An intelligent control method for virtual coupling train group operation in severe environment

Abstract

The application relates to the technical field of train operation intelligent control, and discloses an intelligent control method for virtual coupling train group operation under a severe environment, which comprises the following steps: analyzing the influence of the severe environment on the operation process of the virtual coupling high-speed railway train; analyzing the dynamics of the virtual coupling high-speed railway train under the severe environment; constructing a state space model of the virtual coupling high-speed railway train under the severe environment; and constructing an optimal problem of the intelligent control of the cooperative operation of the high-speed railway train group under the severe environment. The method can effectively cope with the interference of the severe environment on the train operation, control the virtual coupling high-speed railway train group to restore and maintain the cooperative stable operation state under the severe environment, ensure the safe operation of the train, effectively reduce the train operation interval, and improve the line transport capacity.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of train operation intelligent control, and particularly relates to an intelligent control method based on virtual coupling train group operation in a harsh environment. BACKGROUND

[0002] With the rapid development of economy and the continuous growth of the number of travelers, high-speed railway passenger transport plays an increasingly important role, and the traditional high-speed railway transport mode faces the challenge of insufficient transport capacity. Without expanding the scale of the existing infrastructure, adopting new train operation control technology to reduce train operation intervals and realize high-density transport organization has become an important means to improve line capacity and transport capacity. Among them, based on the virtual coupling technology, the safe and stable operation of the virtual coupling train group (VCTS) is the core of the intelligent control problem of train group operation. However, during the operation of the virtual coupling train group, the occurrence of harsh weather, sudden geological disasters and other harsh environments is inevitable, which will cause changes in the speed limit conditions and train operation state along the line, directly affecting the stable operation of the virtual coupling train group of the high-speed railway, making the train deviate from the stable state, and negatively affecting the safety and stability of the virtual coupling train group. SUMMARY

[0003] In view of the above problems in the prior art, the present application provides an intelligent control method based on virtual coupling train group operation in a harsh environment, which considers the influence factors of harsh environment, solves the insecurity and stability of virtual coupling train group operation under the influence of harsh environmental factors, effectively deals with the influence of harsh environmental factors, clearly interferes and minimizes the disturbance through the intelligent control method, ensures the safety of train operation, and maintains the cooperative and stable operation of the train.

[0004] In order to achieve the above-mentioned application purposes, the technical scheme adopted by the present application is as follows:

[0005] An intelligent control method based on virtual coupling train group operation in a harsh environment, comprising the following steps:

[0006] S1, analyzing the influence of harsh environment on the running resistance of virtual coupling high-speed railway train, and calculating the running resistance of virtual coupling high-speed railway train in a harsh environment;

[0007] Among them, the running resistance includes unit basic resistance, other additional resistance and unit line additional resistance;

[0008] S2, analyzing the influence of harsh environment on the safe operation speed of virtual coupling high-speed railway train, and obtaining the safe operation index and speed limit state of virtual coupling high-speed railway train in a harsh environment;

[0009] S3, analyze the single-particle stress state of the virtual coupling high-speed railway train in the harsh environment, and construct a dynamics model of the virtual coupling high-speed railway train in the harsh environment according to the running resistance of the virtual coupling high-speed railway train in the harsh environment;

[0010] S4, according to the dynamics model of the virtual coupling high-speed railway train in the harsh environment, the dynamic programming method is used to obtain the position, speed, unit control quantity and acceleration of the leading virtual coupling high-speed railway train, and based on the target speed and position relationship between adjacent trains, the target state of each following virtual coupling high-speed railway train is obtained;

[0011] S5, according to the target state of each following virtual coupling high-speed railway train, the state quantity of the virtual coupling high-speed railway train is obtained, and the unit control quantity of the virtual coupling high-speed railway train is added to generate a new state quantity, and a state space model of the virtual coupling high-speed railway train in the harsh environment is constructed;

[0012] S6, according to the state space model of the virtual coupling high-speed railway train in the harsh environment, the estimated value of the new state quantity in the prediction time domain is obtained and simplified into a matrix form, a prediction state matrix is obtained, and a prediction output quantity is obtained;

[0013] S7, according to the prediction output quantity and the safety operation index and speed limit state of the virtual coupling high-speed railway train in the harsh environment, the intelligent control optimization target and optimization target constraint condition of the cooperative operation of the virtual coupling high-speed railway train group in the harsh environment are constructed, and after being converted into a standard quadratic programming problem with constraints, it is solved to realize the safe and stable operation of the virtual coupling high-speed railway train group in the harsh environment.

[0014] The present application has the following beneficial effects:

[0015] The intelligent control method for virtual coupling train group operation in harsh environment provided by the present application can complete the intelligent control of the virtual coupling high-speed railway train in harsh environment, significantly shorten the train operation interval while ensuring the safe operation of the train, realize high-density transportation organization, and effectively improve the transportation capacity of high-speed railway line. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 The flowchart of the intelligent control method for virtual coupling train group operation in harsh environment provided by the present application is shown in the figure;

[0017] Figure 2 The single-particle stress state diagram of the virtual coupling high-speed railway train is shown in the figure;

[0018] Figure 3 The position relationship diagram of each following virtual coupling high-speed railway train and the preceding train is shown in the figure;

[0019] Figure 4 schematic diagram for safe braking constraint;

[0020] Figure 5 schematic diagram for line speed limit condition and target speed curve of leading train in severe environment;

[0021] Figure 6 schematic diagram for cooperative running control effect of virtual coupled high-speed railway trains;

[0022] Figure 7 schematic diagram for running interval between adjacent trains in virtual coupled train group and moving block. DETAILED DESCRIPTION

[0023] The specific embodiments of the present application are described below to facilitate the understanding of the present application for those skilled in the art, but it should be clear that the present application is not limited to the scope of the specific embodiments, and for those skilled in the art, it is obvious that various changes are within the spirit and scope of the present application defined and determined by the appended claims, and all the inventions utilizing the concept of the present application are within the scope of protection.

