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高级不确定的多代理系统在切换定向图的缩放位置共识
IEEE transactions on cybernetics
|October 12, 2023
概括
这项研究在复杂的多代理系统中实现了规模化的位置共识. 一个新的分布式自适应算法确保了代理人达到规模化的共识,尽管存在不确定性和切换网络拓.
科学领域:
- 控制理论 控制理论
- 网络化系统 网络化系统
- 机器人技术 机器人技术 机器人技术
背景情况:
- 在多代理系统中达成共识对于协调行为至关重要.
- 高级系统和定向图表带来了重要的控制挑战.
- 缩放位置共识,即代理商在缩放状态上达成一致,是不太探索的.
研究的目的:
- 开发一种分布式适应算法,用于在高阶多代理系统中进行缩放位置共识.
- 应对包括参数不确定性,切换定向图和有限的信息交换在内的挑战.
- 实现完全分布式控制,没有共享或全球收益.
主要方法:
- 用相对缩放的位置信息为每个代理设计一个高级参考模型.
- 建议转换以简化从高阶到第一阶系统的缩放位置共识.
- 基于MRACon方案开发一个分布式自适应算法.
主要成果:
- 理论分析证实了在切换定向图表上达成规模化的共识.
- 数字模拟验证了拟议的算法的有效性.
- 通过调整代理尺度来展示集体行为,如传统,双边和集群共识.
结论:
- 提出的分布式自适应算法有效地在复杂的多代理系统中实现了缩放的位置共识.
- 该方法对参数不确定性具有稳定性,并且可以在均连接的交换定向图上运行.
- 这种方法通过精确的尺度选择来促进多样化的集体行为.
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