Deep Reinforcement Learning Based Active Pantograph Control Strategy in High-Speed Railway

Abstract

The pantograph-catenary system (PCS) is the essential power supply system in the high-speed railway, but its coupling performance is influenced significantly by the rapidly increasing train speed. The actively controlled pantograph is one of the promising technologies to suppress the fluctuation of the pantograph-catenary contact force (PCCF). In this paper, we propose a novel pantograph control strategy based on deep reinforcement learning (DRL) to overcome the complex time-varying characteristic of PCS, which distorts the system identification of the classical control methods. First, a non-linear pantograph-catenary system model is established based on the finite element and multi-body dynamics theory as the simulation environment in DRL. Then, the state space, action space, and reward in DRL are redesigned to train the agent, which is suitable for PCS. Finally, the effectiveness and robustness of our proposed method are verified under various working conditions and parameter disturbances. The experiment results show that our control strategy can reduce the PCCF fluctuation up to 40% and reject parametric perturbation while achieving state-of-the-art performance on the benchmarks.