Hybrid Model Predictive Control for Virtual Coupling of Heavy-haul Trains

Virtual Coupling (VC) has the potential to signif-icantly increase the capacity of heavy-haul railways. However, existing controllers do not fully exploit this potential due to inaccuracies in the model of heavy-haul trains. To address this issue, we propose a hybrid model predictive control framework to implement the virtual coupling of heavy haul trains. Firstly, we model the traction and brake system in which the discrete notch is directly considered as the control input. Then we approximate the nonlinearities in propulsion resistance, track gradient, curvature and traction/brake force by piecewise affine functions. The control law can be solved by mixed integer linear programming (MILP). Finally, we simulate the convoy operation and show that the proposed method can improve the performance of convoy and enable the track to accommodate more trains.