[0024] As shown in Figure 1 a kind of intelligent control method based on virtual coupled train group operation in severe environment, comprising the following steps S1-S7:

[0025] S1, analyze the influence of severe environment on the running resistance of virtual coupled high-speed railway train, calculate the running resistance of virtual coupled high-speed railway train in severe environment;Wherein, running resistance includes unit basic resistance, other additional resistance and unit line additional resistance.

[0026] In this embodiment, the purpose of analyzing the influence of severe environment on the running resistance of virtual coupled high-speed railway train is to explore the influence of severe environment on the running resistance of virtual coupled high-speed railway train, to analyze the expression of the resistance of virtual coupled train in each severe environment scene, and to lay a theoretical foundation for the construction of subsequent dynamic model.

[0027] Specifically, step S1 specifically includes S11-S14:

[0028] S11, obtain severe environment factors including strong wind factor, rainfall factor, lightning factor, visibility factor, earthquake factor and debris flow factor.

[0029] S12, if the severe environment factor is the strong wind factor, the unit basic resistance of the virtual coupled high-speed railway train under the strong wind factor is calculated, that is:

[0030]

[0031] wherein r represents the unit basic resistance received by the virtual coupled high-speed railway train under the strong wind factor, c0 represents the rolling mechanical damping coefficient of the virtual coupled high-speed railway train, c1 represents the other mechanical resistance coefficient of the virtual coupled high-speed railway train, v represents the speed of the virtual coupled high-speed railway train, p represents the air density, S w represents the cross-sectional area of the virtual coupled high-speed railway train, c2 represents the external air damping coefficient received by the virtual coupled high-speed railway train, v h represents the resultant speed of the virtual coupled high-speed railway train speed and the wind speed, C1 represents that the wind direction is the same as the running direction of the virtual coupled high-speed railway train, C2 represents that the wind direction is opposite to the running direction of the virtual coupled high-speed railway train, C3 represents that the wind direction is at a certain angle to the running direction of the virtual coupled high-speed railway train, v w represents the instantaneous wind speed, cos represents the cosine function, a represents the wind direction angle, and b represents the side slip angle.

[0032] In the embodiment, the unit basic resistance r includes friction resistance and air resistance, the unit is KN / t; the speed v, the unit is km / h; the rolling mechanical damping coefficient c0, the unit is KN / t; the other mechanical resistance coefficient c1, the unit is KN / (km / h·t); the external air damping coefficient c2, the unit is KN / (km 2 / h 2 ·t); the air density p, the unit is kg / m 3 ; the cross-sectional area S w , the unit is m 2 ; the resultant speed v h , the unit is km / h; the instantaneous wind speed v w , the unit is km / h; in addition, the wind direction angle a refers to the included angle between the wind direction and the line direction; the side slip angle b refers to the included angle between the resultant speed of the virtual coupled high-speed railway train reverse speed and the wind speed and the line direction.

[0033] S13, if the adverse environment factor is other factors except the strong wind factor, the other additional resistance received by the virtual coupled high-speed railway train under other factors except the strong wind factor is updated in real time by sampling.

[0034] In this embodiment, since the influence of other adverse environmental factors on train operation resistance is uncertain and nonlinear, the other additional resistance ω is represented, and its value is updated in real time by sampling. In the actual train operation process, the value of the other additional resistance ω can be calculated and updated online. That is, from the train acceleration expression: a = u - r - g - ω, where u is the unit control amount (traction / braking) of the train. By setting a reasonable sampling frequency, the control amount u at the sampling time is known, and the acceleration a is known through the train acceleration sensor. Then the other additional resistance ω can be calculated, and then it is used as the value at the next time, which is updated in real time by sampling to calculate the unit control amount of the train.

[0035] S14, calculate the additional resistance of the virtual coupled high-speed railway train under different line conditions under the influence of adverse environmental factors, that is, the unit line additional resistance of the virtual coupled high-speed railway train under the influence of adverse environmental factors, that is:

[0036]

[0037] where G represents the unit line additional resistance of the virtual coupled high-speed railway train, g s represents the unit slope additional resistance of the virtual coupled high-speed railway train, g r represents the unit curve additional resistance of the virtual coupled high-speed railway train, g L represents the unit tunnel additional resistance of the virtual coupled high-speed railway train, g represents the acceleration of gravity, slope represents the slope of the slope where the virtual coupled high-speed railway train is located, e1 represents the empirical curve resistance coefficient, R represents the curve radius of the curve where the virtual coupled high-speed railway train is located, L1 represents the empirical tunnel resistance coefficient, and L tn represents the length of the tunnel where the virtual coupled high-speed railway train is located.

[0038] In this embodiment, the unit slope additional resistance g s is KN / t; the unit curve additional resistance g r is KN / t; the unit tunnel additional resistance g L is KN / t; the length L tn of the tunnel where the virtual coupled high-speed railway train is located is m; the empirical curve resistance coefficient e1 is 600, and the empirical tunnel resistance coefficient L1 is 0.00013.

[0039] S2, analyze the influence of adverse environments on the safe operation speed of the virtual coupled high-speed railway train, and obtain the safe operation index and speed limit state of the virtual coupled high-speed railway train under adverse environments.

[0040] In this embodiment, the purpose of analyzing the influence of the adverse environment on the safe running speed of the virtual coupled high-speed train is to obtain the safe running index and speed limit condition of the virtual coupled high-speed train under each adverse environment scene, so as to determine the speed constraint of the train at each time in the optimal control model.

[0041] Specifically, step S2 specifically includes S21-S27:

[0042] S21, if the adverse environment factor is the strong wind factor, the instantaneous wind speed is selected as the safe running index of the virtual coupled high-speed train, and the speed limit condition of the virtual coupled high-speed train under the instantaneous wind speed standard is obtained according to the instantaneous wind speed standard.

