Model Predictive Current Control of Six-Phase Switched Reluctance Motor With Enhanced Robustness Based on Improved Lehuy Model

Abstract

This article proposes a model predictive current control (MPCC) with enhanced robustness for a six-phase switched reluctance motor (six-phase SRM) based on an improved Lehuy model (ILM). First, based on the six-phase SRM’s nonlinear characteristics and the conventional Lehuy model (CLM), a high-fidelity ILM is established, demonstrating its advantages in reducing modeling errors. Next, the MPCC for the six-phase SRM is introduced, including current prediction calculation, selection, and optimization of candidate voltage vectors (CVVs). Then, the impact of parameter mismatch during motor operation is quantitatively analyzed. Sliding mode disturbance observer (SMDO) is used to compensate for predicted current and observe parameter disturbances in real-time, a composite control system for the six-phase SRM is constructed. Experimental results suggest that compared with conventional MPCC, the composite MPCC controller based on ILM can effectively address the degradation of control performance caused by parameter mismatch. The implementation of this method provides an effective notion for six-phase SRM’s advanced control.