@inproceedings{3f53569ea18e41ceb26239147232f06d,
title = "Distributed State Estimation for Multi-agent Systems Under Consensus Control",
abstract = "Distributed state estimation and consensus control for linear time-invariant multi-agent systems under strongly connected directed graph are addressed in this paper. The distributed output tracking algorithm and the local state estimator are designed for each agent to estimate the output and state of the entire multi-agent system, despite having access only to local output measurements that are insufficient to directly reconstruct the entire state. The consensus control protocol is further designed based on each agent{\textquoteright}s own entire state estimation. Neither distributed state estimation nor consensus control protocol design requires state information from neighboring agents, eliminating the transmission of the values of state estimations during the whole process. The theoretical analysis demonstrates that the realization of distributed output tracking and state estimation. Moreover, all agents achieve consensus. Finally, numerical simulations are worked out to show the effectiveness of the proposed algorithm.",
keywords = "Consensus control, Distributed output tracking, Distributed state estimation, Multi-agent systems",
author = "Yan Li and Jiazhu Huang and Yuezu Lv and Jialing Zhou",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; 30th International Conference on Neural Information Processing, ICONIP 2023 ; Conference date: 20-11-2023 Through 23-11-2023",
year = "2024",
doi = "10.1007/978-981-99-8079-6_17",
language = "English",
isbn = "9789819980789",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "214--225",
editor = "Biao Luo and Long Cheng and Zheng-Guang Wu and Hongyi Li and Chaojie Li",
booktitle = "Neural Information Processing - 30th International Conference, ICONIP 2023, Proceedings",
address = "Germany",
}