| 基于人工智能负载估计系统的磁浮列车垂向振动主动控制 |
| |
| 引用本文： | 陈琛,徐俊起,倪菲,林国斌,吴晗. 基于人工智能负载估计系统的磁浮列车垂向振动主动控制[J]. 同济大学学报(自然科学版), 2020, 48(9): 1344-1352 |
| |
| 作者姓名： | 陈琛 徐俊起 倪菲 林国斌 吴晗 |
| |
| 作者单位： | 1.同济大学 道路与交通工程教育部重点实验室，上海 201804;2.同济大学 磁浮交通工程技术研究中心，上海 201804;3.同济大学 交通运输工程学院，上海 201804;4.中国科学院力学研究所 流固耦合系统力学重点实验室，北京 100109 |
| |
| 基金项目： | “十三五”国家重点研发计划（2016YFB1200602）；中国工程院重大咨询课题（2018-ZD-16） |
| |
| 摘 要： | 提出了基于人工智能负载估计系统的磁浮列车悬浮系统主动控制方法。给出单点悬浮数学模型,并基于劳斯-赫尔维兹判据证明该模型开环不稳定;考虑负载特征和实时悬浮变化,利用多层人工神经网络对悬浮系统控制量的输出进行主动控制;采用非支配排序遗传算法(NSGA)对系统参数进行优化。结果表明:所提出的控制方法具有较好的鲁棒性,在较大负载扰动时仍然能够保持相对较小的误差。
|
| 关 键 词： | 磁浮列车 悬浮系统非线性模型 人工神经网络 负载估计 主动控制 |
| 收稿时间： | 2020-02-24 |
Active Control of Vertical Vibration for Maglev Train Based on Artificial Intelligence Load Estimation System |
| |
| CHEN Chen,XU Junqi,NI Fei,LIN Guobin,WU Han. Active Control of Vertical Vibration for Maglev Train Based on Artificial Intelligence Load Estimation System[J]. Journal of Tongji University(Natural Science), 2020, 48(9): 1344-1352 |
| |
| Authors: | CHEN Chen XU Junqi NI Fei LIN Guobin WU Han |
| |
| Affiliation: | 1.Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China;2.Maglev Transportation Engineering R&D Center, Tongji University, Shanghai 201804, China;3.College of Transportation, Tongji University, Shanghai 201804, China;4.Key Laboratory for Mechanics in Fluid Solid Coupling Systems, Institute of Mechanics of Chinese Academy of Sciences, Beijing 100109, China |
| |
| Abstract: | An active control strategy of maglev train suspension system based on artificial intelligence load estimation system is proposed in this paper. Firstly, the mathematical model of single-point levitation is given, and the open-loop instability is proven by the Routh-Herwitz criterion. Secondly, considering the load characteristics and the real-time suspension changes, a multi-layer artificial neural network is constructed to control the output of the control variables for the suspension system. Thirdly, the non-dominated sorting genetic algorithm (NSGA) is used to optimize the system parameters. The results show that the proposed control method has better robustness and can still keep relatively small error under large load disturbance. |
| |
| Keywords: | maglev train nonlinear model of levitation system artificial neural network load estimation active control |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《同济大学学报(自然科学版)》浏览原始摘要信息 |
|
点击此处可从《同济大学学报(自然科学版)》下载全文 |
