Intelligent Decoupling Control Study of PMSM Based on the Neural Network Inverse System

This study obtains the analytical inverse system of a permanent magnet synchronous motor (PMSM) model based on traditional magnetic field orientation decoupling control mode by analyzing quality PMSM. Using neural network’s excellent approximation ability and well learning functions, network (NNIS) was established identifying offline training back propagation (BPNN) radial basis function (RBFNN). The data collected from PMSM are used to analyze compare prediction accuracy running time network, so as optimize structure parameters network. simulation results three systems show that RBF NNIS has good dynamic static performance, robustness.