Ultra-Local Model-Free Predictive Current Control Based on Nonlinear Disturbance Compensation for Permanent Magnet Synchronous Motor

During motor operation, the motor parameters change, which causes parameter drift. They are also affected by internal and external unknown disturbances, which lead to reduced motor control performance, poor anti-interference performance, and low robustness. A method termed ultra-local model-free predictive current control (MFPCC) has previously been proposed to solve this problem; it uses only the input and output of the system and does not involve any motor parameters, because of which it is free of problems caused by model mismatch. However, the conventional MFPCC method requires adjustment of several control parameters and the estimated value of the total disturbance of the system has a certain deviation and a large pulsation, which result in obvious chattering of the motor output, low stability, reduced anti-interference performance, and low robustness. Therefore, this paper proposes an MFPCC method based on nonlinear disturbance compensation (NDC). This method does not involve any motor parameters, and it can more accurately and stably estimate the total system disturbance, and feedforward compensation, real-time update control information, only need to adjust two control parameters, the workload is small. Simulation results show that the proposed control method has high anti-interference performance, high robustness, small output ripple, and improved dynamic characteristics and that it can estimate the system disturbance accurately and stably.