PSO-Based Neural Network Controller for Speed Sensorless Control of PMSM

In this paper, estimation of rotor speed and position by using model reference adaptive system (MRAS) with multilayer perceptron (MLP) for PMSM sensorless control are presented. Conventional controller which is PI controller for adaptation scheme still hunger with high accuracy information of PMSM for low speed region. Based on PI controller, the MLP which is more well-known about their learning efficiency and performance. This paper proposes a method for training an MLP network using Particles Swarm Optimization (PSO) called MLP-PSO. The PSO is used to find the optimum weights and biases in the MLP network. Finally, the proposed method is evaluated by comparing with PI controller in controlling the speed and position of PMSM. Simulation results under various speed and load conditions has shown that the MLP-PSO achieved well results than the PI controller in terms of system parameter such as rise time (T-r), settling time (T-s), percent overshoot (%OS), and root mean square error (RMSE).