Model predictive control for systems with stochastic multiplicative uncertainty and probabilistic constraints
Robust predictive control handles constrained systems that are subject to stochastic uncertainty but propagating the effects of uncertainty over a prediction horizon can be computationally expensive and conservative. This paper overcomes these issues through an augmented autonomous prediction formul...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
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2009
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| _version_ | 1797051060877000704 |
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| author | Cannon, M Kouvaritakis, B Wu, X |
| author_facet | Cannon, M Kouvaritakis, B Wu, X |
| author_sort | Cannon, M |
| collection | OXFORD |
| description | Robust predictive control handles constrained systems that are subject to stochastic uncertainty but propagating the effects of uncertainty over a prediction horizon can be computationally expensive and conservative. This paper overcomes these issues through an augmented autonomous prediction formulation, and provides a method of handling probabilistic constraints and ensuring closed loop stability through the use of an extension of the concept of invariance, namely invariance with probability p. © 2008 Elsevier Ltd. All rights reserved. |
| first_indexed | 2024-03-06T18:14:28Z |
| format | Journal article |
| id | oxford-uuid:0423f741-c410-4924-9488-423271ba09aa |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T18:14:28Z |
| publishDate | 2009 |
| record_format | dspace |
| spelling | oxford-uuid:0423f741-c410-4924-9488-423271ba09aa2022-03-26T08:50:07ZModel predictive control for systems with stochastic multiplicative uncertainty and probabilistic constraintsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:0423f741-c410-4924-9488-423271ba09aaEnglishSymplectic Elements at Oxford2009Cannon, MKouvaritakis, BWu, XRobust predictive control handles constrained systems that are subject to stochastic uncertainty but propagating the effects of uncertainty over a prediction horizon can be computationally expensive and conservative. This paper overcomes these issues through an augmented autonomous prediction formulation, and provides a method of handling probabilistic constraints and ensuring closed loop stability through the use of an extension of the concept of invariance, namely invariance with probability p. © 2008 Elsevier Ltd. All rights reserved. |
| spellingShingle | Cannon, M Kouvaritakis, B Wu, X Model predictive control for systems with stochastic multiplicative uncertainty and probabilistic constraints |
| title | Model predictive control for systems with stochastic multiplicative uncertainty and probabilistic constraints |
| title_full | Model predictive control for systems with stochastic multiplicative uncertainty and probabilistic constraints |
| title_fullStr | Model predictive control for systems with stochastic multiplicative uncertainty and probabilistic constraints |
| title_full_unstemmed | Model predictive control for systems with stochastic multiplicative uncertainty and probabilistic constraints |
| title_short | Model predictive control for systems with stochastic multiplicative uncertainty and probabilistic constraints |
| title_sort | model predictive control for systems with stochastic multiplicative uncertainty and probabilistic constraints |
| work_keys_str_mv | AT cannonm modelpredictivecontrolforsystemswithstochasticmultiplicativeuncertaintyandprobabilisticconstraints AT kouvaritakisb modelpredictivecontrolforsystemswithstochasticmultiplicativeuncertaintyandprobabilisticconstraints AT wux modelpredictivecontrolforsystemswithstochasticmultiplicativeuncertaintyandprobabilisticconstraints |