噪声和时延环境中具有内在非线性动态特性的异构多代理系统的跟踪控制

IF 9.4 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Cybernetics Pub Date : 2024-10-15 DOI:10.1109/TCYB.2024.3474974
Kewei Zhang;Yuanyuan Zhang;Xiaofeng Zong;Xiwang Dong
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引用次数: 0

摘要

研究了具有内在非线性动力学的异构多智能体系统在噪声和时滞环境下的跟踪控制问题。首先,利用适当的Lyapunov-Krasovskii泛函,给出了具有乘性噪声和时变时滞的非线性随机时滞系统的稳定性判据。然后,基于所提出的稳定性判据,导出了具有内在非线性动力学的非均方(m.s.)和几乎肯定(a.s.)跟踪的充分条件。之后,上述充分条件进一步退化为积分器非均质质量。特别是当时滞消失时,以标量不等式的形式得到了积分器异构质量的显式条件,可以直观地反映噪声强度与控制增益之间的关系。最后,仿真结果验证了所提控制协议的有效性。
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Tracking Control of Heterogeneous Multiagent Systems With Intrinsic Nonlinear Dynamics in Noisy and Time-Delayed Environments
This article investigates the tracking control problem of heterogeneous multiagent systems (MASs) with intrinsic nonlinear dynamics in noisy and time-delayed environments. First, a stability criterion for nonlinear stochastic delay systems with multiplicative noise and time-varying delay is proposed by applying the appropriate Lyapunov-Krasovskii functional. Then, based on the proposed stability criterion, sufficient conditions are derived for mean square (m.s.) and almost sure (a.s.) tracking of heterogeneous MASs with intrinsic nonlinear dynamics. Afterward, the above sufficient conditions further degenerate to integrator heterogeneous MASs. In particular, when the time-delay vanishes, the explicit conditions are obtained for the integrator heterogeneous MASs in the form of scalar inequalities, which can intuitively reflect the relationship between noise intensity and control gains. Finally, simulation results validate the effectiveness of the proposed control protocol.
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来源期刊
IEEE Transactions on Cybernetics
IEEE Transactions on Cybernetics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, CYBERNETICS
CiteScore
25.40
自引率
11.00%
发文量
1869
期刊介绍: The scope of the IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or machine, human, and organizations. The scope includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, cognitive systems, decision making, and robotics, to the extent that they contribute to the theme of cybernetics or demonstrate an application of cybernetics principles.
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