subject:(predictive model)

Delft University of Technology

1. Lindo, H.M.A. (author). Predicting the Individual Pathway of the Kidney Donor and Acceptor Patient based on Preoperative Patient Data.

Degree: 2014, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:c41946a4-011d-4c66-89a4-6db15b054145

► Background – With the growing costs in healthcare and the growing need for care, prediction models offer great opportunities for improving efficiency and patient outcome.… (more)

▼ Background – With the growing costs in healthcare and the growing need for care, prediction models offer great opportunities for improving efficiency and patient outcome. With these models, one can manage the different outcomes as efficient as possible and next to that, understand which variables influence the patient’s pathway. The Erasmus Medical Centre in Rotterdam provided the data of the kidney transplantation patients (donors and acceptors) to examine the effects of different preoperative variables, such as age, body mass index and gender, on the operating time, the surgical complexity, the occurrence of peri- and postoperative complications and the length of stay. Prediction models using this data can give insight into the patients and the processes, it can improve the efficiency, and make it possible to steer the process when necessary. Goal – The goal of this retrospective thesis study is to gain insight into the patient groups and processes and to create a model able to predict the patients pathway through analyzing preoperative patient data. This model should lead to higher efficiency and better patient outcomes and give insight into the variables influencing the patients pathway for both the kidney donors and acceptors. Additionally, this study should provide a practical guideline for prediction model use to achieve the best results in efficiency and patient outcome. Materials and methods - Two sets of clinical histories were used for both the kidney donors and the acceptors: one for deriving the predictive equations and another for validation. For deriving the predictive equations, preoperative patient data was collected from 282 donors and 269 acceptors admitted to the Erasmus Medical Center (EMC) in the period of 2011-2012. For the validation of the equations, patient data was collected from 141 donor patients and 135 acceptor patients admitted in the year 2013. The data includes variables such as gender, age, patient’s body mass index (BMI) and medical history. The variables were selected with the consultation of previous research and experts in the kidney transplantation field. Regression analysis was used to identify the risk factors for each outcome and to estimate the predictive equations used for predicting the patients pathway. Results – For the 80% best predictions, the validation shows that the operating time can be predicted with an average deviation of 11,9% (21,4 minutes) from the actual operating time for the donors and 12,8% (17,0 minutes) for the acceptors. Relevant features for predicting the operating time include gender, BMI, number of veins and arteries, surgeon and surgical technique for the donors and gender, ethnicity and previous transplantations for the acceptors. The length of stay can be predicted with an average deviation of 17,9% (0,6 days) from the actual length of stay for the donors and 16,6% (2,3 days) for the acceptors. The relevant features for predicting the length of stay include gender and pulmonary history for the donors and age of the donor, BMI of the donor and… Advisors/Committee Members: Dankelman, J. (mentor), Guedon, A.C.P. (mentor).

Subjects/Keywords: Predictive model

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APA (6^th Edition):

Lindo, H. M. A. (. (2014). Predicting the Individual Pathway of the Kidney Donor and Acceptor Patient based on Preoperative Patient Data. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:c41946a4-011d-4c66-89a4-6db15b054145

Chicago Manual of Style (16^th Edition):

Lindo, H M A (author). “Predicting the Individual Pathway of the Kidney Donor and Acceptor Patient based on Preoperative Patient Data.” 2014. Masters Thesis, Delft University of Technology. Accessed January 23, 2021. http://resolver.tudelft.nl/uuid:c41946a4-011d-4c66-89a4-6db15b054145.

MLA Handbook (7^th Edition):

Lindo, H M A (author). “Predicting the Individual Pathway of the Kidney Donor and Acceptor Patient based on Preoperative Patient Data.” 2014. Web. 23 Jan 2021.

Vancouver:

Lindo HMA(. Predicting the Individual Pathway of the Kidney Donor and Acceptor Patient based on Preoperative Patient Data. [Internet] [Masters thesis]. Delft University of Technology; 2014. [cited 2021 Jan 23]. Available from: http://resolver.tudelft.nl/uuid:c41946a4-011d-4c66-89a4-6db15b054145.

Council of Science Editors:

Lindo HMA(. Predicting the Individual Pathway of the Kidney Donor and Acceptor Patient based on Preoperative Patient Data. [Masters Thesis]. Delft University of Technology; 2014. Available from: http://resolver.tudelft.nl/uuid:c41946a4-011d-4c66-89a4-6db15b054145

University of Waterloo

2. 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 January 23, 2021. http://hdl.handle.net/10012/14276.

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

MLA Handbook (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. 23 Jan 2021.

Vancouver:

Tejeda-Iglesias M. A Framework for Explicit Model Predictive Control using Adjustable Robust Optimization and Economic Optimization of an Industrial-Scale Sulfuric Acid Plant. [Internet] [Thesis]. University of Waterloo; 2018. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10012/14276.

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

Council of Science Editors:

Tejeda-Iglesias M. A Framework for Explicit Model Predictive Control using Adjustable Robust Optimization and Economic Optimization of an Industrial-Scale Sulfuric Acid Plant. [Thesis]. University of Waterloo; 2018. Available from: http://hdl.handle.net/10012/14276

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

Delft University of Technology

3. de carvalho monteiro, patrik (author). Model Predictive Control for Reticle Cooling.

Degree: 2019, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:5e2cdcb4-6c86-40a4-8c36-bc2df84cc5b6

►

During the exposure process of a wafer scanner, problems occur due to reticle heating. In this project, the consequences of reticle heating in terms of… (more)

Subjects/Keywords: model predictive control

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

APA (6^th Edition):

de carvalho monteiro, p. (. (2019). Model Predictive Control for Reticle Cooling. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:5e2cdcb4-6c86-40a4-8c36-bc2df84cc5b6

Chicago Manual of Style (16^th Edition):

de carvalho monteiro, patrik (author). “Model Predictive Control for Reticle Cooling.” 2019. Masters Thesis, Delft University of Technology. Accessed January 23, 2021. http://resolver.tudelft.nl/uuid:5e2cdcb4-6c86-40a4-8c36-bc2df84cc5b6.

MLA Handbook (7^th Edition):

de carvalho monteiro, patrik (author). “Model Predictive Control for Reticle Cooling.” 2019. Web. 23 Jan 2021.

Vancouver:

de carvalho monteiro p(. Model Predictive Control for Reticle Cooling. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 23]. Available from: http://resolver.tudelft.nl/uuid:5e2cdcb4-6c86-40a4-8c36-bc2df84cc5b6.

