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Model predictive control for systems with stochastic multiplicative uncertainty and probabilistic constraints

Abstract:
Robust predictive control handles constrained systems that are subject to stochastic uncertainty but propagating the effects of uncertainty over a prediction horizon can be computationally expensive and conservative. This paper overcomes these issues through an augmented autonomous prediction formulation, and provides a method of handling probabilistic constraints and ensuring closed loop stability through the use of an extension of the concept of invariance, namely invariance with probability p. © 2008 Elsevier Ltd. All rights reserved.
Publication status:
Published

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Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
Role:
Author
Journal:
AUTOMATICA
Volume:
45
Issue:
1
Pages:
167-172
Publication date:
2009-01-05
DOI:
ISSN:
0005-1098
URN:
uuid:0423f741-c410-4924-9488-423271ba09aa
Source identifiers:
63625
Local pid:
pubs:63625

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