GWO-RBFNN Dual-parameter Collaborative Intelligent Optimal Control of Chaotic Motion of a Class of Permanent Magnet Synchronous Motor - LI Ningzhou; QIU Sixuan; WEI Xiaojuan; LI Xiaoqi; LI Gaosong (Journal of Harbin University of Science and Technology, 2024)
Aiming at the chaos control of permanent magnet synchronous motor, a dual-parameter collaborative intelligent optimal control method based on GWO-RBFNN was proposed. Starting from the perspective that the controller can automatically search for the expected motion state, the distance between two points on the Poincaré cross section is selected as the controller input. And considering the coupling effect of system parameters on the dynamic behavior of the system, a dual-parameter cooperative controller is designed based on radial basis function neural network (RBFNN) ; Grey Wolf Optimization (GWO) algorithm is used to optimize and select controller parameters to achieve the best controller performance; The system is controlled from a chaotic state to the expected motion state by adjusting the two controllable parameters in the PMSM system with minor disturbances. The results show that compared with the single-parameter intelligent optimization control method based on GWO-RBFNN, the dual-parameter collaborative intelligent optimal control method based on GWO-RBFNN has better performance. Although both methods can achieve chaotic motion control, the control speed of the dual-parameter collaborative intelligent optimization control method based on GWO-RBFNN is faster and overshoot is smaller.