Adaptive model-based predictive control with changing operation points

Abstract

Most industrial processes are nonlinear and experience frequent variations in the operating point, which can make them impractical for real-time Model-based Predictive Control (MPC) implementation. This research explores the design and analysis of MPC formulations developed within the context of Linear Parameter Varying (LPV) model framework. These methods take into account the scheduling parameters of the multi-model and perform online process-model adaptation, obtaining a linear prediction model that allows representing the nonlinear process at each instant. Additionally, necessary conditions are established to guarantee the asymptotic stability of the feasible equilibrium set for all models contained in the LPV model. This enables the consideration of changes in operating points that occur during the normal operation of the process. The article concludes with realistic simulation results of two typical unit operations in the process industry, comparing the analyzed MPC techniques with a linear MPC present in the literature. Discussions are presented on the results in terms of performance, effectiveness, computational effort and disturbance rejection, in the presence of changing operating points.