Aperiodic sampled-data MPC strategy for LPV systems

This paper addresses the design of a sampled-data model predictive control (MPC) strategy for linear parameter-varying (LPV) systems. A continuous-time prediction model, which takes into account that samples are not necessarily periodic and plant parameters vary continuously with time, is considered. Moreover, it explicitly assumed value used to compute optimal sequence measured only at sampling instants. The MPC approach proposed by Kothare et al. [1], where basic idea consists in solving an infinite horizon guaranteed cost problem each time using matrix inequalities (LMI) based formulations, adopted. In this context, conditions computing stabilizing LPV law provides quadratic performance criterion under input saturation derived. These obtained from parameter-dependent looped-functional generalized sector condition. convex optimization problems receding policy therefore proposed. It shown guarantees feasibility step leads asymptotic stability origin. conservatism reduction provided results, respect similar ones literature, illustrated through numerical examples.