Sensorless Control of Induction-Motor Drive Based on Robust Kalman Filter and Adaptive Speed Estimation

This study deals with robust estimation of rotor flux and speed for sensorless control of motion control systems with an induction motor. Instead of using sixth order extended Kalman filters, rotor flux is estimated by means of a fourth order descriptor-type robust Kalman filter which explicitly takes into account motor parameter uncertainties, whereas the speed is estimated using a recursive least-squares algorithm starting from the knowledge of the rotor flux itself. It is shown that the descriptor-type structure allows for a direct translation of parameter uncertainties into variations of the coefficients appearing in the model, and this improves the degree of robustness of the estimates. Experimental findings, carried out on a closed loop system consisting of a low power induction motor-load system, a PI-type controller and the proposed estimator, are shown with the aim of verifying the goodness of the whole closed loop control system.