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<p>To suppress the extreme fluctuations of pantograph-catenary contact force (PCCF) taking account of wheel-rail excitation of high-speed trains, a real-time active control framework based on a&#xa0;hybrid deep reinforcement learning (DRL) algorithm is proposed. Specifically, the influence of wheel-rail excitation on the vertical vibration amplitudes of the pantograph is clarified by establishing the multi-body dynamic model based on the interaction mechanism between the rail-vehicle (RV) system and the pantograph-catenary (PC) system. Subsequentially, a hybrid framework based on the feedback control (FC)-guided DRL algorithm is established for applying active control force to the pantograph. A full-state Linear Quadratic Regulator (LQR) is implemented in the FC section to optimize the primary dynamic states of the train, with the forces applied to the pantograph play the guiding role. For more nuanced policy exploration, a self-attention-enhanced DRL-based controller employs the Soft Actor-Critic (SAC) algorithm with the Hindsight Experience Replay (HER) mechanism to train a policy aimed at eliminating extreme PCCF values. The reward function designed for time-varying speed conditions, along with the structure of the FC-DRL, is detailed extensively. Compared with the baseline algorithm, the simulation results based on random track irregularity input show that our control scheme reduces the standard deviation of PCCF by a maximum of 41.85% and the average error by a maximum of 78.25%. The proposed framework converges stably, and the extreme contact force can be eliminated effectively in various testing environments.</p>

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FC-DRL-based framework considering wheel-rail excitation: a speed-interval-oriented active control scheme for high-speed railway pantographs

  • 期刊论文

  • 发表日期：

    2025年11月24日

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  • Zhun Han,
  • Qingsheng Feng,
  • Wangyang Liu,
  • Hangtao Yang,
  • Yan Cui,
  • Hong Li,
  • Yinping Shao

摘要

To suppress the extreme fluctuations of pantograph-catenary contact force (PCCF) taking account of wheel-rail excitation of high-speed trains, a real-time active control framework based on a hybrid deep reinforcement learning (DRL) algorithm is proposed. Specifically, the influence of wheel-rail excitation on the vertical vibration amplitudes of the pantograph is clarified by establishing the multi-body dynamic model based on the interaction mechanism between the rail-vehicle (RV) system and the pantograph-catenary (PC) system. Subsequentially, a hybrid framework based on the feedback control (FC)-guided DRL algorithm is established for applying active control force to the pantograph. A full-state Linear Quadratic Regulator (LQR) is implemented in the FC section to optimize the primary dynamic states of the train, with the forces applied to the pantograph play the guiding role. For more nuanced policy exploration, a self-attention-enhanced DRL-based controller employs the Soft Actor-Critic (SAC) algorithm with the Hindsight Experience Replay (HER) mechanism to train a policy aimed at eliminating extreme PCCF values. The reward function designed for time-varying speed conditions, along with the structure of the FC-DRL, is detailed extensively. Compared with the baseline algorithm, the simulation results based on random track irregularity input show that our control scheme reduces the standard deviation of PCCF by a maximum of 41.85% and the average error by a maximum of 78.25%. The proposed framework converges stably, and the extreme contact force can be eliminated effectively in various testing environments.