Improved Grey Wolf Optimizer and Backpropagation Neural Network for Fractional-Order Control of PMSM

Permanent magnet synchronous motors (PMSM), with their high efficiency and power density, are widely used in industrial applications. For PMSM speed servo systems, fractional-order proportional-integral (FOPI) controllers demonstrate superior robustness and speed control performance compared to conventional PI controllers. However, FOPI controllers involve more parameters with insufficient tuning experience, making their parameter design more challenging. To address the aforementioned problems, this paper proposes a novel FOPI control strategy for PMSM that integrates an improved grey wolf optimizer (IGWO) with backpropagation neural networks (BPNNs). The conventional grey wolf optimizer (GWO) is enhanced in this study, and the test results demonstrate that the proposed IGWO exhibits improved convergence and robustness. BPNNs combined with IGWO are employed to fit the relationship between controller parameters and control performance indicators. IGWO employs the fitted values from BPNNs as evaluation criteria to perform online tuning of the FOPI controller parameters. The simulation results show that under the IGWO-BPNN-FOPI control strategy, the overshoot of the PMSM speed step response is only 1.958%, the settling time is reduced by 80%, and the load disturbance rejection performance of the PMSM is significantly improved.