Consensus of Multi-Agent Systems under Unbounded Communication Delays Via Adaptive Distributed Observers
IEEE Transactions on Control of Network Systems(2025)
College of Artificial Intelligence | Department of Electrical Engineering | Department of Biomedical Engineering
Abstract
This paper considers the leader-following consensus problem of multi-agent systems subject to unbounded distributed communication delays under an condition that only the neighboring agents of the leader have access to the information on both the system matrix and the state of the leader. A novel adaptive distributed observer is proposed to estimate both the system matrix and the state of the leader under unbounded distributed communication delays, without requiring that the information of the unbounded delays is known a priori . A key technical result is firstly established and a novel distributed controller is then developed based on the proposed distributed observer. It is shown that the resulting closed-loop system achieves the desired consensus. Finally, the effectiveness of the theoretical results is validated by two simulation examples.
MoreTranslated text
Key words
Adaptive distributed observers,leader-following consensus,multi-agent systems,unbounded communication delays
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper