Online Magnetization Trajectory Prediction and Current Control for a Variable-flux Permanent Magnet Machine

Online magnetization current control is a critical concern of the variable-flux permanent magnet machine control. Increasing the magnetization speed benefits the machine system, which reduces manipulation losses and mechanical impact. Thus, this paper proposes an online trajectory prediction method that increases the DC-link voltage utilization, boosting the manipulation speed. The prediction method decouples the rotating voltage and the induction voltage in the machine model. The induction voltage is the source of di/dt, which influences the magnetization manipulation speed. The proposed method updates the maximum available induction voltage at every manipulation stage by excluding the rotating voltage from the DC-link voltage limitation. Based on the induction voltage, the magnetization current trajectory is predicted. The trajectory prediction is cooperated with a feed-forward current controller to increase the control dynamics. Verified by various experiments, the proposed method achieves fast manipulation speed with high control accuracy. Besides, the proposed method shows self-adaptive capabilities in variable-speed and variable-voltage conditions.