网络攻击下的多代理系统的固定时间最佳共识:分层控制方法
IEEE transactions on cybernetics
|July 18, 2025
概括
本研究介绍了面临网络攻击的未知多代理系统 (MAS) 的固定时间共识方法. 它使用深度神经网络和强化学习来进行强大的领导追随控制,确保系统稳定性和最佳性能.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 多代理系统 (MAS) 在遭受拒绝服务 (DoS) 和虚假数据注入 (FDI) 等网络攻击时面临着达成共识的挑战.
- 现有的方法往往缺乏固定时间的融合,或与未知的系统动态和复杂的攻击场景作斗争.
研究的目的:
- 为在DoS和FDI攻击下的未知MAS开发一个固定时间的最佳领导者遵循的共识策略.
- 为了提高系统的弹性和性能,尽管网络威胁.
- 为了减少计算复杂性,以确定结算时间.
主要方法:
- 一个新的固定时间稳定定理对于DoS攻击.
- 深度神经网络 (DNN) 使用投影操作员来近似未知的系统动态.
- 使用分布式和基于Luenberger的观察者进行状态估计的等级控制方法.
- 一个事件触发机制 (ETM) 为最佳的控制策略设计.
- 一个只有批评的强化学习 (RL) 算法,用于在线最佳控制学习.
主要成果:
- 在多次恶意攻击下使用已开发的观察员实现了固定时间状态重建.
- 展示了能够在固定的时间内跟踪观测状态的最佳控制政策.
- 通过模拟自动化车辆排队控制,验证了拟议技术的有效性.
结论:
- 拟议的层次控制框架有效地解决了在复杂的网络攻击下未知的MAS中固定时间最佳领导者遵循的共识问题.
- DNN,RL和ETM的整合提供了一个强大的,计算效率高的解决方案.
- 该方法确保稳定性和最佳性能,适用于现实世界的场景,如自动驾驶汽车协调.
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