@inproceedings{2b4a405590134c4bbdd054c9a55dee84,
title = "DEEP LEARNING ASSISTED SEMI-ACTIVE CONTROL OF MAGNETORHEOLOGICAL BUFFER LANDING GEAR",
abstract = "The load alleviation during aircraft landing is an important challenge in the field of aeronautical engineering, and the dynamic characteristics design of landing gear buffers is the key to solving this problem. The damping parameters of traditional landing gear systems are usually fixed, making it difficult to cope with complex landing load conditions. In response to this, this article adopts a damping adjustable magnetorheological (MR) buffer and proposes a convolutional neural network (CNN) control strategy, which can obtain the optimal current under different landing conditions and conduct landing response simulation. The simulation results show that the convolutional neural network method proposed in this paper can significantly reduce the peak landing load for any landing condition, and has good landing load alleviation ability.",
keywords = "convolutional neural network, landing gear with magnetorheological buffer, landing response simulation, Semi-active control",
author = "Pengwei Jiang and Le Wang and Te Yang and Zhichun Yang",
note = "Publisher Copyright: {\textcopyright} 2024 Proceedings of the International Congress on Sound and Vibration. All rights reserved.; 30th International Congress on Sound and Vibration, ICSV 2024 ; Conference date: 08-07-2024 Through 11-07-2024",
year = "2024",
language = "英语",
series = "Proceedings of the International Congress on Sound and Vibration",
publisher = "Society of Acoustics",
editor = "\{van Keulen\}, Wim and Jim Kok",
booktitle = "Proceedings of the 30th International Congress on Sound and Vibration, ICSV 2024",
}