• DocumentCode
    2084651
  • Title

    A discrete-time AILC for systems with non-parametric uncertainties

  • Author

    Chi Ronghu ; Hou Zhongsheng ; Jin Shangtai

  • Author_Institution
    Sch. of Autom. & Electr. Eng., Qingdao Univ. of Sci. & Technol., Qingdao, China
  • fYear
    2010
  • fDate
    29-31 July 2010
  • Firstpage
    5877
  • Lastpage
    5882
  • Abstract
    In this paper, a discrete adaptive ILC is presented for a time-varying nonlinear system with nonparametric uncertainties. A novel estimation of nonparametric uncertainties is constructed just using the past I/O data, and the uncertainties are completely compensated such that the output tracking error is only affected by external disturbance. As a main contribution of this paper, all the discussions are done with the random initial condition and the iteration-varying target trajectories. Both the rigorous mathematical analysis and the simulation results illustrate the correctness and effectiveness of the proposed approach.
  • Keywords
    adaptive control; discrete time systems; iterative methods; learning systems; mathematical analysis; parameter estimation; AILC; adaptive iterative learning control; discrete-time system; iteration-varying target trajectory; mathematical analysis; nonlinear system; nonparametric uncertainty estimation; time-varying system; Adaptive control; Artificial neural networks; Bismuth; Target tracking; Trajectory; Uncertainty; Adaptive Iterative learning control; Discrete-time Systems; Iteration-varying Trajectories; Non-parametric Uncertainties; Random Initial Condition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control Conference (CCC), 2010 29th Chinese
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4244-6263-6
  • Type

    conf

  • Filename
    5572554