Council of Science Editors:

de carvalho monteiro p(. Model Predictive Control for Reticle Cooling. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:5e2cdcb4-6c86-40a4-8c36-bc2df84cc5b6

Penn State University

4. Fang, Yizhou. Carleman Linearization-based Nonlinear Model Predictive Control.

Degree: 2016, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/27590

► The need of tight operating conditions in chemical, pharmaceutical, and petroleum industries has given rise to the development of advanced process control. Model Predictive Control… (more)

▼ The need of tight operating conditions in chemical, pharmaceutical, and petroleum industries has given rise to the development of advanced process control. Model Predictive Control (MPC) started gaining attention three decades ago for optimal transitions between operating modes. Nonlinear MPC converts a constrained control problem of a nonlinear system into an optimization problem. This basic architecture makes Nonlinear MPC capable of handling large state-space multi-variable systems with constraints, and dealing with model-mismatches and disturbances readily. The computation time of control policy is required to be less than one sampling time for online operation. However, this requirement is most of the times impossible to meet when the system has high nonlinearity. That becomes one of the most significant reasons holding back the application of Nonlinear MPC. As a result, there is strong motivation to develop an advanced formulation of Nonlinear MPC that demands less computational effort and thus decides the control actions faster. The primary focus of this thesis is to develop an advanced formulation of Nonlinear MPC that decreases the amount of computational effort in order to circumvent feedback delay, to improve controller performance and to maintain stability of the system. Multiple mathematics tools combined with optimization techniques are implemented for the purpose of accelerated searching algorithms. The optimal control problem is formulated as a receding horizon one. An optimization problem is solved at each time the finite horizon moves on. Based on Carleman Linearization, the states of the system are extended to higher orders following the Kronecker product rule. The nonlinear dynamic process can thus be modeled with a bilinear representation while keeping nonlinear dynamic information. It enables analytical anticipation of system states and provides the searching algorithm with analytically computed sensitivity of the cost function to the control signals. The proposed method resembles both collocation and shooting methods for the following reasons. First, the states of the system are discretized explicitly in time while the sensitivity of the control signals is computed analytically. Second, the states are nonlinear functions of the control signals, releasing the optimization problem from equality constraints and reducing the number of design variables. This thesis presents an introduction to MPC, Carleman Linearization and detailed derivations of the proposed method in Chapter 1 and 2. It also provides detailed description of resetting extended states to compensate for the simulation errors caused by Carleman Linearization as an independent Chapter 3. Chapter 4 presents case-study examples to indicate the applications of the proposed method. Chapter 5 concludes the work and future plans. A part of the work presented in this thesis has been published at American Control Conference, Chicago, IL on July 1st, 2015. Advisors/Committee Members: Antonios Armaou, Thesis Advisor/Co-Advisor.

Subjects/Keywords: Process Control; Model Predictive Control

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

APA (6^th Edition):

Fang, Y. (2016). Carleman Linearization-based Nonlinear Model Predictive Control. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/27590

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

Chicago Manual of Style (16^th Edition):

Fang, Yizhou. “Carleman Linearization-based Nonlinear Model Predictive Control.” 2016. Thesis, Penn State University. Accessed January 23, 2021. https://submit-etda.libraries.psu.edu/catalog/27590.

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

MLA Handbook (7^th Edition):

Fang, Yizhou. “Carleman Linearization-based Nonlinear Model Predictive Control.” 2016. Web. 23 Jan 2021.

Vancouver:

Fang Y. Carleman Linearization-based Nonlinear Model Predictive Control. [Internet] [Thesis]. Penn State University; 2016. [cited 2021 Jan 23]. Available from: https://submit-etda.libraries.psu.edu/catalog/27590.

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

Council of Science Editors:

Fang Y. Carleman Linearization-based Nonlinear Model Predictive Control. [Thesis]. Penn State University; 2016. Available from: https://submit-etda.libraries.psu.edu/catalog/27590

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

Delft University of Technology

5. Kreuzen, C. (author). Cooperative adaptive cruise control: Using information from multiple predecessors in combination with MPC.

Degree: 2012, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:dd0c766a-5f16-4799-8c84-39abbddbce9e

► Cooperative adaptive cruise control (CACC) makes the vehicle follow its predecessor at a close but safe distance, and uses information received from other vehicles to… (more)

▼ Cooperative adaptive cruise control (CACC) makes the vehicle follow its predecessor at a close but safe distance, and uses information received from other vehicles to accomplish this task. In literature and in practice, the control method mostly applied for CACC is proportional integral derivative (PID) control, possibly with some refinement for gear shifting or comfort. The control method called model predictive control (MPC) can also be used for CACC, and from literature it appears to be more promising than PID, because of its ability to anticipate future situations and to implement constraints directly into the control algorithm. MPC applies the first input of a control input sequence that optimises a performance index calculated from predicted system behaviour, based on a prediction model, subject to operational constraints, in a receding horizon approach. Furthermore, literature has shown that with PID the use of state information from the second predecessor or the platoon leader, in addition to the direct predecessor’s states, can improve the CACC performance. Therefore, in this thesis the approach of using such additional communicated information from either the second predecessor or the platoon leader is combined with the use of MPC as control method. The goal is to investigate whether any of these two configurations give an increase in performance compared with similar configurations with PID as control method, and compared with a more basic configuration that uses just the direct predecessor’s state information with either MPC or PID. Also, the possibly added value of using communicated predicted states, in addition to current states, with MPC is investigated. The CACC controllers are designed to control the throttle, the brakes, and the gears, subject to operational constraints on acceleration, velocity, and vehicle-to-vehicle distance. The PID-based CACC controller contains a proportional feedback of the errors in velocity, position, and acceleration, combined with an automatic transmission scheme, and the control input is restricted at time instants at which a constraint is (almost) violated. The MPC-based CACC controller at each time step minimises the expected errors in position and velocity and the corresponding input variation. The MPC prediction model is obtained by approximating a nonlinear vehicle model by a piecewise affine (PWA) model, and converting the MPC optimisation problem into a mixed integer linear programming (MILP) problem. In this project, tuning is done by applying simulated annealing for a scenario involving four CACC-controlled vehicles following a platoon leader. Then, the tuned controllers are implemented in a validation scenario comprising a larger platoon undergoing a longer simulation. The results from simulating this validation scenario show that the PID-based CACC controller has a low responsiveness, compared with MPC, and lets the first two vehicles crash. With MPC several peaks and oscillations in throttle/brake input and acceleration occur, and it is expected that with… Advisors/Committee Members: De Schutter, B. (mentor).

Subjects/Keywords: system & control; model predictive control

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

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 (author). “Cooperative adaptive cruise control: Using information from multiple predecessors in combination with MPC.” 2012. Masters Thesis, Delft University of Technology. Accessed January 23, 2021. http://resolver.tudelft.nl/uuid:dd0c766a-5f16-4799-8c84-39abbddbce9e.

MLA Handbook (7^th Edition):

Kreuzen, C (author). “Cooperative adaptive cruise control: Using information from multiple predecessors in combination with MPC.” 2012. Web. 23 Jan 2021.

Vancouver:

Kreuzen C(. Cooperative adaptive cruise control: Using information from multiple predecessors in combination with MPC. [Internet] [Masters thesis]. Delft University of Technology; 2012. [cited 2021 Jan 23]. Available from: http://resolver.tudelft.nl/uuid:dd0c766a-5f16-4799-8c84-39abbddbce9e.

Council of Science Editors:

Kreuzen C(. Cooperative adaptive cruise control: Using information from multiple predecessors in combination with MPC. [Masters Thesis]. Delft University of Technology; 2012. Available from: http://resolver.tudelft.nl/uuid:dd0c766a-5f16-4799-8c84-39abbddbce9e

University of Ontario Institute of Technology

6. Gillett, Jonathan. Predicting Bitcoin: a robust model for predicting Bitcoin price directions based on network influencers.

