Dual-parameter collaborative intelligent optimal control of chaotic motion of permanent magnet synchronous motor

Abstract

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 dualparameter cooperative controller is designed based on radial basis function neural network (RBFNN); Grey Wolf Optimization (GWO) algorithm is used to optimize the selection of controller parameters; By slightly adjusting the two controllable parameters of the system, the chaotic motion of the PMSM system is controlled to the expected motion state. In the simulation study, compared with the single-parameter intelligent optimization control method based on GWO-RBFNN, the results show that 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.