Stochastic MPC with offline uncertainty sampling

• Autores: Matthias Lorenzen, Fabrizio Dabbene, Roberto Tempo, Frank Allgöwer
• Localización: Automatica: A journal of IFAC the International Federation of Automatic Control, ISSN 0005-1098, Vol. 81, 2017, págs. 176-183
• Idioma: inglés
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• Resumen

  • Abstract 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.