Efficient Train Operation via Shrinking Horizon Parametrized Predictive Control

The problem of driver assistance for the energy-efficient operation of trains is considered. The goal is to control the traction/braking forces applied to the train, while satisfying speed limits and reaching the next station at the prescribed arrival time. Moreover, the control input has to belong to a discrete set of values and/or operating modes, which a human driver has to implement. A nonlinear model predictive control (MPC) approach is proposed, featuring a shrinking horizon and an input-parametrization strategy to retain a continuous optimization problem. Theoretical convergence guarantees are derived, and the approach is tested in realistic simulations