Speed Observer Based on ICA Trained Neural Network in DTC drive of IPMSM

In this paper a speed observer based on Imperialist Competitive Algorithm (ICA) trained artificial neural network is presented. The proposed speed observer is used in sensorless Direct Torque Control (DTC) IPMSM drive scheme. A multilayer perception is trained using imperialist competitive algorithm to estimate the rotor speed. Due to artificial neural network characteristics the proposed speed observer works in wide range speed as opposed to previous observers that doesn’t works low speed or high speeds. Since neural network is trained with ICA, optimum weights of neural network are obtained. Simulation results on different conditions show the good performance of proposed speed observer.

Keywords:

Artificial Neural Network , DTC , Speed Observer , IPMSM