DJ. Efficient Constrained Algebraic Model Predictive Control for Aerospace Applications . [Internet] [Thesis]. University of Sydney; 2016. [cited 2020 Feb 27]. Available from: http://hdl.handle.net/2123/16331.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lamburn DJ. Efficient Constrained Algebraic Model Predictive Control for Aerospace Applications . [Thesis]. University of Sydney; 2016. Available from: http://hdl.handle.net/2123/16331

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Newcastle

24. 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

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th 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 (16^th Edition):

Kong, He. “A unified approach to linear design and predictive control of constrained systems.” 2014. Doctoral Dissertation, University of Newcastle. Accessed February 27, 2020. http://hdl.handle.net/1959.13/1055900.

MLA Handbook (7^th Edition):

Kong, He. “A unified approach to linear design and predictive control of constrained systems.” 2014. Web. 27 Feb 2020.

Vancouver:

Kong H. A unified approach to linear design and predictive control of constrained systems. [Internet] [Doctoral dissertation]. University of Newcastle; 2014. [cited 2020 Feb 27]. 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

University of Oxford

25. 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)

Subjects/Keywords: 629.8; Control engineering; predictive control; model predictive control; robust control; stochastic MPC; probabilistic control; control theory; wind power; wind turbine control

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APA (6^th 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 (16^th Edition):

Evans, Martin A. “Multiplicative robust and stochastic MPC with application to wind turbine control.” 2014. Doctoral Dissertation, University of Oxford. Accessed February 27, 2020. 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 (7^th Edition):

Evans, Martin A. “Multiplicative robust and stochastic MPC with application to wind turbine control.” 2014. Web. 27 Feb 2020.

Vancouver:

Evans MA. Multiplicative robust and stochastic MPC with application to wind turbine control. [Internet] [Doctoral dissertation]. University of Oxford; 2014. [cited 2020 Feb 27]. 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

Cornell University

26. Dobbs, Justin. Toward A Streamlined Software Tool Chain For Cyber-Physical Buildings .

Degree: 2015, Cornell University

URL: http://hdl.handle.net/1813/41112

► Heating, ventilation, and air conditioning (HVAC) systems account for more than half of commercial building energy usage. Unfortunately, bottlenecks in early-stage design, control system implementation,… (more)

Subjects/Keywords: model order reduction; model predictive control; Markov occupancy model

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Dobbs, J. (2015). Toward A Streamlined Software Tool Chain For Cyber-Physical Buildings . (Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/41112

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Dobbs, Justin. “Toward A Streamlined Software Tool Chain For Cyber-Physical Buildings .” 2015. Thesis, Cornell University. Accessed February 27, 2020. http://hdl.handle.net/1813/41112.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Dobbs, Justin. “Toward A Streamlined Software Tool Chain For Cyber-Physical Buildings .” 2015. Web. 27 Feb 2020.

Vancouver:

Dobbs J. Toward A Streamlined Software Tool Chain For Cyber-Physical Buildings . [Internet] [Thesis]. Cornell University; 2015. [cited 2020 Feb 27]. Available from: http://hdl.handle.net/1813/41112.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Dobbs J. Toward A Streamlined Software Tool Chain For Cyber-Physical Buildings . [Thesis]. Cornell University; 2015. Available from: http://hdl.handle.net/1813/41112

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Southern California

27. Sun, Zhijie. Performance monitoring and disturbance adaptation for model predictive control.

Degree: PhD, Chemical Engineering, 2012, University of Southern California

URL: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/92439/rec/4985

► Model predictive control (MPC) is a widely used advanced process control technique in the process industry. According to the internal model principle, the internal model… (more)

