Distributed Model Predictive Control For Train Regulation In Urban Metro Transportation

Train regulation plays an important role in urban metro transportation, and most existing studies on train regulation are based on centralized control. Motivated by the real-time control demand and the rapid development of vehicle based train control (VBTC) technology, this paper investigates the train regulation problem by employing distributed model predictive control (DMPC). We firstly present a distributed control framework for train regulation in metro loop lines, where each train is assumed self-organized with the capability of computation and communication with its predecessor. Then we propose a DMPC algorithm for train regulation in metro loop lines, in which each train decides its control input by optimizing a local cost function subject to operational constraints. We finally provide numerical examples to verify the effectiveness of the proposed DMPC method, showing that it exhibits comparable performance with the centralized MPC and its computation cost is significantly reduced.