基于粒子群优化的感应电机模糊扩展卡尔曼滤波器转速估计方法

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Abstract This paper presents a speed estimation method of particle swarm optimization fuzzy extended Kalman filter (PFEKF) for induction motors, to weaken the impacts of priori measurement noise model on the estimation accuracy of extended Kalman filter (EKF). The proposed algorithm modifies the measurement noise covariance of EKF recursively and chooses a fuzzy factor to make its noise model close to real noise model adaptively, based on PSO fuzzy controller which monitors the degree of divergence (DOD) parameters. Accordingly the optimal estimation is realized, and the impacts of system performance under gross external error and unknown measurement noises are weakened. Simulation and experimental results verify the proposed method for induction motors.

Key words： Particle swarm optimization fuzzy extended Kalman filter induction motor speed estimation

Received: 07 April 2014 Published: 01 April 2016

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TM343

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	Articles by authors
	Yin Zhonggang
	Xiao Lu
	Sun Xiangdong
	Liu Jing
	Zhong Yanru

Cite this article:

Yin Zhonggang,Xiao Lu,Sun Xiangdong等. A Speed Estimation Method of Fuzzy Extended Kalman Filter for Induction Motors Based on Particle Swarm Optimization[J]. Transactions of China Electrotechnical Society, 2016, 31(6): 55-65.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2016/V31/I6/55

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