Adaptive neural network internal model control for PMSM speed regulation

摘要

In this paper, based on the combination of particle swarm optimization (PSO) algorithm and neural network (NN), an adaptive neural network internal model control (NNIMC) is designed for a permanent magnet synchronous motor (PMSM). Firstly, in order to accelerate the convergent speed and to prevent problems of trapping in local minimum, PSO algorithm is applied in feedforward neural network to optimize the NN model's and the NN controller’s parameters. For the adaptation of the learning algorithm of the NN controller, gradient descent method is used, secondly, to achieve high-performance speed tracking. The robustness and effectiveness of the proposed PMSM drive scheme is confirmed by simulation tests in the MATLAB/SIMULINK.