[0043] S22, if the adverse environment factor is the rainfall factor, the rainfall is selected as the safe running index of the virtual coupled high-speed train, and the speed limit condition of the virtual coupled high-speed train under the rainfall standard is obtained according to the rainfall standard.

[0044] S23, if the adverse environment factor is the lightning factor, the lightning is selected as the safe running index of the virtual coupled high-speed train, and the speed limit condition of the virtual coupled high-speed train under the lightning standard is obtained according to the lightning standard.

[0045] S24, if the adverse environment factor is the visibility factor, the visibility distance is selected as the safe running index of the virtual coupled high-speed train, and the speed limit condition of the virtual coupled high-speed train under the visibility distance standard is obtained according to the visibility distance standard.

[0046] S25, if the adverse environment factor is the earthquake factor, the predicted or measured peak ground motion acceleration is selected as the safe running index of the virtual coupled high-speed train, and the speed limit condition of the virtual coupled high-speed train under the peak ground motion acceleration standard is obtained according to the peak ground motion acceleration standard.

[0047] S26, if the adverse environment factor is the debris flow, the debris flow is selected as the safe running index of the virtual coupled high-speed train, and the speed limit condition of the virtual coupled high-speed train under the debris flow standard is obtained according to the debris flow standard.

[0048] S27, if multiple adverse environment factors exist at the same time, the minimum speed under the adverse environment factor standard is selected as the speed limit condition of the virtual coupled high-speed train.

[0049] In this embodiment, according to the above steps S21-S26, the safe running index and speed limit condition of the virtual coupled high-speed train under the adverse environment are specifically shown in Table 1:

[0050] Table 1 safe running index and speed limit condition table

[0051]

[0052]

[0053] S3, analyze the single-particle force status of the virtual coupled high-speed railway train in a harsh environment, and construct a dynamics model of the virtual coupled high-speed railway train in the harsh environment according to the running resistance of the virtual coupled high-speed railway train in the harsh environment.

[0054] In this embodiment, the purpose of analyzing the single-particle force status of the virtual coupled high-speed railway train in a harsh environment is to analyze the single-particle force status of the virtual coupled high-speed railway train in a harsh environment, which includes the traction force or braking force and the running resistance of the train. The acceleration expression of the train can be obtained through single-particle force analysis to construct a train dynamics model. In addition, according to the single-particle force status of the virtual coupled high-speed railway train in a harsh environment and the running resistance of the virtual coupled high-speed railway train in a harsh environment, the Newton kinematics theorem is used to construct a dynamics model of the virtual coupled high-speed railway train in a harsh environment. The purpose is to calculate the target speed curve of the lead train and construct the subsequent state space model according to the dynamics model of the virtual coupled high-speed railway train in a harsh environment.

[0055] Specifically, step S3 specifically includes S31-S32:

[0056] S31, obtain the traction force or braking force and the running resistance of the virtual coupled high-speed railway train in a harsh environment.

[0057] In this embodiment, the single-particle force status of the virtual coupled high-speed railway train in a harsh environment is as shown in Figure 2 , including the traction force F, the running resistance f, and the braking force B. The traction force F and the braking force B are collectively referred to as control quantities. When the virtual coupled high-speed railway train needs to accelerate, it receives the traction force F. When the train travels at a reduced speed, it receives the braking force B.

[0058] S32, construct a dynamics model of the virtual coupled high-speed railway train in a harsh environment according to the traction force or braking force and the running resistance of the virtual coupled high-speed railway train in a harsh environment, that is:

[0059]

[0060] where s i (t) represents the position of the ith virtual coupled high-speed railway train at time t, represents the first derivative of the position of the ith virtual coupled high-speed railway train at time t, v i (t) represents the speed of the ith virtual coupled high-speed railway train at time t, denotes the first derivative of the speed of the i-th virtual coupled high-speed railway train at the t-th moment, a i (t) denotes the acceleration of the i-th virtual coupled high-speed railway train at the t-th moment, u i (t) denotes the unit control amount of the i-th virtual coupled high-speed railway train at the t-th moment, r i (t) denotes the unit basic resistance received by the i-th virtual coupled high-speed railway train at the t-th moment, G i [s i (t) denotes the unit line additional resistance received by the i-th virtual coupled high-speed railway train at the t-th moment, w i [s i (t) denotes other additional resistance received by the i-th virtual coupled high-speed railway train at the t-th moment.

[0061] S4, according to the dynamics model of the virtual coupled high-speed railway train in the severe environment, the position, speed, unit control amount and acceleration of the leading virtual coupled high-speed railway train are obtained by using the dynamic programming method, and the target state of each following virtual coupled high-speed railway train is obtained based on the target speed and position relationship between adjacent trains; wherein the target state includes target position and target speed.

[0062] In this embodiment, according to the train speed limit condition in each severe environment, by means of the dynamics model of the virtual coupled high-speed railway train in the severe environment constructed, the target speed curve of the leading train can be obtained by using the dynamic programming method, as shown in Figure 5 At the same time, based on the target speed and position relationship between adjacent trains, the target position and target speed of each following train at each discrete moment can be obtained, which provides a target for train operation control.

[0063] Specifically, step S4 specifically includes S41-S42:

[0064] S41, according to the dynamics model of the virtual coupled high-speed railway train in the severe environment, the position, speed, unit control amount and acceleration of the leading virtual coupled high-speed railway train are obtained by using the dynamic programming method.

[0065] In this embodiment, the position of the leading virtual coupled high-speed railway train obtained is s0(t), the speed is v0(t), the unit control amount is u0(t), and the acceleration is a0(t).

[0066] S42, according to the target speed and position relationship between adjacent trains, the target state of each following virtual coupled high-speed railway train is obtained, specifically:

[0067] The target speed of each following virtual coupled high-speed railway train is:

[0068]

[0069] in, Let v represent the target speed of the i-th virtual coupled high-speed railway train at time t. i-1 (t) represents the speed of the (i-1)th virtual coupled high-speed railway train at time t.

