A Robust Optimal Control Strategy for PMSM Based on VGPDO and Actor-Critic Neural Network Against Flux Weakening and Mismatched Load Torque
Yangyu Niu, Haibin Shi · Mathematics · 2025
In this paper, a novel robust optimal control strategy is proposed for permanent magnet synchronous motors (PMSMs), simultaneously addressing two critical challenges in speed regulation: flux linkage degradation during long-term operation and abrupt load torque variations. The robust optimal control strategy is implemented through a combination of feedforward control and feedback control. A novel Variable-Gain Proportional Disturbance Observer (VGPDO) is proposed to simultaneously estimate time-varying flux linkage and torque disturbances in PMSM systems. The estimated disturbances are then compensated via a feedforward control loop, significantly improving the system’s robustness against parameter variations and external load changes. An optimal controller based on an actor-critic neural network provides feedback for optimal control performance. The uniform ultimate boundedness (UUB) of the proposed strategy is proved through Lyapunov stability analysis, and comprehensive simulation studies demonstrate the efficacy of both the proposed VGPDO and the proposed robust optimal control strategy.