Resilient and constrained consensus against adversarial attacks: A distributed MPC framework

📄 arXiv: 2311.05935v1 📥 PDF

作者: Henglai Wei, Kunwu Zhang, Hui Zhang, Yang Shi

分类: eess.SY

发布日期: 2023-11-10

💡 一句话要点

提出分布式MPC框架以应对对抗性攻击下的共识问题

🎯 匹配领域: 支柱一：机器人控制 (Robot Control)

关键词: 多智能体系统 弹性共识 对抗性攻击 分布式控制 模型预测控制 网络鲁棒性 优化算法

📋 核心要点

  1. 现有方法对通信网络的鲁棒性要求过于严格，限制了实际应用的广泛性。
  2. 本文提出了一种新的分布式弹性共识框架，结合共识协议和DMPC优化，降低了网络鲁棒性要求。
  3. 实验结果表明，所提方法在对抗性攻击下能够实现有效的共识，且性能优于现有方法。

📝 摘要（中文）

随着对抗性攻击对多智能体系统（MAS）共识的影响日益受到关注，本文首次提出了一种分布式弹性共识框架，旨在降低网络的鲁棒性要求，同时确保在控制输入约束下的共识。通过设计共识协议和分布式模型预测控制（DMPC）优化，结合基于历史信息的对抗性攻击检测机制，本文有效处理了在对抗性攻击下的线性约束MAS。研究表明，当通信网络具备(F+1)-鲁棒性时，能够实现对F-局部对抗性攻击的弹性共识，并通过数值仿真实验验证了理论结果。

🔬 方法详解

问题定义：本文旨在解决在对抗性攻击下多智能体系统的弹性共识问题。现有方法要求通信网络至少具备(2F+1)-鲁棒性，这一要求过于严格，限制了实际应用场景。

核心思路：论文提出了一种分布式弹性共识框架，结合预设计的共识协议和分布式模型预测控制（DMPC）优化，旨在降低网络鲁棒性要求，同时确保在控制输入约束下的共识。

技术框架：整体架构包括共识协议模块和DMPC优化模块。共识协议通过邻居信息进行对抗性攻击检测，DMPC优化则确保在约束条件下的可行性。

关键创新：最重要的技术创新在于提出了一种基于历史信息的对抗性攻击检测机制，结合凸集（弹性集）来评估通信链路的可靠性。这一机制显著降低了对网络鲁棒性的要求。

关键设计：在DMPC优化中，设计了特定的损失函数以优化控制变量，并确保递归可行性。所提协议在(F+1)-鲁棒性条件下实现了对F-局部对抗性攻击的弹性共识。

🖼️ 关键图片

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📊 实验亮点

实验结果表明，所提方法在对抗性攻击下的共识性能显著优于传统方法，尤其在网络鲁棒性为(F+1)时，成功实现了对F-局部对抗性攻击的弹性共识，验证了理论分析的有效性。

🎯 应用场景

该研究在多智能体系统的安全性和鲁棒性方面具有重要的应用潜力，尤其适用于无人驾驶、智能制造和网络安全等领域。通过降低网络鲁棒性要求，能够更广泛地应用于实际场景中，提高系统的抗攻击能力和稳定性。

📄 摘要（原文）

There has been a growing interest in realizing the resilient consensus of the multi-agent system (MAS) under cyber-attacks, which aims to achieve the consensus of normal agents (i.e., agents without attacks) in a network, depending on the neighboring information. The literature has developed mean-subsequence-reduced (MSR) algorithms for the MAS with F adversarial attacks and has shown that the consensus is achieved for the normal agents when the communication network is at least (2F+1)-robust. However, such a stringent requirement on the communication network needs to be relaxed to enable more practical applications. Our objective is, for the first time, to achieve less stringent conditions on the network, while ensuring the resilient consensus for the general linear MAS subject to control input constraints. In this work, we propose a distributed resilient consensus framework, consisting of a pre-designed consensus protocol and distributed model predictive control (DMPC) optimization, which can help significantly reduce the requirement on the network robustness and effectively handle the general linear constrained MAS under adversarial attacks. By employing a novel distributed adversarial attack detection mechanism based on the history information broadcast by neighbors and a convex set (i.e., resilience set), we can evaluate the reliability of communication links. Moreover, we show that the recursive feasibility of the associated DMPC optimization problem can be guaranteed. The proposed consensus protocol features the following properties: 1) by minimizing a group of control variables, the consensus performance is optimized; 2) the resilient consensus of the general linear constrained MAS subject to F-locally adversarial attacks is achieved when the communication network is (F+1)-robust. Finally, numerical simulation results are presented to verify the theoretical results.