Simulation of Comfort Algorithm for Automatic Driving of Urban Rail Train

With the rapid development of China's economy, subways, high-speed railways and bullet trains have become an important tool for more people to travel. Passenger comfort has become another issue to be considered in automatic train control. How to meet the comfort requirements of passengers while improving the speed is another challenge in front of us. Feng et al. transformed the train model into solving the optimal constraint problem, simplified the optimal driving problem by reducing the variable dimension, considered the infinite-dimensional driving problem with finite-dimensional decision variables, and designed a controller with predictive correction to solve the tracking path, so as to establish a driving strategy with optimal ride comfort. In the invention patent of Dong Li Jing et al., in order to improve the comfort of trains, it is considered that the jerk degree is directly involved in the design of the controller, and a high-order control method for the jerk degree is proposed. Combined with the distributed controller design, the comfort of trains can be effectively improved, and the control of multi-train formation also increases the overall operation efficiency.

In the research results of more scholars, the train comfort is considered as a posterior index. After completing the design process of the train controller, whether the controller meets the design requirements of the controller comfort is verified through the constraints and design requirements of the comfort. By comparing the advantages and disadvantages of fuzzy control, neural network control and genetic algorithm in the system, the control performance of ATO system is designed and optimized. Such a design method does not consider the comfort of the train, resulting in the design of the train controller in some extreme cases may not meet the comfort requirements.

In the process of high-speed train operation, due to the different operation scenarios, the resistance is nonlinear and cannot be accurately expressed, which brings disturbance to the whole control system is inevitable. According to the track constraints of maglev train and the design index of passenger comfort, Long proposed an adaptive disturbance control algorithm and verified its feasibility and superiority by simulation. Ze et al. studied the adaptive fault compensation problem for high-speed trains in the presence of time-varying system parameters, disturbances and actuator faults, and discussed the adaptive failure compensation problem with unknown bounds of disturbances in the presence of parameter failures. By introducing nonlinear damping into the controller, a fault compensation controller [47 is proposed for the model where the system parameters are not separable to achieve an arbitrary degree of position tracking accuracy. In the process of train operation, the loss caused by the traction process and the braking process is inevitable. In view of this phenomenon, Mi gen long et al., considering the time-varying external disturbance in the process of real train operation, the basic running resistance and additional resistance of the train are regarded as disturbance terms, and an optimal preview control algorithm is designed. Finally, the prediction and tracking of train speed were realized. Mssashi Asuka et al proposed an Automatic Train Control method to adapt to the situation of train disturbance. This method assumes that digital automatic train control (ATC) equipment is used to transmit the detection time of the track limit to the train approaching the station. Using this information, each train controls its acceleration through a method consisting of two methods: First, by setting a specified limit speed, the train controls its running time to arrive at the next station in accordance with the predicted delay. Second, the train predicts the time to arrive at the current braking profile generated by the digital ATC, and the time to transition the braking profile forward. By comparison, the train correctly chooses the coast driving mode in advance to avoid slowing down due to the current braking profile The effectiveness of the proposed method in terms of driving conditions, energy consumption and delay reduction is evaluated through simulations.

In the process of train operation, there are many factors that affect the comfort of the train, among which the main influencing factors are interior noise, pressure, temperature, odor, toilet facilities, vibration, etc. [10]. Many evaluation criteria of ride comfort have been proposed abroad, and their research on ride comfort evaluation criteria is relatively mature, such as UIC513 standard of the International Railway Union, Sperling standard of Germany, ISO2631 standard of the International Organization for Standardization, etc. [13]. Many domestic scholars have studied how to evaluate the riding comfort of urban rail transit trains by using the UIC513 standard. UIC513 comfort evaluates the riding comfort by using the train's lateral, longitudinal and vertical acceleration [14,15,16]. Sperling smoothness index is mainly used to comprehensively evaluate the lateral and vertical vibration acceleration of vehicle operation [17]. IS02631 standard quantified the vibration exposure limit value of human body in the range of vibration frequency from 1 Hz to 80Hz.

