Tuning of Extended Kalman Filter using Improved Particle Swarm Optimization for Sensorless Control of Induction Motor ⋆

نویسندگان

  • Guohan LIN
  • Zhang JING
  • Zhaohua LIU
چکیده

Extended Kalman filter (EKF) is widely used for speed estimation in sensorless vector control of induction motor. The major and unsolved issue in the practical implementation of the EKF is the choice of the process and measurement noise covariance matrices. In this paper, a speed estimation method using EKF optimized by improved particle swarm optimization (IPSO) is proposed. By combining the advantages of genetic algorithm and standard particle swarm optimization (PSO). The EKF noise covariance matrices are optimized by IPSO. Simulation studies on field-oriented control system under various operating conditions demonstrate that the speed estimation with IPSO-optimized EKF has better estimation accuracy compare to those obtained by genetic algorithm (GA) and standard PSO algorithm and differential evolution (DE) algorithm.

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تاریخ انتشار 2014