侧风环境下城轨列车自动驾驶控制策略研究OA

This paper primarily investigated automatic train operation in urban rail transit systems,with a particular focus on energy-saving and safety control strategies under crosswind conditions.It detailed the construction of the train dynamics model,multi-objective optimization modeling,and impacts of crosswind conditions on operation.An automatic train operation strategy based on Deep Reinforcement Learning(DRL)was proposed.The sparse reward problem was addressed through Tr(Time redundancy)reference planning,and a detailed reward function was designed to guide the agent's learning process.In addition,the DQN-Tr algorithm combined Tr reference planning with Deep Q-Network(DQN),improving the convergence speed and stability of the algorithm.Experimental simulations were conducted using a subway line in Chongqing as a case study,and the approach was validated.The results showed that the DQN-Tr algorithm performed well in stopping accuracy and punctuality,while achieving significant energy savings.Compared with traditional PID control,DQN-Tr increased accuracy by 0.05％and reduced energy consumption by 11.45％,effectively lowering energy use while ensuring operational safety,thereby providing an efficient and energy-saving control method for urban rail transit.