Nonlinear Hybrid Multipoint Model of High‐Speed Train with Traction/Braking Dynamic and Speed Estimation Law

Jia, Chao; Xu, Haibo; Wang, Longsheng

doi:10.1155/2019/1364657

Cited by 4 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The testing of the indicator requires data sets describing the dynamic profile of train driving under different conditions, as shaped by [27][28][29]: In the case of a run consisting of two phases: starting (continuous line) and driving at momentum to stop (dotted line), the energy consumption will be minimal (jmin), while passenger comfort will the highest, but the run time will be the longest (tmax). In the case of a forced drive, i.e., driving with a direct transition from start to stop (dashed line), the travel time will be the shortest (tmin), but the energy consumption will be maximal (jmax), and the passenger comfort will be the lowest.…”

Section: Neural Train Emulatormentioning

confidence: 99%

“…The testing of the indicator requires data sets describing the dynamic profile of train driving under different conditions, as shaped by [27][28][29] The assessment of the indicator for the different profiles described above will require the actual mapping of the train's behavior over a specific section of the rail network for different driving techniques. In other words, to carry out the tests, it is necessary to acquire the actual driving speed profiles obtained using the train modeling simulator, the value of which are train speed (V), a parameter input value of the traction (drive) adjuster and brake adjuster, and track infrastructure parameters.…”

Section: Neural Train Emulatormentioning

confidence: 99%

See 1 more Smart Citation

Testing the Smooth Driving of a Train Using a Neural Network

Koper

Kochan

2020

Sustainability

View full text Add to dashboard Cite

This article deals with the extraction of a new original parameter to characterize a railway traffic driving smoothness indicator, and its investigation is based on data obtained from a neural train emulator. This indicator of driving smoothness is an example of the sustainable value of control command and signaling technology. The pro-social and pro-environmental aspects of smooth driving are indicated and the article proposes the introduction of a new indicator for assessing the quality of rail traffic, taking into account traffic on a micro scale—the driving smoothness of a single train (also called driving flow), derived from a parameter identified in the literature—and traffic smoothness (also called traffic flow), describing traffic quality on a macro scale. At the same time, the concept of a neural train emulator is presented, providing input data to determine the value of the proposed indicator for different train models and track systems in order to test the indicator’s properties. The concept proposes the structure of an artificial neural network, the technique of obtaining test data sets and the conditions of training the network as well. An emulator based on the neural network enables the simulation of train driving, taking into account its nonlinearity and data acquisition for indicator research.

show abstract

Section: Neural Train Emulatormentioning

confidence: 99%

Section: Neural Train Emulatormentioning

confidence: 99%

Testing the Smooth Driving of a Train Using a Neural Network

Koper

Kochan

2020

Sustainability

View full text Add to dashboard Cite

show abstract

“…Compared with the former, the latter treats each train carriage as an individual point, considering coupling relationships between carriages and analyzing the forces acting on each carriage [4]. Hence, the multi-point train model can be more effective in characterizing the dynamic behavior of trains and is widely used in ATO control under complex conditions [5]- [7]. For example, a multi-point train model connected by flexible couplers is constructed in [5], which can be cruise control for trains.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, the multi-point train model can be more effective in characterizing the dynamic behavior of trains and is widely used in ATO control under complex conditions [5]- [7]. For example, a multi-point train model connected by flexible couplers is constructed in [5], which can be cruise control for trains. In [6], it is presented a nonlinear multi-point train model based on integer variables that can represent carriage types and operation states.…”

Section: Introductionmentioning

confidence: 99%

Stability-Enhanced Model Predictive Control for Urban Rail Transit Train

Wang,

Xing,

Wang

2024

IEEE Access

View full text Add to dashboard Cite

Automatic train operation (ATO) control is a pivotal part of urban rail transit development, where designing the dynamics models and controllers for the ATO control scenarios presents a formidable challenge. To begin with, considering the fundamental resistances encountered by trains during the operation process, including elemental running resistance and time-varying slope resistance, we treat relative distance and relative speed between train carriages as state variables in the control modeling. Considering changes in traction/braking outputs as control variables, we formulate a meticulous dynamics model for urban rail transit trains (URTT). Furthermore, a stability-enhanced model predictive control (SEMPC) approach is proposed for ATO control in URTT, with a terminal term being added to the control objective design for stability requirements. This approach anticipates the future dynamic behaviors of the control system, yielding a stable and convergent predictive controller for the ATO system. Lastly, utilizing operational data from a specific urban rail line as an illustrative example, we conduct comparative analyses of the operational control performance among various controllers in scenarios of the single section, multi-section, and disturbance. Experimental results demonstrate that the proposed SEMPC controller exhibits superior performance to the compared controllers for ATO control in URTT.INDEX TERMS Automatic train operation (ATO), urban rail transit train (URTT), stability-enhanced model predictive control (SEMPC), speed control.

show abstract

“…It enables the study of the impact of movements and vibrations on objects, components, and people, which is crucial for design and testing in environments requiring high precision and realistic simulation conditions. Works [32][33][34][35][36][37][38][39] focused on the validation process of railway vehicle dynamics models used in training simulators. The validation process, called the Dynamic Modeling Validation Process (DyMVaP), is crucial to ensuring the credibility of the simulators, which play an important role in driver training.…”

mentioning

confidence: 99%

Model of Electric Locomotive Simulator Cabin Excitations

Chudzikiewicz,

Góra,

Gerlici

et al. 2024

Energies

View full text Add to dashboard Cite

Striving to increase the speed of rail vehicles and thus improve the comfort of traveling passengers at the same time, undertakes activities in the sphere of ensuring an appropriate level of safety of rail, passenger, and freight transport. One of the elements of activities in this area is the training of train drivers. Until recently, this training consisted of a theoretical and practical part on the vehicle, alongside an experienced train driver. Considering the increasing level of automation of railway traffic control systems and locomotive equipment, as well as training costs and requirements related to the introduction of TSI, it is becoming an increasingly common requirement to conduct practical training on railway vehicle traffic simulators, while the conditions in the simulator cabin and the trainee’s feelings should correspond to the actual driving conditions. A locomotive driving simulator is a system consisting of a cabin of a suitable type of locomotive or EMU, mapped in 1:1 scale, coupled with a motion excitation system and computer programs connected together forming the software of the cab visualization and dynamics system. The basic program simulating the dynamics and kinematics of the cabin’s motion is a program containing a motion dynamics model that generates signals forcing the movement of the exciters on which the cabin’s platform is mounted. The correct operation of the simulation model depends on the created mathematical model, which can be built in several ways. This article presents the issue of building a mathematical model describing the dynamics of the rail vehicle motion, which can then be used in the simulation model of the simulator cabin motion. Two ways of proceeding in the process of approaching the construction of a mathematical model of rail vehicle motion dynamics will be presented, with the possibility of later use in creating a simulation model of the motion of the locomotive simulator cabin. One of the possible routes was used in the past in the construction of the EP09 locomotive simulator.

show abstract

Nonlinear Hybrid Multipoint Model of High‐Speed Train with Traction/Braking Dynamic and Speed Estimation Law

Cited by 4 publications

References 19 publications

Testing the Smooth Driving of a Train Using a Neural Network

Testing the Smooth Driving of a Train Using a Neural Network

Stability-Enhanced Model Predictive Control for Urban Rail Transit Train

Model of Electric Locomotive Simulator Cabin Excitations

Contact Info

Product

Resources

About