Stable receding-horizon scenario predictive control for Markov-jump linear systems
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comunitat-uji-handle2:10234/7034
comunitat-uji-handle3:10234/8619
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https://doi.org/10.1016/j.automatica.2017.07.032 |
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Title
Stable receding-horizon scenario predictive control for Markov-jump linear systemsDate
2017-12Publisher
ElsevierISSN
0005-1098Bibliographic citation
SALA, Antonio; HERNÁNDEZ-MEJÍAS, Manuel; ARIÑO, Carlos. Stable receding-horizon scenario predictive control for Markov-jump linear systems. Automatica, 2017, vol. 86, p. 121-128.Type
info:eu-repo/semantics/articleVersion
info:eu-repo/semantics/publishedVersionSubject
Abstract
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, ... [+]
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. [-]
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Automatica, 2017, vol. 86Rights
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