Self-tuning controller for servo motor with an adaptive disturbance observer

In this paper, the state-space disturbance observer was successfully applied to servo motors to estimate and compensate for load variation. Furthermore, an auto-tuning procedure was developed accordingly to identify the varied parameters for state-space disturbance observer of the motor. Then, a real-time IP position controller based on identified parameters is designed by neural network for permanent magnet synchronous motor (PMSM) servo system. The neural networks configuration is simple and reasonable, and the weight has definitely physical meaning. It has rapidly adjusting character to realize the real-time control. The simulation results show that the proposed control scheme not only enhances the fast tracking performance, but also increases the robustness of the servo system.