Projects per year
Abstract
In this paper, the possibility of detecting topological variations by observing output trajectories from networked linear time-invariant systems is investigated, where the network topology can be general, but the nodes have identical higher-dimensional dynamics. A necessary and sufficient condition on the discernibility of topological variations is derived, in terms of the eigenspaces of the original and the modified network configurations. By taking the specific network structures into consideration, some lower-dimensional conditions are derived, which reveal how the network topologies, sensor locations, node-system dynamics and output, as well as inner interactions altogether affect the discernibility. Furthermore, the output discernibility of topological changes for networked multi-agent systems is revisited, showing that some criterion reported in the literature does not hold. Consequently, a modified necessary and sufficient condition is established. The effectiveness of the results is demonstrated through several examples. © 2024 Elsevier Ltd
Original language | English |
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Article number | 111547 |
Journal | Automatica |
Volume | 163 |
Online published | 15 Feb 2024 |
DOIs | |
Publication status | Published - May 2024 |
Funding
This work is supported by the National Natural Science Foundation of China under Grants 12172020 , 11932003 , T2121002 , in part by the Beijing Natural Science Foundation under Grant 1222010 , in part by the Young Elite Scientists Sponsorship Program by CAST under Grant 2022 QNRC001, and in part by the Hong Kong Research Grants Council under the GRF Grant CityU 11206320 .
Research Keywords
- Discernibility
- Networked systems
- Output trajectory
- Topological variation
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Dive into the research topics of 'Discernibility of topological variations for networked LTI systems based on observed output trajectories'. Together they form a unique fingerprint.Projects
- 1 Finished
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GRF: Analyzing the Robustness of Network Controllability against Malicious Attacks
CHEN, G. (Principal Investigator / Project Coordinator) & TANG, K. S. W. (Co-Investigator)
1/01/21 → 28/05/24
Project: Research