@inproceedings{50960ced20c4420e87dc0732f882512c,
title = "Neural Network Feedforward Aided Composite Anti-disturbance Control for Hypersonic Morphing Vehicle",
abstract = "In addressing the attitude tracking control problem of the hypersonic morphing vehicle (HMV), a composite control method including feedforward and feedback control is proposed in this paper. Firstly, a feedback controller is established using the backstepping method and active disturbance rejection control (ADRC) scheme to overcome the external disturbances and model uncertainties. Next, a feedforward controller based on long short-term memory (LSTM) neural network is introduced to enhance the HMV{\textquoteright}s response speed, disturbance rejection capability and adaptability to the aerodynamic characteristics variations and uncertainties. Then, the feedback controller is redesigned to compensate the lumped disturbances including the errors generated by feedforward control. Finally, the stability of the proposed composite control method is proved through theoretical analysis and the effectiveness of the proposed method is validated by digital simulations.",
keywords = "Composite anti-disturbance control, Feedforward control, Hypersonic morphing vehicle, Neural network",
author = "Xingyu Wu and Honglun Wang and Yuebin Lun and Bin Ren",
note = "Publisher Copyright: {\textcopyright} 2025 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; International Conference on Guidance, Navigation and Control, ICGNC 2024 ; Conference date: 09-08-2024 Through 11-08-2024",
year = "2025",
doi = "10.1007/978-981-96-2244-3\_26",
language = "英语",
isbn = "9789819622436",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "264--273",
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 12",
address = "德国",
}