Saturation compensation control of induction motors using adaptive neural networks

In this paper, we present a new adaptive technique of induction motors systems with unknown saturation. The method is systematic and robust to parameter variations Neural network is adopted to estimate unknown function of systems and approximate the unknown input compensation part of actuator. Another most prominent feature of the scheme is which can ensure the system is uniformly ultimately bounded which is proved by Lyapunov theory, and considering the network reconstruction error and the system's external disturbance. The tracking error can be freely adjusted by known form. The simulation example is given to illustrate the effectiveness of this method.