Prescribed-time consensus for multi-agent systems using finite switching time-varying gain

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS International Journal of Robust and Nonlinear Control Pub Date : 2024-08-13 DOI:10.1002/rnc.7570
Yinsheng Li, Bing Wang, Yuquan Chen
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Abstract

In this paper, the prescribed-time consensus problem of multi-agent systems with finite control gain is investigated. A novel control Lyapunov function (CLF) framework for prescribed-time stability is developed by using the time space deformation approach. For both leaderless and leader-following prescribed-time consensus, new switching time-varying gain-based protocols are proposed, in which, the infinite time-varying control gain is turned off before the prescribed time and the global boundedness of control gain is thus guaranteed. It is mathematically proved that the agents equipped with the proposed protocols can achieve less conservative prescribed-time consensus in both leaderless cases and leader-following cases, on the basis of the developed CLF framework. The superiority of the proposed prescribed-time protocols in terms of consensus accuracy, control energy consumption, and control peak value is demonstrated through comparison simulations using illustrative examples.

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使用有限开关时变增益的多代理系统的规定时间共识
本文研究了具有有限控制增益的多代理系统的规定时间共识问题。利用时间空间变形方法,为规定时间稳定性建立了一个新的控制 Lyapunov 函数(CLF)框架。针对无领导和领导跟随的规定时间共识,提出了新的基于切换时变增益的协议,其中,无限时变控制增益在规定时间之前被关闭,从而保证了控制增益的全局有界性。数学证明,在所开发的 CLF 框架基础上,采用所提协议的代理在无领导和领导跟随两种情况下都能实现较保守的规定时间共识。通过使用示例进行对比模拟,证明了所提出的规定时间协议在共识精度、控制能耗和控制峰值方面的优越性。
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来源期刊
International Journal of Robust and Nonlinear Control
International Journal of Robust and Nonlinear Control 工程技术-工程:电子与电气
CiteScore
6.70
自引率
20.50%
发文量
505
审稿时长
2.7 months
期刊介绍: Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.
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Issue Information Disturbance observer based adaptive predefined-time sliding mode control for robot manipulators with uncertainties and disturbances Issue Information Issue Information A stabilizing reinforcement learning approach for sampled systems with partially unknown models
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