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Sampled-Data Control for Nonlinear Continuous-Time Systems

  • Dong ShenEmail author
  • Xuefang Li
Chapter

Abstract

In this chapter, sampled-data iterative learning control (ILC) method is extended to a class of continuous-time nonlinear systems with iteration-varying trial lengths. In order to propose a unified ILC algorithm, the tracking errors will be redefined when the trial length is shorter or longer than the desired one. Based on the modified tracking errors, two sampled-data ILC schemes are proposed to handle the randomly varying trial lengths. Sufficient conditions are derived rigorously to guarantee the convergence of the nonlinear system at each sampling instant. To verify the effectiveness of the proposed ILC laws, simulations for a nonlinear system are performed. The simulation results show that if the sampling period is set to be small enough, the convergence of the learning algorithms can be achieved as the iteration number increases.

References

  1. 1.
    Sun M, Wang D (2001) Sampled-data iterative learning control for nonlinear systems with arbitrary relative degree. Automatica 37:283–289MathSciNetCrossRefGoogle Scholar
  2. 2.
    Wang L, Li X, Shen D (2018) Sampled-data iterative learning control for continuous-time nonlinear systems with iteration-varying lengths. Int J Robust Nonlinear Control 28(8):3073–3091MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.College of Information Science and TechnologyBeijing University of Chemical TechnologyBeijingChina
  2. 2.Department of Electrical and Electronic EngineeringImperial College LondonLondonUK

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