在输入和下,分散的最佳领导者遵循MAS的共识控制:斯塔克尔伯格游戏方法
IEEE transactions on cybernetics
|March 5, 2026
概括
本研究提出了一种新的方法,用于在非线性多代理系统 (MAS) 中进行最佳状态观测和领导者遵循共识,使用Stackelberg游戏和模糊增强学习,确保系统稳定性.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能的人工智能
- 游戏理论 游戏理论
背景情况:
- 多代理系统 (MAS) 在状态观察和共识方面面临挑战,其中存在未知的动态和有限的领导状态可访问性.
- 在非线性MAS中输入和使控制设计和状态估计复杂化.
研究的目的:
- 为了在Stackelberg游戏框架下实现非线性MAS与输入和的最佳状态观测和领导者遵循共识.
- 制定一个强大的控制策略,解决未知的系统动态和部分领导状态信息.
主要方法:
- 一个用于领导状态估计的分布式估计算法.
- 一个基于游戏的观察者设计,考虑双向动态交互.
- 模糊增强学习以近似未知动态并导出最佳控制器.
- 利亚普诺夫稳定性分析,以保证闭环信号的统一终极边界性.
主要成果:
- 为观察者开发了一个最佳的辅助控制器和一个最佳的共识控制器.
- 模糊增强学习方法成功地近似了未知的动态.
- 所有闭环信号都被证明是统一的最终边界,证实了系统的稳定性.
结论:
- 提出的方法有效地在复杂的非线性MAS中实现了最佳状态观察和领导者遵循的共识.
- 斯塔克尔伯格游戏理论和模糊增强学习的整合为强大的控制设计提供了一个强大的框架.
- 模拟结果验证了开发的方法的实际适用性和性能.
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