[0070] In this embodiment, the expected value of the target speed for each following virtual coupled high-speed railway train is equal to the speed of the preceding train, that is:

[0071] The target positions of each virtual-coupled high-speed railway train are as follows:

[0072]

[0073] in, Let s represent the target position of the i-th virtual coupled high-speed railway train at time t. i-1 (t) represents the position of the (i-1)th virtual coupled high-speed railway train at time t, and d des d0 represents the expected distance between adjacent trains, d0 represents the safety margin, and l represents the length of the virtual coupled high-speed railway train.

[0074] In this embodiment, the positional relationship between each following virtual coupled high-speed railway train and the preceding train is as follows: Figure 3 As shown, where Δs i Let Δs be the difference between the distance between the virtual coupled high-speed railway train i and the preceding train and the desired distance. Based on the control objective, we have: i =0, therefore the target position of each following virtual coupled high-speed railway train is

[0075] S5. Based on the target states of each following virtual coupled high-speed railway train, obtain the state variables of the virtual coupled high-speed railway train, add the unit control variables of the virtual coupled high-speed railway train, generate new state variables, and construct the state space model of the virtual coupled high-speed railway train under harsh conditions.

[0076] In this embodiment, a state-space model of a virtual coupled high-speed railway train under harsh conditions is constructed, which provides a model basis for subsequent model predictive control. The model predictive control method needs to make predictions based on the state-space model.

[0077] Specifically, step S5 includes S51-S52:

[0078] S51. Based on the target states of each following virtual coupled high-speed railway train, obtain the state variables of the virtual coupled high-speed railway train, namely:

[0079]

[0080] wherein x i (t) represents the state quantity of the i th virtual coupled high-speed railway train at the t th moment, Δs i (t) represents the deviation of the position of the i th virtual coupled high-speed railway train at the t th moment from the target position, Δv i (t) represents the deviation of the speed of the i th virtual coupled high-speed railway train at the t th moment from the target speed, and T represents transposition.

[0081] S52, according to the state quantity of the virtual coupled high-speed railway train, a forward Euler method is used for discretization processing, and a unit control quantity of the virtual coupled high-speed railway train is added to the state quantity to generate a new state quantity, and a state space model of the virtual coupled high-speed railway train in a harsh environment is constructed, that is:

[0082]

[0083] wherein, represents the new state quantity of the i th virtual coupled high-speed railway train at the t+1 th moment, represents the new state quantity of the i th virtual coupled high-speed railway train at the t th moment, A, B, C, and D represent different coefficient matrices, respectively, and y i (t) represents the control output variable of the i th virtual coupled high-speed railway train at the t th moment, u i (t-1) represents the unit control quantity of the i th virtual coupled high-speed railway train at the t-1 th moment, Δu i (t) represents the difference between the unit control quantity of the i th virtual coupled high-speed railway train at the t th moment and the unit control quantity of the i th virtual coupled high-speed railway train at the t-1 th moment, a i-1 (t) represents the acceleration of the i-1 th virtual coupled high-speed railway train at the t th moment, and E represents a control output coefficient matrix.

[0084] In this embodiment,

[0085]

[0086] represents a sampling period; h i (t) represents the coefficient of the i th virtual coupled high-speed railway train acceleration expression after first-order Taylor expansion v i (t), and l i (t) represents the constant term of the i th virtual coupled high-speed railway train acceleration expression after first-order Taylor expansion. In addition, if the control output variable y i (t) = [Δs i(t), Δv i (t)] T Then, there are:

[0087] S6, according to the state space model of the virtual coupled high-speed railway train in the harsh environment, the estimated value of the new state quantity in the prediction time domain is obtained and simplified into a matrix form, a prediction state matrix is obtained, and a prediction output quantity is obtained.

[0088] In this embodiment, the estimated value of the state quantity is derived and simplified into a matrix form, which can be used to obtain the estimated value of the new state quantity of the i-th virtual coupled high-speed railway train at each time in the prediction time domain N p at time t.

[0089] Specifically, the estimated value of the new state quantity in the prediction time domain in step S6 is:

[0090]

[0091] Wherein, N p represents the prediction time domain, N c represents the control time domain, represents the state quantity of the i-th virtual coupled high-speed railway train at time t+1 predicted at time t, i.e. the estimated value of the new state quantity when the control time domain is 1, represents the state quantity of the i-th virtual coupled high-speed railway train at time t+2 predicted at time t, Δu i (t+1|t) represents the unit control quantity increment of the i-th virtual coupled high-speed railway train at time t+1 predicted at time t, a i-1 (t+1) represents the acceleration of the i-1-th virtual coupled high-speed railway train at time t+1, represents the state quantity of the i-th virtual coupled high-speed railway train at time t+N p predicted at time t, Δu i (t+N c -1|t) represents the unit control quantity increment of the i-th virtual coupled high-speed railway train at time t+N c -1 predicted at time t, a i-1 (t+N p -1) represents the acceleration of the i-1-th virtual coupled high-speed railway train at time t+N p -1.

[0092] In this embodiment, according to the state space model of the virtual coupled high-speed railway train in the harsh environment, a model predictive control method is used to obtain the estimated value of the new state quantity in the prediction time domain. The model predictive control method is to complete the prediction of future dynamic output based on the current measured state and the state space model through model prediction, feedback correction and rolling optimization.

[0093] Specifically, the predicted state matrix in step S6 is:

[0094]

[0095] wherein X i (t) represents the predicted state matrix of the ith virtual coupled high-speed railway train at the tth moment, ψ、 δ and φ represent the recursive coefficient matrixes of each new state variable, respectively, and ΔU i represents the unit control amount increment matrix of the ith virtual coupled high-speed railway train predicted at the tth moment from the tth moment to the t+N c -1th moment, represents the acceleration matrix of the (i-1)th virtual coupled high-speed railway train from the tth moment to the t+N p -1th moment.

