13.1 Introduction

An important characteristic of model predictive control (MPC) is the explicit use of the system models for selecting the optimal actuations. Considering that the parameter values may vary in some systems while in other cases it is difficult to get a precise value of the parameters, it is important to evaluate how MPC schemes behave in the presence of errors in the model parameters.

Due to the nonlinear nature of the predictive control scheme presented in this book, it is not possible to perform a simple analytical study in order to evaluate the behavior of predictive control in the presence of model parameter errors. This chapter presents a simple empirical approach to test the effect of model parameter errors at steady state and transient operation of the system. As an example, the current control of a three-phase inverter is considered. As references for a comparison of the results, a classical control scheme based on PI controllers in rotating coordinates with PWM and the well-known deadbeat controller, has been selected.