Observer-based adaptive neural iterative learning control for a class of time-varying nonlinear systems

  • Jianming Wei (韦建明)
  • Youan Zhang (张友安)
  • Jingmao Liu (刘京茂)
Article
  • 45 Downloads

Abstract

In this paper an adaptive iterative learning control scheme is presented for the output tracking of a class of nonlinear systems. An observer is designed to estimate the tracking errors. A mixed time domain and s-domain representation is constructed to derive an error model with relative degree one for our purpose. And time-varying radial basis function neural network is employed to deal with system uncertainty. A new signal is constructed by using a first-order filter, which removes the requirement of strict positive real (SPR) condition and identical initial condition of iterative learning control. Based on property of hyperbolic tangent function, the system tracing error is proved to converge to the origin as the iteration tends to infinity by constructing Lyapunov-like composite energy function, while keeping all the closed-loop signals bounded. Finally, a simulation example is presented to verify the effectiveness of the proposed approach.

Keywords

adaptive iterative learning control (AILC) time-varying nonlinear systems output tracking observer filter 

CLC number

TP 273 

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Copyright information

© Shanghai Jiaotong University and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Jianming Wei (韦建明)
    • 1
  • Youan Zhang (张友安)
    • 2
  • Jingmao Liu (刘京茂)
    • 3
  1. 1.Department of Control EngineeringNaval Aeronautical UniversityShandongChina
  2. 2.Department of Electrical and Electronic EngineeringYantai Nanshan UniversityShandongChina
  3. 3.Shandong Nanshan International Flight Co., Ltd.ShandongChina

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