通过通用模糊的超标模型对具有状态约束的未知非线性系统进行自适应安全的有限时间最佳控制
概括
本研究引入了一个新的适应式批评学习 (ACL) 框架,用于未知的非线性系统. 它确保了非零和微分游戏的安全,有限时间的解决方案与状态约束.
科学领域:
- 控制理论 控制理论
- 机器学习 机器学习
- 非线性系统是非线性系统.
背景情况:
- 非线性系统和差分游戏带来了重要的控制挑战.
- 国家约束和未知的动态使得实现稳定,最佳的解决方案变得复杂.
- 现有的方法往往需要持续的激发,限制了应用性.
研究的目的:
- 开发一种新的自适应式批评学习 (ACL) 框架,用于具有未知连续时间 (CT) 非线性系统和状态约束的非零和 (NZS) 微分游戏.
- 确保有限时间对纳什平衡解决方案的收,同时保证有限时间稳定性.
- 为了消除对持续刺激 (PE) 条件的需要.
主要方法:
- 基于通用模糊的超标模型 (GFHM) 的标识符来重建未知的系统动态.
- 一个ACL框架采用一个具有安全的有限时间体验重播转换法的关键网络.
- 对于有限时间稳定的利亚普诺夫分析和引入一个等级条件来取代PE条件.
- 整合直接成本函数和控制障碍函数 (CBFs) 以执行国家约束.
主要成果:
- 拟议的ACL框架成功地使用GFHM标识符重建未知的系统动态.
- 对于每个玩家来说,每个玩家都实现了对纳什平衡解决方案的有限时间收.
- 通过利亚普诺夫分析来保证有限时间的稳定性.
- 开发的方法通过引入一个等级条件,消除了对持续激发 (PE) 条件的需要.
- 控制屏障功能 (CBF) 确保系统状态保持在安全边界内.
结论:
- 新的ACL框架为未知CT非线性系统和状态约束的NZS差分游戏提供了有效的解决方案.
- 该方法保证了有限时间的融合和稳定性,而不需要PE条件.
- 集成CBF可确保控制战略的安全性和稳定性.
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