Model-free predictive control for PMSM based on a nonlinear autoregressive exogenous model and an adaptive recursive least squares algorithm

Abstract

This paper proposes a novel nonlinear autoregressive with exogenous input-based adaptive recursive least squares model-free predictive control (NARX-ARLS-MFPC) strategy for permanent magnet synchronous motor (PMSM) drives. The core challenge addressed is the performance degradation of conventional model predictive control (MPC) under inevitable motor parameter mismatches. The proposed method integrates the NARX model with an ARLS algorithm featuring a variable forgetting factor to dynamically track system changes. Comprehensive simulation studies validate the superior robustness of the strategy. Under significant inductance and flux linkage mismatches, the proposed method reduces current total harmonic distortion (THD) by 28.3% and 12.3% compared to conventional finite-control-set model predictive control (FCS-MPC) and a baseline control method, respectively. It maintains stable performance under moderate sensor noise with appropriate tuning. During load transients combined with resistance and inductance mismatches, it achieves THD reductions of 24.1% and 15.7% versus the two benchmark methods, respectively. Statistical analysis under parameter perturbations confirms its overall superior performance across key dynamic and steady-state metrics. The results demonstrate that the synergistic integration of the nonlinear model and adaptive identification effectively suppresses current harmonics caused by model inaccuracies while enhancing dynamic performance.