Degree: 2016, University of Ontario Institute of Technology

URL: http://hdl.handle.net/10155/884

► The ability to predict financial markets has tremendous potential to limit exposure to risk and provide better assurances of annualized gains. In this thesis, a… (more)

▼ The ability to predict financial markets has tremendous potential to limit exposure to risk and provide better assurances of annualized gains. In this thesis, a model for predicting the future daily price of Bitcoin is proposed and evaluated in comparison to that of a purely random model. Bitcoin is a novel digital currency that relies on cryptography instead of a central authority to verify transactions. Without a central authority, Bitcoin requires a complete list of all transactions to be made public so that they can be verified by all users. This unique feature of Bitcoin, where all transactions are public, is exploited by the model to predict the future price directions based on the actions of Bitcoin users. The daily activity of the markets, aggregate network features, and the actions of major network influencers are all used as features for the predictive model. Where major network influencers are defined as users that accumulate a disproportionate amount of wealth within the Bitcoin network compared to others. The information about the actions of all Bitcoin users are extracted from the blockchain and stored in a relational database for ease of use. Two metrics were created to identify the major network influencers based on the history of their actions recorded on the blockchain. The first metric, based on the concept of an h-index, often used in academia to rank authors by their citations, is used to rank users by the amount of wealth they accumulate monthly. The second metric is based on the optimization of multiple objectives, the maximum increase in wealth with the least amount of activity using Pareto optimization. All of the major network influencers identified were then used as features, in combination with aggregate network features, and market data, to test and evaluate several predictive models. The models created were based on non-linear equations, support vector machines, decision trees, and XGBoost; all evaluated and compared using the same data. The XGBoost model consistently proved to be much more accurate than all other models and was used for the final set of experiments. The XGBoost model was compared to that of a purely random Monte Carlo model using the entire history of data for the period of 2013???2016. The first set of experiments were conducted using various sizes of training and testing data, in each case the XGBoost model had an accuracy 20% greater than that of the Monte Carlo model. For the final experiments, the model was tested in a realistic scenario, predicting the price direction for each future day, while also being re-trained using the results of each new day. The XGBoost model achieved a much better performance in comparison to the Monte Carlo model, which had approximately 50% accuracy, whereas the XGBoost model had 70%???79% accuracy. Advisors/Committee Members: Rahnamayan, Shahryar, Makrehchi, Masoud.

Subjects/Keywords: Bitcoin; Predictive model; Machine learning

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

APA (6^th Edition):

Gillett, J. (2016). Predicting Bitcoin: a robust model for predicting Bitcoin price directions based on network influencers. (Thesis). University of Ontario Institute of Technology. Retrieved from http://hdl.handle.net/10155/884

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):

Gillett, Jonathan. “Predicting Bitcoin: a robust model for predicting Bitcoin price directions based on network influencers.” 2016. Thesis, University of Ontario Institute of Technology. Accessed January 23, 2021. http://hdl.handle.net/10155/884.

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

MLA Handbook (7^th Edition):

Gillett, Jonathan. “Predicting Bitcoin: a robust model for predicting Bitcoin price directions based on network influencers.” 2016. Web. 23 Jan 2021.

Vancouver:

Gillett J. Predicting Bitcoin: a robust model for predicting Bitcoin price directions based on network influencers. [Internet] [Thesis]. University of Ontario Institute of Technology; 2016. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10155/884.

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

Council of Science Editors:

Gillett J. Predicting Bitcoin: a robust model for predicting Bitcoin price directions based on network influencers. [Thesis]. University of Ontario Institute of Technology; 2016. Available from: http://hdl.handle.net/10155/884

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

University of Waterloo

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

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 January 23, 2021. http://hdl.handle.net/10012/9982.

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

MLA Handbook (7^th Edition):

Santander, Omar. “Economic Model Predictive Control of Chemical Processes.” 2015. Web. 23 Jan 2021.

Vancouver:

Santander O. Economic Model Predictive Control of Chemical Processes. [Internet] [Thesis]. University of Waterloo; 2015. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10012/9982.

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

Council of Science Editors:

Santander O. Economic Model Predictive Control of Chemical Processes. [Thesis]. University of Waterloo; 2015. Available from: http://hdl.handle.net/10012/9982

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

University of Waterloo

8. Daoud, Mohamed Ashraf Gameleldin. Simultaneous Local Motion Planning and Control, Adjustable Driving Behavior, and Obstacle Representation for Autonomous Driving.

Degree: 2020, University of Waterloo

URL: http://hdl.handle.net/10012/16436

► The evolving autonomous driving technology has been attracting significant research efforts in both academia and industry because of its promising potentials. Eliminating the human intervention… (more)

▼ The evolving autonomous driving technology has been attracting significant research efforts in both academia and industry because of its promising potentials. Eliminating the human intervention in driving will drastically improve the road safety and will create more mobility freedom for the humankind. Decision-making system in autonomy pipeline is the last module that interacts directly with the surrounding environment. Typical decision-making systems perform a variety of tasks including local motion planning, obstacle avoidance, and path-following in a sequential manner. An alternative approach is to perform these tasks simultaneously to obtain faster decision-making actions. This thesis focuses on designing an optimization-based simultaneous controller that performs obstacle avoidance, local motion planning, and vehicle control on roads regardless of their orientation while following a target path, and also incorporates adjustable driving behavior. Firstly, a decision-making scheme that enables autonomous driving for long trips while expanding the usage of the available computational resources and ensuring obstacle avoidance functionality is proposed. The proposed scheme utilizes a parallel architecture for local motion planning and control layers to increase time efficiency. In addition, a novel feasibility-guaranteed lane change and double lane change planners are introduced for path planning and obstacle avoidance. Finally, an online parameterized curve generator is proposed and integrated with a recently developed path-following controller. Next, a nonlinear model predictive control (NMPC) scheme is developed for the path-following control of autonomous vehicles. In addition, a dual-objective cost function which is composed of a regulation part and an economic part is introduced. By tuning the weights of this cost, a driving behavior can be implemented; two different driving behaviors are designed, namely, energy-efficient and sport driving modes. Finally, a kinematic bicycle model is used for predicting the vehicle motion while a longitudinal motion dynamic model is used for estimating the energy consumption. Finally, a novel representation framework for static maps and obstacles based on Fourier Series is proposed. The framework relies on Complex Fourier Series analysis to reduce the computation time and outputs mathematical equations to describe the shape of the considered object. Furthermore, two methods were proposed, namely; offline method and online method. The offline method is used to create accurate representations for static maps and most common obstacles an ego vehicle may encounter. The online method is used to model the free space around the vehicle when dealing with uncertain environments. The proposed contributions fill significant gaps in the autonomous driving problem. All the proposed work is tested and validated using numerical simulations and some experiments. The results show the effectiveness of the proposed contributions.

Subjects/Keywords: autonomous driving; model predictive control

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APA (6^th Edition):

Daoud, M. A. G. (2020). Simultaneous Local Motion Planning and Control, Adjustable Driving Behavior, and Obstacle Representation for Autonomous Driving. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/16436

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

Chicago Manual of Style (16^th Edition):

Daoud, Mohamed Ashraf Gameleldin. “Simultaneous Local Motion Planning and Control, Adjustable Driving Behavior, and Obstacle Representation for Autonomous Driving.” 2020. Thesis, University of Waterloo. Accessed January 23, 2021. http://hdl.handle.net/10012/16436.

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

MLA Handbook (7^th Edition):

Daoud, Mohamed Ashraf Gameleldin. “Simultaneous Local Motion Planning and Control, Adjustable Driving Behavior, and Obstacle Representation for Autonomous Driving.” 2020. Web. 23 Jan 2021.

Vancouver:

Daoud MAG. Simultaneous Local Motion Planning and Control, Adjustable Driving Behavior, and Obstacle Representation for Autonomous Driving. [Internet] [Thesis]. University of Waterloo; 2020. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10012/16436.

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

Council of Science Editors:

Daoud MAG. Simultaneous Local Motion Planning and Control, Adjustable Driving Behavior, and Obstacle Representation for Autonomous Driving. [Thesis]. University of Waterloo; 2020. Available from: http://hdl.handle.net/10012/16436

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

Université de Grenoble

9. 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 January 23, 2021. 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. 23 Jan 2021.

