Formal analysis of state estimation for nonlinear model predictive control

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dc.contributor.advisorTsoeu Mohohloen_ZA
dc.contributor.authorMoeti, Sekhonyanaen_ZA
dc.date.accessioned2016-06-22T08:51:26Z
dc.date.available2016-06-22T08:51:26Z
dc.date.issued2015en_ZA
dc.description.abstractThe main goal of this study is to carry out a closed-loop performance analysis of state estimation methods when implemented in the formulation of nonlinear model predictive control. The analysis is facilitated by two nonlinear optimal state estimation methods: augmented state EKF (ASEKF) and augmented state UKF (ASUKF) for comparison purposes. Each state estimation method is coupled to the same NMPC controller to form state estimation-based NMPC controllers, that is, to form the ASEKF-NMPC and ASUKFNMPC controllers. The resulting NMPC controllers are applied for position control of the magnetic levitation system to validate their closed-loop performances. The ASEKFNMPC and ASUKF-NMPC controllers are further applied for the angular position control of the inverted pendulum mounted on a cart system for comparative analysis. The controlled system is perturbed with different error sources: output step disturbance and 5%parametric plant-model mismatch. Output step disturbance is introduced to the system to disturb the pendulum from its upright position while the 5% mismatch is applied to the parameters of the model of the controlled system throughout the simulation. To facilitate fair analysis, Pareto front ranking method is chosen as an evaluation method whereby the cost functions are defined according to the author's preferences. The cost functions served as performance markers for analyzing performance of ASEKF and ASUKF in NMPC formulation in multidimensional space. The tunable parameters of the ASEKFNMPC and ASUKF-NMPC controllers are chosen to be the decision variables of the evaluation problem. The state estimation methods are evaluated in terms of estimation accuracy, system's response time, peak overshoot and control performance. The Level Diagrams tool is used for good visualization of the Pareto fronts to evaluate which estimator performs better in the closed-loop. Finally, the points on the Level Diagrams which provide a performance closest to the desired are selected and tested through simulation runs on the inverted pendulum on a moving cart system.en_ZA
dc.identifier.apacitationMoeti, S. (2015). <i>Formal analysis of state estimation for nonlinear model predictive control</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/20065en_ZA
dc.identifier.chicagocitationMoeti, Sekhonyana. <i>"Formal analysis of state estimation for nonlinear model predictive control."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2015. http://hdl.handle.net/11427/20065en_ZA
dc.identifier.citationMoeti, S. 2015. Formal analysis of state estimation for nonlinear model predictive control. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Moeti, Sekhonyana AB - The main goal of this study is to carry out a closed-loop performance analysis of state estimation methods when implemented in the formulation of nonlinear model predictive control. The analysis is facilitated by two nonlinear optimal state estimation methods: augmented state EKF (ASEKF) and augmented state UKF (ASUKF) for comparison purposes. Each state estimation method is coupled to the same NMPC controller to form state estimation-based NMPC controllers, that is, to form the ASEKF-NMPC and ASUKFNMPC controllers. The resulting NMPC controllers are applied for position control of the magnetic levitation system to validate their closed-loop performances. The ASEKFNMPC and ASUKF-NMPC controllers are further applied for the angular position control of the inverted pendulum mounted on a cart system for comparative analysis. The controlled system is perturbed with different error sources: output step disturbance and 5%parametric plant-model mismatch. Output step disturbance is introduced to the system to disturb the pendulum from its upright position while the 5% mismatch is applied to the parameters of the model of the controlled system throughout the simulation. To facilitate fair analysis, Pareto front ranking method is chosen as an evaluation method whereby the cost functions are defined according to the author's preferences. The cost functions served as performance markers for analyzing performance of ASEKF and ASUKF in NMPC formulation in multidimensional space. The tunable parameters of the ASEKFNMPC and ASUKF-NMPC controllers are chosen to be the decision variables of the evaluation problem. The state estimation methods are evaluated in terms of estimation accuracy, system's response time, peak overshoot and control performance. The Level Diagrams tool is used for good visualization of the Pareto fronts to evaluate which estimator performs better in the closed-loop. Finally, the points on the Level Diagrams which provide a performance closest to the desired are selected and tested through simulation runs on the inverted pendulum on a moving cart system. DA - 2015 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2015 T1 - Formal analysis of state estimation for nonlinear model predictive control TI - Formal analysis of state estimation for nonlinear model predictive control UR - http://hdl.handle.net/11427/20065 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/20065
dc.identifier.vancouvercitationMoeti S. Formal analysis of state estimation for nonlinear model predictive control. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2015 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/20065en_ZA
dc.language.isoEngen_ZA
dc.publisher.departmentDepartment of Electrical Engineeringen_ZA
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherElectrical Engineeringen_ZA
dc.titleFormal analysis of state estimation for nonlinear model predictive controlen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMSc (Eng)en_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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