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Analysis and control of high-speed train lateral vibration on the basis of a conditionally triggered model predictive control strategy

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Abstract

The lateral vibration of the car body affects the lateral stability of high-speed trains (HSTs), and the aim of this study is to design a good control strategy to decrease the lateral vibration of the car body. First, the Maxwell model of the anti-yaw damper was transformed into a Voigt model by the equivalence method, and a modified 17-degree-of-freedom (DOF) lateral vibration model that includes the anti-yaw damper was established. After the design difficulty and control accuracy of the control strategy were weighed, the lateral and yaw motions of the wheelsets were regarded as disturbances, and a disturbance observer of wheelset motions was designed to simplify the 17-DOF model to a 9-DOF lateral vibration model. Second, a model predictive control (MPC) strategy for HST lateral vibration was designed, and the effects of the key parameters on the lateral vibration acceleration of the HST car body and on the iterative computation time of the MPC strategy were studied. In addition, a conditionally triggered model predictive control (CMPC) strategy was proposed. Last, the control effects of the CMPC strategy under the working conditions of speed change, turnout passage, and aerodynamic load were studied by simulation, which verified the validity of the proposed model and control strategy.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Nos. U2034210, 51975487, and 52372402).

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Authors and Affiliations

  1. School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, China

    Ruqiang Mou, Chunjun Chen, Chaoyue Chen & Yaowen Zhang

  2. Department of Automation Engineering, The Engineering & Technical College, Chengdu University of Technology, Leshan, 614000, China

    Ruqiang Mou

  3. Technology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province, Southwest Jiaotong University, Chengdu, 610031, China

    Chunjun Chen

Authors
  1. Ruqiang Mou
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  3. Chaoyue Chen
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Corresponding authors

Correspondence to Ruqiang Mou or Chunjun Chen.

Additional information

Ruqiang Mou received his M.A. degree in Mechatronic Engineering from Sichuan University in 2016, where he is currently pursuing the Ph.D. degree in Mechatronic Engineering from Southwest Jiaotong University, Chengdu, China. He is a lecturer of the Department of Automation Engineering, The Engineering & Technical College of Chengdu University of Technology. His research interests include robot mechanism and control, vehicle system dynamics and vibration control, mechatronics design.

Chunjun Chen received the Ph.D. degree from Southwest Jiaotong University in 2006 and the M.A. degree from University of Electronic Science and Technology of China in 1993. He is a Professor of School of Mechanical Engineering, Southwest Jiaotong University, Director of Department of Measure-ment and Control and Mechano-electronic Measurement and Control Laboratorial Center and Deputy Director of the Technology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province. His research interests include vibration, noise and aerodynamics of high-speed trains, traffic equipment, electromechanical systems, advanced control and measurement theory, electromechanical control and measurement system.

Chaoyue Chen is currently pursuing the Ph.D. degree in Mechatronic Engineering from Southwest Jiaotong University, Chengdu, China. His research interests include vehicle system dynamics, mechanical system dynamics and advanced control strategy.

Yaowen Zhang received the B.S. degree in mechanical engineering from Southwest Jiaotong University, Chengdu, China, in 2020, where he is currently pursuing the Ph.D. degree in Mechatronic Engineering from Southwest Jiaotong University. His current research interests include semi-active control and vehicle system dynamics.

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Mou, R., Chen, C., Chen, C. et al. Analysis and control of high-speed train lateral vibration on the basis of a conditionally triggered model predictive control strategy. J Mech Sci Technol 38, 1703–1717 (2024). https://doi.org/10.1007/s12206-024-0307-6

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  • DOI: https://doi.org/10.1007/s12206-024-0307-6

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