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Observer-Based 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

In this chapter, a deep investigation is carried out for the AILC problem of nonlinear systems with states un-measurable and two kinds of observer-based AILC schemes are proposed, which overcomes the design difficulty from time delays, input saturation and the absence of measurement of states. In the state observer-based AILC scheme, state observer is designed on the basis of neural network compensation. The observer gain is determined by using LMI method, which avoids the SPR condition. In the error observer-based AILC scheme, a new error variable is defined by introducing filter, which removes the identical initial condition and SPR condition. A new robust learning term is chosen by using hyperbolic tangent function and series convergent sequence to guarantee the learning convergence.

Suggested Citation

  • Jianming Wei & Hong Wang & Fang Liu, 2022. "Observer-Based AILC of Nonlinear Time-Delay Systems," Springer Books, in: Iterative Learning Control for Nonlinear Time-Delay System, chapter 0, pages 111-160, Springer.
    • Handle: RePEc:spr:sprchp:978-981-19-6317-9_5
    DOI: 10.1007/978-981-19-6317-9_5
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