Brain Emotional Learning Control Based on Radial Basis Function for Permanent Magnet Synchronous Motor

Abstract

It is proposed to use a brain emotional learning control (BELC) system that is based on radial basis function (RBF) in order to enhance the performance of the speed control system of a permanent magnet synchronous motor (PMSM) and its capacity to remain stable following an unexpected load. First, the shortcomings of the traditional PI control in the PMSM speed-control system are explained. The intelligent control system has excellent learning ability and can effectively improve the control effect. The brain emotional learning control is great for nonlinear system control. Thus, it was utilized as the PMSM speed controller in place of the conventional PI control. The RBF neural network was used to optimize some parameters of BELC. Therefore, the process of adjusting parameters in BELC was simplified and the controller ability to resist disturbances was enhanced. The results showed that the brain-based emotional learning control based on RBF optimization (RBF-based BELC) not only improved the speed-control effect of the PMSM system but also enhanced the stability of the torque and current.