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NN AILC of Nonlinear Time-Delay Systems

In: Iterative Learning Control for Nonlinear Time-Delay System

Author

Listed:
  • Jianming Wei

    (Naval University of Engineering, College of Weapons Engineering)

  • Hong Wang

    (Naval Aviation University)

  • Fang Liu

    (Naval University of Engineering, College of Weapons Engineering)

Abstract

This chapter presents an adaptive iterative learning control (AILC) scheme for a class of nonlinear systems with unknown time-varying delays and unknown input dead-zone. A novel nonlinear form of dead-zone nonlinearity is presented. The assumption of identical initial condition for iterative learning control (ILC) is removed by introducing boundary layer function. The uncertainties with time-varying delays are compensated for by using appropriate Lyapunov-Krasovskii functional and Young0s inequality. Radial basis function neural networks are used to model the time-varying uncertainties. The hyperbolic tangent function is employed to avoid the problem of singularity. According to the property of hyperbolic tangent function, the system output is proved to converge to a small neighborhood of the desired trajectory by constructing Lyapunov-like composite energy function (CEF) in two cases, while keeping all the closed-loop signals bounded. Finally, simulation examples are presented to verify the effectiveness of the proposed approach.

Suggested Citation

  • Jianming Wei & Hong Wang & Fang Liu, 2022. "NN AILC of Nonlinear Time-Delay Systems," Springer Books, in: Iterative Learning Control for Nonlinear Time-Delay System, chapter 0, pages 53-79, Springer.
    • Handle: RePEc:spr:sprchp:978-981-19-6317-9_3
    DOI: 10.1007/978-981-19-6317-9_3
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