An Adaptive Speed Estimation Method Based on a Strong Tracking Extended Kalman Filter with a Least-Square Algorithm for Induction Motors | Zendy

Zhonggang Yin | Zendy; Guoyin Li | Zendy; Chao Du | Zendy; Xiangdong Sun | Zendy; Jing Liu | Zendy; Yanru Zhong | Zendy

Open Access

An Adaptive Speed Estimation Method Based on a Strong Tracking Extended Kalman Filter with a Least-Square Algorithm for Induction Motors

Author(s) -

Zhonggang Yin,

Guoyin Li,

Chao Du,

Xiangdong Sun,

Jing Liu,

Yanru Zhong

Publication year - 2017

Publication title -

journal of power electronics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.23

H-Index - 33

eISSN - 2093-4718

pISSN - 1598-2092

DOI - 10.6113/jpe.2017.17.1.149

Subject(s) - kalman filter , control theory (sociology) , fading , algorithm , correctness , covariance matrix , minimum mean square error , computer science , filter (signal processing) , sequence (biology) , tracking (education) , matrix (chemical analysis) , mathematics , artificial intelligence , control (management) , decoding methods , statistics , computer vision , psychology , pedagogy , materials science , estimator , biology , composite material , genetics

To improve the performance of sensorless induction motor (IM) drives, an adaptive speed estimation method based on a strong tracking extended Kalman filter with a least-square algorithm (LS-STEKF) for induction motors is proposed in this paper. With this method, a fading factor is introduced into the covariance matrix of the predicted state, which forces the innovation sequence orthogonal to each other and tunes the gain matrix online. In addition, the estimation error is adjusted adaptively and the mutational state is tracked fast. Simultaneously, the fading factor can be continuously self-tuned with the least-square algorithm according to the innovation sequence. The application of the least-square algorithm guarantees that the information in the innovation sequence is extracted as much as possible and as quickly as possible. Therefore, the proposed method improves the model adaptability in terms of actual systems and environmental variations, and reduces the speed estimation error. The correctness and the effectiveness of the proposed method are verified by experimental results.

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