[0096] In the embodiment, wherein n represents the lower bound of the cumulative symbol.

[0097] Specifically, the predicted output in step S6 is:

[0098]

[0099] wherein Y i (t) represents the predicted output of the ith virtual coupled high-speed railway train at the tth moment, y i (t+1|t) represents the state output of the ith virtual coupled high-speed railway train at the t+1th moment predicted at the tth moment, i.e., the position deviation and the speed deviation at the t+1th moment, y i (t+N p |t) represents the state output of the ith virtual coupled high-speed railway train at the t+N p th moment predicted at the tth moment, i.e., the position deviation and the speed deviation at the t+N p th moment, y i (t+j1|t) represents the state output of the ith virtual coupled high-speed railway train at the t+j1th moment predicted at the tth moment, i.e., the position deviation and the speed deviation at the t+j1th moment, represents the state quantity of the ith virtual coupled high-speed railway train at the t+j1th moment predicted at the tth moment, ψ′, φ′, δ′, and φ′ represent the recursive coefficient matrixes of the predicted output, respectively.

[0100] S7, constructing the intelligent control optimization goal and the optimization goal constraint condition of the virtual coupled high-speed railway train group in the harsh environment according to the predicted output and the safety operation index and speed limit state of the virtual coupled high-speed railway train in the harsh environment, and solving the standard quadratic programming problem with constraints to realize the safe and stable operation of the virtual coupled high-speed railway train group in the harsh environment.

[0101] In this embodiment, the cooperative control goal of the virtual coupled high-speed railway train group is to minimize the train position error and speed error, and at the same time, in the control process, frequent switching between traction and braking is avoided as much as possible, and energy consumption is reduced; therefore, the weighted sum of the position error, speed error and control increment can be used to construct the intelligent control optimization goal of the virtual coupled high-speed railway train group in the harsh environment, and a scalar relaxation factor is added in the optimization goal to enhance the solving ability of the optimization goal. In addition, in the operation process of the virtual coupled high-speed railway train group, the train is limited by its own performance and affected by the running environment, and the optimization goal constraint condition should be considered in the operation intelligent control to ensure the running safety and performance of the train. At the same time, the standard quadratic programming problem with constraints is solved, and the unit control increment from t-1 time to t time can be obtained, so that the unit control at each time can be obtained.

[0102] Specifically, step S7 specifically includes S71-S73:

[0103] S71, constructing the intelligent control optimization goal of the virtual coupled high-speed railway train group in the harsh environment according to the predicted output and the safety operation index and speed limit state of the virtual coupled high-speed railway train in the harsh environment, that is:

[0104]

[0105] Wherein, J i (t) represents the intelligent control optimization goal of the ith virtual coupled high-speed railway train at the tth time, Δu i (t+j2-1|t) represents the unit control increment of the ith virtual coupled high-speed railway train at the t+N c -1 time predicted at the tth time, ε represents the relaxation factor, q1 represents the intermediate variable, diag represents the diagonal matrix, q2, ρ' represent the weight coefficients of the position error, speed error, control increment and relaxation factor respectively.

[0106] S72, establishing the optimization goal constraint condition including the control performance constraint, the safety braking constraint and the safety operation speed constraint, which is specifically:

[0107] The control performance constraint is:

[0108] uMin ≤u i (t)≤u Max

[0109] Among them, u Min u Max These represent the maximum braking deceleration and maximum traction acceleration of the virtual coupled train, respectively.

[0110] In this embodiment, during train operation, its traction and braking performance are inherently constrained by the train design. This constraint is specifically reflected in the train's unit control quantity, namely: u Min ≤u i (t)≤u Max .

[0111] The safety braking constraint is:

[0112]

[0113] Where max represents taking the maximum value, v i (t) represents the speed of the i-th virtual coupled high-speed railway train at time t, v i-1 (t) represents the speed of the (i-1)th virtual coupled high-speed railway train at time t.

[0114] In this embodiment, to ensure the safe stopping of the train in an emergency, the distance between adjacent trains must be strictly greater than their relative braking distance, i.e. like Figure 4 As shown, that is Let be the emergency braking distance of the i-th virtual coupled high-speed railway train. Let be the emergency braking distance of the (i-1)th virtual coupled high-speed railway train. When the emergency braking distance of the i-th virtual coupled high-speed railway train is greater than that of the (i-1)th virtual coupled high-speed railway train, the difference distance must be reserved to ensure that the i-th virtual coupled high-speed railway train will not rear-end the (i-1)th virtual coupled high-speed railway train when it brakes to a stop, and to maintain a safety margin distance, that is, to meet the following safe operating speed constraints.

[0115] The safe operating speed constraint is:

[0116] 0≤v i (t)≤v lim (s i (t))

[0117] Among them, v lim Indicates the safe operating speed.

[0118] In this embodiment, the running speed of the virtual coupled train needs to meet the safe running speed and be less than the speed limit in the line to reduce the safety risk of train operation under the influence of line conditions and various uncertain environmental factors (adverse environmental factors). In addition, the safe running speed v lim The specific value is expressed by a segmented function according to the speed limit condition under each adverse environmental factor.

[0119] S73, the virtual coupled high-speed railway train group coordination running intelligent control optimization goal and optimization goal constraint condition under adverse environment is converted into a vector form of standard type quadratic programming problem with constraints and solved, realizing the safe and stable operation of the virtual coupled high-speed railway train group under adverse environment.

