Physics-informed deep learning for virtual rail train trajectory following control

Yuanjin Ji, Youpei Huang, Maozhenning Yang, Han Leng, Lihui Ren, Hongda Liu and Yuejian Chen

Reliability Engineering and System Safety, 2025, vol. 261, issue C

Abstract: Trajectory-following control is a crucial challenge for virtual rail trains (VRTs), directly impacting tracking accuracy, path width requirements, and operational safety. Traditional model-based control methods, struggle with nonlinear dynamics and require highly accurate system models, while purely data-driven deep learning methods lack physical interpretability and robustness. To address these challenges, this paper proposes a novel Physics-Informed Deep Learning Control Strategy that integrates Lagrangian dynamics equations into a deep neural network, forming a Deep Lagrangian Neural Network (DLNN). This approach ensures that the learned control model retains essential physical properties while capturing complex vehicle dynamics. The DLNN serves as an inverse model within the control framework, mapping desired trajectories to control inputs. Experimental results on circular, lane-change, and obstacle-avoidance maneuvers demonstrate that the DLNN model significantly reduces lateral deviation and yaw rate errors compared to traditional Multi-Layer Perceptron (MLP)-based models. The DLNN exhibits strong generalization capability across different trajectory geometries and benefits from online learning, allowing continuous adaptation to new driving conditions. These findings highlight the potential of physics-informed deep learning in intelligent rail transit systems, providing a more accurate, interpretable, and robust control framework for virtual rail train trajectory following.

Keywords: Trajectory-following control; Deep Lagrangian neural network; Physic-informed; Virtual rail train; Intelligent transportation systems (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0951832025002935
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:261:y:2025:i:c:s0951832025002935

DOI: 10.1016/j.ress.2025.111092

Access Statistics for this article

Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares

More articles in Reliability Engineering and System Safety from Elsevier
Bibliographic data for series maintained by Catherine Liu ().