通过固定时间收增强学习,为多无人机系统提供安全的封闭控制.
IEEE transactions on cybernetics
|March 3, 2025
概括
这项研究开发了针对网络攻击的多个自动驾驶飞行器的安全制控制. 一种新的强化学习方法确保了系统稳定性和快速融合,并通过模拟和实验验证.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 网络物理系统安全 网络物理系统安全
- 自主系统中的人工智能
背景情况:
- 自动驾驶飞行器面临控制控制的挑战,因为网络攻击操纵控制命令.
- 现有的控制方法可能容易受到复杂的网络威胁,可能会导致制失败.
- 多车辆系统的安全制需要强有力的策略来应对敌对行动.
研究的目的:
- 为解决网络攻击下多个自主飞行器的安全制控制问题.
- 开发基于强化学习的框架,用于设计安全的分布式控制政策.
- 提高控制系统的收速度和稳定性,使用固定时间收技术.
主要方法:
- 制定了安全封闭控制作为一个零和图形游戏,一个最小-最大优化问题.
- 在强化学习 (RL) 框架内采用一个只有批评者的神经网络 (NN) 结构来解决汉密尔顿-雅各比-艾萨克斯 (HJI) 方程.
- 集成的固定时间收和经验重播机制,以加速RL收和放松激发条件.
主要成果:
- 通过使用RL解决合的HJI方程,实现了最佳的分布式安全控制策略.
- 对于拟议的控制方案,已经证明了NN趋同和闭环稳定性分析.
- 通过解决汉密尔顿-雅各比-贝尔曼方程,获得了态度循环的最佳反控制规律.
结论:
- 拟议的固定时间收增强学习方法有效地确保了多个自动驾驶飞行器的安全控制.
- 模拟和四旋翼实验验证了开发的安全控制策略的实际有效性和稳定性.
- 该方法在保护自动驾驶飞行器系统免受网络物理威胁方面取得了重大进展.
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