Decoupling control of PMSM based on artificial neural network inverse method

A decoupling control method based on artificial neural network (ANN) inverse system theory was applied for the permanent magnet synchronous motor (PMSM), which was a nonlinear and high coupling system. The analytical inverse system of PMSM was obtained by analyzing the reversibility of the mathematical model, which is constituted by two pseudo-linear subsystems, first-order linear flux subsystem and second-order speed subsystem. The dynamic decoupling control between flux and speed of PMSM were realized by using PID algorithm to design closed-loop controller for the two subsystems. The dSPACE platform was built to realized the ANN inverse system control method for a real PMSM, and to obtain the data, which was sampled to train the ANN. The results show that ANN inverse system control strategy was of a high dynamic performance for PMSM, even though there were different ways of load torque disturbance.