Fully Distributed Model-Free Flocking of Multiple Euler-Lagrange Systems

IF 3 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-06-26 DOI:10.1109/TSIPN.2024.3419437

Mingkang Long;Yin Chen;Housheng Su

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Abstract

In this paper, we investigate the leader-follower flocking issue of multiple Euler-Lagrange systems (MELSs) with time-varying input disturbances and completely unknown model parameter information under a proximity graph. Particularly, each follower can only access information from other agents that the relative distance between them is not greater than communication distance. Firstly, based on adaptive control theory, we propose a model-free leader-follower flocking algorithm with constant coupling gains, that is the controller design does not require any dynamic parameter information. Then, for fully distributed design (i.e. no requirement for any global information of the communication graph), edge-based adaptive coupling gains are applied for the above algorithm. The leader-follower flocking of MELSs can be achieved by all proposed algorithms under a connected and no-collision initial proximity graph. Finally, we show some simulation results to illustrate the effectiveness of all proposed flocking algorithms.

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多个欧拉-拉格朗日系统的全分布式无模型成群飞行

本文研究了具有时变输入干扰和完全未知模型参数信息的多欧拉-拉格朗日系统（MELS）在邻近图下的领导者-追随者成群问题。特别是，每个跟随者只能从相对距离不大于通信距离的其他代理获取信息。首先，基于自适应控制理论，我们提出了一种耦合增益恒定的无模型领导者-追随者成群算法，即控制器设计不需要任何动态参数信息。然后，为了实现全分布式设计（即不需要任何通信图的全局信息），上述算法采用了基于边的自适应耦合增益。在连通且无碰撞的初始邻近图下，所有提出的算法都能实现 MELS 的领导者-追随者成群。最后，我们展示了一些仿真结果，以说明所有建议的成群算法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Signal and Information Processing over Networks Computer Science-Computer Networks and Communications

CiteScore

5.80

自引率

12.50%

发文量

期刊介绍： The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.