Abstract
Genetic algorithm (GA) is applied in this paper to optimize parameters of the extended Kalman filter (EKF) in a speed-senserless field-oriented controller (FOC) system. The main parameters of EKF are the covariance matrics Q and R, which are bound respectively to the state and measurement noises. As for speed-sensorless FOC system, the convergence and precision of both rotor speed and flux estimation depend on the accuracy of the models of system noise and measurement noise, i.e. Q and R. A GA training simulation system of optimum parameters of EKF is given and the simulation results show the efficiency and rationality of the algorithm.
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