Robust Parallel Cooperative Control of Cable-Driven Robot System via Adaptive Integral Sliding Mode

IF 5.3 2区 计算机科学 Q2 ROBOTICS IEEE Robotics and Automation Letters Pub Date : 2025-04-10 DOI:10.1109/LRA.2025.3559837
Xiaolei Li;Qixin Kui;Weiran Yao;Ligang Wu
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Abstract

This paper develops an adaptive integral sliding mode control scheme to manipulate the cable-driven robots, realizing robust parallel cooperation performance for the variable loads. The considered cable-driven robot system (CDRS) employs multiple flexible cables to cooperatively regulate the robotics platform, which results in the control issues of parallel and distributed manipulations. In this regard, a parallel cooperation strategy is proposed to regulate the parallel cooperation performance of CDRS adaptively. It presents a cross-coupled cooperation principle based on a ring topology, effectively reducing the computational redundancy caused by global cooperative errors in parallel and distributed manipulations. Based on this, an adaptive integral sliding mode cooperative controller is developed to regulate multiple flexible cables in a cooperative manner, which can achieve a smoother control process by reducing the chattering issues of flexible cables. Finally, the effectiveness and superiority of the proposed scheme are verified by multiple groups of robotics experiments with variable loads.
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基于自适应积分滑模的索驱动机器人鲁棒并联协同控制
本文开发了一种操纵缆索驱动机器人的自适应积分滑模控制方案,实现了可变负载下稳健的并行合作性能。所考虑的缆索驱动机器人系统(CDRS)采用多条柔性缆索协同调节机器人平台,这导致了并行和分布式操纵的控制问题。为此,提出了一种并行合作策略,用于自适应调节 CDRS 的并行合作性能。它提出了一种基于环形拓扑结构的交叉耦合合作原理,有效减少了并行和分布式操纵中全局合作误差造成的计算冗余。在此基础上,开发了一种自适应积分滑模协同控制器,以协同方式调节多根柔性电缆,通过减少柔性电缆的颤振问题实现更平滑的控制过程。最后,通过多组变负载机器人实验验证了所提方案的有效性和优越性。
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来源期刊
IEEE Robotics and Automation Letters
IEEE Robotics and Automation Letters Computer Science-Computer Science Applications
CiteScore
9.60
自引率
15.40%
发文量
1428
期刊介绍: The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.
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