Vancouver:

Lamoudi MY. Commande prédictive distribuée pour la gestion de l'énergie dans le bâtiment Distributed model predictive control for energy management in building : Distributed Predictive Control for energy management in buildings. [Internet] [Doctoral dissertation]. Université de Grenoble; 2012. [cited 2021 Jan 23]. Available from: http://www.theses.fr/2012GRENT097.

Council of Science Editors:

Lamoudi MY. Commande prédictive distribuée pour la gestion de l'énergie dans le bâtiment Distributed model predictive control for energy management in building : Distributed Predictive Control for energy management in buildings. [Doctoral Dissertation]. Université de Grenoble; 2012. Available from: http://www.theses.fr/2012GRENT097

KTH

10. Bengtsson, Ivar. Autonomous Overtaking with Learning Model Predictive Control.

Degree: Optimization and Systems Theory, 2020, KTH

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276691

►

We review recent research into trajectory planning for autonomous overtaking to understand existing challenges. Then, the recently developed framework Learning Model Predictive Control (LMPC)… (more)

▼

We review recent research into trajectory planning for autonomous overtaking to understand existing challenges. Then, the recently developed framework Learning Model Predictive Control (LMPC) is presented as a suitable method to iteratively improve an overtaking manoeuvre each time it is performed. We present recent extensions to the LMPC framework to make it applicable to overtaking. Furthermore, we also present two alternative modelling approaches with the intention of reducing computational complexity of the optimization problems solved by the controller. All proposed frameworks are built from scratch in Python3 and simulated for evaluation purposes. Optimization problems are modelled and solved using the Gurobi 9.0 Python API gurobipy. The results show that LMPC can be successfully applied to the overtaking problem, with improved performance at each iteration. However, the first proposed alternative modelling approach does not improve computational times as was the intention. The second one does but fails in other areas.

Vi går igenom ny forskning inom trajectory planning för autonom omkörning för att förstå de utmaningar som finns. Därefter föreslås ramverket Learning Model Predictive Control (LMPC) som en lämplig metod för att iterativt förbättra en omkörning vid varje utförande. Vi tar upp utvidgningar av LMPC-ramverket för att göra det applicerbart på omkörningsproblem. Dessutom presenterar vi också två alternativa modelleringar i syfte att minska optimeringsproblemens komplexitet. Alla tre angreppssätt har byggts från grunden i Python3 och simulerats i utvärderingssyfte. Optimeringsproblem har modellerats och lösts med programvaran Gurobi 9.0s python-API gurobipy. Resultaten visar att LMPC kan tillämpas framgångsrikt på omkörningsproblem, med förbättrat utförande vid varje iteration. Den första alternativa modelleringen minskar inte beräkningstiden vilket var dess syfte. Det gör däremot den andra alternativa modelleringen som dock fungerar sämre i andra avseenden.

Subjects/Keywords: Autonomous vehicles; Learning Model Predictive Control; Model Predictive Control; Autonoma fordon; Learning Model Predictive Control; Modellprediktiv reglering; Mathematics; Matematik

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

APA (6^th Edition):

Bengtsson, I. (2020). Autonomous Overtaking with Learning Model Predictive Control. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276691

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

Chicago Manual of Style (16^th Edition):

Bengtsson, Ivar. “Autonomous Overtaking with Learning Model Predictive Control.” 2020. Thesis, KTH. Accessed January 23, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276691.

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

MLA Handbook (7^th Edition):

Bengtsson, Ivar. “Autonomous Overtaking with Learning Model Predictive Control.” 2020. Web. 23 Jan 2021.

Vancouver:

Bengtsson I. Autonomous Overtaking with Learning Model Predictive Control. [Internet] [Thesis]. KTH; 2020. [cited 2021 Jan 23]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276691.

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

Council of Science Editors:

Bengtsson I. Autonomous Overtaking with Learning Model Predictive Control. [Thesis]. KTH; 2020. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276691

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

University of Melbourne

11. Gowri Sankar, Gokul Siva Sankar. Robust predictive control of diesel airpath.

Degree: 2019, University of Melbourne

URL: http://hdl.handle.net/11343/230607

► In an automotive diesel airpath application, the controller is required to track reference values of intake manifold (boost) pressure and exhaust gas recirculation (EGR) rate.… (more)

▼ In an automotive diesel airpath application, the controller is required to track reference values of intake manifold (boost) pressure and exhaust gas recirculation (EGR) rate. EGR rate is the ratio of the EGR outflow rate to the combined EGR and compressor outflow rates. The reference values for these output variables are determined by a high level controller based on the current engine operating condition characterised by an engine rotational speed and a fuelling rate. Perfect tracking of these references in a drive cycle ensures optimal driver demand responsiveness, fuel efficiency, and satisfaction of legislated emission limits. However, diesel engine airpath control is challenging owing to its nonlinear multivariable nature. Conventional approaches use look-up tables and proportional-integral-differential (PID) loops ignoring the cross-sensitivities that can affect the controller performance. Furthermore, these approaches do not guarantee satisfaction of operating and reliability constraints on manifold pressures and physical limitations of the actuators. On the other hand, the constraint handling ability of model predictive control (MPC) makes it an ideal choice of control architecture for constrained multi-input multi-output systems, such as diesel engines. However, efficient calibration of typical implementations of MPC is hindered by the high number of tuning parameters and their non-intuitive correlation with the output response. Additionally, modelling errors might lead to constraint violation and loss of recursive feasibility. This thesis proposes a robust switched linear time invariant (LTI) MPC architecture for diesel airpath that utilises multiple local controllers that require low calibration effort. In order to reduce the number of effective tuning parameters to aid rapid calibration, first, structural modifications are introduced to the traditional MPC formulation by constraining the outputs within exponentially decaying envelopes and an appropriate cost function parameterisation. Subsequently, a methodology based on sequential convex program (SCP) is proposed to estimate maximal disturbance set that can be handled by each local controller under limited control authority arising due to input saturations typically observed in diesel engine operation. Constraint tightening approach is used to provide robust feasibility guarantees in the face of uncertainties originating from the maximal disturbance set and due to controller switching. In addition, a procedure is proposed for identifying the switched controllers requiring further tuning using feedback from performance over drive cycles. The developed switched LTI-MPC architecture is implemented on rapid prototyping hardware and experimentally demonstrated on a diesel engine test rig. The calibration efficacy of the controller architecture is demonstrated at a steady state condition for fuelling steps and over drive cycles. Furthermore, the control system is shown to track output references while maintaining all input and state constraints in…

Subjects/Keywords: model predictive control; robust model predictive control; model predictive controller calibration; advanced automotive control; diesel engines

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APA (6^th Edition):

Gowri Sankar, G. S. S. (2019). Robust predictive control of diesel airpath. (Doctoral Dissertation). University of Melbourne. Retrieved from http://hdl.handle.net/11343/230607

Chicago Manual of Style (16^th Edition):

Gowri Sankar, Gokul Siva Sankar. “Robust predictive control of diesel airpath.” 2019. Doctoral Dissertation, University of Melbourne. Accessed January 23, 2021. http://hdl.handle.net/11343/230607.

MLA Handbook (7^th Edition):

Gowri Sankar, Gokul Siva Sankar. “Robust predictive control of diesel airpath.” 2019. Web. 23 Jan 2021.

Vancouver:

Gowri Sankar GSS. Robust predictive control of diesel airpath. [Internet] [Doctoral dissertation]. University of Melbourne; 2019. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/11343/230607.

