Abstract

For heavy haul trains, it is difficult to get global information due to the limited range of communication. This paper proposed a novel distributed optimal control based on game strategy, in which the global optimization is achieved by equilibrizing subsystems’ performance just utilizing local information. To online solve the game control, an efficient multivariable extremum seeking algorithm was adapted to approximate the partial differential equation deduced by optimal condition. The convergence of the proposed approximate algorithm is proved by constructing a fictitious Lie bracket system using Lyapunov function. Finally, the proposed distributed optimal control is valuated rigorously by case study according to the configuration of Daqin railway in China.

Highlights

  • Heavy haul trains are used broadly in many countries with high demand for transporting mineral, petroleum, coals, and so on

  • A segment of track, desired speed profile Figure 3, and simulation parameters of trains are given in Table 1, which comes from heavy haul trains running on Daqin railway in China

  • Traveling time, energy consumption, and in-train forces are considered in the performance index

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Summary

Introduction

Heavy haul trains are used broadly in many countries with high demand for transporting mineral, petroleum, coals, and so on. Heavy haul trains are distributed powered networked system constituted with many locomotives and wagons. The basic control problem for heavy haul trains is tracking to the target speed profile while considering some performance index, including the traveling time, energy consumption, and in-train forces. Energy consumption and traveling time focused optimal control was studied in these works for either passenger trains or ordinary freight trains. Much larger dynamic in-train forces were imposed in heavy haul trains due to unreasonable control of the distributed power, undulating grades, and the lager train lengths. The couplings used in heavy haul trains wear out due to large in-train forces [1]. The optimization techniques used for passenger trains or ordinary freight trains may not be directly applicable to heavy haul trains

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