Model Predictive Control Approach for Recovery from Delays in Railway Systems

Abstract

The model predictive control (MPC) framework, a very popular controller design method in the process industry, is extended to transfer coordination in railway systems. In fact, the proposed approach can also be used for other systems with both hard and soft synchronization constraints, such as logistic operations. The main aim of the control is to optimally recover from delays by breaking connections (at a cost). In general, the MPC control design problem for railway systems leads to a nonlinear, nonconvex optimization problem. Computing an optimal MPC strategy using an extended linear complementarity problem is demonstrated. Also presented is an extension with an extra degree of freedom to recover from delays by letting some trains run faster than usual (again at a cost). The resulting extended MPC railway problem can also be solved using an extended linear complementarity problem.