通过事件触发机制的高斯过程进行多代理系统控制的合作在线学习
IEEE transactions on neural networks and learning systems
|September 16, 2024
概括
本研究介绍了使用高斯过程 (GP) 回归的多代理系统 (MAS) 的在线合作学习算法. 一个事件触发机制提高了数据效率和控制性能,在MAS的动态未知.
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
- 机器学习 机器学习
背景情况:
- 具有未知的动态的多代理系统 (MAS) 带来了重大控制挑战.
- 高斯过程 (GP) 回归提供了灵活的非线性函数建模和不确定性推理的预测误差界限.
- 在线学习通过在操作过程中整合新数据来增强GP模型预测.
研究的目的:
- 调查多代理系统 (MAS) 控制的在线合作学习算法.
- 开发事件触发的数据选择机制,以提高基于GP的MAS控制中的数据效率.
- 验证拟议的基于学习的控制策略的实际收和跟踪性能.
主要方法:
- 在线合作学习算法利用高斯过程 (GP) 回归.
- 事件触发数据选择机制,灵感来自于集中事件触发 (CET) 分析.
- 利亚普诺夫理论用于验证系统融合和跟踪性能保证.
主要成果:
- 拟议的在线学习算法确保了MAS的实际融合.
- 为多代理系统实现了保证的跟踪性能.
- 事件触发机制有效地减少了模型更新频率,并提高了数据效率.
- 对于单个代理商来说,Zeno的行为被证明是排除在外的.
结论:
- 开发的由事件触发的在线学习方法对具有未知动态的MAS的合作控制有效.
- 该方法通过高效的数据利用来提高预测准确性和控制性能.
- 通过利亚普诺夫稳定性分析提供了对趋同和跟踪性能的理论保证.
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