Journal
AUTOMATICA
Volume 86, Issue -, Pages 121-128Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.automatica.2017.07.032
Keywords
Markov-jump linear systems; Model predictive control; Receding-horizon; Constrained linear systems; Mean-square stability
Funding
- Spanish Ministry of Economy
- European Union [DPI2016-81002-R]
- Universitat Jaume I [P1.1B2015-36]
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This paper discusses predictive control for constrained discrete-time Markov-jump linear systems (MJLS) which jump between a finite set of modes according to a Markov probabilistic transition/observation model, minimising an average cost. Due to the exponential explosion of the number of possible realisations as horizon grows, scenario approaches consider only a subset of them. Prior works cast the problem as a tree-based optimisation one, but enforce stability and feasibility via artificial Lyapunov-related constraints. The proposed approach avoids this route, proposing instead 'terminal ingredients' and tree properties (trim-contained, strictly-complete) properly generalising the stability/feasibility ideas in linear and MJLS literature. (C) 2017 Elsevier Ltd. All rights reserved.
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