Abstract
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.
Highlights
One of the main challenges in virtual coupling is the synchronous operations of virtually coupled trains
A typical train speed curve and target speed curve are shown in Figure 2. e greatest recommended speed (GRS) is a speed curve calculated by the train control system based on the current line speed limit, used to prevent the train from overspeeding. e train operation process can be divided into three phases: traction phase, cruise phase, and braking phase. e control algorithm designed in this paper aims to control the following train in the cruise phase
In order to further verify the performance of the proposed control algorithm, first, we compare the proposed control algorithm with the traditional train control architecture. e following train operates according to the preplanned speed curve and protection curve. e design of the controller is that the following train tracks the target’s speed curve and maintains a safe distance from the preceding train. e controller does not predict the behavior of the preceding train
Summary
One of the main challenges in virtual coupling is the synchronous operations of virtually coupled trains. An unstable tracking distance between trains results in a longer arrival time of virtually coupled trains at a station. Erefore, the control target of the following train is a predicted operation state of the preceding train. In conventional control methods of virtual coupling, the approximation and prediction of the preceding train occur through the rule-based dynamics model of the train [4, 5]. The prediction accuracy of these methods is limited, and they are unable to achieve ideal train convoy stability To solve this problem, we propose a long short-term memory (LSTM) based method to predict the operations of preceding trains. Liu et al [9] proposed an optimal control method to maintain the speed of all trains in the virtual coupling consistent and safe distance. Sliding mode control is used to achieve the same control effect [12]
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