基于安全和安全关键学习的多代理系统的协作控制
IEEE transactions on neural networks and learning systems
|January 26, 2024
概括
本研究引入了多代理系统 (MAS) 的新控制框架,以在拒绝服务 (DoS) 攻击和环境挑战期间保持安全的通信和安全的形成. 该系统增强了自动驾驶汽车车队的弹性和稳定性.
科学领域:
- 控制理论 控制理论 控制理论
- 人工智能的人工智能是人工智能.
- 机器人技术 机器人技术 机器人技术
背景情况:
- 多代理系统 (MAS) 由于拒绝服务 (DoS) 攻击,模型不确定性和环境障碍,在通信安全和形成安全方面面临挑战.
- 现有的控制框架往往难以应对非线性系统中的这些联合威胁.
研究的目的:
- 为非线性MAS开发一种基于学习的新型协作控制框架.
- 在DoS攻击,模型不确定性和环境约束的情况下,确保通信安全和阵营安全.
- 增强MAS在动态环境中的弹性和强度.
主要方法:
- 一个分布式和分离的框架,集成网络层和物理层设计.
- 一个基于立方函数编程 (RCLF-QP) 的弹性控制Lyapunov观察器,用于对DoS攻击进行安全状态估计.
- 深度强化学习 (RL) 和控制障碍功能 (CBF) 对于安全关键的阵列控制器.
主要成果:
- 拟议的框架成功地确保了在DoS攻击下安全的参考状态估计.
- 设计了一种安全关键的阵列控制器,使不确定因素之间的安全合作成为可能.
- 使用自动驾驶汽车的实验结果显示,系统的弹性和强度有了显著的改善.
结论:
- 这种基于学习的新型框架有效地解决了MASS的通信安全和形成安全问题.
- 解的网络物理设计增强了对DoS攻击和环境不确定性的适应性.
- 该框架对需要强大和安全的多代理协调的应用有希望,例如自动驾驶车辆组成.
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