High-speed Railway最新文献_第4页

High-speed Railway Pub Date : 2024-06-01 DOI: 10.1016/j.hspr.2024.05.003

Jiaxu Guo , Ding Ding , Peihan Yang , Qi Zou , Yaping Huang

{"title":"A related degree-based frequent pattern mining algorithm for railway fault data","authors":"Jiaxu Guo , Ding Ding , Peihan Yang , Qi Zou , Yaping Huang","doi":"10.1016/j.hspr.2024.05.003","DOIUrl":"10.1016/j.hspr.2024.05.003","url":null,"abstract":"<div><p>It is of great significance to improve the efficiency of railway production and operation by realizing the fault knowledge association through the efficient data mining algorithm. However, high utility quantitative frequent pattern mining algorithms in the field of data mining still suffer from the problems of low time-memory performance and are not easy to scale up. In the context of such needs, we propose a related degree-based frequent pattern mining algorithm, named Related High Utility Quantitative Item set Mining (RHUQI-Miner), to enable the effective mining of railway fault data. The algorithm constructs the item-related degree structure of fault data and gives a pruning optimization strategy to find frequent patterns with higher related degrees, reducing redundancy and invalid frequent patterns. Subsequently, it uses the fixed pattern length strategy to modify the utility information of the item in the mining process so that the algorithm can control the length of the output frequent pattern according to the actual data situation and further improve the performance and practicability of the algorithm. The experimental results on the real fault dataset show that RHUQI-Miner can effectively reduce the time and memory consumption in the mining process, thus providing data support for differentiated and precise maintenance strategies.</p></div>","PeriodicalId":100607,"journal":{"name":"High-speed Railway","volume":"2 2","pages":"Pages 101-109"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949867824000333/pdfft?md5=7ddc6c2c1df15b6be817951e15c67c9e&pid=1-s2.0-S2949867824000333-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141031330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An improved YOLOv7 for the state identification of sliding chairs in railway turnout 用于铁路道岔滑动椅状态识别的改进型 YOLOv7

High-speed Railway Pub Date : 2024-06-01 DOI: 10.1016/j.hspr.2024.04.002

Yuan Cao , Zongbao Liu , Feng Wang , Shuai Su , Yongkui Sun , Wenkun Wang

{"title":"An improved YOLOv7 for the state identification of sliding chairs in railway turnout","authors":"Yuan Cao , Zongbao Liu , Feng Wang , Shuai Su , Yongkui Sun , Wenkun Wang","doi":"10.1016/j.hspr.2024.04.002","DOIUrl":"10.1016/j.hspr.2024.04.002","url":null,"abstract":"<div><p>The sliding chairs are important components that support the switch rail conversion in the railway turnout. Due to the harsh environmental erosion and the attack from the wheel vibration, the failure rate of the sliding chairs accounts for up to 10% of the total failure number in turnout. However, there is little research carried out in the existing literature to diagnose the deterioration states of the sliding chairs. To fill out this gap, by utilizing the images containing the sliding chairs, we propose an improved You Only Look Once version 7 (YOLOv7) to identify the state of the sliding chairs. Specifically, to meet the challenge brought by the small inter-class differences among the sliding chair states, we first integrate the Convolutional Block Attention Module (CBAM) into the YOLOv7 backbone to screen the information conducive to state identification. Then, an extra detector for a small object is customized into the YOLOv7 network in order to detect the small-scale sliding chairs in images. Meanwhile, we revise the localization loss in the objective function as the Efficient Intersection over Union (EIoU) to optimize the design of the aspect ratio, which helps the localization of the sliding chairs. Next, to address the issue caused by the varying scales of the sliding chairs, we employ K-means++ to optimize the priori selection of the initial anchor boxes. Finally, based on the images collected from real-world turnouts, the proposed method is verified and the results show that our method outperforms the basic YOLOv7 in the state identification of the sliding chairs with 4% improvements in terms of both mean Average [email protected] ([email protected]) and F1-score.</p></div>","PeriodicalId":100607,"journal":{"name":"High-speed Railway","volume":"2 2","pages":"Pages 71-76"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S294986782400028X/pdfft?md5=c34de2ade9026bad6418a20d3cc740e0&pid=1-s2.0-S294986782400028X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140779833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bearing fault diagnosis based on a multiple-constraint modal-invariant graph convolutional fusion network 基于多约束模态不变图卷积融合网络的轴承故障诊断

