Scheduling quasi-min-max model predictive control algorithm for nonlinear systems

Abstract

In this paper, a model predictive control algorithm, scheduling quasi-min-max MPC algorithm, is designed for nonlinear systems. Combination of a linear model with a linear parameter varying model approximates the nonlinear behavior. The linear model expresses the current nonlinear dynamics, and the linear parameter varying model covers the future nonlinear behavior. In the algorithm, a 'quasi worst case' value of infinite horizon objective function is minimized. Closed-loop stability is guaranteed when the algorithm is implemented in a receding horizon fashion by including a Lyapunov constraint in the formulation. The proposed approach is applied to control a jacketed styrene polymerization reactor.