Control of High-Speed Linear Induction Motor Using Artificial Neural Networks

This paper presents a discrete-time control for a Linear Induction Motor (LIM). First, an identifier is proposed with a nonlinear block controllable form (NBC) structure. This identifier is based on a discrete-time high order neural network trained on-line with an extended Kalman filter (EKF)-based algorithm. The backstepping control and Artificial Neural Networks (ANN ) are combined in order to design a robust controller that is capable of preserving the drive system robustness subject to all parameter variations and uncertainties. The overall system stability is proved by Lyaponuv theory.. The neural control performance is illustrated via simulations.