▼ Model predictive control (MPC) is a widely used advanced process control technique in the process industry. According to the internal model principle, the internal model of MPC has to include both exact plant and disturbance models to be optimal. However, in practice, the MPC usually assumes a step-like disturbance or a fixed disturbance model. As a result, the MPC will be suboptimal when disturbance changes slowly. Moreover, it lacks a tool assessing the optimality of control performance in terms of the MPC model. ❧ In this dissertation, a new MPC disturbance adaptation method is presented. Starting from a single-input-single-output (SISO) semiconductor manufacturing process, we replaced the conventional run-to-run controller by an adaptive EWMA controller. It is shown that the plant model mismatch can be compensated by adapting the disturbance model. Analysis has been done to show that the adaptive controller is stable and converges to the optimal controller. ❧ The proposed method is then extended to multi-input-multi-output (MIMO) systems. For the ease of practical applications, the integrated moving average (IMA) model with order (1,1) is recommended. The equivalence between the IMA(1,1) parameter and the prediction error filter constant in commercial MPC has been established. Implementation of disturbance adaptation is explained. ❧ Another disturbance modeling tool is presented. It focuses on the closed-loop identification of a nonparametric disturbance model. The method incorporates the plant model information during the conversion from observer Markov parameters to system Markov parameters. ❧ A new control performance assessment method evaluating MPC model quality is then presented. Feedback invariant principle is introduced, based on which a method estimating disturbance innovations is given. A model quality index is developed as the performance benchmark, which compares prediction errors with disturbance innovations. It is shown that the model quality index related to the MPC performance index. ❧ Most industrial processes are optimized by a linear programming (LP) problem on top of the MPC. A new control performance monitoring method for cascaded LP-MPC system is developed. The block-lower-triangular interactor matrix is introduced to form a new method that is able to determine the performance benchmark based on controlled variable (CV) priorities coming from the LP results. Advisors/Committee Members: Qin, S. Joe (Committee Chair), Wang, Pin (Committee Member), Safonov, Michael G. (Committee Member).

Subjects/Keywords: model predictive control; run-to-run control; control performance monitoring

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Sun, Z. (2012). Performance monitoring and disturbance adaptation for model predictive control. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/92439/rec/4985

Chicago Manual of Style (16^th Edition):

Sun, Zhijie. “Performance monitoring and disturbance adaptation for model predictive control.” 2012. Doctoral Dissertation, University of Southern California. Accessed February 27, 2020. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/92439/rec/4985.

MLA Handbook (7^th Edition):

Sun, Zhijie. “Performance monitoring and disturbance adaptation for model predictive control.” 2012. Web. 27 Feb 2020.

Vancouver:

Sun Z. Performance monitoring and disturbance adaptation for model predictive control. [Internet] [Doctoral dissertation]. University of Southern California; 2012. [cited 2020 Feb 27]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/92439/rec/4985.

Council of Science Editors:

Sun Z. Performance monitoring and disturbance adaptation for model predictive control. [Doctoral Dissertation]. University of Southern California; 2012. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/92439/rec/4985

University of Cambridge

28. Shekhar, Rohan Chandra. Variable horizon model predictive control : robustness and optimality.

Degree: PhD, 2012, University of Cambridge

URL: https://www.repository.cam.ac.uk/handle/1810/244210 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.564035

► Variable Horizon Model Predictive Control (VH-MPC) is a form of predictive control that includes the horizon length as a decision variable in the constrained optimisation… (more)

