Optimal operation of heavy haul trains using model predictive control methodology

Abstract

An optimal operation problem of heavy haul trains is formulated and solved by a model predictive control (MPC) approach. The operation objective is to minimize the trade-off among energy consumption, in-train forces, and velocity tracking errors in a long journey. The practical operational constraints are taken into consideration in the controller design. A cascade mass model of a train, which facilitates the analysis of in-train dynamics, is adopted. The model is firstly transformed to take the origin as an equilibrium, and then, linearized and discretized. An MPC approach is then employed in the controller design with the discretized model. Simulation demonstrates the feasibility and advantage of the approach proposed.