Council of Science Editors:

Gowri Sankar GSS. Robust predictive control of diesel airpath. [Doctoral Dissertation]. University of Melbourne; 2019. Available from: http://hdl.handle.net/11343/230607

University of California – Berkeley

12. Danielson, Claus. Symmetric Constrained Optimal Control: Theory, Algorithms, and Applications.

Degree: Mechanical Engineering, 2014, University of California – Berkeley

URL: http://www.escholarship.org/uc/item/7dq9176d

► This dissertation develops the theory of symmetry for constrained linear systems. We use symmetry to design model predictive controllers for constrained linear systems with reduced… (more)

▼ This dissertation develops the theory of symmetry for constrained linear systems. We use symmetry to design model predictive controllers for constrained linear systems with reduced complexity.The dissertation is divided into three parts. In the first part we review the relevant results from model predictive control and group theory. In particular we present algorithms and data structures from computational group theory used to efficiently search groups.In the second part we develop the theory of symmetry for constrained linear systems and model predictive control problems. Symmetries of constrained linear systems are linear transformations that preserve the dynamics and constraints. A symmetry of a model predictive control problem is a symmetry of the underlying constrained system that also preserves the cost. We use a group theoretic formalism to derive properties of symmetric constrained linear systems and symmetric model predictive control problems. We prove conditions under which the model predictive controller is symmetric and present a procedure for efficiently computing the symmetries of constrained linear systems and model predictive control problems. Our method transforms the problem of finding generators for the symmetry group into a graph automorphism problem. These symmetries are used to design model predictive control algorithms with reduced complexity.We also present two novel explicit model predictive control designs. Both reduce memory requirements by discarding symmetrically redundant pieces of the control-law. The control-law in the eliminated pieces can be reconstructed online using symmetry. We show that storing the symmetries of the problem requires less memory than storing the controller pieces.In the third part of this dissertation we apply our symmetry theory to the battery balancing problem. We use symmetry to reduce the memory requirements for explicit model predictive controllers for seven battery-balancing hardware designs proposed in the literature. This application demonstrates that our symmetric controller designs can significantly reduce the memory requirements of explicit model predictive control. In particular for four out of seven of the designs in our numerical study, the number of pieces in the symmetric controller did not increase as the battery pack-size was increased.

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

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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 January 23, 2021. http://www.escholarship.org/uc/item/7dq9176d.

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

MLA Handbook (7^th Edition):

Danielson, Claus. “Symmetric Constrained Optimal Control: Theory, Algorithms, and Applications.” 2014. Web. 23 Jan 2021.

Vancouver:

Danielson C. Symmetric Constrained Optimal Control: Theory, Algorithms, and Applications. [Internet] [Thesis]. University of California – Berkeley; 2014. [cited 2021 Jan 23]. Available from: http://www.escholarship.org/uc/item/7dq9176d.

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

Council of Science Editors:

Danielson C. Symmetric Constrained Optimal Control: Theory, Algorithms, and Applications. [Thesis]. University of California – Berkeley; 2014. Available from: http://www.escholarship.org/uc/item/7dq9176d

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

Oregon State University

13. 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 January 23, 2021. 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. 23 Jan 2021.

Vancouver:

Fernández DC. Model Predictive Control for Underwater Robots in Ocean Waves. [Internet] [Masters thesis]. Oregon State University; 2015. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/1957/57267.

Council of Science Editors:

Fernández DC. Model Predictive Control for Underwater Robots in Ocean Waves. [Masters Thesis]. Oregon State University; 2015. Available from: http://hdl.handle.net/1957/57267

Cornell University

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

Degree: PhD, Mechanical Engineering, 2016, Cornell University

URL: http://hdl.handle.net/1813/44364

► Probabilistic inference and reasoning is applied to two major application areas: HVAC controls in buildings and autonomous vehicle perception. Although the physical domains differ vastly,… (more)

▼ Probabilistic inference and reasoning is applied to two major application areas: HVAC controls in buildings and autonomous vehicle perception. Although the physical domains differ vastly, across both applications the presented novel contributions share real-time inference of stochastic systems for improved control capability and performance. Besides performing simple state estimation, Kalman Filters in both applications are extended for model inference-estimating thermal model parameters and disturbances in buildings and dynamic object classification for perception in autonomous vehicles. Part one of this study proposes a general, scalable method to learn controloriented thermal models of buildings that could enable wide-scale deployment of cost-effective predictive controls. An Unscented Kalman Filter augmented for parameter and disturbance estimation is shown to accurately learn and predict a building's thermal response. By leveraging building topology and measurement data, the filter quickly learns parameters of a thermal network during periods of known or constrained loads and then characterizes unknown loads in order to provide accurate 24+ hour energy predictions. Performance was validated with EnergyPlus simulation data across a year-long study of a passive building. The method is extended to multi-zone actively controlled buildings by using the controller to excite unknown portions of the building's dynamics. A simulation study demonstrates self-excitation improves model estimation. Formalization of parameterization, disturbance estimation, and self-excitation routines is shown with an observability analysis. Comparing against a baseline thermostat controller, a Model Predictive Control (MPC) framework, which anticipates weather uncertainty and time-varying temperature set-points, is shown to improve energy savings and occupant comfort. Part two of this study presents a novel probabilistic perception algorithm as a real-time joint solution to data association, object tracking, and object classification for an autonomous ground vehicle (AGV) in all-weather conditions. The presented algorithm extends a Rao-Blackwellized Particle Filter originally built for Cornell's AGV for the DARPA Urban Challenge (DUC) to include multiple model tracking for classification. Additionally a state-of-the-art vision detection algorithm that includes heading information for AGV applications was implemented. Cornell's AGV from the DUC was upgraded and used to experimentally examine if and how state-of-the-art vision algorithms can complement or replace lidar and radar sensors. Sensor and algorithm performance in adverse weather and lighting conditions is tested. Experimental evaluation demonstrates that sensor diversity with a joint probabilistic perception algorithm provides robust all-weather data association, tracking, and classification. Advisors/Committee Members: Campbell,Mark (chair), Snavely,Keith Noah (committee member), Hencey,Brandon M. (committee member).

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

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

APA (6^th Edition):

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

Chicago Manual of Style (16^th Edition):

Radecki, Peter. “Applied Probabilistic Inference: Model Estimation For Hvac Predictive Controls And All-Weather Perception For Autonomous Vehicles.” 2016. Doctoral Dissertation, Cornell University. Accessed January 23, 2021. http://hdl.handle.net/1813/44364.

MLA Handbook (7^th Edition):

Radecki, Peter. “Applied Probabilistic Inference: Model Estimation For Hvac Predictive Controls And All-Weather Perception For Autonomous Vehicles.” 2016. Web. 23 Jan 2021.

Vancouver:

Radecki P. Applied Probabilistic Inference: Model Estimation For Hvac Predictive Controls And All-Weather Perception For Autonomous Vehicles. [Internet] [Doctoral dissertation]. Cornell University; 2016. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/1813/44364.

Council of Science Editors:

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

Cornell University

15. Du, Yingda. Observations and a Geometric Explanation of the Effects of Humic Acid on Flocculation.

Degree: M.S., Civil and Environmental Engineering, Civil and Environmental Engineering, 2017, Cornell University

URL: http://hdl.handle.net/1813/59054

► Natural organic matter (NOM) is found in all surface, ground and soil waters. NOM in water has a significant effect on drinking water treatment. The… (more)

Subjects/Keywords: flocculation; humic acid; predictive model; Environmental engineering

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APA (6^th Edition):

Du, Y. (2017). Observations and a Geometric Explanation of the Effects of Humic Acid on Flocculation. (Masters Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/59054

Chicago Manual of Style (16^th Edition):

Du, Yingda. “Observations and a Geometric Explanation of the Effects of Humic Acid on Flocculation.” 2017. Masters Thesis, Cornell University. Accessed January 23, 2021. http://hdl.handle.net/1813/59054.

