相关实验视频
事件触发的RNN基于弹性模型预测共识控制非线性多代理系统在DoS攻击下:多无人机网络中的案例研究
IEEE transactions on cybernetics
|March 3, 2026
概括
本研究介绍了针对非线性多代理系统 (MAS) 的弹性共识控制器,该控制器可以承受拒绝服务 (DoS) 攻击. 基于事件触发的新型循环神经网络模型预测共识控制器 (RNN-MPCC) 确保即使在网络中断的情况下也可靠运行.
科学领域:
- 控制理论 控制理论
- 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) 在网络攻击下保持共识时面临挑战.
- 拒绝服务 (DoS) 攻击破坏通信,降低系统性能和可靠性.
- 现有的控制策略往往缺乏对动态和持久网络威胁的稳定性.
研究的目的:
- 为非线性MAS开发一个有弹性的共识控制策略,以应对DoS攻击.
- 设计一个由事件触发的控制器,尽量减少通信开销,同时确保稳定性.
- 将数据驱动的预测与强大的控制相结合,以提高系统的弹性.
主要方法:
- 经常性神经网络 (RNN) 用于以数据为导向的预测,以保证的误差界限来预测代理动态.
- 集成了一个DoS意识的通信模型来管理攻击对共识的影响.
- 一个事件触发的,模型预测共识控制器 (RNN-MPCC) 已开发,优化一个成本函数在一个回落的地平线.
- 为了防止事件触发系统中的Zeno行为,强制执行了最小的间接事件时间.
主要成果:
- 拟议的RNN-MPCC有效地在DoS攻击下的非线性MAS中实现了弹性共识.
- 事件触发方法减少了通信负载和计算需求.
- 在多个无人机网络上的模拟验证了控制器的性能.
- 控制器成功地处理了不同的攻击频率和持续时间.
结论:
- 开发的RNN-MPCC提供了一个强大的解决方案,用于在面临DoS攻击的MAS中达成共识.
- 事件触发控制与RNN预测相结合,提高了系统的弹性和效率.
- 这种方法为安全可靠的分布式控制系统提供了基础.
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