Please wait a minute...
Front. Inform. Technol. Electron. Eng.  2011, Vol. 12 Issue (2): 124-131    DOI: 10.1631/jzus.C0910795
    
Moments and Pasek’s methods for parameter identification of a DC motor
Mounir Hadef*, Mohamed-Rachid Mekideche
Université de Jijel, Laboratoire LAMEL, B.P. 98, Ouled Aissa, 18000 Jijel, Algérie
Download:   PDF(562KB)
Export: BibTeX | EndNote (RIS)      

Abstract  Time moments have been introduced in automatic control because of the analogy between the impulse response of a linear system and a probability function. Pasek described a testing procedure for determining the DC parameters from the current response to a step in the armature voltage motor. In this paper, two identification algorithms developed based on the moments and Pasek’s methods are introduced and applied to the parameter identification of a DC motor. The simulation and experimental results are presented and compared, showing that the moments method makes the model closer to reality, especially in a transient regime.

Key wordsIdentification      Moments method      Pasek’s identification method      Separately excited DC motor     
Received: 26 December 2009      Published: 08 February 2011
CLC:  TM33  
Cite this article:

Mounir Hadef, Mohamed-Rachid Mekideche. Moments and Pasek’s methods for parameter identification of a DC motor. Front. Inform. Technol. Electron. Eng., 2011, 12(2): 124-131.

URL:

http://www.zjujournals.com/xueshu/fitee/10.1631/jzus.C0910795     OR     http://www.zjujournals.com/xueshu/fitee/Y2011/V12/I2/124


Moments and Pasek’s methods for parameter identification of a DC motor

Time moments have been introduced in automatic control because of the analogy between the impulse response of a linear system and a probability function. Pasek described a testing procedure for determining the DC parameters from the current response to a step in the armature voltage motor. In this paper, two identification algorithms developed based on the moments and Pasek’s methods are introduced and applied to the parameter identification of a DC motor. The simulation and experimental results are presented and compared, showing that the moments method makes the model closer to reality, especially in a transient regime.

关键词: Identification,  Moments method,  Pasek’s identification method,  Separately excited DC motor 
[1] Jiong FU, Xue-shan LUO, Ai-min LUO, Jun-xian LIU. Enterprise-level business component identification in business architecture integration   [J]. Front. Inform. Technol. Electron. Eng., 2017, 18(9): 1320-1335.
[2] You LIU , Qing SHEN , Dong-li MA , Xiang-jiang YUAN. Steering control for underwater gliders[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(7): 898-914.
[3] Xiong-bin PENG, Guo-fang GONG , Hua-yong YANG , Hai-yang LOU , Wei-qiang WU, Tong LIU. Quantitative feedback controller design and test for an electro-hydraulic position control system in a large-scale reflecting telescope[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(10): 1624-1634.
[4] Hong SONG, Jia-heng ZHANG, Ping YANG, Hao-cai HUANG, Shu-yue ZHAN, Teng-jun LIU, Yi-lu GUO, Hang-zhou WANG, Hui HUANG, Quan-quan MU, Mei-fen FANG, Ming-yuan YANG. Modeling of a dynamic dual-input dual-output fast steering mirror system[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(10): 1488-1498.
[5] Yun-xiang Zhao, Wan-xin Zhang, Dong-sheng LI, Zhen Huang, Min-ne Li, Xi-cheng Lu. Pegasus: a distributed and load-balancing fingerprint identification system[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(8): 766-780.
[6] Qiang Liu, Jia-chen Ma. Subspace-based identification of discrete time-delay system[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(6): 566-575.
[7] De-xuan Zou, Li-qun Gao, Steven Li. Volterra filter modeling of a nonlinear discrete-time system based on a ranked differential evolution algorithm[J]. Front. Inform. Technol. Electron. Eng., 2014, 15(8): 687-696.
[8] Can Wang, Hong Liu, Xing Liu. Contact-free and pose-invariant hand-biometric-based personal identification system using RGB and depth data[J]. Front. Inform. Technol. Electron. Eng., 2014, 15(7): 525-536.
[9] Yin Tian, Hong-hui Dong, Li-min Jia, Si-yu Li. A vehicle re-identification algorithm based on multi-sensor correlation[J]. Front. Inform. Technol. Electron. Eng., 2014, 15(5): 372-382.
[10] Hasan Abbasi Nozari, Hamed Dehghan Banadaki, Mohammad Mokhtare, Somayeh Hekmati Vahed. Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks[J]. Front. Inform. Technol. Electron. Eng., 2012, 13(6): 403-412.
[11] Zhen-gong Cai, Xiao-hu Yang, Xin-yu Wang, Aleksander J. Kavs. A fuzzy formal concept analysis based approach for business component identification[J]. Front. Inform. Technol. Electron. Eng., 2011, 12(9): 707-720.
[12] Jian Niu, Zu-hua Xu, Jun Zhao, Zhi-jiang Shao, Ji-xin Qian. Model predictive control with an on-line identification model of a supply chain unit[J]. Front. Inform. Technol. Electron. Eng., 2010, 11(5): 394-400.