Mean-variance receding horizon control for discrete time linear stochastic systems
A control strategy based on a mean-variance objective and expected value constraints is proposed for systems with additive and multiplicative stochastic uncertainty. Subject to a mean square stabilizability condition, the receding horizon objective can be obtained by solving a system of Lyapunov equ...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
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2008
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| _version_ | 1797075781918130176 |
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| author | Cannon, M Kouvaritakis, B Couchman, P |
| author_facet | Cannon, M Kouvaritakis, B Couchman, P |
| author_sort | Cannon, M |
| collection | OXFORD |
| description | A control strategy based on a mean-variance objective and expected value constraints is proposed for systems with additive and multiplicative stochastic uncertainty. Subject to a mean square stabilizability condition, the receding horizon objective can be obtained by solving a system of Lyapunov equations. An algorithm is proposed for computing the unconstrained optimal control law, which is the solution of a pair of coupled algebraic Riccati equations, and conditions are given for its convergence. A receding horizon controller based on quasi-closed loop predictions is defined. The control law is shown to provide a form of stochastic convergence of the state, and to ensure that the time average of the state variance converges to known bounds. Copyright © 2007 International Federation of Automatic Control All Rights Reserved. |
| first_indexed | 2024-03-06T23:55:02Z |
| format | Journal article |
| id | oxford-uuid:73e8d6c8-39e9-4124-b44e-435e6cde40a3 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T23:55:02Z |
| publishDate | 2008 |
| record_format | dspace |
| spelling | oxford-uuid:73e8d6c8-39e9-4124-b44e-435e6cde40a32022-03-26T19:59:28ZMean-variance receding horizon control for discrete time linear stochastic systemsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:73e8d6c8-39e9-4124-b44e-435e6cde40a3EnglishSymplectic Elements at Oxford2008Cannon, MKouvaritakis, BCouchman, PA control strategy based on a mean-variance objective and expected value constraints is proposed for systems with additive and multiplicative stochastic uncertainty. Subject to a mean square stabilizability condition, the receding horizon objective can be obtained by solving a system of Lyapunov equations. An algorithm is proposed for computing the unconstrained optimal control law, which is the solution of a pair of coupled algebraic Riccati equations, and conditions are given for its convergence. A receding horizon controller based on quasi-closed loop predictions is defined. The control law is shown to provide a form of stochastic convergence of the state, and to ensure that the time average of the state variance converges to known bounds. Copyright © 2007 International Federation of Automatic Control All Rights Reserved. |
| spellingShingle | Cannon, M Kouvaritakis, B Couchman, P Mean-variance receding horizon control for discrete time linear stochastic systems |
| title | Mean-variance receding horizon control for discrete time linear stochastic systems |
| title_full | Mean-variance receding horizon control for discrete time linear stochastic systems |
| title_fullStr | Mean-variance receding horizon control for discrete time linear stochastic systems |
| title_full_unstemmed | Mean-variance receding horizon control for discrete time linear stochastic systems |
| title_short | Mean-variance receding horizon control for discrete time linear stochastic systems |
| title_sort | mean variance receding horizon control for discrete time linear stochastic systems |
| work_keys_str_mv | AT cannonm meanvariancerecedinghorizoncontrolfordiscretetimelinearstochasticsystems AT kouvaritakisb meanvariancerecedinghorizoncontrolfordiscretetimelinearstochasticsystems AT couchmanp meanvariancerecedinghorizoncontrolfordiscretetimelinearstochasticsystems |