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Yi Shi

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Proceedings Article | 11 October 2023 Paper

Research on energy-efficient speed control for automatic train operation based on dueling-DQN algorithm

Tian Lu, Bohong Liu, Yi Shi

Proceedings Volume 12800, 128006H (2023) https://doi.org/10.1117/12.3003921

KEYWORDS: Education and training, Evolutionary algorithms, Neural networks, Automatic control, Motion models, Control systems, Simulations, Deep learning, Machine learning, Transportation

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In order to realize a better train autopilot energy-saving operation control strategy, a train autopilot method based on Dueling-DQN algorithm is proposed with the train as the research object. Firstly, the train control model is established by considering the train operation demand comprehensively. Secondly, the idea of dueling network is introduced to change the structure of DQN network and prevent the problem of overestimation of value function. Finally, experimental simulations are conducted to compare with the DQN method and explore the feasibility of the method. The experiments are carried out to verify the train operation strategy method by simulating the actual line conditions. From the experimental results, it can be seen that the train operation meets the actual requirements. The energy consumption is 25.4% more energy-efficient than the actual operation, and the convergence speed of the algorithm is improved by about 40% compared with DQN. The Dueling-DQN method can improve the efficiency of the algorithm and can form an energy-efficient operation strategy that is better than the speed control in actual operation under the condition of satisfying the automatic train driving performance, which is meaningful for improving the intelligence of automatic train operation.

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