From the perspective of ATO system, in order to ensure the comfort of passengers, the acceleration of the train should not be greater than 1.52m/s² [18]. TB/T2543-1995 “Passenger train Longitudinal Impulse Evaluation Method” points out that the train impulse acceleration change rate can be used to evaluate the train driver's operation stability, and on this basis, the train longitudinal acceleration change rate (i.e., impact rate) is used as an index to evaluate the comfort of high-speed ATO system [19]. The analysis shows that the change in acceleration leads to worse comfort. The rate of change of acceleration is the impact rate, the higher the impact rate, the worse the comfort. The expression formula of impact rate is as follows. (2) J=ΔaΔt J = {{\Delta a} \over {\Delta t}}

In the expression, J is the impact rate, a is the acceleration, and t is the time. The comfort index of ATO system of high-speed train: the impact rate is not greater than 0.5m / s³ in starting and stopping stages, and not greater than 0.4m / s³ in other stages.

In order to improve the ride comfort of urban rail transit, the precondition is to set the train operation target curve that meets the comfort requirements.

Through the analysis, it is concluded that there are two typical stages of train operation: speed adjustment stage and constant speed operation stage. In the constant speed operation phase, the acceleration is equal to zero, so it meets the comfort requirements. However, the speed adjustment phase is certainly accompanied by a change in the acceleration of the combined external force, and the change in acceleration and its acceleration itself are key factors affecting comfort. Therefore, this paper mainly designs the target curve of the train starting phase.

When ATO regulates the speed of the train, a large traction force cannot be applied to the train immediately, otherwise the ride comfort will be seriously affected; therefore, the traction stage should be gradually increased to reduce the impact rate. In the process of train operation, the traction force cannot be withdrawn immediately after the desired target speed is reached [20], so as not to cause harm to passengers. Therefore, the traction force should be gradually increased and then gradually decreased in the starting phase, which not only increases the ride comfort of the train, but also saves energy.

Considering the longitudinal impact between the train vehicles, a certain margin is set for the comfort parameters, and the maximum value of the impact rate is 0.4m / s³ and the maximum value of the acceleration is 1.2m / s² in the starting phase. In order to reduce the impact on the rapidity of the automatic driving system, the train acceleration was linearly increased with the impact rate of 0.4m / s³ and the impact rate was reduced to zero when the acceleration reached 1.2m / s² Then the velocity is linearly increased with an acceleration of 1.2m / s² Finally, with an impact rate of −0.4m / s³ the train acceleration linearly decreases to zero, the train speed reaches the target speed, and the start phase is completed.

The target speed of 60km/h in the starting phase is taken as the target for the design, and the curve of the impact rate, acceleration, speed and running distance of the train as a function of speed is shown in Figure 1.

Figure 1

Affected by the characteristics of the line, the driver must keep the speed of the train within the speed limit when operating the train, so as not to trigger the ATO to brake the train and cause unnecessary harm to the passengers. Therefore, the design of the train operation target curve is transformed into the design of the change law of the speed with the running distance. Therefore, by using the speed and distance change law with time, the change law of the train starting stage is obtained by calculation and data fitting, as shown in Table I. The change law of the target speed in the train starting stage with the travel distance is listed in Table 1. When the train reaches the target speed, the train enters the constant speed running state, and the target curve of train operation used in this paper is shown in Figure 1.

Figure 2

In the table above, a₀ = 13.64, a₁ = −1.108, b₁ = −3.603, a₂ = 0.5904, b₂ = −0.7355, a₃ = 0.1957, b₃ = −0.01202, a₄ = 0.01595, b₄ = 0.01363, w = 0.0326.

According to the model created in Figures 4 and 6, the simulation is carried out to facilitate the analysis of the correlation index with the comfort of the train in the next step. The tracking situation of train speed under the action of PID controller is shown in Figure 7, and the results of train speed control by fuzzy PID controller are compared. The changes of impact rate and acceleration with time under the action of two control methods are shown in Figure 9 and 10, respectively.

Figure 7

Figure 8

Figure 9

Figure 10

Combining Figure 7 and Figure 8, it can be seen that under the action of PID controller and fuzzy PID controller, the tracking characteristics of the train to the target speed curve are ideal, however, under the action of fuzzy PID control, the overshoot of the speed is relatively small. As can be seen from FIG. 9, the impact rate in the process of train operation under the two control functions meets the comfort requirements. However, under the PID control condition, the impact rate has an overshoot of 10% relative to the set value of 0.4m / s³, and the fuzzy PID fully meets the requirements of the set value without overshoot. As can be seen from Figure 10, the acceleration of the train in the process of operation under the two kinds of control meets the comfort requirements, but the acceleration has an overshoot of no more than 5% compared with the set value 1.2m / s², while the overshoot of fuzzy PID control is smaller. It can also be seen from Figs. 9 and 10 that both controllers fully meet the requirements of comfort when moving from the starting phase to the constant speed operation phase.