Journal article
Maximising the guaranteed feasible set for stochastic MPC with chance constraints
- Abstract:
- This paper proposes a method of approximating positively invariant sets and n-step controllable sets of uncertain linear systems that are subject to chance constraints. The computed sets are robustly invariant and are guaranteed to satisfy the probabilistic constraints of the control problem. In contrast, existing methods based on random sampling are only able to satisfy such constraints with a fixed level of confidence. The proposed approach uses explicitly parametrised auxiliary disturbance sets, which are optimised subject to a constraint on their probability measure so as to maximise the relevant positively invariant or n-step controllable set. The results are illustrated by numerical examples.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Accepted manuscript, pdf, 219.8KB, Terms of use)
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- Publisher copy:
- 10.1016/j.ifacol.2017.08.1388
Authors
- Publisher:
- Elsevier
- Journal:
- IFAC-PapersOnLine More from this journal
- Volume:
- 50
- Issue:
- 1
- Pages:
- 8220-8225
- Publication date:
- 2017-10-18
- Acceptance date:
- 2017-02-27
- DOI:
- ISSN:
-
2405-8963
- Keywords:
- Pubs id:
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pubs:744773
- UUID:
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uuid:729bcb2e-9303-4d72-84c0-ea01a61b122c
- Local pid:
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pubs:744773
- Source identifiers:
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744773
- Deposit date:
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2017-12-01
Terms of use
- Copyright holder:
- © 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd
- Copyright date:
- 2017
- Notes:
- This is the author accepted manuscript following peer review version of the article. The final version is available online from Elsevier at: 10.1016/j.ifacol.2017.08.1388
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