A Tube Model Predictive Control Approach for Virtually Coupled Train Set with Nonlinear Relative-Braking Distance

Virtual coupling deploys the relative-braking distance (RBD) to keep a minimal distance between successive units in a virtually coupled train set (VCTS). However, it is still challenging to design a control approach that addresses RBD in a stable and computation-efficient way. Thus, this paper proposes a tube model predictive control (MPC) approach to solve this problem. The nonlinear model is formulated to capture the movement of VCTS with RBD. In the prediction horizon, we linearize the model at each instant, such that the controller is calculated by solving a computationally efficient quadratic programming problem. Tubes are constructed to confine the tracking errors from linearization. Finally, experiments are carried out and relevant results show that the proposed tube MPC can operate VCTS as well as nonlinear MPC, but save by over 70% computation time.