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Tytuł:
Research on speed control of high-speed train based on multi-point model
Autorzy:
Hou, Tao
Guo, Yang-yang
Niu, Hong-xia
Powiązania:
https://bibliotekanauki.pl/articles/224081.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
high-speed train
multi-point model
predictive control
fuzzy control
PID control
speed control
pociąg ekspresowy
model wielopunktowy
sterowanie predykcyjne
sterowanie rozmyte
sterowanie PID
prędkość
Opis:
The traditional train speed control research regards the train as a particle, ignoring the length of the train and the interaction force between carriages. Although this method is simple, the control error is large for high-speed trains with the characteristics of power dispersion. Moreover, in the control process, if the length of the train is not considered, when the train passes the slope point or the curvature point, the speed will jump due to the change of the line, causing a large control error and reducing comfort. In order to improve the accuracy of high-speed train speed control and solve the problem of speed jump when the train runs through variable slope and curvature, the paper takes CRH3 EMU data as an example to establish the corresponding multi-point train dynamics model. In the control method, the speed control of high-speed train needs to meet the fast requirement. Comparing the merits and demerits of classical PID control, fuzzy control and fuzzy adaptive PID control in tracking the ideal running curve of high-speed train, this paper chooses the fuzzy adaptive PID control with fast response. Considering that predictive control can predict future output, a predictive fuzzy adaptive PID controller is designed, which is suitable for high-speed train model based on multi-point. The simulation results show that the multi-point model of the high-speed train can solve the speed jump problem of the train when passing through the special lines, and the predictive fuzzy adaptive PID controller can control the speed of the train with multi-point model, so that the train can run at the desired speed, meeting the requirements of fast response and high control accuracy.
Źródło:
Archives of Transport; 2019, 50, 2; 35-46
0866-9546
2300-8830
Pojawia się w:
Archives of Transport
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Research on speed control of high speed trains based on hybrid modeling
Autorzy:
Hou, Tao
Tang, Li.
Niu, Hong-xia
Zhao, Tingyang
Powiązania:
https://bibliotekanauki.pl/articles/27311802.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
high-speed train
hybrid modeling
speed control
error compensation
pociąg ekspresowy
modelowanie hybrydowe
kontrola prędkości
kompensacja błędów
Opis:
With the continuous improvement of train speed, the automatic driving of trains instead of driver driving has become the development direction of rail transit in order to realize traffic automation. The application of single modeling methods for speed control in the automatic operation of high-speed trains lacks exploration of the combination of train operation data information and physical model, resulting in low system modeling accuracy, which impacts the effectiveness of speed control and the operation of high-speed trains. To further increase the dynamic modeling accuracy of high-speed train operation and the high-speed train's speed control effect, a high-speed train speed control method based on hybrid modeling of mechanism and data drive is put forward. Firstly, a model of the high-speed train's mechanism was created by analyzing the train's dynamics. Secondly, the improved kernel-principal component regression algorithm was used to create a data-driven model using the actual operation data of the CRH3 (China Railway High-speed 3) high-speed train from Huashan North Railway Station to Xi'an North Railway Station of "Zhengxi High-speed Railway," completing the mechanism model compensation and the error correction of the speed of the actual operation process of the high-speed train, and realizing the hybrid modeling of mechanism and data-driven. Finally, the prediction Fuzzy PID control algorithm was developed based on the natural line and train characteristics to complete the train speed control simulation under the hybrid model and the mechanism model, respectively. In addition, analysis and comparison analysis were conducted. The results indicate that, compared to the high-speed train speed control based on the mechanism model, the high-speed train speed control based on hybrid modeling is more accurate, with an average speed control error reduced by 69.42%. This can effectively reduce the speed control error, improve the speed control effect and operation efficiency, and demonstrate the efficacy of the hybrid modeling and algorithm. The research results can provide a new ideal of multi-model fusion modeling for the dynamic modeling of high-speed train operation, further improve control objectives such as safety, comfort, and efficiency of high-speed train operation, and provide a reference for automatic driving and intelligent driving of high-speed trains.
Źródło:
Archives of Transport; 2023, 66, 2; 77--82
0866-9546
2300-8830
Pojawia się w:
Archives of Transport
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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