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논문 기본 정보

자료유형
학술저널
저자정보
Yanqing Zhang (Xi’an University of Technology) Zhonggang Yin (Xi’an University of Technology) Guoyin Li (CRRC SRI Chongqing S&T) Jing Liu (Xi’an University of Technology) Xiangqian Tong (Xi’an University of Technology)
저널정보
대한전기학회 Journal of Electrical Engineering & Technology Journal of Electrical Engineering & Technology Vol.13 No.1
발행연도
2018.1
수록면
287 - 297 (11page)

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초록· 키워드

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To improve the performance of sensorless induction motor (IM) drives, a novel speed estimation method based on the real-time adaptive extended Kalman filter (RAEKF) is proposed in this paper. In this algorithm, the fuzzy factor is introduced to tune the measurement covariance matrix online by the degree of mismatch between the actual innovation and the theoretical. Simultaneously, the fuzzy factor can be continuously self-tuned tuned by the fuzzy logic reasoning system based on Takagi–Sugeno (T-S) model. Therefore, the proposed method improves the model adaptability to the actual systems and the environmental variations, and reduces the speed estimation error. Furthermore, a simple exponential function based on the fuzzy theory is used to reduce the computational burden, and the real-time performance of the system is improved. The correctness and the effectiveness of the proposed method are verified by the simulation and experimental results.

목차

Abstract
1. Introduction
2. EKF Observer
3. Real-Time Adaptive EKF Observer
4. Experimental Results
5. Conclusion
References

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UCI(KEPA) : I410-ECN-0101-2018-560-001704738