ARMA-based Model-Free Two-Degree-of-Freedom Predictive Control Strategy for SPMSM Drives

To realize collaborative improvement for the inner controller and outer controller, and improve control performances suited for high-end applications for model-free predictive control, in the surface-mounted permanent magnet synchronous motor (SPMSM) driving system, an autoregressive moving average (ARMA) -based model-free two-degree-of-freedom PCC (MF2DoF-PCC) strategy is proposed in this paper. The motor is online fitted as an ARMA model, in which the coefficients are updated by the recursive gradient correction (RGC) algorithm. A secondary controller is designed based on the coefficients of the ARMA model to decouple the dynamics, and the suitable orders of the model are also determined. According to the simulation and experimental results, the effectiveness of the proposed method is demonstrated, as well as the advantages including the improved dynamics and current quality with suitable robustness.