Finite-time SMC-based admittance controller design of macro-micro robotic system for complex surface polishing operations

IF 9.1 1区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Robotics and Computer-integrated Manufacturing Pub Date : 2024-09-23 DOI:10.1016/j.rcim.2024.102881
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Abstract

In the field of robotic polishing, achieving uniform material removal typically involves addressing the issue of constant contact force control. However, multi-source external disturbances in the polishing scenarios of complex workpiece surfaces can severely affect the robot’s force control accuracy. To enhance the responsiveness and disturbance rejection capabilities of robots in the compliant polishing process, this paper proposes an adaptive admittance controller with practical finite-time stability. A virtual control input is introduced into the basic admittance control framework in light of the state space theory, aiming to provide flexibility for common adaptive law designs. On this basis, a robust sliding mode control (SMC) algorithm is proposed to suppress external disturbances. The force tracking error is theoretically proven to achieve finite-time convergence when applying the proposed control strategy. Experimental results across various polishing scenarios demonstrate that, compared with the existing admittance control strategies, the proposed method can reduce fluctuations of the polishing force and improve the surface quality, thus verifying its effectiveness.
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基于有限时间 SMC 的复杂表面抛光操作宏微型机器人系统导纳控制器设计
在机器人抛光领域,实现均匀的材料去除通常需要解决恒定接触力控制问题。然而,在复杂工件表面抛光场景中,多源外部干扰会严重影响机器人的力控制精度。为了提高机器人在顺应式抛光过程中的响应速度和干扰抑制能力,本文提出了一种具有实用有限时间稳定性的自适应导纳控制器。根据状态空间理论,在基本的导纳控制框架中引入了虚拟控制输入,旨在为常见的自适应法则设计提供灵活性。在此基础上,提出了一种抑制外部干扰的鲁棒滑模控制(SMC)算法。理论证明,在应用所提出的控制策略时,力跟踪误差可实现有限时间收敛。各种抛光场景的实验结果表明,与现有的导纳控制策略相比,所提出的方法可以减少抛光力的波动,提高表面质量,从而验证了其有效性。
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来源期刊
Robotics and Computer-integrated Manufacturing
Robotics and Computer-integrated Manufacturing 工程技术-工程:制造
CiteScore
24.10
自引率
13.50%
发文量
160
审稿时长
50 days
期刊介绍: The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.
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