Finite-time composite learning control for nonlinear teleoperation systems under networked time-varying delays

IF 7.3 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yana Yang, Huixin Jiang, Changchun Hua, Junpeng Li
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引用次数: 0

Abstract

The robust finite-time synchronization control problem is investigated for master-slave networked nonlinear telerobotics systems (NNTSs) in this article. Although there have been some research achievements on finite-time control for the NNTSs, these studies are based on the strong assumptions of communication time delays or can only achieve finite-time bounded convergence even when the external forces are zero. Accordingly and in view of the importance of these issues, a novel robust composite learning adaptive control scheme rendering the finite-time master-slave synchronization is proposed in this paper. In particular, the influence of time delays on finite-time convergence of the system is analyzed by employing the multi-dimension finite-time small-gain framework. Meanwhile, in order to achieve accurate and fast estimation of uncertain parameters of the system, both the online historical and the instantaneous data of the estimation data are explored to derive the new parameter adaptive law under a more realizable interval-excitation (IE) condition. Therefore, the convergence of the position/force synchronization errors and the adaptive parameter estimation errors is obtained in finite time, and enhanced robustness of the closed-loop system will also be ensured. Finally, the superior performance of the proposed control algorithms is validated by numerical simulations and hardware experiments.

网络时变延迟下非线性远程操纵系统的有限时间复合学习控制
本文研究了主从联网非线性远程机器人系统(NNTS)的鲁棒性有限时间同步控制问题。虽然已有一些关于 NNTS 有限时间控制的研究成果,但这些研究都基于通信时间延迟的强假设,或者即使在外力为零时也只能实现有限时间有界收敛。因此,鉴于这些问题的重要性,本文提出了一种渲染有限时间主从同步的新型鲁棒复合学习自适应控制方案。其中,通过采用多维有限时间小增益框架,分析了时间延迟对系统有限时间收敛性的影响。同时,为了实现对系统不确定参数的精确、快速估计,本文同时探讨了估计数据的在线历史数据和瞬时数据,在更可实现的区间激励(IE)条件下推导出新的参数自适应规律。因此,位置/力同步误差和自适应参数估计误差将在有限时间内收敛,闭环系统的鲁棒性也将得到增强。最后,通过数值模拟和硬件实验验证了所提控制算法的优越性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Science China Information Sciences
Science China Information Sciences COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
12.60
自引率
5.70%
发文量
224
审稿时长
8.3 months
期刊介绍: Science China Information Sciences is a dedicated journal that showcases high-quality, original research across various domains of information sciences. It encompasses Computer Science & Technologies, Control Science & Engineering, Information & Communication Engineering, Microelectronics & Solid-State Electronics, and Quantum Information, providing a platform for the dissemination of significant contributions in these fields.
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