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Distributed stochastic MPC of linear systems with parameter uncertainty and disturbances

Abstract:

In this paper, we propose a distributed stochastic model predictive control (DSMPC) algorithm for a team of linear subsystems sharing coupled probabilistic constraints. Each subsystem is subject to both parameter uncertainty and stochastic disturbances. To handle the probabilistic constraints, we first decompose the state trajectory into a nominal part and an uncertain part. The latter one is further divided into two parts: one is bounded by probabilistic tubes that are calculated offl...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/ChiCC.2016.7554022

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
Publisher:
Technical Committee on Control Theory Publisher's website
Journal:
Proceedings of the ,5th Chinese Control Conference Journal website
Pages:
4312-4317
Host title:
2016 35th Chinese Control Conference (CCC)
Publication date:
2016-08-29
Acceptance date:
2016-04-01
DOI:
EISSN:
1934-1768
Source identifiers:
629612
ISBN:
9789881563910
Keywords:
Pubs id:
pubs:629612
UUID:
uuid:f18a045e-09c8-427b-b01b-29d07fa575ec
Local pid:
pubs:629612
Deposit date:
2016-06-24

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