• DocumentCode
    1797925
  • Title

    Tension identification of two-motor system based on neural network left-inverse

  • Author

    Zhennan Cai ; Guohai Liu ; Wenxiang Zhao ; Hao Zhang ; Yan Jiang ; Yaojie Mi

  • Author_Institution
    Sch. of Electr. & Inf. Eng., Jiangsu Univ., Zhenjiang, China
  • fYear
    2014
  • fDate
    6-11 July 2014
  • Firstpage
    2167
  • Lastpage
    2170
  • Abstract
    Tension detection is a key to improve performance of two-motor system under sensorless operation. This paper presents a new identification method for two-motor system based on artificial neural network and the left-inverse theory. Considering that the system parameters are time-variant and the mathematic model of left-inverse identification is complex, BP neural network is used to build the left-inverse model in this method, which is easy to implement. A simulation model of a two-motor system is developed. The simulated results verify the proposed method. By using this control strategy, the tension can be identified quickly and accurately, in which satisfactory robustness is offered.
  • Keywords
    MIMO systems; backpropagation; neurocontrollers; nonlinear control systems; sensorless machine control; synchronous motors; BP neural network; artificial neural network; left-inverse identification mathematic model; multiinput multioutput system; multimotor synchronous system; neural network left-inverse; nonlinear system; sensorless operation; strong coupling control; tension identification; time-variant system parameter; two-motor system; Artificial neural networks; Induction motors; Mathematical model; Rotors; Synchronous motors; Torque;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks (IJCNN), 2014 International Joint Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4799-6627-1
  • Type

    conf

  • DOI
    10.1109/IJCNN.2014.6889688
  • Filename
    6889688