Trading performance for state constraint feasibility in stochastic constrained control: A randomized approach

摘要

Constrained control for stochastic linear systems is generally a difficult task due to the possible infeasibility of state constraints. In this paper, we focus on a finite control horizon and propose a design methodology where the constrained control problem is formulated as a chance-constrained optimization problem depending on some parameter. This parameter can be tuned so as to decide the appropriate trade-off between control cost minimization and state constraints satisfaction. An approximate solution is computed via a randomized algorithm. Precise guarantees about its feasibility for the original chance constrained problem are provided. A numerical example shows the efficacy of the proposed methodology. (C) 2016 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.