Citation

Shi, Yang and Gong, Ye and Li, Shuai and Cao, Xinwei and Ting, Choo Yee and Wang, Jiyun (2026) A Distributed Cloud Control Framework Based on RNN for Trajectory Tracking of Cable-Driven Planar Platform. IEEE Transactions on Industrial Electronics. pp. 1-12. ISSN 0278-0046

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Abstract

In recent years, cable-driven planar platforms have been adopted in a wide range of application scenarios. However, most existing control methods for such platforms still rely on single-machine execution, which can limit the implementation and scalability of the recursive computations required by discrete-time recurrent neural network (DTRNN)-based methods. To enable multinode deployment, this article proposes a distributed cloud DTRNN (DC-DTRNN) model for trajectory tracking and develops a corresponding three-node distributed cloud control framework. The platform structure and kinematic formulation are presented, and the DC-DTRNN model is constructed and analyzed under different sampling periods. In the proposed framework, trajectory generation is carried out in a segment-wise manner across multiple cloud nodes with centralized aggregation on the head node, while the platform-side execution loop remains unchanged. Physical experiments on a real-world testbed show consistent execution under a three-node deployment with a fixed sampling period. The model-based head-node estimated tracking error and the waypoint-generation time are quantitatively evaluated in the experiments, and the results show that distributed multinode trajectory generation and ordered execution can be implemented within the proposed cloud-control framework.

Item Type:	Article
Uncontrolled Keywords:	Cable-driven planar platform, distributed cloud control, distributed cloud discrete-time recurrent neural network (DC-DTRNN), ray computing cluster, trajectory tracking
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	04 Sep 2026 00:23
Last Modified:	04 Sep 2026 00:23
URII:	http://shdl.mmu.edu.my/id/eprint/16653

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