Stochastic Tubes in Model Predictive Control With Probabilistic Constraints
Stochastic model predictive control (MPC) strategies can provide guarantees of stability and constraint satisfaction, but their online computation can be formidable. This difficulty is avoided in the current technical note through the use of tubes of fixed cross section and variable scaling. A model...
| Príomhchruthaitheoirí: | , , , |
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| Formáid: | Journal article |
| Teanga: | English |
| Foilsithe / Cruthaithe: |
2011
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| _version_ | 1826301357372473344 |
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| author | Cannon, M Kouvaritakis, B Rakovic, S Cheng, Q |
| author_facet | Cannon, M Kouvaritakis, B Rakovic, S Cheng, Q |
| author_sort | Cannon, M |
| collection | OXFORD |
| description | Stochastic model predictive control (MPC) strategies can provide guarantees of stability and constraint satisfaction, but their online computation can be formidable. This difficulty is avoided in the current technical note through the use of tubes of fixed cross section and variable scaling. A model describing the evolution of predicted tube scalings facilitates the computation of stochastic tubes; furthermore this procedure can be performed offline. The resulting MPC scheme has a low online computational load even for long prediction horizons, thus allowing for performance improvements. The efficacy of the approach is illustrated by numerical examples. © 2010 IEEE. |
| first_indexed | 2024-03-07T05:31:12Z |
| format | Journal article |
| id | oxford-uuid:e255fcfd-3f83-4dd4-bf79-ad40a6a59cb6 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T05:31:12Z |
| publishDate | 2011 |
| record_format | dspace |
| spelling | oxford-uuid:e255fcfd-3f83-4dd4-bf79-ad40a6a59cb62022-03-27T10:00:21ZStochastic Tubes in Model Predictive Control With Probabilistic ConstraintsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:e255fcfd-3f83-4dd4-bf79-ad40a6a59cb6EnglishSymplectic Elements at Oxford2011Cannon, MKouvaritakis, BRakovic, SCheng, QStochastic model predictive control (MPC) strategies can provide guarantees of stability and constraint satisfaction, but their online computation can be formidable. This difficulty is avoided in the current technical note through the use of tubes of fixed cross section and variable scaling. A model describing the evolution of predicted tube scalings facilitates the computation of stochastic tubes; furthermore this procedure can be performed offline. The resulting MPC scheme has a low online computational load even for long prediction horizons, thus allowing for performance improvements. The efficacy of the approach is illustrated by numerical examples. © 2010 IEEE. |
| spellingShingle | Cannon, M Kouvaritakis, B Rakovic, S Cheng, Q Stochastic Tubes in Model Predictive Control With Probabilistic Constraints |
| title | Stochastic Tubes in Model Predictive Control With Probabilistic Constraints |
| title_full | Stochastic Tubes in Model Predictive Control With Probabilistic Constraints |
| title_fullStr | Stochastic Tubes in Model Predictive Control With Probabilistic Constraints |
| title_full_unstemmed | Stochastic Tubes in Model Predictive Control With Probabilistic Constraints |
| title_short | Stochastic Tubes in Model Predictive Control With Probabilistic Constraints |
| title_sort | stochastic tubes in model predictive control with probabilistic constraints |
| work_keys_str_mv | AT cannonm stochastictubesinmodelpredictivecontrolwithprobabilisticconstraints AT kouvaritakisb stochastictubesinmodelpredictivecontrolwithprobabilisticconstraints AT rakovics stochastictubesinmodelpredictivecontrolwithprobabilisticconstraints AT chengq stochastictubesinmodelpredictivecontrolwithprobabilisticconstraints |