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Adaptive Neural Tracking Control for a Two‐Joint Robotic Manipulator with Unknown Time‐Varying Delays

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  • Jiayao Wang
  • Yang Cui

Abstract

This paper presents an adaptive neural tracking control approach for a two‐joint robotic manipulator with unknown time‐varying delays. In order to work out the effect of unknown time‐varying delays on the two‐joint robotic manipulator, the appropriate Lyapunov–Krasovskii functionals (LKFs) and separation technology are chosen to settle this matter. The neural networks work as an approximator that has the advantage of estimating the unknown function in the system. In this paper, Lyapunov stability analysis can prove that all signals of the closed‐loop system are semiglobal uniformly ultimately bounded and the tracking error can converge to a compact neighborhood with respect to zero. The simulation consequences demonstrate the availability of the feedforward control approach.

Suggested Citation

  • Jiayao Wang & Yang Cui, 2022. "Adaptive Neural Tracking Control for a Two‐Joint Robotic Manipulator with Unknown Time‐Varying Delays," Complexity, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:complx:v:2022:y:2022:i:1:n:7230853
    DOI: 10.1155/2022/7230853
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    1. Yeong-Chan Chang & Ming-Fang Wu, 2016. "Robust tracking control for a class of flexible-joint time-delay robots using only position measurements," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(14), pages 3336-3349, October.
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