Torque Ripple Minimization in Permanent Magnet Synchronous Motors Based on Neural Networks

The method of torque estimation is generally adopted for compensating the torque ripple of permanent magnet synchronous motors(PMSM).But at different points and with changing motor parameters,precise estima- tion of the instantaneous torque is difficult.Therefore,a method is proposed,which uses radical basis function neural networks(RBF NN)as the torque ripple compensation to control the PMSM.First,the torque ripple is compensated by on-line approximating the nonlinear factors and external disturbances with neural networks (NN),and then by Backstepping control method the rules of modulating the weights of NN and the output of the controller are obtained.Simulation and digital signal processing(DSP)experiment results show that the proposed method combines the advantages of both NN and Backstepping and can realize different kinds of torque compen- sation.