• DocumentCode
    1797801
  • Title

    Neural-based adaptive integral sliding mode tracking control for nonlinear interconnected systems

  • Author

    Wen-Shyong Yu ; Chien-Chih Weng

  • Author_Institution
    Dept. of Electr. Eng., Tatung Univ., Taipei, Taiwan
  • fYear
    2014
  • fDate
    6-11 July 2014
  • Firstpage
    1344
  • Lastpage
    1351
  • Abstract
    It is proposed here to use a robust tracking design neural-based adaptive integral sliding mode control technique to control nonlinear interconnected systems with unknown coupled uncertainty in which each uncertainty is assumed to be bounded by an unknown gain. A neural network for nonlinear interconnected systems is then proposed for solving the uncertainties of nonlinear interconnected systems. On-line estimation schemes are developed to overcome the uncertainties and identify the gains of the unknown coupled uncertainty, simultaneously. By the concept of parallel distributed compensation (PDC), we combine adaptive neural scheme the integral sliding mode control scheme to resolve the system uncertainties, unknown coupled uncertainties, and the external disturbances such that H∞ tracking performance is achieved. Simulation results are further presented to show the effectiveness and performance of the proposed control scheme.
  • Keywords
    adaptive control; compensation; control system synthesis; interconnected systems; neurocontrollers; nonlinear control systems; robust control; uncertain systems; variable structure systems; NN; PDC; adaptive integral sliding mode tracking control; neural network; nonlinear interconnected systems; on-line estimation schemes; parallel distributed compensation; robust tracking design; unknown coupled uncertainty; Adaptive systems; Artificial neural networks; Interconnected systems; Manifolds; Sliding mode control; Uncertainty; Vectors;
  • 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.2014.6889627
  • Filename
    6889627