On sampled-data models for model predictive control

Abstract

In this paper we discuss how to obtain accurate and simple sampled-data models for model predictive control (MPC) in power electronics. We highlight the role that relative degree plays in the model accuracy. To support our presentation, we review examples from the literature where model complexity, time-varying parameters, and nonlinearities make the discretization procedure a key issue to achieve good performance in MPC strategies. Moreover, we propose a general discretization procedure based on a simple Taylor series expansion, which provides a sampled model with higher accuracy than Euler approximation.