- 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
- Publisher copy:
- 10.1016/j.automatica.2008.06.017
-
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- 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
- Language:
- English
- Keywords:
- Copyright date:
- 2009
Journal article
Model predictive control for systems with stochastic multiplicative uncertainty and probabilistic constraints
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