Stochastic MPC with Offline Uncertainty Sampling

For discrete-time linear systems subject to parametric uncertainty described by random variables, we develop a sampling-based Stochastic Model Predictive Control algorithm. Unlike earlier results employing a scenario approximation, we propose an offline sampling approach in the design phase instead...

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Bibliographic Details
Main Authors Lorenzen, Matthias, Dabbene, Fabrizio, Tempo, Roberto, Allgöwer, Frank
Format Journal Article
LanguageEnglish
Published 20.06.2016
Subjects
Computer Science - Systems and Control
Mathematics - Optimization and Control
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DOI10.48550/arxiv.1606.06056

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Summary:For discrete-time linear systems subject to parametric uncertainty described by random variables, we develop a sampling-based Stochastic Model Predictive Control algorithm. Unlike earlier results employing a scenario approximation, we propose an offline sampling approach in the design phase instead of online scenario generation. The paper highlights the structural difference between online and offline sampling and provides rigorous bounds on the number of samples needed to guarantee chance constraint satisfaction. The approach does not only significantly speed up the online computation, but furthermore allows to suitably tighten the constraints to guarantee robust recursive feasibility when bounds on the uncertain variables are provided. Under mild assumptions, asymptotic stability of the origin can be established.
DOI:10.48550/arxiv.1606.06056