Mathematical Modelling of the Energy Optimum Modes of Driving the Train Taking Into Account Perturbation

Yurenko, Konstantin

doi:10.17213/0321-2653-2015-3-34-44

Cited by 5 publications

(2 citation statements)

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“…It compensated a small reduction in energy consumption. It should also be noted that deviations of actual parameters from those calculated in real operating conditions more influenced for control quality [3] at the programmed control method than control with LLM.…”

Section: Computational Experimentsmentioning

confidence: 99%

“…The automation of train traffic control is one of the main ways to improve its safety, save energy resources, improve the working conditions of locomotive crews with strict observance of the traffic schedule [1,2]. The system of train auto-driving must solve the problem of determining and realizing the optimal trajectory of motion in real time mode taking into account the perturbations [3]. This task belongs to the class of terminal (or finite, according Bellman) control.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Train Operation Control on-based of Logical-Linguistic Model

Yurenko¹,

Kharchenko²,

Fandeev³

et al. 2018

EAI Endorsed Transactions on Energy Web

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The article presents a new approach to control of train movement based on a combination of terminal (finite) and fuzzy control methods using a logical-linguistic model whose numerical parameters are determined on the basis of search optimization and simulation modeling. The results of computational experiments are presented and given recommendations to the application and further development of the proposed approach. In particular, the proposed logicallinguistic model can be used in the construction of on-board systems of suburban electric trains.

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Section: Computational Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%