High-speed Railway Pub Date : 2024-06-01 DOI: 10.1016/j.hspr.2024.04.003

Zhongmei Wang , Pengxuan Nie , Jianhua Liu , Jing He , Haibo Wu , Pengfei Guo

{"title":"Bearing fault diagnosis based on a multiple-constraint modal-invariant graph convolutional fusion network","authors":"Zhongmei Wang , Pengxuan Nie , Jianhua Liu , Jing He , Haibo Wu , Pengfei Guo","doi":"10.1016/j.hspr.2024.04.003","DOIUrl":"10.1016/j.hspr.2024.04.003","url":null,"abstract":"<div><p>Multisensor data fusion method can improve the accuracy of bearing fault diagnosis, in order to address the problems of single-sensor data types and the insufficient exploration of redundancy and complementarity between different modal data in most existing multisensor data fusion methods for bearing fault diagnosis, a bearing fault diagnosis method based on a Multiple-Constraint Modal-Invariant Graph Convolutional Fusion Network (MCMI-GCFN) is proposed in this paper. Firstly, a Convolutional Autoencoder (CAE) and Squeeze-and-Excitation Block (SE block) are used to extract features of raw current and vibration signals. Secondly, the model introduces source domain classifiers and domain discriminators to capture modal invariance between different modal data based on domain adversarial training, making use of the redundancy and complementarity between multimodal data. Then, the spatial aggregation property of Graph Convolutional Neural Networks (GCN) is utilized to capture the dependency relationship between current and vibration modes with similar time step features for accurately fusing contextual semantic information. Finally, the validation is conducted on the public bearing damage current and vibration dataset from Paderborn University. The experimental results showed that the delivered fusion method achieved a bearing fault diagnosis accuracy of 99.6 %, which was about 9 %–11.4 % better than that with nonfusion methods.</p></div>","PeriodicalId":100607,"journal":{"name":"High-speed Railway","volume":"2 2","pages":"Pages 92-100"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949867824000291/pdfft?md5=77c0cb7bf500e84117361557c994ede3&pid=1-s2.0-S2949867824000291-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140768214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Train wheel-rail force collaborative calibration based on GNN-LSTM 基于 GNN-LSTM 的列车轮轨力协同校准

High-speed Railway Pub Date : 2024-06-01 DOI: 10.1016/j.hspr.2024.05.002

Changfan Zhang , Zihao Yu , Lin Jia

{"title":"Train wheel-rail force collaborative calibration based on GNN-LSTM","authors":"Changfan Zhang , Zihao Yu , Lin Jia","doi":"10.1016/j.hspr.2024.05.002","DOIUrl":"10.1016/j.hspr.2024.05.002","url":null,"abstract":"<div><p>Accurate wheel-rail force data serves as the cornerstone for analyzing the wheel-rail relationship. However, achieving continuous and precise measurement of this force remains a significant challenge in the field. This article introduces a calibration algorithm for the wheel-rail force that leverages graph neural networks and long short-term memory networks. Initially, a comprehensive wheel-rail force detection system for trains was constructed, encompassing two key components: an instrumented wheelset and a ground wheel-rail force measuring system. Subsequently, utilizing this system, two distinct datasets were acquired from the track inspection vehicle: instrumented wheelset data and ground wheel-rail force data, a feedforward neural network was employed to calibrate the instrumented wheelset data, referencing the ground wheel-rail force data. Furthermore, ground wheel-rail force data for the locomotive was obtained for the corresponding road section. This data was then integrated with the calibrated instrumented wheelset data from the track inspection vehicle. Leveraging the GNN-LSTM network, the article establishes a mapping relationship model between the wheel-rail force of the track inspection vehicle and the locomotive wheel-rail force. This model facilitates continuous measurement of locomotive wheel-rail forces across three typical scenarios: straight sections, long and steep downhill sections, and small curve radius sections.</p></div>","PeriodicalId":100607,"journal":{"name":"High-speed Railway","volume":"2 2","pages":"Pages 85-91"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949867824000321/pdfft?md5=22fe4eda6bf192d5a6a4dc4d24ca2848&pid=1-s2.0-S2949867824000321-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141027047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Determining future high speed rail review topics through bibliometric analysis 通过文献计量分析确定未来高速铁路审查主题

