Sensorless indirect adaptive control with parameters and load-torque estimation of induction motor

In this paper we use a high gain output feedback controller to control the induction motor (IM) by a sensorless indirect adaptive strategy. In contrast to pervious works, an adaptive high gain observer is synthesized to compensate the uncertainty of the IM model, furthermore it allows a full state estimation including the unknown load torque, given that the stator current is the only measured output. The states and parameters estimates are used to build up the high gain output feedback control law, which is designed in such a way to stabilize the controlled system including the observer and to ensure a global asymptotique speed tracking, even in the presence of parameters and state uncertainty. Simulation results were presented to show the above control strategy efficiency.