Parameter Robustness of Model Predictive Control in Multiphase Machine Drives: A Comprehensive Review

Abstract

Multiphase machines offer low per-phase power stress and strong fault-tolerant capability and have therefore attracted substantial interest in both academia and industry. Model predictive control (MPC) is an advanced control approach that can improve the dynamic performance and efficiency of motor-drive systems. However, predictive models for multiphase machines are particularly sensitive to parameter mismatch, which poses additional challenges for MPC. This paper reviews general approaches for improving the parameter robustness of MPC and summarizes their development in multiphase machine drives from three perspectives: prediction model improvement, disturbance/uncertainty estimation and compensation (DUEC), and online parameter identification. Although parameter robustness and DUEC are closely related, they are not equivalent. DUEC methods can improve parameter robustness by estimating and compensating for prediction errors caused by parameter variations, while also rejecting unmodeled dynamics and other nonparametric disturbances. A quantitative comparison is also provided to assess industrial feasibility. Emerging robustness challenges associated with additional parameters in harmonic subspace and fault-tolerant operation are then analyzed. Finally, future research directions are discussed, and several promising topics are identified.