High-speed Railway Pub Date : 2024-03-01 DOI: 10.1016/j.hspr.2024.01.005

Heather Steele, Marcelo Blumenfeld, Paul Plummer

{"title":"Determining future high speed rail review topics through bibliometric analysis","authors":"Heather Steele, Marcelo Blumenfeld, Paul Plummer","doi":"10.1016/j.hspr.2024.01.005","DOIUrl":"10.1016/j.hspr.2024.01.005","url":null,"abstract":"<div><p>As High Speed Rail (HSR) has proliferated globally, so has a related research field dedicated to exploring and addressing its unique issues. Yet, studies to understand and classify the HSR research domain are limited. This paper addresses the gap, using bibliometric analysis to identify future research areas and 20 candidate topics for literature review based on keyword analysis through VOSviewer. Article and review papers related to HSR published in the last 20 years (2003–2022) were retrieved from Scopus, and then analyzed to determine the split in knowledge between languages, the collaboration between countries and institutions, highly productive and cited journals, and research topics which have and have not been reviewed. Approximately 30% of the search results were published exclusively in Chinese, highlighting the importance of extending the evaluation to cover both languages. This is a novel aspect of the work, which has enabled the recognition of potential knowledge gaps. It is recommended that future reviews incorporate works in both languages, possibly through international collaboration. Institutions in China and other countries that are strong collaborators have been identified, as well as relevant, highly cited journals.</p></div>","PeriodicalId":100607,"journal":{"name":"High-speed Railway","volume":"2 1","pages":"Pages 17-29"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949867824000114/pdfft?md5=f6e42f3f0afc55ccfa32f97930b65177&pid=1-s2.0-S2949867824000114-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139829971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A review of artiﬁcial intelligence applications in high-speed railway systems 高速铁路系统中的人工智能应用综述

High-speed Railway Pub Date : 2024-03-01 DOI: 10.1016/j.hspr.2024.01.002

Xuehan Li , Minghao Zhu , Boyang Zhang , Xiaoxuan Wang , Zha Liu , Liang Han

{"title":"A review of artiﬁcial intelligence applications in high-speed railway systems","authors":"Xuehan Li , Minghao Zhu , Boyang Zhang , Xiaoxuan Wang , Zha Liu , Liang Han","doi":"10.1016/j.hspr.2024.01.002","DOIUrl":"https://doi.org/10.1016/j.hspr.2024.01.002","url":null,"abstract":"<div><p>In recent years, the global surge of High-speed Railway (HSR) revolutionized ground transportation, providing secure, comfortable, and punctual services. The next-gen HSR, fueled by emerging services like video surveillance, emergency communication, and real-time scheduling, demands advanced capabilities in real-time perception, automated driving, and digitized services, which accelerate the integration and application of Artiﬁcial Intelligence (AI) in the HSR system. This paper ﬁrst provides a brief overview of AI, covering its origin, evolution, and breakthrough applications. A comprehensive review is then given regarding the most advanced AI technologies and applications in three macro application domains of the HSR system: mechanical manufacturing and electrical control, communication and signal control, and transportation management. The literature is categorized and compared across nine application directions labeled as intelligent manufacturing of trains and key components, forecast of railroad maintenance, optimization of energy consumption in railroads and trains, communication security, communication dependability, channel modeling and estimation, passenger scheduling, traﬃc ﬂow forecasting, high-speed railway smart platform. Finally, challenges associated with the application of AI are discussed, offering insights for future research directions.</p></div>","PeriodicalId":100607,"journal":{"name":"High-speed Railway","volume":"2 1","pages":"Pages 11-16"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949867824000023/pdfft?md5=0ef443bd7ceb0b69fa7757a81c6c6a33&pid=1-s2.0-S2949867824000023-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140187272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Influence of span-to-depth ratio on dynamic response of vehicle-turnout-bridge system in high-speed railway 跨深比对高速铁路车辆-道岔-桥梁系统动态响应的影响

