Position Sensorless Control for PMLSM Using Elman Neural Network

This paper presents an approach of position sensorless control for permanent magnet linear synchronous motors (PMLSM) based on Elman neural network. The Elman neural network observer can be considered as a special kind of feed-forward neural network with additional memory neurons and local feedback. Because of the context neurons and local recurrent connections between the context layer and the hidden layer, it facilitates the nonlinear states estimation for the sensorless control of PMLSM. The Elman neural network is trained both off-line and on-line. In the off-line training process with the training data, the connective weights of the Elman neural network are trained by the Levenberg-Marquardt algorithm, while on-line learning, the connective weights of the Elman neural network are trained using supervised gradient decent method. The effectiveness of the proposed observer is confirmed by the digital simulations results.