• DocumentCode
    2832759
  • Title

    Adaptive PID control based on RBF neural network identification

  • Author

    Ming-guang, Zhang ; Xing-Gui, Wang ; Man-Qiang, Liu

  • Author_Institution
    Sch. of Electr. & Inf. Eng., Lanzhou Univ. of Technol.
  • fYear
    2005
  • fDate
    16-16 Nov. 2005
  • Lastpage
    683
  • Abstract
    Radial basis function (RBF) neural network (NN) is powerful computational tools that have been used extensively in the areas of pattern recognition, systems modeling and identification. This paper proposes an adaptive PID control method based on RBF neural network identification. This approach can on-line identify the controlled plant with the RBF neural network identifier and the weights of the adaptive PID controller are adjusted timely based-on the identification of the plant and self-learning capability of RBFNN. Simulation result shows that the proposed controller has the adaptability, strong robustness and satisfactory control performance in the nonlinear and time varying system
  • Keywords
    adaptive control; identification; neurocontrollers; radial basis function networks; three-term control; unsupervised learning; RBF neural network identification; adaptive PID control; nonlinear system; radial basis function neural network; satisfactory control performance; self-learning; time varying system; Adaptive control; Computer networks; Modeling; Neural networks; Nonlinear control systems; Pattern recognition; Programmable control; Robust control; Three-term control; Time varying systems; Adaptive PID control; Neural Network; Radial Basis Function (RBF); Simulation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Tools with Artificial Intelligence, 2005. ICTAI 05. 17th IEEE International Conference on
  • Conference_Location
    Hong Kong
  • ISSN
    1082-3409
  • Print_ISBN
    0-7695-2488-5
  • Type

    conf

  • DOI
    10.1109/ICTAI.2005.26
  • Filename
    1563016