@incollection{eprints429,
            year = {2009},
           title = {Scenario-based model predictive control of stochastic constrained linear systems},
           pages = {6333--6338},
       booktitle = {48th IEEE Conference on Decision and Control},
          author = {Daniele Bernardini and Alberto Bemporad},
       publisher = {IEEE},
         journal = {Decision and Control},
            note = {Proceeding of the 48th IEEE Conference on Decision and Control, Shanghai, China},
        keywords = {Control systems; Convergence; Linear matrix inequalities; Linear systems; Predictive control; Predictive models; Robust control; Stability; Stochastic processes; Stochastic systems },
             url = {http://eprints.imtlucca.it/429/},
        abstract = {In this paper we propose a stochastic model predictive control (MPC) formulation based on scenario generation for linear systems affected by discrete multiplicative disturbances. By separating the problems of (1) stochastic performance, and (2) stochastic stabilization and robust constraints fulfillment of the closed-loop system, we aim at obtaining a less conservative control action with respect to classical robust MPC schemes, still enforcing convergence and feasibility properties for the controlled system. Stochastic performance is addressed for very general classes of stochastic disturbance processes, although discretized in the probability space, by adopting ideas from multi-stage stochastic optimization. Stochastic stability and recursive feasibility are enforced through linear matrix inequality (LMI) problems, which are solved off-line; stochastic performance is optimized by an on-line MPC problem which is formulated as a convex quadratically constrained quadratic program (QCQP) and solved in a receding horizon fashion. The performance achieved by the proposed approach is shown in simulation and compared to the one obtained by standard robust and deterministic MPC schemes.}
}