Citation

Shi, Yang and Peng, Yilong and Li, Shuai and Cao, Xinwei and Ting, Choo Yee and Wang, Jiyun (2026) Discrete Trajectory Tracking Control of Planar Cable-Actuated Platform via Recurrent Neural Network With Disturbance Rejection. IEEE Transactions on Industrial Informatics. pp. 1-11. ISSN 1551-3203

Text 1.pdf - Published Version Restricted to Repository staff only Download (2MB)

Abstract

Conventional discrete-time recurrent neural networks (RNNs) often suffer from limited tracking accuracy and inadequate disturbance rejection when dealing with time-variant disturbances in the control of planar cable-actuated platform. To address these challenges, this article proposes a novel four-step discrete-form integralreinforcing recurrent neural network (DF-IR-RNN) approach for disturbance-rejection trajectory tracking control. By incorporating an integral-reinforcing mechanism into the discrete RNN error dynamics, the proposed approach enhances disturbance-rejection capability and improves steady-state tracking precision. The effectiveness of the proposed DF-IR-RNN approach is first validated through numerical simulations under various disturbance conditions on a planar cable-actuated platform. Furthermore, physical experiments are conducted on the platform to further verify the effectiveness and practical feasibility of the proposed approach. The results demonstrate that the proposed approach achieves high trajectory tracking accuracy and robust disturbance rejection compared to conventional neural network approaches. This work provides a practical and effective solution for discrete-time control of planar cableactuated platform in real-world applications.

Item Type:	Article
Uncontrolled Keywords:	Recurrent neural network (RNN), trajectory tracking.
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:	07 Jul 2026 05:15
Last Modified:	07 Jul 2026 05:15
URII:	http://shdl.mmu.edu.my/id/eprint/16210

Downloads

Edit (login required)