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

Jianhui Wang, Yongping Du, Yuanqing Zhang, Yixiang Gu and Kairui Chen ()
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Jianhui Wang: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Yongping Du: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Yuanqing Zhang: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Yixiang Gu: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Kairui Chen: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China

Mathematics, 2023, vol. 11, issue 23, 1-15

Abstract: 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.

Keywords: robotic systems; output constraints; fast finite-time stability; event-triggered mechanism (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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