• DocumentCode
    1559458
  • Title

    Fuzzy reinforcement learning control for compliance tasks of robotic manipulators

  • Author

    Tzafestas, S.G. ; Rigatos, G.G.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Nat. Tech. Univ. of Athens, Greece
  • Volume
    32
  • Issue
    1
  • fYear
    2002
  • fDate
    2/1/2002 12:00:00 AM
  • Firstpage
    107
  • Lastpage
    113
  • Abstract
    A fuzzy reinforcement learning (FRL) scheme which is based on the principles of sliding-mode control and fuzzy logic is proposed. The FRL uses only immediate reward. Sufficient conditions for the convergence of the FRL to the optimal task performance are studied. The validity of the method is tested through simulation examples of a robot which deburrs a metal surface
  • Keywords
    compliance control; fuzzy control; fuzzy logic; intelligent control; learning (artificial intelligence); manipulators; variable structure systems; FRL scheme; compliance tasks; fuzzy logic; fuzzy reinforcement learning; fuzzy reinforcement learning control; hybrid hierarchical control; immediate reward; impedance control; metal surface deburring; optimal task performance; robotic manipulators; sliding-mode control; sufficient conditions; Control systems; Convergence; Deburring; Fuzzy control; Fuzzy logic; Iterative algorithms; Learning; Manipulators; Service robots; Sliding mode control;
  • fLanguage
    English
  • Journal_Title
    Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1083-4419
  • Type

    jour

  • DOI
    10.1109/3477.979965
  • Filename
    979965
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1559458