Internal pressure iterative learning control for high-speed trains based on condition and performance matching under morphologically similar tunnel pressure waves

Lu Yang; Chunjun Chen; Ji Deng; Qin Zheng

doi:10.1117/12.2689838

7 September 2023 Internal pressure iterative learning control for high-speed trains based on condition and performance matching under morphologically similar tunnel pressure waves

Lu Yang, Chunjun Chen, Ji Deng, Qin Zheng

Author Affiliations +

Proceedings Volume 12790, Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023); 127903M (2023) https://doi.org/10.1117/12.2689838
Event: 8th International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023), 2023, Hangzhou, China

Abstract

When a high-speed train repeatedly passes through a tunnel, tunnel pressure waves induce pressure fluctuation inside the carriage. The traditional passive control strategy of closing the air ducts for a fixed period may fail to meet the requirements of pressure comfort and air quality. The similar properties of morphologically-similar tunnel pressure waves provide a new perspective for performing quasi-periodic repetitive tasks by iterative learning control (ILC) algorithm. In order to break the limitation of strict repeatability of tracking targets in traditional ILC algorithm, this paper proposes a novel ILC based on condition and performance matching algorithm. By matching the current working condition and expected performance with the condition and performance of the historical database, and processing the matched control input with a variable time scale, the optimal control input can be obtained. The control input and the historical database can be updated based on the error and performance after control. The simulation results show that, compared with the uncontrolled, traditional control and traditional open-loop ILC, the proposed algorithm can effectively suppress the internal pressure fluctuation under morphologically-similar tunnel pressure waves, and has higher control accuracy and faster convergence speed.

Citation Download Citation

Lu Yang, Chunjun Chen, Ji Deng, and Qin Zheng "Internal pressure iterative learning control for high-speed trains based on condition and performance matching under morphologically similar tunnel pressure waves", Proc. SPIE 12790, Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023), 127903M (7 September 2023); https://doi.org/10.1117/12.2689838

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Databases

Control systems

Detection and tracking algorithms

Passive control

Design and modelling

Computer simulations

Mechanical engineering

Keywords/Phrases

Search In:

Publication Years