Connection and speed control in railway systems – A model predictive control approach

We further enhance our previous results on model predictive control (MPC) for railway systems with both hard and soft connection constraints, i.e., railway systems where, if necessary, some connections may be broken (but then a penalty is incurred). In this paper we extend the previous model by also including variable traveling times, which offers an extra degree of freedom for control. We present an MPC framework for railway systems, where the main aim of the control is to recover from delays in an optimal way by breaking connections and/or letting some trains run faster than usual (both at a cost). In general, the MPC control design problem for railway systems leads to a nonlinear nonconvex optimization problem, but we show that the optimal MPC strategy can be computed using extended linear complementarity problems or integer programming.