AI-Enhanced Model Predictive and Active Disturbance Rejection Control for High-Performance Permanent Magnet Synchronous Motor Drives

Permanent magnet synchronous motors (PMSMs) suffer performance degradation under parameter uncertainties and external load disturbances, reducing the effectiveness of conventional proportional-integral and field-oriented control (FOC) schemes. This paper presents an artificial intelligence (AI) enhanced hybrid controller that combines finite-control-set model predictive control (FCS-MPC) and active disturbance rejection control (ADRC). The FCS-MPC optimizes inverter switching states by minimizing a cost function through predicted current trajectories. Additionally, the ADRC employs an extended state observer to estimate and compensate for aggregated disturbances. A lightweight radial basis function neural network is utilized, whose centers and widths are initialized offline based on k-means clustering on representative data, while its output weights are updated online via a Lyapunov-based adaptive law. This network dynamically adjusts the MPC cost function weights and ADRC observer bandwidth according to real-time operating conditions, while enabling online identification of key motor parameters. MATLAB/Simulink R2024a simulations under step load torque conditions verify that the proposed method achieves a speed deviation within 3% of the rated value, an over 90% reduction in torque ripple compared to FOC, and a settling time of less than 5 ms. Although it incurs a moderate computational cost, the proposed controller exhibits improved tracking accuracy and enhanced robustness under simulated conditions. Consequently, the AI-enhanced MPC-ADRC strategy shows strong potential for high-performance applications, subject to future experimental validation.