Mathematics (Dec 2023)

Adaptive Event-Triggered Neural Network Fast Finite-Time Control for Uncertain Robotic Systems

  • Jianhui Wang,
  • Yongping Du,
  • Yuanqing Zhang,
  • Yixiang Gu,
  • Kairui Chen

DOI
https://doi.org/10.3390/math11234841
Journal volume & issue
Vol. 11, no. 23
p. 4841

Abstract

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A fast convergence adaptive neural network event-triggered control strategy is proposed for the trajectory tracking issue of uncertain robotic systems with output constraints. To cope with the constraints on the system output in the actual industrial field while reducing the burden on communication resources, an adaptive event-triggered mechanism is designed by using logarithm-type barrier Lyapunov functions and an event-triggered mechanism. Meanwhile, the combination of neural networks and fast finite-time stability theory can not only approximate the unknown nonlinear function of the system, but also construct the control law and adaptive law with a fractional exponential power to accelerate the system’s convergence speed. Furthermore, the tracking errors converge quickly to a bounded and adjustable compact set in finite time. Finally, the effectiveness of the strategy is verified by simulation examples.

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