[0120] To verify the effectiveness of the intelligent control method based on virtual coupled train group operation under adverse environment proposed in the application, based on the adverse environment conditions shown in Table 2, that is:

[0121] Table 2 Virtual coupled high-speed railway train position and speed limit condition table under adverse environment

[0122] Location (km) Adverse environmental factors Speed limit conditions (km / h) 22-32 Wind, v w = 20 m / s 300 45-60 Rain, R = 200 mm / s 200 110-135 Low visibility, L = 150 m 250 190-200 With lightning 200 265-285 Wind direction angle 45°, v w = 20 m / s 300

[0123] Based on the data in Table 2, the running state of the following train is simulated by using the method proposed in the application and MATLAB, wherein according to the train speed limit condition under each adverse environment, the target speed curve of the leading train can be obtained by using the train dynamics model and dynamic programming method, as shown in Figure 5 . Further, the state space model can be used to solve the virtual coupled high-speed railway train coordination running control effect based on the model predictive control method and by using the constructed control model, as shown in Figure 6 From Figure 6 , it can be seen that the method proposed in the application can effectively cope with the interference of adverse environmental factors on the running of the virtual coupled train, control the virtual coupled train group to quickly restore and maintain a stable coordination state, maximize the control of the train group to run at the same speed and with the expected interval, and maintain the train position error and speed error at a small value, ensuring the safe operation of the train group.

[0124] At the same time, based on the method proposed in the application, compared with the traditional moving block mode, the virtual coupling technology effectively shortens the running interval between trains, as shown in Figure 7As shown. It is calculated that in the present embodiment, the minimum train interval under the moving block mode is 2429 meters on average, while the train interval under the VCTS mode is maintained at 150 meters most of the time, which is equal to the set expected interval plus a safety margin, and the average interval of the virtual coupling of each adjacent train is reduced by 93.77% compared with the minimum train interval under the moving block mode. Based on the control effect of the method proposed in the present application, the expected interval simulation value can be further reduced on the basis of ensuring train safety, so as to realize smaller train running interval.

[0125] In summary, the intelligent control method for train group operation based on virtual coupling under harsh environment proposed in the present application can effectively cope with the interference of harsh environment on train operation, control the virtual coupling of high-speed railway train group to restore and maintain a coordinated and stable operation state under harsh environment, ensure safe train operation, effectively reduce train running interval, and improve line transport capacity.

[0126] The principle and implementation mode of the present application are described in the specific embodiments in the present application, and the above embodiment description is only used to help understand the method of the present application and its core idea; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range can be changed, and the above description should not be understood as a limitation of the present application.

[0127] Those skilled in the art will realize that the embodiments described herein are for the purpose of understanding the principle of the present application and should be understood as not limiting the protection scope of the present application. Those skilled in the art can make various other specific modifications and combinations according to the technical inspiration disclosed in the present application without departing from the essence of the present application, and these modifications and combinations are still within the protection scope of the present application.

Claims

1. An intelligent control method based on virtual coupled train group operation in a severe environment, characterized in that, The method comprises the following steps: S1, analyzing the influence of the adverse environment on the running resistance of the virtual coupled high-speed railway train, and calculating the running resistance of the virtual coupled high-speed railway train in the adverse environment; The running resistance comprises unit basic resistance, other additional resistance and unit line additional resistance; S2, analyzing the influence of the adverse environment on the safe running speed of the virtual coupled high-speed railway train, and obtaining the safe running index and speed limit state of the virtual coupled high-speed railway train in the adverse environment; S3, analyzing the single-particle force state of the virtual coupled high-speed railway train in the adverse environment, and constructing a dynamics model of the virtual coupled high-speed railway train in the adverse environment according to the running resistance of the virtual coupled high-speed railway train in the adverse environment; S4, obtaining the position, speed, unit control quantity and acceleration of the leading virtual coupled high-speed railway train by using the dynamic programming method according to the dynamics model of the virtual coupled high-speed railway train in the adverse environment, and obtaining the target state of each following virtual coupled high-speed railway train based on the target speed and position relationship between adjacent trains; S5, obtaining the state quantity of the virtual coupled high-speed railway train according to the target state of each following virtual coupled high-speed railway train, adding the unit control quantity of the virtual coupled high-speed railway train, generating a new state quantity, and constructing a state space model of the virtual coupled high-speed railway train in the adverse environment; S6, obtaining the estimated value of the new state quantity in the prediction time domain and simplifying it into a matrix form according to the state space model of the virtual coupled high-speed railway train in the adverse environment, obtaining a prediction state matrix, and obtaining a prediction output quantity; S7, constructing the intelligent control optimization target and optimization target constraint condition of the coordinated operation of the virtual coupled high-speed railway train group in the adverse environment according to the prediction output quantity, the safe running index and the speed limit state of the virtual coupled high-speed railway train in the adverse environment, converting the intelligent control optimization target and the optimization target constraint condition into a standard quadratic programming problem with constraints, and solving the problem to realize the safe and stable operation of the virtual coupled high-speed railway train group in the adverse environment.

2. The intelligent control method for virtual coupled train group operation in severe environment according to claim 1, characterized in that, Step S1 specifically comprises: S11, obtaining adverse environment factors including strong wind factor, rainfall factor, lightning factor, visibility factor, earthquake factor and debris flow factor; S12, if the adverse environment factor is the strong wind factor, the unit basic resistance of the virtual coupled high-speed railway train under the strong wind factor is calculated, that is: Wherein, r represents the unit basic resistance received by the virtual coupled high-speed railway train under the strong wind factor, c0 represents the rolling mechanical damping coefficient of the virtual coupled high-speed railway train, c1 represents the other mechanical resistance coefficient of the virtual coupled high-speed railway train, v represents the speed of the virtual coupled high-speed railway train, ρ represents the air density, S w represents the cross-sectional area of the virtual coupled high-speed railway train, c2 represents the external air damping coefficient received by the virtual coupled high-speed railway train, v h represents the combined speed of the virtual coupled high-speed railway train speed and the wind speed, C1 represents that the wind direction is the same as the running direction of the virtual coupled high-speed railway train, C2 represents that the wind direction is opposite to the running direction of the virtual coupled high-speed railway train, C3 represents that the wind direction is at a certain angle with the running direction of the virtual coupled high-speed railway train, v w represents the instantaneous wind speed, cos represents the cosine function, α represents the wind direction angle, and β represents the side slip angle; S13, if the adverse environment factor is other factors except the strong wind factor, the other additional resistance of the virtual coupled high-speed railway train under other factors except the strong wind factor is updated in real time by sampling; S14, calculating the additional resistance of the virtual coupled high-speed railway train under different line conditions, that is, the unit line additional resistance of the virtual coupled high-speed railway train under the adverse environment factor, that is: wherein G represents a unit line additional resistance received by the virtual coupled high-speed railway train, g s represents a unit slope additional resistance of the virtual coupled high-speed railway train, g r represents a unit curve additional resistance of the virtual coupled high-speed railway train, g L represents a unit tunnel additional resistance of the virtual coupled high-speed railway train, g represents an acceleration of gravity, slope represents a slope percentage of a slope where the virtual coupled high-speed railway train is located, e1 represents an empirical curve resistance coefficient, R represents a curve radius of a curve where the virtual coupled high-speed railway train is located, L1 represents an empirical tunnel resistance coefficient, L tn represents a length of a tunnel where the virtual coupled high-speed railway train is located.

