An Enhanced Active Disturbance Rejection Control of PMSM Based on ILC and Parameter Self-tuning

Conventional model-based permanent magnet synchronous motor (PMSM) drivers suffer deteriorated dynamic performance from the inward and outward disturbance. A new control method is proposed to improve the robustness of PMSM drivers in transient-state operation in this paper. Ant colony optimization (ACO) is utilized to tune parameters of active disturbance rejection control (ADRC). By using ACO's self-learning ability and multiple iterative calculations, the optimal solution can be quickly calculated, thereby reducing the difficulty of ADRC parameter adjustment. Besides, the torque ripple changes periodically with the rotor position and causes speed fluctuations, which reduces the PMSM system's dynamic performance. Usually, the PI controller and iterative learning control (ILC) in parallel are used to suppress torque fluctuations. However, it is very sensitive to the system uncertainty and external interference, that is, it will be paralyzed by non-periodic interference. Therefore, the ILC-ADRC is proposed in this paper to both reduce the ripple and guarantee robustness. The simulation results demonstrate the superior robustness of the proposed ADRC to that of the traditional method in transientstate and steady-state operations.