Fast model predictive control
<p>This thesis develops efficient optimization methods for Model Predictive Control (MPC) to enable its application to constrained systems with fast and uncertain dynamics. The key contribution is an active set method which exploits the parametric nature of the sequential optimization problem...
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| Format: | Thesis |
| Language: | English |
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2013
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| _version_ | 1826278009078808576 |
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| author | Buerger, J |
| author2 | Cannon, M |
| author_facet | Cannon, M Buerger, J |
| author_sort | Buerger, J |
| collection | OXFORD |
| description | <p>This thesis develops efficient optimization methods for Model Predictive Control (MPC) to enable its application to constrained systems with fast and uncertain dynamics. The key contribution is an active set method which exploits the parametric nature of the sequential optimization problem and is obtained from a dynamic programming formulation of the MPC problem. This method is first applied to the nominal linear MPC problem and is successively extended to linear systems with additive uncertainty and input constraints or state/input constraints. The thesis discusses both offline (projection-based) and online (active set) methods for the solution of controllability problems for linear systems with additive uncertainty. The active set method uses first-order necessary conditions for optimality to construct parametric programming regions for a particular given active set locally along a line of search in the space of feasible initial conditions. Along this line of search the homotopy of optimal solutions is exploited: a known solution at some given plant state is continuously deformed into the solution at the actual measured current plant state by performing the required active set changes whenever a boundary of a parametric programming region is crossed during the line search operation. The sequence of solutions for the finite horizon optimal control problem is therefore obtained locally for the given plant state. This method overcomes the main limitation of parametric programming methods that have been applied in the MPC context which usually require the offline precomputation of all possible regions. In contrast to this the proposed approach is an online method with very low computational demands which efficiently exploits the parametric nature of the solution and returns exact local DP solutions. The final chapter of this thesis discusses an application of robust tube-based MPC to the nonlinear MPC problem based on successive linearization.</p> |
| first_indexed | 2024-03-06T23:37:30Z |
| format | Thesis |
| id | oxford-uuid:6e296415-f02c-4bc2-b171-3bee80fc081a |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T23:37:30Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | oxford-uuid:6e296415-f02c-4bc2-b171-3bee80fc081a2022-03-26T19:22:32ZFast model predictive controlThesishttp://purl.org/coar/resource_type/c_db06uuid:6e296415-f02c-4bc2-b171-3bee80fc081aOperations research,mathematical programmingCalculus of variations and optimal controlControl engineeringEnglishOxford University Research Archive - Valet2013Buerger, JCannon, MKouvaritakis, B<p>This thesis develops efficient optimization methods for Model Predictive Control (MPC) to enable its application to constrained systems with fast and uncertain dynamics. The key contribution is an active set method which exploits the parametric nature of the sequential optimization problem and is obtained from a dynamic programming formulation of the MPC problem. This method is first applied to the nominal linear MPC problem and is successively extended to linear systems with additive uncertainty and input constraints or state/input constraints. The thesis discusses both offline (projection-based) and online (active set) methods for the solution of controllability problems for linear systems with additive uncertainty. The active set method uses first-order necessary conditions for optimality to construct parametric programming regions for a particular given active set locally along a line of search in the space of feasible initial conditions. Along this line of search the homotopy of optimal solutions is exploited: a known solution at some given plant state is continuously deformed into the solution at the actual measured current plant state by performing the required active set changes whenever a boundary of a parametric programming region is crossed during the line search operation. The sequence of solutions for the finite horizon optimal control problem is therefore obtained locally for the given plant state. This method overcomes the main limitation of parametric programming methods that have been applied in the MPC context which usually require the offline precomputation of all possible regions. In contrast to this the proposed approach is an online method with very low computational demands which efficiently exploits the parametric nature of the solution and returns exact local DP solutions. The final chapter of this thesis discusses an application of robust tube-based MPC to the nonlinear MPC problem based on successive linearization.</p> |
| spellingShingle | Operations research,mathematical programming Calculus of variations and optimal control Control engineering Buerger, J Fast model predictive control |
| title | Fast model predictive control |
| title_full | Fast model predictive control |
| title_fullStr | Fast model predictive control |
| title_full_unstemmed | Fast model predictive control |
| title_short | Fast model predictive control |
| title_sort | fast model predictive control |
| topic | Operations research,mathematical programming Calculus of variations and optimal control Control engineering |
| work_keys_str_mv | AT buergerj fastmodelpredictivecontrol |