High-speed Railway Pub Date : 2024-03-01 DOI: 10.1016/j.hspr.2024.02.002

Chuanqing Dai , Tao Xin , Shenlu Qiao , Yanan Zhang , Pengsong Wang , Mahantesh M. Nadakatti

{"title":"Influence of span-to-depth ratio on dynamic response of vehicle-turnout-bridge system in high-speed railway","authors":"Chuanqing Dai , Tao Xin , Shenlu Qiao , Yanan Zhang , Pengsong Wang , Mahantesh M. Nadakatti","doi":"10.1016/j.hspr.2024.02.002","DOIUrl":"10.1016/j.hspr.2024.02.002","url":null,"abstract":"<div><p>For high-speed railways, the smoothness of the railway line significantly affects the operational speed of trains. When the train passes through the turnout on a long-span bridge, the wheel-rail impacts caused by the turnout structure irregularities, and the instability arising from the bridge's flexural deformation lead to a strong coupling effect in the vehicle-turnout-bridge system. This significantly affects both ride comfort and operational safety. For addressing this issue, the present study considered a long-span continuous rigid-frame bridge as an example and established a train-turnout-bridge coupled dynamic model of high-speed railway. Utilizing a self-developed dynamic simulation program, the study analysed the dynamic response characteristics when the train passes through the turnouts on the bridge. It also investigated the influence of different span-to-depth ratios of the bridge on the vehicle dynamic response when the train passes through the main line and branch line of turnouts and then proposed a span-to-depth ratio limit value for a long-span continuous rigid-frame bridge. The research findings suggest that the changes in the span-to-depth ratio have a relatively minor impact on the train’s operational performance but significantly affect the dynamic characteristics of the bridge structure. Based on the findings and a comprehensive assessment of safety indicators, it is advisable to establish a span-to-depth ratio limit of 1/4500 for a long-span continuous rigid-frame bridge.</p></div>","PeriodicalId":100607,"journal":{"name":"High-speed Railway","volume":"2 1","pages":"Pages 30-41"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949867824000126/pdfft?md5=73a24e63746c889095a3e5b568b432c7&pid=1-s2.0-S2949867824000126-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139887243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-speed railway track components inspection framework based on YOLOv8 with high-performance model deployment 基于 YOLOv8 的高速铁路轨道部件检测框架与高性能模型部署

High-speed Railway Pub Date : 2024-03-01 DOI: 10.1016/j.hspr.2024.02.001

Youzhi Tang, Yu Qian

{"title":"High-speed railway track components inspection framework based on YOLOv8 with high-performance model deployment","authors":"Youzhi Tang, Yu Qian","doi":"10.1016/j.hspr.2024.02.001","DOIUrl":"10.1016/j.hspr.2024.02.001","url":null,"abstract":"<div><p>Railway inspection poses significant challenges due to the extensive use of various components in vast railway networks, especially in the case of high-speed railways. These networks demand high maintenance but offer only limited inspection windows. In response, this study focuses on developing a high-performance rail inspection system tailored for high-speed railways and railroads with constrained inspection timeframes. This system leverages the latest artificial intelligence advancements, incorporating YOLOv8 for detection. Our research introduces an efficient model inference pipeline based on a producer-consumer model, effectively utilizing parallel processing and concurrent computing to enhance performance. The deployment of this pipeline, implemented using C++, TensorRT, float16 quantization, and oneTBB, represents a significant departure from traditional sequential processing methods. The results are remarkable, showcasing a substantial increase in processing speed: from 38.93 Frames Per Second (FPS) to 281.06 FPS on a desktop system equipped with an Nvidia RTX A6000 GPU and from 19.50 FPS to 200.26 FPS on the Nvidia Jetson AGX Orin edge computing platform. This proposed framework has the potential to meet the real-time inspection requirements of high-speed railways.</p></div>","PeriodicalId":100607,"journal":{"name":"High-speed Railway","volume":"2 1","pages":"Pages 42-50"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949867824000102/pdfft?md5=b5d63f0780710b9790174134eb70af22&pid=1-s2.0-S2949867824000102-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139826433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A study on temperature monitoring method for inverter IGBT based on memory recurrent neural network 基于记忆递归神经网络的逆变器 IGBT 温度监控方法研究

