Robust Backstepping Control of Induction Motor Drives Using Artificial Neural Networks

In this paper, using the three-phase induction motor (IM) fifth order model in a stationary two axis reference frame with stator current and rotor flux as state variables, a conventional backstepping controller is designed first for speed and rotor flux control of an IM drive. Then in order to make the control system stable and robust against the parameter uncertainties as well as the unknown load torque, in the next stage the backstepping controller is combined with an artificial neural network (ANN). It will be shown that the proposed composite controller is capable of compensating the parameters variations and rejecting the external load torque disturbance. The overall system stability is proved by Lyapunov theory. It is also shown that the method of ANN training, guarantees the boundedness of errors and ANN weighs. The validity and effectiveness of the controller is verified by computer simulation