Paper
14 February 2024 Research on optimal train adhesion control based on nonlinear model predictive control
Xiaomao He, Song Wang, Bolaji Balogun Alanamu
Author Affiliations +
Proceedings Volume 13018, International Conference on Smart Transportation and City Engineering (STCE 2023); 130180P (2024) https://doi.org/10.1117/12.3024103
Event: International Conference on Smart Transportation and City Engineering (STCE 2023), 2023, Chongqing, China
Abstract
The phenomenon of wheel idling, resulting from poor adhesion conditions, not only reduces traction utilization but also poses a safety risk to train operation. A unique peak adhesion point is observed in the wheel-rail adhesion characteristic curve. To maintain stable train operation at this peak adhesion point, it is crucial to enhance adhesion utilization. This paper proposes a nonlinear model predictive control algorithm for optimizing train adhesion control. Initially, the dynamic forgetting factor recursive least squares algorithm is employed to online identify track parameters. These identified online track parameters are subsequently utilized by the nonlinear model predictive controller to achieve optimal adhesion control. The aim is to position the train's operation point as closely as possible to the adhesion curve's peak, thereby achieving optimal adhesion control. A MATLAB/Simulink simulation model for vehicle wheel-rail adhesion is developed, and the simulation results demonstrate the superior effectiveness of the proposed adhesion control method when compared to linear time-varying model prediction adhesion control. Additionally, a higher average adhesion utilization rate is achieved.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Xiaomao He, Song Wang, and Bolaji Balogun Alanamu "Research on optimal train adhesion control based on nonlinear model predictive control", Proc. SPIE 13018, International Conference on Smart Transportation and City Engineering (STCE 2023), 130180P (14 February 2024); https://doi.org/10.1117/12.3024103
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KEYWORDS
Adhesion

Nonlinear control

Control systems

Nonlinear optimization

Systems modeling

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