基于神经网络的强化学习,用于在攻击下对混合网络海洋系统的定时形成跟踪
Gao-Fei Zhao1, Tao Han1, Bo Xiao1
1School of Electrical Engineering and Automation, Hubei Normal University, Huangshi 435005, PR China.
ISA transactions
|August 21, 2025
概括
这项研究引入了一种新型的海上系统控制框架,以在拒绝服务和物理攻击的情况下实现可靠的时间变化的形成跟踪,确保在设定的时间内保持稳定.
科学领域:
- 机器人和控制系统
- 海洋工程
- 网络系统
背景情况:
- 由于网络物理攻击,海上和水下混合网络海洋系统 (HNMS) 在协调控制方面面临挑战.
- 在动态环境中使用自动水面车辆 (ASV) 和自动水下车辆 (AUV) 保持形成跟踪是复杂的.
- 现有的控制策略可能无法在规定的时间内充分解决联合拒绝服务 (DoS) 和物理攻击.
研究的目的:
- 在HNMS中制定一个可靠的时间变化的形成跟踪 (TVFT) 控制战略.
- 解决DoS和物理攻击对系统性能的影响.
- 在规定的时间内确保稳定和趋同.
主要方法:
- 提出了一个规定的时间层次控制 (PTHC) 框架.
- 规定的时间分布式估计器旨在估计受到DoS攻击的领导状态.
- 与基于神经网络的控制器集成的强化学习算法减轻了物理攻击,干扰和未建模的水力动力学.
主要成果:
- 拟议的PTHC框架允许在联合攻击下对HNMS进行TVFT.
- 分布式估计器通过使用相对信息有效处理DoS攻击.
- 神经网络控制器确保规定的时间稳定性和准确的训练跟踪.
- 莱普诺夫稳定理论证实了系统的保证时间稳定性.
结论:
- 开发的方法为面临复杂攻击的HNMS提供了强大而有效的TVFT解决方案.
- 规定的时间控制,分布式估计和强化学习的整合为海洋系统控制提供了强大的框架.
- 模拟结果验证了拟议的控制策略的实际适用性和性能.
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