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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
Version:
Accepted Manuscript

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Publisher copy:
10.1109/ChiCC.2016.7554022

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Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
Role:
Author
Publisher:
Technical Committee on Control Theory Publisher's website
Pages:
4312-4317
Publication date:
2016-08-29
DOI:
EISSN:
1934-1768
URN:
uuid:f18a045e-09c8-427b-b01b-29d07fa575ec
Source identifiers:
629612
Local pid:
pubs:629612
ISBN:
978-9-8815-6391-0

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