Adaptive Predictive Control for Industrial Processes

Abstract

In this work a predictive controller formulation is developed within a linear parameter-varying formalism, which serves as a non-linear process model. The proposed strategy is an adaptive Model-based Predictive Controller (MPC), designed with terminal set constraints and considering the scheduling polytope of the model. At each sample time, two Quadratic Programming (QP) problems are solved: the first QP considers a backward horizon to find a virtual model-process tuning variable that defines the best LTI prediction model, considering the vertices of the polytopic system; then, the second QP uses this LTI model to optimise performances along a forward horizon. This paper ends with a realistic solar thermal collector process simulation, comparing the proposed MPC to other techniques from the literature. Discussions regarding the results, the design procedure and the computational effort are presented.