Efficient robust predictive control
Predictive constrained control of time-varying and/or uncertain linear systems has been effected through the use of ellipsoidal invariant sets. Linear matrix inequalities (LMI's) have been used to design a state-dependent state-feedback law that maintains the state vector inside invariant feasi...
| Prif Awduron: | , , |
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| Fformat: | Journal article |
| Iaith: | English |
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IEEE
2000
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| _version_ | 1826261703648608256 |
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| author | Kouvaritakis, B Rossiter, J Schuurmans, J |
| author_facet | Kouvaritakis, B Rossiter, J Schuurmans, J |
| author_sort | Kouvaritakis, B |
| collection | OXFORD |
| description | Predictive constrained control of time-varying and/or uncertain linear systems has been effected through the use of ellipsoidal invariant sets. Linear matrix inequalities (LMI's) have been used to design a state-dependent state-feedback law that maintains the state vector inside invariant feasible sets. For the purposes of prediction however, at each time instant, the state feedback law is assumed constant. In addition, due to the large number of LMI's involved, online computation becomes intractable for anything other than small dimensional systems. Here we propose a new approach that deploys a fixed state-feedback law but introduces extra degrees of freedom through the use of perturbations on the fixed state-feedback law. The problem is so formulated that all demanding computations can be performed offline leaving only a simple optimization problem to be solved online. Over and above the very significant reduction in computational cost, the extra degrees of freedom allow for better performance and wider applicability. |
| first_indexed | 2024-03-06T19:25:31Z |
| format | Journal article |
| id | oxford-uuid:1b904175-4e83-4289-adf7-4f7c8f1b35fa |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T19:25:31Z |
| publishDate | 2000 |
| publisher | IEEE |
| record_format | dspace |
| spelling | oxford-uuid:1b904175-4e83-4289-adf7-4f7c8f1b35fa2022-03-26T11:01:07ZEfficient robust predictive controlJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:1b904175-4e83-4289-adf7-4f7c8f1b35faEnglishSymplectic Elements at OxfordIEEE2000Kouvaritakis, BRossiter, JSchuurmans, JPredictive constrained control of time-varying and/or uncertain linear systems has been effected through the use of ellipsoidal invariant sets. Linear matrix inequalities (LMI's) have been used to design a state-dependent state-feedback law that maintains the state vector inside invariant feasible sets. For the purposes of prediction however, at each time instant, the state feedback law is assumed constant. In addition, due to the large number of LMI's involved, online computation becomes intractable for anything other than small dimensional systems. Here we propose a new approach that deploys a fixed state-feedback law but introduces extra degrees of freedom through the use of perturbations on the fixed state-feedback law. The problem is so formulated that all demanding computations can be performed offline leaving only a simple optimization problem to be solved online. Over and above the very significant reduction in computational cost, the extra degrees of freedom allow for better performance and wider applicability. |
| spellingShingle | Kouvaritakis, B Rossiter, J Schuurmans, J Efficient robust predictive control |
| title | Efficient robust predictive control |
| title_full | Efficient robust predictive control |
| title_fullStr | Efficient robust predictive control |
| title_full_unstemmed | Efficient robust predictive control |
| title_short | Efficient robust predictive control |
| title_sort | efficient robust predictive control |
| work_keys_str_mv | AT kouvaritakisb efficientrobustpredictivecontrol AT rossiterj efficientrobustpredictivecontrol AT schuurmansj efficientrobustpredictivecontrol |