干扰攻击下多代理系统的数据驱动自适应弹性漏斗共识追踪

IF 3.2 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

International Journal of Robust and Nonlinear Control Pub Date : 2024-08-15 DOI:10.1002/rnc.7593

Shan‐Shan Sun, Yuan‐Xin Li

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引用次数: 0

摘要

本文研究了干扰攻击下非线性多代理系统的无模型自适应漏斗控制框架。我们利用动态线性化方法为代理建立了一个等效的时变线性数据模型。然后，提出一个预定义的漏斗函数，使跟踪误差在预定范围内演化。通过调整漏斗函数的参数值，可以改善跟踪误差的瞬态和稳态行为。此外，考虑到影响干扰攻击率的传输信道状态，还采用了斯塔克尔伯格博弈来确定发射机和干扰机之间的最佳传输功率。在漏斗控制的基础上，提出了一种无模型自适应漏斗控制方案，以保证跟踪误差保持在漏斗边界内（即使存在干扰攻击），并改善瞬态和稳态性能。通过仿真进一步证明了所提算法的有效性。

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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Data‐driven adaptive resilient funnel consensus tracking of multiagent systems under jamming attacks

In this article, we investigate a model‐free adaptive funnel control framework for nonlinear multiagent systems under jamming attacks. An equivalent time‐varying linear data model is built for agents by utilizing the dynamic linearization approach. Then, a predefined funnel function is presented such that the tracking errors are expected to evolve within a predetermined range. The transient and steady‐state behaviors of the tracking error can be improved by adjusting the parameter values of the funnel function. Moreover, the Stackelberg game is employed to determine the optimal transmission power between the transmitters and jammers considering the state of the transmission channel, which influences the jamming attack rate. Based on funnel control, a model‐free adaptive funnel control scheme is presented to guarantee that the tracking error remains within a funnel boundary (even in the presence of jamming attacks) and the transient and steady‐state performances are improved. The effectiveness of the proposed algorithm is further demonstrated through simulations.

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来源期刊

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.