Leader-Following Consensus of Multiple Uncertain Rigid Body Systems by a Sampled-Data Adaptive Distributed Observer

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Changran He, Jie Huang
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引用次数: 0

Abstract

In this paper, we study the leader-following attitude consensus problem for multiple uncertain rigid body systems by a sampled-data adaptive distributed observer. Unlike the existing sampled-data distributed observer, which can only asymptotically estimate the state of the leader, the sampled-data adaptive distributed observer can estimate both the state and the system matrix of the leader exponentially. We synthesize a distributed control law utilizing sampled-data communications to solve the leader-following attitude consensus problem for multiple uncertain rigid body systems based on the sampled-data adaptive distributed observer. Moreover, we present a sufficient condition for guaranteeing the convergence of the estimated unknown parameters to their true values. Compared with a distributed control law that uses continuous-time communications, the distributed control law utilizing sampled-data communications consumes fewer communication resources and is more robust to communication failures. The effectiveness of our approach is verified by a numerical example.

Abstract Image

基于采样数据自适应分布式观测器的多不确定刚体系统的领导-跟随一致性
本文利用采样数据自适应分布式观测器研究了多不确定刚体系统的领导-跟随姿态一致性问题。与现有的采样数据分布式观测器只能渐近估计leader的状态不同,采样数据自适应分布式观测器可以指数估计leader的状态和系统矩阵。基于采样数据自适应分布式观测器,综合了一种利用采样数据通信的分布式控制律,解决了多不确定刚体系统的leader- follower姿态一致性问题。并给出了估计的未知参数收敛于其真值的充分条件。与使用连续时间通信的分布式控制律相比,使用采样数据通信的分布式控制律消耗的通信资源更少,对通信故障的鲁棒性更强。通过一个算例验证了该方法的有效性。
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来源期刊
International Journal of Robust and Nonlinear Control
International Journal of Robust and Nonlinear Control 工程技术-工程:电子与电气
CiteScore
6.70
自引率
20.50%
发文量
505
审稿时长
2.7 months
期刊介绍: Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.
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