Stochastic MPC with offline uncertainty sampling

For discrete-time linear systems subject to multiplicative disturbance 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 inst...

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Vydáno v:	Automatica (Oxford) Ročník 81; s. 176 - 183
Hlavní autoři:	Lorenzen, Matthias, Dabbene, Fabrizio, Tempo, Roberto, Allgöwer, Frank
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.07.2017
Témata:	Control of constrained systems Data-based control Receding horizon control Stochastic control Stochastic model predictive control Receding horizon control Stochastic control Stochastic model predictive control Data-based control Control of constrained systems
ISSN:	0005-1098, 1873-2836
On-line přístup:	Získat plný text
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Popis
Shrnutí:	For discrete-time linear systems subject to multiplicative disturbance 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.
ISSN:	0005-1098 1873-2836
DOI:	10.1016/j.automatica.2017.03.031