Citation

Jiang, Chao and Cheng, Zixuan and Wang, Jiacheng and Yee, Ting Choo and Gerontitis, Dimitrios K. and Wang, Jiyun (2025) A Discrete-Time AD-ZNN for Robust Trajectory Tracking of Robotic Manipulators. In: 7th International Conference on Electronic Engineering and Informatics, EEI 2025, 7 November 2025 - 9 November 2025, Yangzhou.

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Abstract

This paper presents a discrete-time AntiDisturbance Zeroing Neural Network (AD-ZNN) model for robust trajectory tracking of robotic manipulators under time-varying disturbances. By extending the perturbationinhibited ZNN framework, the proposed AD-ZNN introduces a discrete difference-based damping mechanism to suppress oscillations and mitigate external perturbations during numerical implementation. The developed algorithm ensures exponential convergence of tracking errors while maintaining computational simplicity suitable for real-time control. To validate its performance, two benchmark experiments—a cycloid trajectory and an oscillatory spiral trajectory—are conducted using a five-link planar manipulator. Simulation results demonstrate that the AD-ZNN achieves accurate and stable trajectory tracking even under constant, linear, and cosine time-varying noise, highlighting its potential for real-world time-varying motion control applications.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Zeroing neural network, anti-disturbance control
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	20 Apr 2026 01:56
Last Modified:	20 Apr 2026 01:56
URII:	http://shdl.mmu.edu.my/id/eprint/15747

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