• DocumentCode
    1047538
  • Title

    Semiglobal ISpS Disturbance Attenuation With Output Tracking via Direct Adaptive Design

  • Author

    Ge, Shuzhi Sam ; Han, Thanh-Trung

  • Author_Institution
    Nat. Univ. of Singapore, Singapore
  • Volume
    18
  • Issue
    4
  • fYear
    2007
  • fDate
    7/1/2007 12:00:00 AM
  • Firstpage
    1129
  • Lastpage
    1148
  • Abstract
    Direct adaptive partial state feedback control is presented to achieve semiglobally input-to-state practically stable (ISpS) disturbance attenuation with output tracking for a class of uncertain time-varying nonlinear systems in which the unmeasured dynamics do not possess a constant disturbance attenuation level (CDAL). Identifying a necessary condition for the existence of a CDAL, direct adaptive neural networks (NNs) control is developed, where the universal approximation property of NNs and the domination design are employed together to overcome the difficulties due to the lack of state information, unknown system nonlinearities, and unknown state-dependent disturbance attenuation gain. The proposed method is coherent in the sense that it is applicable to the case in which a CDAL exists.
  • Keywords
    adaptive control; neural nets; nonlinear systems; state feedback; time-varying systems; constant disturbance attenuation level; direct adaptive neural networks; direct adaptive partial state feedback control; output tracking; semiglobal ISpS disturbance attenuation; semiglobally input-to-state practically stable; uncertain time-varying nonlinear systems; Adaptive control; Adaptive systems; Attenuation; Control systems; Neural networks; Nonlinear control systems; Nonlinear systems; Programmable control; State feedback; Time varying systems; Adaptive control; disturbance attenuation; input-to-state stability (ISS); neural network (NN) parametrization; semiglobal solutions; Algorithms; Computer Simulation; Decision Support Techniques; Feedback; Models, Theoretical; Neural Networks (Computer); Nonlinear Dynamics;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/TNN.2007.899159
  • Filename
    4267707