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author:

Bao, Mingkun (Bao, Mingkun.) [1] | Zhou, Yangzhong (Zhou, Yangzhong.) [2] (Scholars:周扬忠) | Zhong, Tianyun (Zhong, Tianyun.) [3] (Scholars:钟天云)

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Abstract:

Aiming at the problems of inaccurate model, coupling of identification parameters, and low identification accuracy in the identification of permanent magnet synchronous linear motor (PMSLM) mechanical parameters, a mechanical parameter identification algorithm based on transmission model reconstruction was proposed in this paper. First, an extended state observer was established based on the reconstruction model to obtain the coupling information of the mover mass and friction force, electromagnetic thrust and initial value of mass identification. Secondly, in order to eliminate this coupling, a two-stage mover mass decoupling identification strategy was proposed. Thirdly, according to the friction characteristics of PMSLM, a friction force model that comprehensively considers the effects of velocity and acceleration was constructed to describe the real friction force more accurately. Finally, a control system based on the mechanical parameter identification algorithm was built, and the effectiveness of the identification algorithm proposed in this paper is proved by simulation and experimental verification. ©2024 Chin.Soc.for Elec.Eng. 1597.

Keyword:

Acceleration Extended Kalman filters Friction Linear motors Parameter estimation Permanent magnets

Community:

  • [ 1 ] [Bao, Mingkun]Fujian Key Laboratory of New Energy Generation and Power Conversion (Fuzhou University, Fujian Province, Fuzhou; 350108, China
  • [ 2 ] [Zhou, Yangzhong]Fujian Key Laboratory of New Energy Generation and Power Conversion (Fuzhou University, Fujian Province, Fuzhou; 350108, China
  • [ 3 ] [Zhong, Tianyun]Fujian Key Laboratory of New Energy Generation and Power Conversion (Fuzhou University, Fujian Province, Fuzhou; 350108, China

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Source :

Proceedings of the Chinese Society of Electrical Engineering

ISSN: 0258-8013

CN: 11-2107/TM

Year: 2024

Issue: 4

Volume: 44

Page: 1597-1607

Cited Count:

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ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 5

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