Virtual Coupling of Railway Vehicles: Gap Reference for Merge and Separation, Robust Control, and Position Measurement

Park, Jaegeun; Lee, Byunghun; Eun, Yongsoon

doi:10.1109/tits.2020.3019979

Cited by 60 publications

(42 citation statements)

References 17 publications

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“…The minimization problem (19) implies to obtain an optimal control input sequence for the next N p time which makes a trade-off between the speed errors and comfort level.…”

Section: Controller For the Leadermentioning

confidence: 99%

“…which is the optimal solution of minimization problem (19). Then, the predicted optimal state is expressed as X 1,k .…”

Section: Controller For the Leadermentioning

confidence: 99%

“…Then, with the accurate information of other trains and a safe protection method, it is supposed to design an efficient and stable train control system to keep the desired distance between trains. In recent publications about VC, feedback control [1], model predictive control (MPC) [10], multi-agent system (MAS) control [18] and sliding mode control (SMC) [19] are used to realize train automatic drive. The feedback control and MAS control need to be carefully designed since the existing limitations with respect to the track condition and train property.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Adaptive Model Predictive Control System for Virtual Coupling in Metros

et al. 2021

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Virtual coupling (VC) is an emerging concept and hot research topic in railways, especially for metro systems. Several unit trains in VC drive with a desired minimum distance, and they, as a whole, are regarded as a single train. In this work, a distributed adaptive model predictive control (AMPC) system is proposed to coordinate the driving of each unit train in VC. To obtain the accurate parameters of train dynamics model in a time varying environment, an estimator of the train dynamics model is designed for each AMPC controller. A variable step descent algorithm along the negative gradient direction is adopted for each estimator, which steers the estimated values of the parameters to real ones. Simulations are conducted and the results are compared with both nominal model predictive control system and AMPC system with fixed steps in the literature. Our proposed AMPC system with variable step (AMPCVS) has better performances than other two systems. Results indicate that there is an improvement of the proposed AMPC system with variable steps system when compared with other two existed systems. A running process of VC in a whole inter-station is also simulated here. Experimental results show that the trains track the desired objective well.

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“…The minimization problem (19) implies to obtain an optimal control input sequence for the next N p time which makes a trade-off between the speed errors and comfort level.…”

Section: Controller For the Leadermentioning

confidence: 99%

“…which is the optimal solution of minimization problem (19). Then, the predicted optimal state is expressed as X 1,k .…”

Section: Controller For the Leadermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Adaptive Model Predictive Control System for Virtual Coupling in Metros

et al. 2021

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“…is approach aims to maintain the desired distance between trains and enrich the ERTMS/ETCS with virtual coupling without changing its working principles. Sliding mode control is used to achieve the same control effect [12].…”

Section: Introductionmentioning

confidence: 99%

Long Short-Term Memory-Based Model Predictive Control for Virtual Coupling in Railways

Chai

Liu

2022

Wireless Communications and Mobile Computing

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The increasing need for capacity has led the railway industry to explore new train control systems based on a concept called virtual coupling. Inspired by the platooning of autonomous vehicles, the safe operation of virtual coupling is guaranteed by a relative brake distance-based train separation method. This paper proposes a novel long short-term memory (LSTM)-based model predictive control (MPC) method for train operations. An MPC-based control design for virtual coupled train operations is presented. The LSTM is introduced to model the dynamics of the preceding train to approximate the actual train operations. With the train dynamics models, the operation trajectories of the preceding train are predicted based on planned control inputs. A study of a metro line in Chengdu was chosen to analyze the proposed control approach. The simulation results of different scenarios show that compared with the conventional MPC methods, the proposed LSTM-based MPC can reduce the speed differences and position differences of tracking trains by up to 35 % and 25 % , respectively.

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“…Song proposed a speed limit curve calculation method based on relative braking distance under the background of virtual coupling [18]. Park and Zhu use sliding mode control and adaptive control methods as the train interval correction scheme, which reduces the influence of external interference on the train [19] [20]. Felez and Cao conducted research from the perspective of virtual coupling train operation control, and proposed using model predictive control to control virtual coupling trains, which can shorten the operation interval between trains [21][22], but did not consider the uncertainty of location and distance.…”

Section: Introductionmentioning

confidence: 99%

Research on Train Dynamic Coupling Strategy Based on Distributed Model Predictive Control

Yang

Yan

2022

J. Phys.: Conf. Ser.

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This paper mainly studies the problem of dynamic coupling under multiple optimization objectives and multiple constraints. Virtual coupling (VC), as a new train operation mode, effectively improves the operating efficiency of the line, but reduces the safety margin between trains, and requires higher control accuracy. As an important scenario of virtual coupling, dynamic formation has not formed a set of applicable train control strategies. Aiming at the dynamic train formation scenario of virtual coupling, propose a control architecture based on distributed model predictive control (DMPC). Numerical simulation using Matlab verifies the effectiveness of the algorithm in this paper. The simulation results show that the train is under the condition of satisfying the operating constraints. Compared with PID control, the method in this paper is more effective and more accurate.

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Virtual Coupling of Railway Vehicles: Gap Reference for Merge and Separation, Robust Control, and Position Measurement

Cited by 60 publications

References 17 publications

An Adaptive Model Predictive Control System for Virtual Coupling in Metros

An Adaptive Model Predictive Control System for Virtual Coupling in Metros

Long Short-Term Memory-Based Model Predictive Control for Virtual Coupling in Railways

Research on Train Dynamic Coupling Strategy Based on Distributed Model Predictive Control

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