Real-time tracking control of squirrel cage induction motor using neural network

This paper presents a real time feedforward control scheme of a squirrel cage induction motor. This scheme uses an artificial neural network (ANN). The objective of this controller is to force the rotor speed to follow an arbitrarily prescribed trajectory. The proposed neural network structure is first trained to identify the inverse dynamics of the drive system. Then the trained neural network is used as a feedforward controller to generate both the input voltage and frequency for the motor to follow the desired trajectory. The training data is obtained from a laboratory setup which implements an LSI circuit (HEF4752V), a PWM inverter, and an induction motor. The main advantage of the proposed scheme is that it does not need a detailed and elaborate model of the drive system. 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 showed a very simple and reliable tracking control system.