Stochastic MPC with Offline Uncertainty Sampling

Autor:	Lorenzen, Matthias, Dabbene, Fabrizio, Tempo, Roberto, Allgöwer, Frank
Rok vydání:	2016
Předmět:	Computer Science - Systems and Control Mathematics - Optimization and Control
Druh dokumentu:	Working Paper
Popis:	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. Comment: Preprint submitted to Automatica
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1606.06056 Zobrazit plný text záznamu View this record from Arxiv