Energy-optimal control of intelligent track inspection trains: design and experiment

Abstract

This study proposes and evaluates the effectiveness of a new energy-efficient control strategy for Intelligent Track Inspection Trains (ITITs). First, a sufficiently simple state-space model for a real ITIT is introduced. This model is employed to formulate and solve a constrained optimization problem to determine the vehicle's optimal speed profile using the pseudo-spectral method to achieve the highest energy savings. Then, we take advantage of the model predictive control (MPC) scheme to create a controller to follow the resulting optimal speed trajectory as closely as possible. The results obtained from co-simulation between a high-fidelity model of the ITIT built within AMESim and the MPC controller developed in the Matlab/Simulink environment show that the optimal speed trajectory is tracked very well when the MPC controller is applied to the vehicle's high-fidelity model. During the co-simulations, the energy consumption in terms of the battery's state of charge (SOC) changes for the MPC-based optimal speed trajectory following was around 6% less than that quantity for a conventional non-optimal cruise controller. Moreover, in the experiment with the real ITIT, the energy consumption in terms of the SOC changes for the non-optimal cruise controller was 5% more than that value for the MPC-based optimal speed trajectory following.