Multimode Intelligent Control Based on Multidata Fusion Filtering in High-Speed Train Traffic Signal and Control

As the speed of high-speed trains continues to increase, the intelligent monitoring of high-speed trains has become a concern of people. This research mainly discusses the application of multimode intelligent control of multidata fusion filtering in high-speed train traffic signal and control. In mu...

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Main Authors: Bin Huang, Ying Huang
Format: Article
Language:English
Published: Hindawi Limited 2021-01-01
Series:Journal of Sensors
Online Access:http://dx.doi.org/10.1155/2021/6081999
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spelling doaj-ae052fe6d387464f8883469e350809b22021-06-14T00:17:45ZengHindawi LimitedJournal of Sensors1687-72682021-01-01202110.1155/2021/6081999Multimode Intelligent Control Based on Multidata Fusion Filtering in High-Speed Train Traffic Signal and ControlBin Huang0Ying Huang1School of Automatic ControlSchool of Automatic ControlAs the speed of high-speed trains continues to increase, the intelligent monitoring of high-speed trains has become a concern of people. This research mainly discusses the application of multimode intelligent control of multidata fusion filtering in high-speed train traffic signal and control. In multimodal intelligent control, BangBang, PI control, adaptive fuzzy PID control, and expert monitoring control under special circumstances can be used, respectively, according to the error and the rate of change of the error, which can achieve the best control effect under safe conditions. Take the allowable speed of ATP as the target speed of the control system, and combine the operation process, operation requirements, traction characteristics, braking characteristics of high-speed trains, and meet the two conditions for improving the operating efficiency of high-speed trains. According to the dynamic expected speed value of high-speed trains, dynamically adjust the switching threshold. This study uses a pulse signal generator to simulate the speed data of the vehicle speed sensor (all pulse data), and then read the speed (pulse) signal data through the pulse signal acquisition card, and display the simulated speed data under the Kingview software. The monitoring computer is used to collect train speed information, display speed information, manage speed information, and output speed information. Then, through OPC technology, the simulation speed data is transmitted to MATLAB software for multidata fusion filtering processing and multimodal control simulation. In the simulation process, the train adopts a multimodal intelligent control response scheme, with a total time of 2183.7 s, which is shortened by 214.5 s and improved by nearly 10%. The multimode intelligent control scheme of multidata fusion filtering proposed in this study can better meet the control of high-speed train traffic signals.http://dx.doi.org/10.1155/2021/6081999
collection DOAJ
language English
format Article
sources DOAJ
author Bin Huang
Ying Huang
spellingShingle Bin Huang
Ying Huang
Multimode Intelligent Control Based on Multidata Fusion Filtering in High-Speed Train Traffic Signal and Control
Journal of Sensors
author_facet Bin Huang
Ying Huang
author_sort Bin Huang
title Multimode Intelligent Control Based on Multidata Fusion Filtering in High-Speed Train Traffic Signal and Control
title_short Multimode Intelligent Control Based on Multidata Fusion Filtering in High-Speed Train Traffic Signal and Control
title_full Multimode Intelligent Control Based on Multidata Fusion Filtering in High-Speed Train Traffic Signal and Control
title_fullStr Multimode Intelligent Control Based on Multidata Fusion Filtering in High-Speed Train Traffic Signal and Control
title_full_unstemmed Multimode Intelligent Control Based on Multidata Fusion Filtering in High-Speed Train Traffic Signal and Control
title_sort multimode intelligent control based on multidata fusion filtering in high-speed train traffic signal and control
publisher Hindawi Limited
series Journal of Sensors
issn 1687-7268
publishDate 2021-01-01
description As the speed of high-speed trains continues to increase, the intelligent monitoring of high-speed trains has become a concern of people. This research mainly discusses the application of multimode intelligent control of multidata fusion filtering in high-speed train traffic signal and control. In multimodal intelligent control, BangBang, PI control, adaptive fuzzy PID control, and expert monitoring control under special circumstances can be used, respectively, according to the error and the rate of change of the error, which can achieve the best control effect under safe conditions. Take the allowable speed of ATP as the target speed of the control system, and combine the operation process, operation requirements, traction characteristics, braking characteristics of high-speed trains, and meet the two conditions for improving the operating efficiency of high-speed trains. According to the dynamic expected speed value of high-speed trains, dynamically adjust the switching threshold. This study uses a pulse signal generator to simulate the speed data of the vehicle speed sensor (all pulse data), and then read the speed (pulse) signal data through the pulse signal acquisition card, and display the simulated speed data under the Kingview software. The monitoring computer is used to collect train speed information, display speed information, manage speed information, and output speed information. Then, through OPC technology, the simulation speed data is transmitted to MATLAB software for multidata fusion filtering processing and multimodal control simulation. In the simulation process, the train adopts a multimodal intelligent control response scheme, with a total time of 2183.7 s, which is shortened by 214.5 s and improved by nearly 10%. The multimode intelligent control scheme of multidata fusion filtering proposed in this study can better meet the control of high-speed train traffic signals.
url http://dx.doi.org/10.1155/2021/6081999
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AT yinghuang multimodeintelligentcontrolbasedonmultidatafusionfilteringinhighspeedtraintrafficsignalandcontrol
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