Rotor resistance identification using neural networks for induction motor drives in the case of insensitivity to load variations

Abstract

For induction motor drives controlled by the indirect rotor flux oriented control (IRFOC), the rotor resistance variation results in an undesirable coupling between the flux and the torque of the machine, and loss of dynamic performance. This paper presents a scheme for the estimation of rotor resistance using a neural networks (NN) block. In this system the flux and torque have been estimated by using stator voltages and currents. A back-propagation NN receives the flux and torque errors and a supposed rotor resistance at the input and estimates the actual rotor resistance at the output, which is used in the control of an indirect vector-controlled drive system. The neural network has been trained off line with the mathematical model of the control scheme in detuning operations. IRFOC control, used with the NN estimator, has been studied in the detuning condition. The performance of the controller is good, even when the rotor time constant is increased from nominal rate to twice the nominal value, as well as torque variations. In this method, estimation is done quickly and accurately, and its design is simple. Simulation results for a 3-hp induction motor driven by a current-regulated pulse width modulation CRPWM inverter with an indirect vector controller are presented to validate the effectiveness of the proposed technique for the purpose of improving the performance and robustness of the drive.