A multi-objective optimization approach for the virtual coupling train set driving strategy

Abstract This paper presents an improved virtual coupling train set (VCTS) operation control framework to deal with the lack of optimization of speed curves in the traditional techniques. The framework takes into account the temporary speed limit on the railway line and the communication delay between trains, and it uses a VCTS consisting of three trains as an experimental object. It creates the virtual coupling train tracking and control process by improving the driving strategy of the leader train and using the leader–follower model. The follower train uses the improved speed curve of the leader train as its speed reference curve through knowledge migration, and this completes the multi-objective optimization of the driving strategy for the VCTS. The experimental results confirm that the deep reinforcement learning algorithm effectively achieves the optimization goal of the train driving strategy. They also reveal that the intrinsic curiosity module prioritized experience replay dueling double deep Q-network (ICM-PER-D3QN) algorithm outperforms the deep Q-network (DQN) algorithm in optimizing the driving strategy of the leader train. The ICM-PER-D3QN algorithm enhances the leader train driving strategy by an average of 57% when compared to the DQN algorithm. Furthermore, the particle swarm optimization (PSO)-based model predictive control (MPC) algorithm has also demonstrated tracking accuracy and further improved safety during VCTS operation, with an average increase of 37.7% in tracking accuracy compared to the traditional MPC algorithm.