Analysis of Finite-Control-Set Model Predictive Current Control With Model Parameter Mismatch in a Three-Phase Inverter

Abstract

It is well known that predictive control methods can be affected by the presence of modeling errors. The extent to which finite-control-set model predictive control (FCS-MPC) is influenced by parametric uncertainties is a recurrent concern at the moment of evaluating the viability of this method for power electronics applications. This paper proposes an analytic approach to examine the influence of model parametric uncertainties on the prediction error of FCS-MPC for current control in a three-phase two-level inverter. The analysis shows that the prediction error is not only determined by parametric mismatch but also by the instantaneous values of load current and inverter output voltage. This implies that within each sampling period of the predictive algorithm several conditions of prediction error are generated, as multiple voltage vectors are evaluated. Simulation and experimental results are provided and discussed showing the effects of inaccuracies in the modeling of load resistance and inductance parameters on the performance of FCS-MPC. Even though steady-state performance is noticeably affected with parameter changes, especially when the load inductance is overestimated by the model, its fast transient step response is less affected by parameter changes.