ROBUST BACKSTEPPING CONTROL OF INDUCTION MOTOR DRIVES USING ARTIFICIAL NEURAL NETWORKS AND SLIDING-MODE FLUX OBSERVERS

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