Stability proof for computationally efficient predictive control in the uncertain case
Large system dimensions and/or a possible need for long horizons restrict the applicability of predictive control. Earlier work showed that by sacrificing a certain degree of optimality it is possible to define efficient algorithms which reduce considerably computational complexity. This note consid...
| Main Authors: | , , , |
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| Format: | Conference item |
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2003
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| _version_ | 1826288873804660736 |
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| author | Rossiter, J Kouvaritakis, B Cannon, M AAC AAC |
| author_facet | Rossiter, J Kouvaritakis, B Cannon, M AAC AAC |
| author_sort | Rossiter, J |
| collection | OXFORD |
| description | Large system dimensions and/or a possible need for long horizons restrict the applicability of predictive control. Earlier work showed that by sacrificing a certain degree of optimality it is possible to define efficient algorithms which reduce considerably computational complexity. This note considers a class of such algorithms which deploy just one degree of freedom. It is shown that it is possible to: (i) derive a priori stability guarantees over much larger regions of the state space and for a larger class of control trajectories; (ii) account for a particular class of model uncertainty and (iii) show that even though, use is made of ellipsoidal invariant sets, nevertheless the stability results are not limited to the volume of such ellipsoids. |
| first_indexed | 2024-03-07T02:20:18Z |
| format | Conference item |
| id | oxford-uuid:a3b1b679-bccd-4ac8-b7f5-d14928a24d0c |
| institution | University of Oxford |
| last_indexed | 2024-03-07T02:20:18Z |
| publishDate | 2003 |
| record_format | dspace |
| spelling | oxford-uuid:a3b1b679-bccd-4ac8-b7f5-d14928a24d0c2022-03-27T02:28:45ZStability proof for computationally efficient predictive control in the uncertain caseConference itemhttp://purl.org/coar/resource_type/c_5794uuid:a3b1b679-bccd-4ac8-b7f5-d14928a24d0cSymplectic Elements at Oxford2003Rossiter, JKouvaritakis, BCannon, MAACAACLarge system dimensions and/or a possible need for long horizons restrict the applicability of predictive control. Earlier work showed that by sacrificing a certain degree of optimality it is possible to define efficient algorithms which reduce considerably computational complexity. This note considers a class of such algorithms which deploy just one degree of freedom. It is shown that it is possible to: (i) derive a priori stability guarantees over much larger regions of the state space and for a larger class of control trajectories; (ii) account for a particular class of model uncertainty and (iii) show that even though, use is made of ellipsoidal invariant sets, nevertheless the stability results are not limited to the volume of such ellipsoids. |
| spellingShingle | Rossiter, J Kouvaritakis, B Cannon, M AAC AAC Stability proof for computationally efficient predictive control in the uncertain case |
| title | Stability proof for computationally efficient predictive control in the uncertain case |
| title_full | Stability proof for computationally efficient predictive control in the uncertain case |
| title_fullStr | Stability proof for computationally efficient predictive control in the uncertain case |
| title_full_unstemmed | Stability proof for computationally efficient predictive control in the uncertain case |
| title_short | Stability proof for computationally efficient predictive control in the uncertain case |
| title_sort | stability proof for computationally efficient predictive control in the uncertain case |
| work_keys_str_mv | AT rossiterj stabilityproofforcomputationallyefficientpredictivecontrolintheuncertaincase AT kouvaritakisb stabilityproofforcomputationallyefficientpredictivecontrolintheuncertaincase AT cannonm stabilityproofforcomputationallyefficientpredictivecontrolintheuncertaincase AT aac stabilityproofforcomputationallyefficientpredictivecontrolintheuncertaincase AT aac stabilityproofforcomputationallyefficientpredictivecontrolintheuncertaincase |