Induction motor control, because of wide range of use, has attracted considerable attention. Vector control has its own advantages and disadvantages. Recently, nonlinear control schemes, specially robust and adaptive control methods are used to control induction motor drives. In this thesis, first, speed?(torque) and flux control of an induction motor drive using some nonlinear control methods, such as input-output feedback linearization, adaptive backstepping and sliding mode control is described. Because of sensitivity of stator and rotor resistances of this motor to thermal variations and skin effect, adaptive backstepping is used to estimate these uncertainties. To make control system free of physical flux sensors, flux observer is a good solution. In addition, sliding mode control has benefits such as insensitivity to uncertainties and fast dynamical response, which is combined with controller structure. Although adaptive backstepping can make control system robust to some special parameters, but drive control system is not robust against all electromechanical uncertainties and external load torque disturbance. One way to overcome this problem, is to use neural networks’s estimation ability. In addition, to omit mechanical speed sensor, neural networks help us again. Performance and ability of above control methods are verified and tested by computer simulations.