3. The intelligent control method for virtual coupled train group operation in severe environment according to claim 2, characterized in that, Step S2 specifically comprises: S21, if the adverse environment factor is the strong wind factor, the instantaneous wind speed is selected as the safe running index of the virtual coupled high-speed railway train, and the speed limit condition of the virtual coupled high-speed railway train under the instantaneous wind speed standard is obtained according to the instantaneous wind speed standard; S22, if the adverse environmental factor is rainfall factor, selecting rainfall as the virtual coupling high-speed railway train safety operation index, and according to the rainfall standard, obtaining the speed limit condition of the virtual coupling high-speed railway train under the rainfall standard; S23, if the adverse environmental factor is lightning factor, selecting lightning as the virtual coupling high-speed railway train safety operation index, and according to the lightning standard, obtaining the speed limit condition of the virtual coupling high-speed railway train under the lightning standard; S24, if the adverse environmental factor is visibility factor, selecting visibility distance as the virtual coupling high-speed railway train safety operation index, and according to the visibility distance standard, obtaining the speed limit condition of the virtual coupling high-speed railway train under the visibility distance standard; S25, if the adverse environmental factor is earthquake factor, selecting the predicted or measured peak ground motion acceleration as the virtual coupling high-speed railway train safety operation index, and according to the peak ground motion acceleration standard, obtaining the speed limit condition of the virtual coupling high-speed railway train under the peak ground motion acceleration standard; S26, if the adverse environmental factor is debris flow, selecting debris flow as the virtual coupling high-speed railway train safety operation index, and according to the debris flow standard, obtaining the speed limit condition of the virtual coupling high-speed railway train under the debris flow standard; S27, if multiple adverse environmental factors exist simultaneously, selecting the minimum speed under the adverse environmental factor standard as the speed limit condition of the virtual coupling high-speed railway train.

4. The intelligent control method for virtual coupled train group operation in severe environment according to claim 3, characterized in that, Step S3 specifically includes: S31, obtaining the traction force or braking force, running resistance of the virtual coupling high-speed railway train under adverse environment; S32, constructing the dynamics model of the virtual coupling high-speed railway train under adverse environment according to the traction force or braking force, running resistance of the virtual coupling high-speed railway train under adverse environment, that is: wherein s i (t) represents the position of the i-th virtual coupled high-speed railway train at the t-th moment, represents the first derivative of the position of the i-th virtual coupled high-speed railway train at the t-th moment, v i (t) represents the speed of the i-th virtual coupled high-speed railway train at the t-th moment, represents the first derivative of the speed of the i-th virtual coupled high-speed railway train at the t-th moment, a i (t) represents the acceleration of the i-th virtual coupled high-speed railway train at the t-th moment, u i (t) represents the unit control amount of the i-th virtual coupled high-speed railway train at the t-th moment, r i (t) represents the unit basic resistance received by the i-th virtual coupled high-speed railway train at the t-th moment, G i [s i (t) represents the unit line additional resistance received by the i-th virtual coupled high-speed railway train at the t-th moment, w i [s i (t) represents other additional resistance received by the i-th virtual coupled high-speed railway train at the t-th moment.

5. The intelligent control method for virtual coupled train group operation in severe environment according to claim 4, characterized in that, Step S4 specifically includes: S41, obtaining the position, speed, unit control quantity and acceleration of the leading virtual coupling high-speed railway train by using dynamic programming method according to the dynamics model of the virtual coupling high-speed railway train under adverse environment; S42, obtaining the target state of each following virtual coupling high-speed railway train according to the target speed and position relationship between adjacent trains, specifically: The target speed of each following virtual coupling high-speed railway train is: wherein, v represents the target speed of the i-th virtual coupled high-speed railway train at the t-th moment, i-1 (t) represents the speed of the i-1-th virtual coupled high-speed railway train at the t-th moment; The target position of each following virtual coupling high-speed railway train is: wherein, s(t) represents the target position of the i-th virtual coupled high-speed railway train at the t-th moment, i-1 s(t) represents the target position of the i-th virtual coupled high-speed railway train at the t-th moment, des s(t) represents the target position of the i-th virtual coupled high-speed railway train at the t-th moment, s(t) represents the target position of the i-th virtual coupled high-speed railway train at the t-th moment, 6. The intelligent control method for virtual coupled train group operation in severe environment according to claim 5, characterized in that, Step S5 specifically includes: S51, obtaining the state quantity of the virtual coupling high-speed railway train based on the target state of each following virtual coupling high-speed railway train, that is: wherein x i (t) denotes the state quantity of the i-th virtual coupled high-speed railway train at the t-th moment, Δs i (t) denotes the deviation of the position of the i-th virtual coupled high-speed railway train at the t-th moment from the target position, Δv i (t) denotes the deviation of the speed of the i-th virtual coupled high-speed railway train at the t-th moment from the target speed, T denotes transposition; S52, discretizing the state quantity of the virtual coupling high-speed railway train by using forward Euler method, and adding the unit control quantity of the virtual coupling high-speed railway train to the state quantity to generate new state quantity, and constructing the state space model of the virtual coupling high-speed railway train under adverse environment, that is: wherein, denotes the new state variable of the i-th virtual coupled high-speed railway train at the t+1 time, denotes the new state variable of the i-th virtual coupled high-speed railway train at the t time, A, B, C, D represent different coefficient matrices respectively, y i (t) denotes the control output variable of the i-th virtual coupled high-speed railway train at the t time, u i (t-1) denotes the unit control variable of the i-th virtual coupled high-speed railway train at the t-1 time, Δu i (t) denotes the difference between the unit control variable of the i-th virtual coupled high-speed railway train at the t time and the unit control variable of the i-th virtual coupled high-speed railway train at the t-1 time, a i-1 (t) denotes the acceleration of the i-1-th virtual coupled high-speed railway train at the t time, E represents the control output coefficient matrix.

