Prescribed‐time distributed direct estimation under relative state measurements

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS International Journal of Robust and Nonlinear Control Pub Date : 2024-09-18 DOI:10.1002/rnc.7644
Jin Ke, Ying Li, Tao Xie
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Abstract

The distributed estimation technology is prevalently utilized to solve the leader‐following multi‐agent tracking problem. This technology poses a challenge in practice, since it generally relies on the available absolute state measurements. For this reason, a novel distributed estimation approach based on relative state measurements is developed in this article. The proposed method directly estimates the tracking error between the leader and each follower, rather than using an existing indirect way of estimating and making subtraction under absolute state measurements. Specifically, a distributed directed estimation is first studied to complete estimation tasks within prescribed time under the known directed networks. Then, a fully distributed directed estimation problem is considered under the unknown directed networks. Both distributed and fully distributed results are extended to the robustness cases to resist external disturbances. Simulation examples, including numerical examples and a multiship coordination example, are provided to demonstrate the effectiveness and advantages of the proposed distributed estimation method.
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相对状态测量下的规定时间分布式直接估计
分布式估算技术被广泛用于解决领导者-跟随者多代理跟踪问题。由于该技术通常依赖于可用的绝对状态测量值,因此在实际应用中面临着挑战。因此,本文提出了一种基于相对状态测量的新型分布式估计方法。所提出的方法直接估算领导者和每个跟随者之间的跟踪误差,而不是使用现有的间接估算方式,并在绝对状态测量下做减法。具体来说,首先研究分布式有向估计,以在已知有向网络下的规定时间内完成估计任务。然后,考虑未知有向网络下的全分布式有向估计问题。分布式和全分布式结果都扩展到了鲁棒性情况,以抵御外部干扰。还提供了包括数值示例和多船协调示例在内的模拟示例,以证明所提出的分布式估计方法的有效性和优势。
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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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