Stochastic Model Predictive Control for Constrained Discrete-time Markovian Switching Systems at Automatica Journal

IMT Institute for Advanced Studies Lucca research in EFFINET stochastic Model Predictive Control has been accepted for publication in Automatica Journal. In this paper IMT Lucca study constrained stochastic optimal control problems for Markovian switching systems, an extension of Markovian jump linear systems (MJLS), where the subsystems are allowed to be nonlinear. We develop appropriate notions of invariance and stability for such systems and provide terminal conditions for stochastic MPC that guarantee mean-square stability and robust constraint fulfillment of the Markovian switching system in closed-loop with the SMPC law under very weak assumptions. In the special but important case of constrained MJLS we present an algorithm for computing explicitly the stochastic MPC control law on-line, that combines dynamic programming with parametric piecewise quadratic optimization.

Download full publication through following link: SMPC4MSS_automatica