7. The intelligent control method for virtual coupled train group operation in severe environment according to claim 6, characterized in that, The estimated value of the new state quantity in the prediction time domain in step S6 is: wherein N p represents a prediction time domain, N c represents a control time domain, represents the state quantity of the i-th virtual coupled high-speed railway train at the (t+1)-th moment predicted at the t-th moment, i.e. the estimation value of the new state quantity when the control time domain is 1, represents the state quantity of the i-th virtual coupled high-speed railway train at the (t+2)-th moment predicted at the t-th moment, Δu i (t+1|t) represents the unit control quantity increment of the i-th virtual coupled high-speed railway train at the (t+1)-th moment predicted at the t-th moment, a i-1 (t+1) represents the acceleration of the (i-1)-th virtual coupled high-speed railway train at the (t+1)-th moment, represents the state quantity of the i-th virtual coupled high-speed railway train at the (t+N p -1|t) represents the unit control quantity increment of the i-th virtual coupled high-speed railway train at the (t+N i -1) moment predicted at the t-th moment, a c (t+N c -1|t) represents the unit control quantity increment of the i-th virtual coupled high-speed railway train at the (t+N i-1 -1) moment predicted at the t-th moment, a p (t+N p -1) represents the acceleration of the (i-1)-th virtual coupled high-speed railway train at the (t+N -1) moment.

8. The intelligent control method for virtual coupled train group operation in severe environment according to claim 7, characterized in that, The prediction state matrix in step S6 is: wherein X i (t) represents the predicted state matrix of the i-th virtual coupled high-speed railway train at the t-th time, ψ, δ, φ represent the recursion coefficient matrix of each new state variable, ΔU i represents the unit control amount increment matrix of the i-th virtual coupled high-speed railway train predicted at the t-th time to the t+N c -1 time, represents the acceleration matrix of the i-1-th virtual coupled high-speed railway train at the t-th time to the t+N p -1 time.

9. The intelligent control method for virtual coupled train group operation in severe environment according to claim 8, characterized in that, The prediction output quantity in step S6 is: wherein Y i (t) represents the predicted output of the i-th virtual coupled high-speed railway train at the t-th moment, y i (t+1|t) represents the state output of the i-th virtual coupled high-speed railway train at the t+1-th moment predicted at the t-th moment, i.e. the position deviation and the speed deviation at the t+1-th moment, y i (t+N p |t) represents the state output of the i-th virtual coupled high-speed railway train at the t+N p -th moment predicted at the t-th moment, i.e. the position deviation and the speed deviation at the t+N p -th moment, y i (t+j1|t) represents the state output of the i-th virtual coupled high-speed railway train at the t+j1-th moment predicted at the t-th moment, i.e. the position deviation and the speed deviation at the t+j1-th moment, represents the state of the i-th virtual coupled high-speed railway train at the t+j1-th moment predicted at the t-th moment, ψ', φ', δ', φ' all represent the recursive coefficient matrix of the predicted output.

10. The intelligent control method for virtual coupled train group operation in severe environment according to claim 9, characterized in that, Step S7 specifically includes: S71, constructing the intelligent control optimization target of the virtual coupling high-speed railway train group cooperative operation under adverse environment according to the prediction output quantity, virtual coupling high-speed railway train safety operation index and speed limit state under adverse environment, that is: wherein J i (t) represents the intelligent control optimization target of the i-th virtual coupled high-speed railway train at the t-th moment, Δu i (t+j2-1|t) represents the unit control amount increment of the i-th virtual coupled high-speed railway train at the t+N c -1 moment predicted at the t-th moment, ε represents a relaxation factor, q1 represents an intermediate variable, diag represents a diagonal matrix, q2, ρ' respectively represent weight coefficients of position error, speed error, control increment and relaxation factor; S72, an optimization objective constraint condition including a control performance constraint, a safety braking constraint and a safe running speed constraint is established, specifically: The control performance constraint is: u Min ≤u i (t)≤u Max where u Min , u Max respectively represent the maximum braking deceleration and the maximum traction acceleration of the virtual coupled train. The safety braking constraint is: where max denotes taking the maximum value, v i (t) denotes the speed of the i-th virtual coupled high-speed railway train at the t-th moment, v i-1 (t) denotes the speed of the i-1-th virtual coupled high-speed railway train at the t-th moment; The safe running speed constraint is: 0 < v i (t) < v lim (s i (t)) wherein v lim represents the safe running speed; S73, the virtual coupling high-speed railway train group cooperative running intelligent control optimization objective and the optimization objective constraint condition under the adverse environment are converted into a standard type quadratic programming problem with constraints in a vector form and solved, so as to realize the safe and stable running of the virtual coupling high-speed railway train group under the adverse environment.

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