Simulation of identification process of parameters of movement of the wagon on monorail transport

Мороз, Н. Н.; Starostenko, Aleksey; Gorbachev, Roman; Kuznetsov, N. A.

doi:10.1109/icaict.2016.7991717

Cited by 2 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…RLS is used in [78] and [79] to estimate the train weight and the running resistance parameters r 0 and r 1 . In both articles RLS is validated using simulation data with added noise representing the effect of wind.…”

Section: B Online Train Motion Model Calibrationmentioning

confidence: 99%

“…Online calibration techniques show a wide variety of ways of analyzing the available measurements. Some of them use only the last measurement available to generate the estimates at each iteration of the algorithm [78], [79], [80], [81], [82], [83], which guarantees a faster computation time at the cost of more sensitivity of the estimates to anomalous data and noise. In contrast, some techniques consider all the available measurements at each iteration, and add the new ones as soon as they are generated [84].…”

Section: Main Findingsmentioning

confidence: 99%

See 1 more Smart Citation

A Literature Review on Train Motion Model Calibration

Cunillera

Bešinović

Lentink

et al. 2023

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

The dynamics of a moving train are usually described by means of a motion model based on Newton's second law. This model uses as input track geometry data and train characteristics like mass, the parameters that model the running resistance, the maximum tractive effort and power, and the brake rates to be applied. It can reproduce and predict train dynamics accurately if the mentioned train characteristics are carefully calibrated. The model constitutes the core element of a broad variety of railway applications, from timetabling tools to Driver Advisory Systems and Automatic Train Operation. Among the existing train motion model calibration techniques, those that use operational data are of particular interest, as they benefit from on-board recorded data, capturing the train dynamics during operation. In this literature review article we provide an overview of the train motion model calibration techniques that have been published in the scientific literature between January 2000 and December 2021 and either use operational data or can be minimally adapted to use it. To this end, we present a critical overview of the existing train motion model calibration approaches, distinguishing online calibration that analyzes data on-the-go and offline calibration that analyzes historical data batchwise. We propose a research agenda and highlight some potential goals to be tackled in the near future: from devising accurate online calibrators for eco-driving applications to quantitizing the physical sources of parameter variation. Last, we discuss practical recommendations for practitioners and scholars inferred from the current state of the art.

show abstract

Section: B Online Train Motion Model Calibrationmentioning

confidence: 99%