MLA Handbook (7^th Edition):

Du, Yingda. “Observations and a Geometric Explanation of the Effects of Humic Acid on Flocculation.” 2017. Web. 23 Jan 2021.

Vancouver:

Du Y. Observations and a Geometric Explanation of the Effects of Humic Acid on Flocculation. [Internet] [Masters thesis]. Cornell University; 2017. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/1813/59054.

Council of Science Editors:

Du Y. Observations and a Geometric Explanation of the Effects of Humic Acid on Flocculation. [Masters Thesis]. Cornell University; 2017. Available from: http://hdl.handle.net/1813/59054

McMaster University

16. 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 January 23, 2021. http://hdl.handle.net/11375/23432.

MLA Handbook (7^th Edition):

Kheradmandi, Masoud. “ADVANCES IN MODEL PREDICTIVE CONTROL.” 2018. Web. 23 Jan 2021.

Vancouver:

Kheradmandi M. ADVANCES IN MODEL PREDICTIVE CONTROL. [Internet] [Doctoral dissertation]. McMaster University; 2018. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/11375/23432.

Council of Science Editors:

Kheradmandi M. ADVANCES IN MODEL PREDICTIVE CONTROL. [Doctoral Dissertation]. McMaster University; 2018. Available from: http://hdl.handle.net/11375/23432

Penn State University

17. Padghan, Pranjali S. COMPLEX AERIAL FLIGHT UTILIZING MODEL PREDICTIVE CONTROL.

Degree: 2018, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/14828psp5131

► In this dissertation a quadcopter model is developed and Model Predictive Control techniques are implemented for trajectory tracking and obstacle avoidance. The major application that… (more)

Subjects/Keywords: Model Predictive Control; Quadcopter Dynamics and control

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

APA (6^th Edition):

Padghan, P. S. (2018). COMPLEX AERIAL FLIGHT UTILIZING MODEL PREDICTIVE CONTROL. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/14828psp5131

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

Chicago Manual of Style (16^th Edition):

Padghan, Pranjali S. “COMPLEX AERIAL FLIGHT UTILIZING MODEL PREDICTIVE CONTROL.” 2018. Thesis, Penn State University. Accessed January 23, 2021. https://submit-etda.libraries.psu.edu/catalog/14828psp5131.

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

MLA Handbook (7^th Edition):

Padghan, Pranjali S. “COMPLEX AERIAL FLIGHT UTILIZING MODEL PREDICTIVE CONTROL.” 2018. Web. 23 Jan 2021.

Vancouver:

Padghan PS. COMPLEX AERIAL FLIGHT UTILIZING MODEL PREDICTIVE CONTROL. [Internet] [Thesis]. Penn State University; 2018. [cited 2021 Jan 23]. Available from: https://submit-etda.libraries.psu.edu/catalog/14828psp5131.

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

Council of Science Editors:

Padghan PS. COMPLEX AERIAL FLIGHT UTILIZING MODEL PREDICTIVE CONTROL. [Thesis]. Penn State University; 2018. Available from: https://submit-etda.libraries.psu.edu/catalog/14828psp5131

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

University of Waterloo

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

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 January 23, 2021. http://hdl.handle.net/10012/14524.

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

MLA Handbook (7^th Edition):

Maitland, Anson. “Nonlinear Model Predictive Control Reduction Strategies for Real-time Optimal Control.” 2019. Web. 23 Jan 2021.

Vancouver:

Maitland A. Nonlinear Model Predictive Control Reduction Strategies for Real-time Optimal Control. [Internet] [Thesis]. University of Waterloo; 2019. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10012/14524.

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

Council of Science Editors:

Maitland A. Nonlinear Model Predictive Control Reduction Strategies for Real-time Optimal Control. [Thesis]. University of Waterloo; 2019. Available from: http://hdl.handle.net/10012/14524

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

Texas A&M University

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

Degree: MS, Electrical Engineering, 2015, Texas A&M University

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

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

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

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

APA (6^th Edition):

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

Chicago Manual of Style (16^th Edition):

Vedam, Narayani. “Terrain-Adaptive Cruise Control: A Human-Like Approach.” 2015. Masters Thesis, Texas A&M University. Accessed January 23, 2021. http://hdl.handle.net/1969.1/156224.

MLA Handbook (7^th Edition):

Vedam, Narayani. “Terrain-Adaptive Cruise Control: A Human-Like Approach.” 2015. Web. 23 Jan 2021.

Vancouver:

Vedam N. Terrain-Adaptive Cruise Control: A Human-Like Approach. [Internet] [Masters thesis]. Texas A&M University; 2015. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/1969.1/156224.

Council of Science Editors:

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

University of Waikato

20. Forsythe, Claire Elizabeth. Predictive Risk Modelling for Hospital Readmissions .

Degree: 2014, University of Waikato

URL: http://hdl.handle.net/10289/8699

► This thesis is concerned with developing a predictive risk model to identify patients that are at high risk of readmission to hospital. Such a model… (more)

▼ This thesis is concerned with developing a predictive risk model to identify patients that are at high risk of readmission to hospital. Such a model should have a desirable level of predictive accuracy but also, should be financially beneficial to the DHB. Logistic regression and Naive Bayes probabilistic classification methods were both considered to build the predictive model. Performance measures such as the positive predictive value (PPV) and cost savings analysis were used to find the optimal days between initial admission and readmission and the optimal threshold for prediction of high risk patients. This study is concerned with Waikato District Health Board (DHB) domiciled patients discharged between 1 July 2009 and 31 October 2013. The dataset includes information about the patients initial admission and the response variable is whether a readmission occurred or not. Using logistic regression, this study found the model that fits the data best includes 21 variables that contain information about the patients initial admission. The two classification methods used produce a risk probability between 0 and 1 for each patient in the study. The logistic regression model performance was better than Naive Bayes as shown by the PPV (the proportion of patients correctly identified as at risk over the total at risk). The 56 day readmission data PPV at a risk threshold of 0.5 for the logistic regression was 48.4% and 30.8% for Naive Bayes. Analysis of the PPV identifies the risk threshold level of 0.5 and readmission period of 56 days as optimal predictive criteria in this study. Cost savings analysis also supports the 56 day model with an intervention cost of $500. The 0.5 cut off point in the 56 day model identifies a reasonable number of patients at risk for intervention, approximately 3,000, which equates to about 2 patients at risk per day over the period of this analysis. This analysis found the optimal model for predicting patients at risk of readmission is a logistic regression model using 56 day readmission data and a risk threshold of 0.5. A key recommendation of this study is that the DHB needs to introduce a method that correctly flags patient admissions. The model should be used on a trial basis at the DHB to see how accurate it performs. Advisors/Committee Members: Joshi, Chaitanya (advisor).

Subjects/Keywords: Logistic regression; Readmissions; Predictive model; Hospital

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

APA (6^th Edition):

Forsythe, C. E. (2014). Predictive Risk Modelling for Hospital Readmissions . (Masters Thesis). University of Waikato. Retrieved from http://hdl.handle.net/10289/8699

Chicago Manual of Style (16^th Edition):

Forsythe, Claire Elizabeth. “Predictive Risk Modelling for Hospital Readmissions .” 2014. Masters Thesis, University of Waikato. Accessed January 23, 2021. http://hdl.handle.net/10289/8699.

