• DocumentCode
    1743695
  • Title

    Adaptive control on manifolds with RBF neural networks

  • Author

    Terekhov, Valeri A. ; Tyukin, Ivan Yu ; Prokhorov, Danil V.

  • Author_Institution
    Dept. of Autom. & Control Process, St. Petersburg State Electr. Eng. Univ., Russia
  • Volume
    4
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    3831
  • Abstract
    We propose a new method of adaptive control on manifolds for nonlinear plants in the full-state feedback case using radial basis function (RBF) neural networks. We introduce a procedure for synthesis of adaptation algorithms based on associated performance criteria. We analyze applicability of the algorithms developed for a quadratic performance criterion
  • Keywords
    adaptive control; neurocontrollers; nonlinear systems; radial basis function networks; state feedback; adaptive control; manifolds; neurocontrol; nonlinear systems; radial basis function neural networks; state feedback; Adaptive control; Algorithm design and analysis; Control system synthesis; Control systems; Control theory; Equations; Network synthesis; Neural networks; State feedback; State-space methods;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Decision and Control, 2000. Proceedings of the 39th IEEE Conference on
  • Conference_Location
    Sydney, NSW
  • ISSN
    0191-2216
  • Print_ISBN
    0-7803-6638-7
  • Type

    conf

  • DOI
    10.1109/CDC.2000.912309
  • Filename
    912309
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1743695