▼ Variable Horizon Model Predictive Control (VH-MPC) is a form of predictive control that includes the horizon length as a decision variable in the constrained optimisation problem solved at each iteration. It has been recently applied to completion problems, where the system state is to be steered to a closed set in finite time. The behaviour of the system once completion has occurred is not considered part of the control problem. This thesis is concerned with three aspects of robustness and optimality in VH-MPC completion problems. In particular, the thesis investigates robustness to well defined but unpredictable changes in system and controller parameters, robustness to bounded disturbances in the presence of certain input parameterisations to reduce computational complexity, and optimal robustness to bounded disturbances using tightened constraints. In the context of linear time invariant systems, new theoretical contributions and algorithms are developed. Firstly, changing dynamics, constraints and control objectives are addressed by introducing the notion of feasible contingencies. A novel algorithm is proposed that introduces extra prediction variables to ensure that anticipated new control objectives are always feasible, under changed system parameters. In addition, a modified constraint tightening formulation is introduced to provide robust completion in the presence of bounded disturbances. Different contingency scenarios are presented and numerical simulations demonstrate the formulation’s efficacy. Next, complexity reduction is considered, using a form of input parameterisation known as move blocking. After introducing a new notation for move blocking, algorithms are presented for designing a move-blocked VH-MPC controller. Constraints are tightened in a novel way for robustness, whilst ensuring that guarantees of recursive feasibility and finite-time completion are preserved. Simulations are used to illustrate the effect of an example blocking scheme on computation time, closed-loop cost, control inputs and state trajectories. Attention is now turned towards mitigating the effect of constraint tightening policies on a VH-MPC controller’s region of attraction. An optimisation problem is formulated to maximise the volume of an inner approximation to the region of attraction, parameterised in terms of the tightening policy. Alternative heuristic approaches are also proposed to deal with high state dimensions. Numerical examples show that the new technique produces substantially improved regions of attraction in comparison to other proposed approaches, and greatly reduces the maximum required prediction horizon length for a given application. Finally, a case study is presented to illustrate the application of the new theory developed in this thesis to a non-trivial example system. A simplified nonlinear surface excavation machine and material model is developed for this purpose. The model is stabilised with an inner-loop controller, following which a VH-MPC controller for autonomous trajectory generation is…

Subjects/Keywords: 620; Model predictive control; Robust control; Variable horizon; Optimal control

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Shekhar, R. C. (2012). Variable horizon model predictive control : robustness and optimality. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/244210 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.564035

Chicago Manual of Style (16^th Edition):

Shekhar, Rohan Chandra. “Variable horizon model predictive control : robustness and optimality.” 2012. Doctoral Dissertation, University of Cambridge. Accessed February 27, 2020. https://www.repository.cam.ac.uk/handle/1810/244210 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.564035.

MLA Handbook (7^th Edition):

Shekhar, Rohan Chandra. “Variable horizon model predictive control : robustness and optimality.” 2012. Web. 27 Feb 2020.

Vancouver:

Shekhar RC. Variable horizon model predictive control : robustness and optimality. [Internet] [Doctoral dissertation]. University of Cambridge; 2012. [cited 2020 Feb 27]. Available from: https://www.repository.cam.ac.uk/handle/1810/244210 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.564035.

Council of Science Editors:

Shekhar RC. Variable horizon model predictive control : robustness and optimality. [Doctoral Dissertation]. University of Cambridge; 2012. Available from: https://www.repository.cam.ac.uk/handle/1810/244210 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.564035

University of Newcastle

29. Mohammadalipour Tofighi, Elham. Robust nonlinear model predictive control of wind turbines using uncertain wind predictions.

Degree: PhD, 2019, University of Newcastle

URL: http://hdl.handle.net/1959.13/1406261

►

Research Doctorate - Doctor of Philosophy (PhD)

Efficient wind turbine control can enhance the performance of the wind turbine operation by increasing the energy yield… (more)

Subjects/Keywords: wind turbine control; robust control; NMPC; nonlinear model predictive control; optimization

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Mohammadalipour Tofighi, E. (2019). Robust nonlinear model predictive control of wind turbines using uncertain wind predictions. (Doctoral Dissertation). University of Newcastle. Retrieved from http://hdl.handle.net/1959.13/1406261

Chicago Manual of Style (16^th Edition):

Mohammadalipour Tofighi, Elham. “Robust nonlinear model predictive control of wind turbines using uncertain wind predictions.” 2019. Doctoral Dissertation, University of Newcastle. Accessed February 27, 2020. http://hdl.handle.net/1959.13/1406261.

MLA Handbook (7^th Edition):

Mohammadalipour Tofighi, Elham. “Robust nonlinear model predictive control of wind turbines using uncertain wind predictions.” 2019. Web. 27 Feb 2020.

Vancouver:

Mohammadalipour Tofighi E. Robust nonlinear model predictive control of wind turbines using uncertain wind predictions. [Internet] [Doctoral dissertation]. University of Newcastle; 2019. [cited 2020 Feb 27]. Available from: http://hdl.handle.net/1959.13/1406261.

