Interpolation in MPC for discrete time bilinear systems
Feedback linearization suffers from a number of restrictions which have limited its use in Model-based Predictive Control. Some of these restrictions do not apply to the case of bilinear systems, but problems with input constraints and unstable zero dynamics persist. The present paper overcomes thes...
| Päätekijät: | , , , |
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| Aineistotyyppi: | Conference item |
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2001
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| _version_ | 1826267807872974848 |
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| author | Bloemen, H Cannon, M Kouvaritakis, B AACC AACC AACC |
| author_facet | Bloemen, H Cannon, M Kouvaritakis, B AACC AACC AACC |
| author_sort | Bloemen, H |
| collection | OXFORD |
| description | Feedback linearization suffers from a number of restrictions which have limited its use in Model-based Predictive Control. Some of these restrictions do not apply to the case of bilinear systems, but problems with input constraints and unstable zero dynamics persist. The present paper overcomes these difficulties by means of an interpolation strategy. Involved in this interpolation is a stabilizing trajectory which is computed through the use of invariant feasible sets (defined for the bilinear model) and a more aggressive trajectory which can be chosen to be either the unconstrained optimal trajectory or an alternative which guarantees that the state vector remains bounded and that the output converges to the origin. |
| first_indexed | 2024-03-06T20:59:53Z |
| format | Conference item |
| id | oxford-uuid:3a88fab7-b83c-4a17-a735-925054e9fc1d |
| institution | University of Oxford |
| last_indexed | 2024-03-06T20:59:53Z |
| publishDate | 2001 |
| record_format | dspace |
| spelling | oxford-uuid:3a88fab7-b83c-4a17-a735-925054e9fc1d2022-03-26T14:02:06ZInterpolation in MPC for discrete time bilinear systemsConference itemhttp://purl.org/coar/resource_type/c_5794uuid:3a88fab7-b83c-4a17-a735-925054e9fc1dSymplectic Elements at Oxford2001Bloemen, HCannon, MKouvaritakis, BAACCAACCAACCFeedback linearization suffers from a number of restrictions which have limited its use in Model-based Predictive Control. Some of these restrictions do not apply to the case of bilinear systems, but problems with input constraints and unstable zero dynamics persist. The present paper overcomes these difficulties by means of an interpolation strategy. Involved in this interpolation is a stabilizing trajectory which is computed through the use of invariant feasible sets (defined for the bilinear model) and a more aggressive trajectory which can be chosen to be either the unconstrained optimal trajectory or an alternative which guarantees that the state vector remains bounded and that the output converges to the origin. |
| spellingShingle | Bloemen, H Cannon, M Kouvaritakis, B AACC AACC AACC Interpolation in MPC for discrete time bilinear systems |
| title | Interpolation in MPC for discrete time bilinear systems |
| title_full | Interpolation in MPC for discrete time bilinear systems |
| title_fullStr | Interpolation in MPC for discrete time bilinear systems |
| title_full_unstemmed | Interpolation in MPC for discrete time bilinear systems |
| title_short | Interpolation in MPC for discrete time bilinear systems |
| title_sort | interpolation in mpc for discrete time bilinear systems |
| work_keys_str_mv | AT bloemenh interpolationinmpcfordiscretetimebilinearsystems AT cannonm interpolationinmpcfordiscretetimebilinearsystems AT kouvaritakisb interpolationinmpcfordiscretetimebilinearsystems AT aacc interpolationinmpcfordiscretetimebilinearsystems AT aacc interpolationinmpcfordiscretetimebilinearsystems AT aacc interpolationinmpcfordiscretetimebilinearsystems |