MLA Handbook (7^th Edition):

Forsythe, Claire Elizabeth. “Predictive Risk Modelling for Hospital Readmissions .” 2014. Web. 23 Jan 2021.

Vancouver:

Forsythe CE. Predictive Risk Modelling for Hospital Readmissions . [Internet] [Masters thesis]. University of Waikato; 2014. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10289/8699.

Council of Science Editors:

Forsythe CE. Predictive Risk Modelling for Hospital Readmissions . [Masters Thesis]. University of Waikato; 2014. Available from: http://hdl.handle.net/10289/8699

Delft University of Technology

21. Jain, R.P.K. (author). Transportation of Cable Suspended Load using Unmanned Aerial Vehicles: A Real-time Model Predictive Control approach.

Degree: 2015, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:4c6b4a94-4f15-4e67-8c30-eb8156aab406

►

Unmanned Aerial Vehicles (UAV) have received an increasing amount of attention recently with many applications being actively investigated across the globe, and several related open… (more)

▼

Unmanned Aerial Vehicles (UAV) have received an increasing amount of attention recently with many applications being actively investigated across the globe, and several related open research questions being actively pursued. Possible applications include search and rescue, disaster relief, environmental monitoring and surveillance, transportation, and construction. Transportation of cable suspended payloads using Unmanned Aerial Vehicles is one such application which is the topic of this research. Autonomous transportation of objects using UAV can contribute to the safe and reliable supply of food and medicine in remote or disaster-affected areas and even in commercial delivery of goods. The state-of-the-art approaches towards the slung load transportation either develop non-linear feedback control laws to stabilize the system to a predefined trajectory or employ open loop off-line trajectory planning schemes to generate optimal control inputs to the system. Most of these techniques often rely on availability of an accurate model of the system backed up with simulation results. Very few results exist which target experimental validation of the proposed method. Based on the findings of the previously conducted literature survey, it appears that the application of closed loop on-line trajectory generation and control schemes to transport a slung payload in swing free manner remains unanswered. The work in this thesis sets off to answer the research questions in this direction and address the issues that come along with experimental validation. Model Predictive Control (MPC) is a promising framework, which provides the means to tackle both the trajectory generation problem and the feedback control problem in an unified manner. As a result, it forms the most important component of this thesis. Specific research problem that is addressed in this thesis is to transport a cable suspended load using quadrotor from one point to another, while minimizing the swing through the use of Linear Time Invariant MPC techniques. A non-linear dynamic model for the quadrotor-slung load system is obtained and the structure within the system dynamics is exploited to decide the control strategy. Two different MPC formulations viz. MPC with integral action and MPC with delta-u formulation are simulated and compared to Linear Quadratic control with integral action which acts as a benchmark controller. Backed with simulation results, it is shown through experimental validation that it is possible to control the swing of cable suspended load using linear control techniques. MPC being an computationally expensive task, state-of-the-art fast optimization solvers such as FORCES PRO is used to achieve on-line implementation of MPC for the quadrotor-slung load system. To this end, a new software framework for implementation of MPC is developed which establishes a wireless link with the quadrotor resulting in a real-time networked control loop.

Systems and Control

Delft Center for Systems and Control

Mechanical, Maritime and Materials…

Advisors/Committee Members: Keviczky, T. (mentor).

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

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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 (author). “Transportation of Cable Suspended Load using Unmanned Aerial Vehicles: A Real-time Model Predictive Control approach.” 2015. Masters Thesis, Delft University of Technology. Accessed January 23, 2021. http://resolver.tudelft.nl/uuid:4c6b4a94-4f15-4e67-8c30-eb8156aab406.

MLA Handbook (7^th Edition):

Jain, R P K (author). “Transportation of Cable Suspended Load using Unmanned Aerial Vehicles: A Real-time Model Predictive Control approach.” 2015. Web. 23 Jan 2021.

Vancouver:

Jain RPK(. Transportation of Cable Suspended Load using Unmanned Aerial Vehicles: A Real-time Model Predictive Control approach. [Internet] [Masters thesis]. Delft University of Technology; 2015. [cited 2021 Jan 23]. Available from: http://resolver.tudelft.nl/uuid:4c6b4a94-4f15-4e67-8c30-eb8156aab406.

Council of Science Editors:

Jain RPK(. Transportation of Cable Suspended Load using Unmanned Aerial Vehicles: A Real-time Model Predictive Control approach. [Masters Thesis]. Delft University of Technology; 2015. Available from: http://resolver.tudelft.nl/uuid:4c6b4a94-4f15-4e67-8c30-eb8156aab406

Delft University of Technology

22. Stive, P.M. (author). 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. (mentor).

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 (author). “Performance assessment of tree-based model predictive control.” 2011. Masters Thesis, Delft University of Technology. Accessed January 23, 2021. http://resolver.tudelft.nl/uuid:79a718a3-cd99-4f44-8391-3de5e93c3dcd.

MLA Handbook (7^th Edition):

Stive, P M (author). “Performance assessment of tree-based model predictive control.” 2011. Web. 23 Jan 2021.

Vancouver:

Stive PM(. Performance assessment of tree-based model predictive control. [Internet] [Masters thesis]. Delft University of Technology; 2011. [cited 2021 Jan 23]. 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

Delft University of Technology

23. Siddaramappa, A. (author). 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.

Systems and Control

Delft Center for Systems and Control

Mechanical, Maritime and Materials Engineering

Advisors/Committee Members: Keviczky, T. (mentor).

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 (author). “Rigid Formation Control using Hovercrafts: A Spatial Model Predictive Control Approach.” 2015. Masters Thesis, Delft University of Technology. Accessed January 23, 2021. http://resolver.tudelft.nl/uuid:3b025de3-52ff-45d3-ab80-05cf2e450ec9.

MLA Handbook (7^th Edition):

Siddaramappa, A (author). “Rigid Formation Control using Hovercrafts: A Spatial Model Predictive Control Approach.” 2015. Web. 23 Jan 2021.

Vancouver:

Siddaramappa A(. Rigid Formation Control using Hovercrafts: A Spatial Model Predictive Control Approach. [Internet] [Masters thesis]. Delft University of Technology; 2015. [cited 2021 Jan 23]. 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

University of Illinois – Urbana-Champaign

24. Havens, Aaron. Model-based approaches for learning control from multi-modal data.

Degree: MS, Aerospace Engineering, 2020, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/108550

► Methods like deep reinforcement learning (DRL) have gained increasing attention when solving very general continuous control tasks in a model-free end-to-end fashion. However, there has… (more)

Subjects/Keywords: model predictive control; reinforcement learning; soft robotics

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APA (6^th Edition):

Havens, A. (2020). Model-based approaches for learning control from multi-modal data. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/108550

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):

Havens, Aaron. “Model-based approaches for learning control from multi-modal data.” 2020. Thesis, University of Illinois – Urbana-Champaign. Accessed January 23, 2021. http://hdl.handle.net/2142/108550.

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

MLA Handbook (7^th Edition):

Havens, Aaron. “Model-based approaches for learning control from multi-modal data.” 2020. Web. 23 Jan 2021.

Vancouver:

Havens A. Model-based approaches for learning control from multi-modal data. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2020. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/2142/108550.

