@inproceedings{359ad6bd4415462c962849166e4e6304,
title = "Data-Driven Formation Control of Multiple Uncertain Hypersonic Vehicles",
abstract = "In this paper, the optimal formation control problem is addressed for multiple cooperative hypersonic vehicles (HVs) under parameter uncertainties. To provide the position references for HV formation leaders and followers in the presence of switching topologies, the distributed observers are developed. By employing the reinforcement learning theory, data-driven optimal formation control strategies are learned from the interaction between the vehicle system and environment. Furthermore, the obtained optimal strategies can be used to identify the unknown parameters of the HV dynamics in real time. Simulation results of a group of HV is offered to confirm the efficacy of the proposed optimal controller.",
keywords = "formation control, hypersonic vehicle, nonlinear system, optimal control, reinforcement learning",
author = "Ming Cheng and Hao Liu and Qing Gao and Haibin Duan",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; International Conference on Guidance, Navigation and Control, ICGNC 2024 ; Conference date: 09-08-2024 Through 11-08-2024",
year = "2025",
doi = "10.1007/978-981-96-2216-0\_2",
language = "英语",
isbn = "9789819622153",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "11--20",
editor = "Liang Yan and Haibin Duan and Yimin Deng",
booktitle = "Advances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control Volume 5",
address = "德国",
}