An Improved Model-Free Adaptive Current Control for PMSM Based on Prior-Optimization

Abstract

Current predictive control methods are sensitive to motor parameters, while model-free control methods often require extensive offline data or involve complex parameter coupling, making tuning challenging. To address these limitations, this article proposes a prior-optimized model-free adaptive current control (POMFACC) method. By using current and voltage signals to create a multiple-input multiple-output system, this method eliminates the need for specific motor knowledge. It also constructs a multiobjective optimization function for current control and uses the nondominated sorting genetic algorithm II to automatically optimize interdependent parameters, addressing the challenges of repeated trial-and-error adjustments and inaccurate settings. Results demonstrate that the POMFACC method is widely applicable, does not depend on offline data, maintains a moderate computational burden, and effectively enhances motor control accuracy and robustness.