Iterative learning control of train internal pressure under extreme tunnel conditions

Xiru Wang; Chunjun Chen

doi:10.1117/12.2689764

7 September 2023 Iterative learning control of train internal pressure under extreme tunnel conditions

Xiru Wang, Chunjun Chen

Proceedings Volume 12790, Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023); 1279037 (2023) https://doi.org/10.1117/12.2689764
Event: 8th International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023), 2023, Hangzhou, China

Abstract

In order to solve the problem of controlling the pressure fluctuation in the oxygen supply train in the extreme tunnel conditions with high altitude, extra-long and large gradient in the western mountainous railway, this paper analyses the gas exchange path of the train, establishes a semi-empirical model of the internal pressure in the oxygen supply train, and verifies the accuracy of the model by using the simulation results of one-dimensional numerical simulation. For the passive pressure control method of controlling the stop valve of the air exchange system, an internal pressure iterative learning control algorithm with oxygen partial pressure as a constraint is proposed to meet the pressure comfort requirements and oxygen supply needs of passengers. The results are compared with those under the uncontrolled and traditional control methods. The comparison results show that the iterative learning control algorithm for internal pressure with oxygen partial pressure as a constraint is effective in reducing pressure fluctuations, and that the oxygen partial pressure level also meets the oxygen supply standard. the iterative learning control reduces the peak pressure in the vehicle without control from 9212.77 Pa to 8062.90 Pa, an improvement of 12.48%.

Citation Download Citation

Xiru Wang and Chunjun Chen "Iterative learning control of train internal pressure under extreme tunnel conditions", Proc. SPIE 12790, Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023), 1279037 (7 September 2023); https://doi.org/10.1117/12.2689764

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