High performance speed control of electric machine using Kalman filter and self-tuning regulator

For high-performance servo drive system, it is important to estimate and control the motor speed precisely in wide speed range. Therefore, the disturbance rejection ability and the robustness to variations of the mechanical parameters such as inertia should be considered. This paper shows that the adaptive state estimator and self-tuning regulator based on RELS (recursive extended least squares) parameter identification method can give high-performance speed control in wide-speed range. RELS method identifies the variations of mechanical parameters, and the estimated mechanical parameters are used to replace the role of manual tuning by adjusting the gain of the speed controller automatically for good dynamic response. Also, these estimated parameters are used to adapt the Kalman filter, which is an optimal state estimator, to provide good estimation for the rotor speed, the rotor position and the disturbance load torque even in noisy environment. The validity of the proposed controller is verified by experimental results