High-speed Railway Pub Date : 2024-03-01 DOI: 10.1016/j.hspr.2024.02.003

Yunhe Liu, Tengfei Guo, Jinda Li, Chunxing Pei, Jianqiang Liu

{"title":"A study on temperature monitoring method for inverter IGBT based on memory recurrent neural network","authors":"Yunhe Liu, Tengfei Guo, Jinda Li, Chunxing Pei, Jianqiang Liu","doi":"10.1016/j.hspr.2024.02.003","DOIUrl":"10.1016/j.hspr.2024.02.003","url":null,"abstract":"<div><p>The power module of the Insulated Gate Bipolar Transistor (IGBT) is the core component of the traction transmission system of high-speed trains. The module's junction temperature is a critical factor in determining device reliability. Existing temperature monitoring methods based on the electro-thermal coupling model have limitations, such as ignoring device interactions and high computational complexity. To address these issues, an analysis of the parameters influencing IGBT failure is conducted, and a temperature monitoring method based on the Macro-Micro Attention Long Short-Term Memory (MMALSTM) recursive neural network is proposed, which takes the forward voltage drop and collector current as features. Compared with the traditional electrical-thermal coupling model method, it requires fewer monitoring parameters and eliminates the complex loss calculation and equivalent thermal resistance network establishment process. The simulation model of a high-speed train traction system has been established to explore the accuracy and efficiency of MMALSTM-based prediction methods for IGBT power module junction temperature. The simulation outcomes, which deviate only 3.2% from the theoretical calculation results of the electric-thermal coupling model, confirm the reliability of this approach for predicting the temperature of IGBT power modules.</p></div>","PeriodicalId":100607,"journal":{"name":"High-speed Railway","volume":"2 1","pages":"Pages 64-70"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949867824000138/pdfft?md5=087cca3eb0d18193c47f24bb07cf80af&pid=1-s2.0-S2949867824000138-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139892851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Disturbance rejection tube model predictive levitation control of maglev trains 磁悬浮列车的干扰抑制管模型预测悬浮控制

High-speed Railway Pub Date : 2024-03-01 DOI: 10.1016/j.hspr.2024.01.001

Yirui Han, Xiuming Yao, Yu Yang

{"title":"Disturbance rejection tube model predictive levitation control of maglev trains","authors":"Yirui Han, Xiuming Yao, Yu Yang","doi":"10.1016/j.hspr.2024.01.001","DOIUrl":"https://doi.org/10.1016/j.hspr.2024.01.001","url":null,"abstract":"<div><p>Magnetic levitation control technology plays a significant role in maglev trains. Designing a controller for the levitation system is challenging due to the strong nonlinearity, open-loop instability, and the need for fast response and security. In this paper, we propose a Disturbance-Observe-based Tube Model Predictive Levitation Control (DO-TMPLC) scheme combined with a feedback linearization strategy for the levitation system. The proposed strategy incorporates state constraints and control input constraints, i.e., the air gap, the vertical velocity, and the current applied to the coil. A feedback linearization strategy is used to cancel the nonlinearity of the tracking error system. Then, a disturbance observer is implemented to actively compensate for disturbances while a TMPLC controller is employed to alleviate the remaining disturbances. Furthermore, we analyze the recursive feasibility and input-to-state stability of the closed-loop system. The simulation results indicate the efficacy of the proposed control strategy.</p></div>","PeriodicalId":100607,"journal":{"name":"High-speed Railway","volume":"2 1","pages":"Pages 57-63"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949867824000011/pdfft?md5=0e6dbc9b12b2460cef9f8a1d1ede1187&pid=1-s2.0-S2949867824000011-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140187274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0