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

Council of Science Editors:

Havens A. Model-based approaches for learning control from multi-modal data. [Thesis]. University of Illinois – Urbana-Champaign; 2020. Available from: http://hdl.handle.net/2142/108550

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

University of Minnesota

25. Pate, Joseph. Improvements in Sparsity Promoting Estimator Design and Network Aware Controller Design for Wireless Structural Control.

Degree: MS, Civil Engineering, 2020, University of Minnesota

URL: http://hdl.handle.net/11299/216749

► Feedback control systems are an attractive approach to limit the structural response to large dynamics loading events such as wind and earthquakes. However, civil structures… (more)

▼ Feedback control systems are an attractive approach to limit the structural response to large dynamics loading events such as wind and earthquakes. However, civil structures often contain too many degrees of freedom to measure all effectively when controlling the structure’s response. Previous work has shown that the Kalman filter can be used effectively to estimate the state response of structures from a limited set of measurements of the structure. Choosing a limited set of sensors from a larger sensor set poses a combinatorial problem. In light of this, we have devised a systematic method to determine a sparse set of measurements that have the largest impact on estimating the state of the structure. To avoid the use of a combinatorial search, we propose the addition of a sparsity promoting parameter and a penalty function to the optimization problem used to determine the Kalman gain. Promoting sparsity within the Kalman gain provides a method of removing sensors by setting the weight for a given sensor to zero which effectively removes the sensor’s measurements from the Kalman filter estimations. The use of wireless sensors in structural control has recently gained interest. How- ever, wireless sensors have an increased probability of packet error and longer communication delays in comparison with wired sensors. Using a sparse set of sensors has the added benefit of requiring fewer network resources, which allows for more efficient communication over wireless networks. Simulation results of a wireless feedback control of a 9 story structure show improved performance in limiting the structural response when using sensors placed according to the KFADMM in comparison to using the full sensor set. To further address the problem of packet loss and communication delays in wireless networks we examine an integrated model of the controller network and structure. Leveraging the integrated control model, we propose a controller capable of simultaneously controlling the network and structure. Preliminary simulation results indicate that the controller is able to effectively control both the network and structure by switching between sub systems within the controller network.

Subjects/Keywords: model predictive control; sensor selection; structural control

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APA (6^th Edition):

Pate, J. (2020). Improvements in Sparsity Promoting Estimator Design and Network Aware Controller Design for Wireless Structural Control. (Masters Thesis). University of Minnesota. Retrieved from http://hdl.handle.net/11299/216749

Chicago Manual of Style (16^th Edition):

Pate, Joseph. “Improvements in Sparsity Promoting Estimator Design and Network Aware Controller Design for Wireless Structural Control.” 2020. Masters Thesis, University of Minnesota. Accessed January 23, 2021. http://hdl.handle.net/11299/216749.

MLA Handbook (7^th Edition):

Pate, Joseph. “Improvements in Sparsity Promoting Estimator Design and Network Aware Controller Design for Wireless Structural Control.” 2020. Web. 23 Jan 2021.

Vancouver:

Pate J. Improvements in Sparsity Promoting Estimator Design and Network Aware Controller Design for Wireless Structural Control. [Internet] [Masters thesis]. University of Minnesota; 2020. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/11299/216749.

Council of Science Editors:

Pate J. Improvements in Sparsity Promoting Estimator Design and Network Aware Controller Design for Wireless Structural Control. [Masters Thesis]. University of Minnesota; 2020. Available from: http://hdl.handle.net/11299/216749

University of Illinois – Urbana-Champaign

26. 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 January 23, 2021. 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. 23 Jan 2021.

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 2021 Jan 23]. 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

University of Minnesota

27. Zoltek, Daniel. Developing a Predictive Model and Novel Imaging Technique for the Failure of Polyethylene Insulators.

Degree: MS, Chemistry, 2019, University of Minnesota

URL: http://hdl.handle.net/11299/208974

► Polyethylene is the most widespread polymer used in insulative cable housings due to its low cost, high chemical resistivity and low permeability to liquids and… (more)

Subjects/Keywords: Aging; Imaging; Insulation; Model; Polyethylene; Predictive

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

APA (6^th Edition):

Zoltek, D. (2019). Developing a Predictive Model and Novel Imaging Technique for the Failure of Polyethylene Insulators. (Masters Thesis). University of Minnesota. Retrieved from http://hdl.handle.net/11299/208974

Chicago Manual of Style (16^th Edition):

Zoltek, Daniel. “Developing a Predictive Model and Novel Imaging Technique for the Failure of Polyethylene Insulators.” 2019. Masters Thesis, University of Minnesota. Accessed January 23, 2021. http://hdl.handle.net/11299/208974.

MLA Handbook (7^th Edition):

Zoltek, Daniel. “Developing a Predictive Model and Novel Imaging Technique for the Failure of Polyethylene Insulators.” 2019. Web. 23 Jan 2021.

Vancouver:

Zoltek D. Developing a Predictive Model and Novel Imaging Technique for the Failure of Polyethylene Insulators. [Internet] [Masters thesis]. University of Minnesota; 2019. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/11299/208974.

Council of Science Editors:

Zoltek D. Developing a Predictive Model and Novel Imaging Technique for the Failure of Polyethylene Insulators. [Masters Thesis]. University of Minnesota; 2019. Available from: http://hdl.handle.net/11299/208974

University of Melbourne

28. 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 January 23, 2021. 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. 23 Jan 2021.

Vancouver:

LAM D. A model predictive approach to optimal path-following and contouring control. [Internet] [Doctoral dissertation]. University of Melbourne; 2012. [cited 2021 Jan 23]. 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

California State University – Sacramento

29. Bhatt, Pratik R. Audio coder using perceptual linear predictive coding.

Degree: MS, Electrical and Electronic Engineering, 2011, California State University – Sacramento

URL: http://hdl.handle.net/10211.9/1045

► Audio coder using perceptual linear predictive coding is a new technique for compressing and decompressing audio signal. This technique codes only the information of an… (more)

Subjects/Keywords: Audio coder; Perceptual model; Linear predictive coding

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

APA (6^th Edition):

Bhatt, P. R. (2011). Audio coder using perceptual linear predictive coding. (Masters Thesis). California State University – Sacramento. Retrieved from http://hdl.handle.net/10211.9/1045

Chicago Manual of Style (16^th Edition):

Bhatt, Pratik R. “Audio coder using perceptual linear predictive coding.” 2011. Masters Thesis, California State University – Sacramento. Accessed January 23, 2021. http://hdl.handle.net/10211.9/1045.

MLA Handbook (7^th Edition):

Bhatt, Pratik R. “Audio coder using perceptual linear predictive coding.” 2011. Web. 23 Jan 2021.

Vancouver:

Bhatt PR. Audio coder using perceptual linear predictive coding. [Internet] [Masters thesis]. California State University – Sacramento; 2011. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10211.9/1045.

Council of Science Editors:

Bhatt PR. Audio coder using perceptual linear predictive coding. [Masters Thesis]. California State University – Sacramento; 2011. Available from: http://hdl.handle.net/10211.9/1045

California State University – Sacramento

30. 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 January 23, 2021. http://hdl.handle.net/10211.3/198833.

MLA Handbook (7^th Edition):

Guo, Zhicheng. “Model predictive control for two-stage photovoltaic system.” 2017. Web. 23 Jan 2021.

Vancouver:

Guo Z. Model predictive control for two-stage photovoltaic system. [Internet] [Masters thesis]. California State University – Sacramento; 2017. [cited 2021 Jan 23]. 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

Open Access Theses and Dissertations