Formal Modeling and Synthesis of Longitudinal Dynamics Controller for Train Platoons

Train platoons are a really innovative application for future railways, in which trains are virtually-coupled via Train-Train communication, so drastically reducing their headways and increasing line capacity. However, as the spacing between trains becomes closer, the influence of disturbances from preceding trains becomes more significant and cannot be ignored. This influence can have a substantial impact on the operational behavior of the following trains, leading to fluctuating spacing within the platoon and potentially compromising the safety and stability of the train spacing. This paper introduces a formalized modeling framework and a controller synthesis method for train platoons, aiming to achieve absolute safety and stable operation in train platoon operations. The framework utilizes Hybrid Priced Two-player Timed Game Automaton (HPTTGA) to describe train platoon dynamics and synthesizes controllers that satisfy safety requirements using an On-the-fly Controller Synthesis (OCS) algorithm, where the effectiveness of the algorithm is demonstrated through a two-train tracking example, and Q-learning is applied to select controller which satisfy local and string stability. Owing to model-based feature, the proposed method is compared to classical Model Predictive Control (MPC) in terms of safety, local stability, and string stability. Experimental results demonstrate that the proposed method consistently ensures train platoon safety and achieves similar control performance to MPC in extreme operating scenarios. Moreover, under velocity limit scenarios, the proposed method successfully achieves the desired following behavior of the trailing train according to the control objectives.