MPC for linear parameter-varying systems in input-output representation

In this paper, we propose a method for model predictive control of linear parameter-varying (LPV) systems described in an input-output (IO) representation and subject to input- and output constraints. By assuming exact knowledge of the future trajectory of the scheduling variable, the on-line computations reduce to the solution of a nominal predictive control problem. An incremental non-minimal state-space representation is used as a prediction model, giving a controller with integral action suitable for tracking piecewise-constant reference signals. Closed-loop asymptotic stability is guaranteed by a terminal cost and terminal set constraint, and the computation of an ellipsoidal terminal set is discussed. Numerical examples demonstrate the properties of the proposed approach. When exact future knowledge of the scheduling variable is not available, we argue and show that good practical performance can be obtained by a scheduling prediction strategy.