Optimization of fuzzy-like Luenberger observer for high speed sensorless induction motor drives using genetic algorithms

This paper presents a new method to optimize the performance of the fuzzy-like Luenberger observer to estimate the stator flux and the speed of an induction motor. The fuzzy-like observer uses the observer matrix values computed off-line at different speeds in the discrete time in order to keep the observer poles inside the stability region. The proposed optimization method determines the best pole placement for the observer using a genetic algorithm that minimizes a cost function related to the time response of the error between the estimated state variables and their real values. In this way, the dynamic response and the stability of the observer are guaranteed at high speed as well as at low speed. The performance of a 4-pole induction motor drive, using the proposed optimization method, has been tested in simulation and experimentally and some results are shown, in the speed range 20-12000 rpm