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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
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基于安全和安全关键学习的多代理系统的协作控制.

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    此摘要是机器生成的。

    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.

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    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.