Model Predictive Control for ARC Motors Using Extended State Observer and Iterative Learning Methods

The arc permanent magnet motor (arc motor) is widely used in large telescope for its high efficiency and high power density. Due to the unique structure, the periodic end torque, cogging torque and flux harmonics will cause certain speed ripples which can affect the performance of the drive system. To solve these problems, the paper proposes a novel model predictive control (MPC) based on extended state observer and iterative learning control, where the MPC is equipped in speed loop and the disturbances estimated by the conventional extended state observer (ESO) are fed forward to the controller to improve the ability of disturbance rejection. A position dependent iterative learning control (ILC) is used to estimate the periodic disturbances at different speeds. The MPC calculates the q axis current reference with the compensation of ESO and ILC, which can have a fast response and disturbance rejection ability for periodic and nonperiodic disturbances. The stability of the proposed method is demonstrated by theoretical analysis. The experimental results validate the effectiveness of the proposed method at different speeds.