Learning Based Approximate Model Predictive Control for Nonlinear Systems

Abstract

The paper presents a systematic design procedure for approximate explicit model predictive control for constrained nonlinear systems described in linear parameter-varying (LPV) form. The method applies a Gaussian process (GP) model to learn the optimal control policy generated by a recently developed fast model predictive control (MPC) algorithm based on an LPV embedding of the nonlinear system. By exploiting the advantages of the GP structure, various active learning methods based on information theoretic criteria, gradient analysis and simulation data are combined to systematically explore the relevant training points. The overall method is summarized in a complete synthesis procedure. The applicability of the proposed method is demonstrated by designing approximate predictive controllers for constrained nonlinear mechanical systems.