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GA-Optimized Extended Kalman Filter for Speed Estimation

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In this chapter, a speed-sensorless controller using an extended Kalman filter (EKF) is investigated. To improve the performance of the speed-sensorless controller, noise covariance and weight matrices of the EKF are optimized by using a real-coded genetic algorithm (GA). MATLAB®/Simulink based simulation results are presented to confirm the efficacy of the GA-optimized EKF for speed estimation in an induction motor drive.