对于使用强化学习和事件触发通信机制的分数顺序多代理网络系统的智能弹性安全控制
IEEE transactions on cybernetics
|March 7, 2025
概括
本研究介绍了使用强化学习 (RL) 进行分数顺序多代理网络系统 (FOMANS) 的智能,弹性事件触发控制. 它增强了对未知的动态和拒绝服务 (DoS) 攻击的稳定性和强度.
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
- 网络化系统 网络化系统
背景情况:
- 分数级多代理网络系统 (FOMANS) 面临着未知动态,执行器故障和拒绝服务 (DoS) 攻击的挑战.
- 现有的控制方法在复杂,不确定的环境中难以适应和性.
研究的目的:
- 为FOMANSs开发一种智能,有弹性的事件触发控制方法.
- 使用强化学习 (RL) 来解决未知的动态,执行器故障和DoS攻击.
主要方法:
- 实现了一个适应性学习法则,用于未知的非线性动态的神经网络和模糊逻辑.
- 结合RL与滑动模式控制,以优化分布式控制协议.
- 制定了一种双事件触发的控制策略,以减轻DoS攻击的影响.
主要成果:
- 实现了强大的跟踪,并保证了闭环系统的Mittag-Leffler稳定性.
- 成功地减轻了DoS攻击的影响,并确保了弹性控制.
- 在单环灵活关节机器人操纵系统上验证了控制策略.
结论:
- 拟议的智能,弹性事件触发控制方法有效地提高了FOMANS的性能.
- 整合RL和双事件触发为受到攻击的网络系统提供了强大的解决方案.
- 这种方法为复杂系统的安全和稳定的控制提供了重大进展.
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