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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Vydané v:Automatica (Oxford) Ročník 81; s. 176 - 183
Hlavní autori: Lorenzen, Matthias, Dabbene, Fabrizio, Tempo, Roberto, Allgöwer, Frank
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.07.2017
Predmet:
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
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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