An Improved Deep Deterministic Policy Gradient Pantograph Active Control Strategy for High-Speed Railways

Abstract

The pantograph–catenary system (PCS) is essential for trains to obtain electrical energy. As the train’s operating speed increases, the vibration between the pantograph and the catenary intensifies, reducing the quality of the current collection. Active control may significantly reduce the vibration of the PCS, effectively lower the cost of line retrofitting, and enhance the quality of the current collection. This article proposes an improved deep deterministic policy gradient (IDDPG) for the pantograph active control problem, which delays updating the Actor and Target–Actor networks and adopts a reconstructed experience replay mechanism. The deep reinforcement learning (DRL) environment module was first established by creating a PCS coupling model. On this basis, the controller’s DRL module is precisely designed using the IDDPG strategy. Ultimately, the control strategy is integrated with the PCS for training, and the controller’s performance is validated on the PCS. Simulation experiments show that the improved strategy significantly reduces the training time, enhances the steady-state performance of the agent during later training stages, and effectively reduces the standard deviation of the pantograph–catenary contact force (PCCF) by an average of over 51.44%, effectively improving the quality of current collection.