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Updated: May 24, 2025

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Operation of the Collaborative Composite Manufacturing CCM System
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安全增强学习:最佳的形成控制与避免碰撞的多个卫星系统
IEEE transactions on cybernetics
|March 3, 2025
概括
本研究引入了一种新的安全强化学习 (RL) 算法,用于卫星形成控制. 它通过屏障函数 (BF) 和适应性学习来确保更安全的卫星运营,确保避免碰撞.
科学领域:
- 航空航天工程 航空航天工程
- 机器人和控制系统 机器人和控制系统
- 人工智能的人工智能
背景情况:
- 多卫星系统需要强大的避免碰撞和形成控制.
- 现有的方法在动态环境中可能缺乏适应性和保证安全性.
研究的目的:
- 提出一种新的安全强化学习 (RL) 算法,用于避免多卫星碰撞和形成控制.
- 通过创新的学习技术增强系统安全性和适应能力.
主要方法:
- 在自适应动态编程框架内开发了一个安全的RL算法.
- 在成本函数中集成了一个屏障函数 (BF),以保证避免碰撞.
- 实施了结合在线和历史数据的自适应式不同距离的学习方法.
主要成果:
- 拟议的算法实现了有效的避免碰撞和稳定的形成控制.
- 数字模拟验证了算法的安全性,稳定性和优于现有方法的优势.
- 该系统展示了适应性形成和自我学习能力.
结论:
- 新的安全RL算法有效地解决了多卫星碰撞避免和形成控制问题.
- 屏障功能和自适应式学习方法确保了系统的安全性和性能.
- 这种方法为自主卫星运营提供了一个有希望的解决方案.
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