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Stochastic MPC for additive and multiplicative uncertainty using sample approximations

Abstract:

We introduce an approach for Model Predictive Control (MPC) of systems with additive and multiplicative stochastic uncertainty subject to chance constraints. Predicted states are bounded within a tube and the chance constraint is considered in a 'one step ahead' manner, with robust constraints applied over the remainder of the horizon. The online optimization is formulated as a chance-constrained program which is solved approximately using sampling. We prove that if the optimization is initia...

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Publication status:
Published
Peer review status:
Peer reviewed

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Files:
  • (Accepted manuscript, pdf, 503.6KB)
Publisher copy:
10.1109/TAC.2018.2887054

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More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
St John's College
Role:
Author
ORCID:
0000-0003-2189-7876
Publisher:
Institute of Electrical and Electronics Engineers
Journal:
IEEE Transactions on Automatic Control More from this journal
Volume:
64
Issue:
9
Pages:
3883-3888
Publication date:
2018-12-17
Acceptance date:
2018-12-02
DOI:
EISSN:
1558-2523
ISSN:
0018-9286
Keywords:
Pubs id:
pubs:951230
UUID:
uuid:04fb0168-6a60-43ed-b282-18d961d1d92a
Local pid:
pubs:951230
Source identifiers:
951230
Deposit date:
2018-12-08

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