Distributed Global Composite Learning Cooperative Control of Virtually Coupled Heavy Haul Train Formations

Emerging communication-based control and virtually coupled train formations are critical foundations for automatic train operations to improve capacity, safety and flexibility toward intelligent rail transportation systems. High-reliability cooperative control of heavy haul train (HHT) formations encounters greater challenges due to the distinctive dynamic characteristics and uncertainties. This paper proposes a communication-based distributed global composite learning cooperative control protocol for virtually coupled heavy haul train formations that enables all HHTs to achieve autonomous dynamic steady coordination with a minimum safe interval and collision avoidance. A pneumatic braking algorithm is first presented for each HHT's vehicles in the formation to satisfy the distinctive electro-pneumatic braking mode of HHTs and attain improved longitudinal impulses. Then, the complicated dynamic characteristics and uncertainties of HHTs are considered based on a constructed control-oriented dynamic model for HHTs and a neural network (NN) approximation. Additionally, corresponding control protocols are proposed, where the interpretability of the NN approximation and the globally uniform ultimate boundedness property hold. Finally, the control feasibility and abilities of the proposed control protocol are analyzed and demonstrated to be effective via simulation experiments.