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Global composite learning velocity tracking control for heavy haul trains

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Abstract

In this paper, the velocity tracking control problem of heavy haul trains (HHTs) is focused on multibody and control-oriented dynamic models. A composite learning algorithm is developed by combining a neural network with a high-order disturbance observer to approximate unknown nonlinearities and compounded disturbances collaboratively, where a prediction error is introduced to assess and improve the approximation accuracy. Since the neural network approximation ability holds only over a compact set, neural network-based control schemes can only ensure semi-globally uniform ultimate boundedness. Thus, a global tracking control scheme for HHTs is proposed that can switch between the composite learning controller and an additional robust controller via a switching mechanism. Finally, the globally uniform ultimate boundedness of closed-loop system signals is proven through Lyapunov theory. Simulation experiments are carried out based on an HXD1-type HHT running on the Da-Qin Line in China, and the results demonstrate the effectiveness of the proposed models and control technique.

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Authors and Affiliations

  1. School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, 330013, China

    Longsheng Chen & Hui Yang

  2. School of Aircraft Engineering, Nanchang Hangkong University, Nanchang, 330063, China

    Longsheng Chen

  3. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, China

    Yong Ren

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Correspondence to Longsheng Chen.

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No conflict of interest exits in the submission of this manuscript, and the manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part. All the authors listed have approved the manuscript that is enclosed.

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This research was supported by the National Natural Science Foundation of China (61963029,U2034211,61733005), the Jiangxi Provincial Natural Science Foundation (20224BAB202027,20232ACB202007) and the Technological Innovation Guidance Program of Jiangxi Province (20203AEI009).

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Chen, L., Yang, H. & Ren, Y. Global composite learning velocity tracking control for heavy haul trains. Nonlinear Dyn 111, 22345–22361 (2023). https://doi.org/10.1007/s11071-023-09033-1

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