Protocol-Based PID Control for Stochastic Multiagent Systems Under Deception Attacks: Handling Quasi-Consensus in Probability

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Jie Ban, Liangdong Guo, Zidong Wang
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引用次数: 0

Abstract

In this paper, we investigate the problem of observer-based proportional-integral-derivative (PID) quasi-consensus control for a specific class of stochastic multi-agent systems (MASs) subject to deception attacks. This study is driven by practical engineering challenges where communication networks are vulnerable to attacks and resource constraints. We consider the scenario where the relative output vector of each agent, after being uniformly quantized and managed through a Round-Robin scheduling protocol, is exposed to deception attacks. To address these challenges, we develop a novel distributed observer-based PID control strategy tailored for stochastic MASs. This strategy effectively accounts for system randomness and defenses against deception attacks. Using matrix inequality and stochastic analysis techniques, we establish a framework of sufficient conditions for achieving quasi-consensus in probability and derive the corresponding gain matrices. The proposed approach is validated through numerical simulations, demonstrating its effectiveness in maintaining consensus despite deception attacks and quantization effects.

欺骗攻击下基于协议的随机多智能体系统PID控制:概率拟一致处理
在本文中,我们研究了受欺骗攻击的一类特定随机多代理系统(MAS)的基于观测器的比例积分派生(PID)准共识控制问题。这项研究是由通信网络易受攻击和资源限制的实际工程挑战驱动的。我们考虑的情况是,每个代理的相对输出向量在经过统一量化并通过循环调度协议进行管理后,会受到欺骗攻击。为了应对这些挑战,我们开发了一种新颖的基于分布式观测器的 PID 控制策略,专为随机 MAS 量身定制。该策略有效地考虑了系统的随机性和对欺骗攻击的防御。利用矩阵不等式和随机分析技术,我们建立了一个在概率上实现准共识的充分条件框架,并推导出相应的增益矩阵。我们通过数值模拟对所提出的方法进行了验证,证明它能有效地在遭受欺骗攻击和量化效应的情况下保持共识。
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
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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