@inproceedings{68fe19a8b4e24ac3a4acde4e316359bf,
title = "An Optimal Strategy for Multi-QUAVs Formation Tracking Control Based on Neural Network and Integral Sliding Mode",
abstract = "This paper studies the optimal formation tracking control problem for the position loop of multiple quadrotor unmanned aerial vehicles (multi-QUAVs) based on neural network and integral sliding mode method. Firstly, a neural network is applied to approximate uncertainties and external disturbances, and an integral sliding mode controller is designed to compensate for the impact of them on the system. Then, the robust optimal tracking control problem of original system is converted into the optimal control problem of a nominal system. An adaptive dynamic programming framework based on a single critic network is proposed to obtain the optimal cost function, and the optimal controller law is calculated based on the optimal cost function. The effectiveness of the proposed method is ultimately verified through numerical simulation.",
keywords = "integral sliding mode control, neural network, optimal formation tracking control",
author = "Yueming Bai and Yang Liu and Ming Shang",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.; 21st Chinese Intelligent Systems Conference, CISC 2025 ; Conference date: 25-10-2025 Through 26-10-2025",
year = "2026",
doi = "10.1007/978-981-95-6553-5\_54",
language = "英语",
isbn = "9789819565528",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "604--612",
editor = "Yingmin Jia and Yang Liu and Weicun Zhang and Yongling Fu",
booktitle = "Proceedings of 2025 Chinese Intelligent Systems Conference",
address = "德国",
}