A High Performance Decoupling Control of Induction Motor with Eficient Flux Estimator

A high performance decoupling control strategy for magnetizing current and torque current of induction motors (IM) is proposed. An extended state equation of induction motor is derived to obtain the coupling matrix elements between magnetizing current and torque current. To cancel these coupling matrix elements, equivalent respective elements of opposite sign are added to the output of the current controllers. In this extended state equation based block diagram of IM, all branches of the summing point have voltage dimension, and thus the equation enables easy understanding of the physical operation of the motor. In the extended state equation after decoupling, the transfer function of the motor becomes simple first order without rotor resistance. So it is free from rotor resistance variation with temperature. Efficient stator flux estimator is developed by Real Time Recurrent Learning (RTRL) algorithm based Recurrent Neural Network (RNN). This estimator is completely free from stator and rotor resistance variation problems. Simulation results are presented to verify the feasibility of the proposed controller.