An Improved Model-Free Current Predictive Control of Permanent Magnet Synchronous Motor Based on High-Gain Disturbance Observer

Predictive current control (PCC) is an advanced control strategy for permanent magnet synchronous motors (PMSM). When the motor drive system is undisturbed, predictive current control exhibits a good dynamic response speed and steady-state performance, but the conventional PCC control performance of PMSM that depends on the motor body model is vulnerable to parameter perturbation. Aiming at this problem, an improved model-free predictive current control (IMFPCC) strategy based on a high-gain disturbance observer (HGDO) is proposed in this paper. The proposed strategy is introduced with the idea of model-free control, relying only on the system input and output to build an ultra-local current prediction model, which gets rid of the constraints of the motor body parameters. In the paper, the ultra-local structure is optimized by comparing and analyzing the equation of the state of the classical ultra-local structure and PMSM system. The system's current state variables are incorporated into the ultra-local system modeling, as a result, the current estimation errors existing in the classical ultra-local structure are eliminated. For the unmodeled and parametric perturbation part of the ultra-local system, a high-gain disturbance observer is designed to estimate it in real time. Finally, the proposed IMFPCC strategy is compared with the conventional model-based predictive current control (MPCC) and the conventional model-free predictive current control (CMFPCC) in simulation and experiment. The results show that the current steady-state error of the IMFPCC strategy in the case of parameter variation is only 50% of the MPCC method, which proves the effectiveness and correctness of the proposed strategy.