具有未知干扰的多个航天器系统的边缘触发式领导者-追随者共识

IF 3 3区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Dong Liang;Shimin Wang;Engang Tian
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引用次数: 0

摘要

多刚体可以模拟各种实际工业系统。然而,由于带宽有限,周期性采样数据通信会对网络造成负荷。本技术论文对一组刚体动力学的领导者-追随者姿态共识问题进行了研究。每个从动装置都会受到未知的外部干扰。为了减轻通信网络的负担,本文提出了一种具有动态触发机制的边缘触发非线性分布式观测器。所提出的观测器能够评估领导者系统的状态,而无需执行邻域信息的连续时间交换。所提出的基于边缘的触发机制是异步的,同时消除了芝诺现象。在非线性观测器的基础上,提出了一种分布式控制协议和自适应法则,以便在衰减未知外部干扰的同时实现领导者与跟随者的姿态一致。最后,我们提供了一个航天器系统集合的示例来验证我们方法的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Edge-Triggered Leader–Follower Consensus of Multiple Spacecraft Systems With Unknown Disturbances
Multiple rigid bodies can model various practical industrial systems. However, periodic sampled-data communication can have a load over the network subject to limited bandwidth. The research on the leader-follower attitude consensus issue for a group of rigid-body dynamics is conducted in this technical paper. The plant of each follower is subject to unknown external disturbances. To reduce the burden of the communication network, an edge-triggered nonlinear distributed observer with dynamic triggering mechanisms is presented. The proposed observer has the ability to evaluate the leader system's state regardless of implementing the continuous-time exchange of the neighborhood information. The proposed edge-based triggering mechanism is asynchronous while eliminating the Zeno phenomenon. Based on the nonlinear observer, a distributed control protocol together with an adaptive law is put forward in order to realize the leader-follower attitude consensus while attenuating the unknown external disturbances. In the end, an illustrative example of a collection of spacecraft systems is provided to verify the feasibility of our methods.
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来源期刊
IEEE Transactions on Signal and Information Processing over Networks
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
56
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.
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