Neural network-based tracking control system for slip-energy recovery drive

This paper studies the implementation of tracking control in a slip-energy recovery induction motor drive. Tracking control is investigated using an artificial neural network-based controller. In this system, the rotor speed can follow an arbitrarily prescribed trajectory. This trajectory may be different from the one used in training the network. The proposed system is capable of achieving accurate tracking control of the speed even when the nonlinear parameters of the motor and the load are unknown. These unknown nonlinear parameters are captured by the trained artificial neural network. The architecture and the training algorithm of the neural network are presented and discussed. The effectiveness of the proposed drive system is investigated using a laboratory model. Laboratory results confirm a very promising tracking control system. This system takes full advantage of the efficient slip-energy recovery induction motor drive.