未知纯反系统的分布式近似聚合优化与采样邻居信息
IEEE transactions on cybernetics
|September 19, 2025
概括
本研究将分布式聚合优化 (DAO) 扩展到高阶非线性系统. 新方法使用辅助变量和控制规律来管理网络中的复杂动态.
科学领域:
- 控制系统工程 控制系统工程
- 网络化系统 网络化系统
- 非线性动力学是一种非线性动力学.
背景情况:
- 分布式聚合优化 (DAO) 对网络系统至关重要.
- 将DAO扩展到具有未知动态的高阶非线性系统具有重大挑战.
研究的目的:
- 开发一种用于分布式聚合优化的新框架,用于高阶非线性系统,未知纯反动态.
- 解决有针对性和不平衡网络中的控制挑战.
主要方法:
- 引入辅助聚合变量来整合代理和邻居信息.
- 为渐进式变量更新开发一个平滑函数.
- 应用动态平均共识原则和一个关键定理,将DAO问题转化为监管问题.
- 使用规定的性能函数来设计控制规律,在有界干扰下进行近似优化.
主要成果:
- 成功地将DAO方法扩展到高阶非线性系统.
- 证明了DAO问题的转化为可解决的监管问题.
- 通过对具有未知的动态和干扰的代理物的数值示例验证了拟议的控制方案的有效性.
结论:
- 提出的方法有效地解决了高阶非线性系统的分布式聚合优化问题.
- 该方法为复杂的网络控制系统提供了可靠的解决方案,其动态和干扰是未知的.
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