Council of Science Editors:

Mohammadalipour Tofighi E. Robust nonlinear model predictive control of wind turbines using uncertain wind predictions. [Doctoral Dissertation]. University of Newcastle; 2019. Available from: http://hdl.handle.net/1959.13/1406261

Georgia Tech

30. Okamoto, Kazuhide. Optimal Covariance Steering: Theory and Its Application to Autonomous Driving.

Degree: PhD, Aerospace Engineering, 2019, Georgia Tech

URL: http://hdl.handle.net/1853/62260

► Optimal control under uncertainty has been one of the central research topics in the control community for decades. While a number of theories have been… (more)

▼ Optimal control under uncertainty has been one of the central research topics in the control community for decades. While a number of theories have been developed to control a single state from an initial state to a target state, in some situations, it is preferable to simultaneously compute control commands for multiple states that start from an initial distribution and converge to a target distribution. This dissertation aims to develop a stochastic optimal control theory that, in addition to the mean, explicitly steers the state covariance. Specifically, we focus on the control of linear time-varying (LTV) systems with additive Gaussian noise. The task is to steer a Gaussian-distributed initial system state distribution to a target Gaussian distribution, while minimizing a state and control expectation-dependent quadratic cost under probabilistic state constraints. Notice that, in such systems, the system state keeps being Gaussian distributed. Because Gaussian distributions can be fully described by the first two moments, the proposed optimal covariance steering (OCS) theory allows us to control the whole distribution of the state and quantify the effect of uncertainty without conducting Monte-Carlo simulations. We propose to use a control policy that is an affine function of filtered disturbances, which utilizes the results of convex optimization theory and efficiently finds the solution. After the OCS theory for LTV systems is introduced, we extend the theory to vehicle path planning problems. While several path planning algorithms have been proposed, many of them have dealt with deterministic dynamics or stochastic dynamics with open-loop un- certainty, i.e., the uncertainty of the system state is not controlled and, typically, increases with time due to exogenous disturbances, which may lead to the design of potentially conservative nominal paths. A typical approach to deal with disturbances is to use a lower-level local feedback controller after the nominal path is computed. This unidirectional dependence of the feedback controller on the path planner makes the nominal path unnecessarily conservative. The path-planning approach we develop based on the OCS theory computes the nominal path based on the closed-loop evolution of the system uncertainty by simultaneously optimizing the feedforward and feedback control commands. We validate the performance using numerical simulations with single and multiple vehicle path planning problems. Furthermore, we introduce an optimal covariance steering controller for linear systems with input hard constraints. As many real-world systems have input constraints (e.g., air- craft and spacecraft have minimum/maximum thrust), this problem formulation will allow us to deal with realistic scenarios. In order to incorporate input hard constraints in the OCS theory framework, we use element-wise saturation functions and limit the effect of disturbance to the control commands. We prove that this problem formulation leads to a convex programming problem and demonstrate the… Advisors/Committee Members: Tsiotras, Panagiotis (advisor), Clarke, Jahn-Paul (committee member), Chernova, Sonia (committee member), Rogers, Jonathan (committee member), Chen, Yongxin (committee member).

Subjects/Keywords: Stochastic Control; Optimal Control; Model Predictive Control; Vehicle Path Planning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Okamoto, K. (2019). Optimal Covariance Steering: Theory and Its Application to Autonomous Driving. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62260

Chicago Manual of Style (16^th Edition):

Okamoto, Kazuhide. “Optimal Covariance Steering: Theory and Its Application to Autonomous Driving.” 2019. Doctoral Dissertation, Georgia Tech. Accessed February 27, 2020. http://hdl.handle.net/1853/62260.

MLA Handbook (7^th Edition):

Okamoto, Kazuhide. “Optimal Covariance Steering: Theory and Its Application to Autonomous Driving.” 2019. Web. 27 Feb 2020.

Vancouver:

Okamoto K. Optimal Covariance Steering: Theory and Its Application to Autonomous Driving. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2020 Feb 27]. Available from: http://hdl.handle.net/1853/62260.

Council of Science Editors:

Okamoto K. Optimal Covariance Steering: Theory and Its Application to Autonomous Driving. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/62260

Open Access Theses and Dissertations