Model predictive control based on LSTM neural network for maglev vehicle' suspension system
文献类型：期刊论文
| 作者 | Liu MJ(刘梦娟)2,3,4; Wu H(吴晗)4; Liang, Xin1,3; Liu, Jiali1,3; Ceng XH(曾晓辉)4; Hu KX(胡凯轩)2,4 |
| 刊名 | ACTA MECHANICA SINICA
 |
| 出版日期 | 2025-08-25 |
| 卷号 | 42期号:5页码:14 |
| 关键词 | Maglev vehicle Suspension system Model predictive control Long short-term memory neural network |
| ISSN号 | 0567-7718 |
| DOI | 10.1007/s10409-025-24572-x |
| 通讯作者 | Wu, Han(wuhan@imech.ac.cn) |
| 英文摘要 | To improve the suspension performance of high-speed maglev vehicles under complex external disturbance, a composite model predictive control (MPC) algorithm based on a neural network is proposed. Firstly, the nonlinear dynamic response prediction model is constructed utilizing the long short-term memory (LSTM) neural network, and this model is trained by machine learning. Subsequently, a rolling optimization controller of the MPC algorithm is designed according to the vehicle suspension system's prediction model and the suspension target. To compensate for the error of the prediction model resulting from changes in the control algorithm, a composite MPC algorithm is devised by combining both the proportional-integral-derivative (PID) algorithm and the MPC algorithm. This composite approach enables the suspension system to switch the selection of control algorithms in the suspension system according to the prediction error. Finally, the effectiveness of the composite MPC algorithm is verified by simulation and experiment. The results show that the prediction model based on the LSTM neural network can effectively predict the future dynamic response of the vehicle. Moreover, the proposed MPC algorithm can effectively suppress the suspension gap fluctuation in the high-speed maglev vehicle, thereby fostering improved stability in the suspension system. |
| 分类号 | 一类 |
| 资助项目 | State Key Laboratory of High-speed Maglev Transportation Technology[SKLM-SFCF-2023-001] ; CAS Project for the Young Scientists in Basic Research[YSBR-045] ; Original Technology Ten-year Cultivation Special Project of CRRC[2023CGY004-1] ; National Natural Science Foundation of China[12372051] |
| WOS研究方向 | Engineering ; Mechanics |
| 语种 | 英语 |
| WOS记录号 | WOS:001560789200001 |
| 资助机构 | State Key Laboratory of High-speed Maglev Transportation Technology ; CAS Project for the Young Scientists in Basic Research ; Original Technology Ten-year Cultivation Special Project of CRRC ; National Natural Science Foundation of China |
| 其他责任者 | 吴晗 |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/103650]  |
| 专题 | 力学研究所_流固耦合系统力学重点实验室(2012-) |
| 作者单位 | 1.CRRC, Qingdao Sifang Co Ltd, Qingdao 266111, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China; 3.CRRC, State Key Lab High Speed Maglev Transportat Techno, Qingdao 266111, Peoples R China; 4.Chinese Acad Sci, Inst Mech, Key Lab Mech Fluid Solid Coupling Syst, Beijing 100190, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Liu MJ,Wu H,Liang, Xin,et al. Model predictive control based on LSTM neural network for maglev vehicle' suspension system[J]. ACTA MECHANICA SINICA,2025,42(5):14. |
| APA | 刘梦娟,吴晗,Liang, Xin,Liu, Jiali,曾晓辉,&胡凯轩.(2025).Model predictive control based on LSTM neural network for maglev vehicle' suspension system.ACTA MECHANICA SINICA,42(5),14. |
| MLA | 刘梦娟,et al."Model predictive control based on LSTM neural network for maglev vehicle' suspension system".ACTA MECHANICA SINICA 42.5(2025):14. |
入库方式： OAI收割
来源：力学研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。