Intelligent control method based on virtual coupling train group operation in severe environment

Abstract

The invention relates to the technical field of train operation intelligent control, and discloses an intelligent control method based on virtual coupling train group operation in a severe environment, which comprises the following steps of: analyzing the influence of the severe environment on the operation process of a virtual coupling high-speed railway train; performing dynamic analysis on the virtual coupling high-speed railway train in a severe environment; building a virtual coupling high-speed railway train state space model in a severe environment; constructing an intelligent control optimal problem of cooperative operation of the high-speed railway train group in a 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 recover and maintain the cooperative stable operation state in the severe environment, ensure the safe operation of the train, effectively reduce the train operation distance and improve the line transportation capacity.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control of train operation, and in particular to an intelligent control method based on virtual coupled train group operation in a harsh environment. Background Art

[0002] With the rapid development of the economy and the continuous increase in the number of travelers, high-speed railway passenger transportation plays an increasingly important role, and the traditional high-speed railway transportation mode faces the challenge of insufficient transportation capacity. On the basis of not expanding the scale of existing infrastructure, adopting new train operation control technology to reduce train operation intervals and achieve high-density transportation organization has become an important means to improve line capacity and transportation capacity. Among them, based on virtual coupling technology, ensuring the safe and stable operation of virtual coupled train groups (VCTS) is the core of the intelligent control problem of train group operation. However, during the operation of virtual coupled train groups, the occurrence of severe environments such as bad weather and sudden geological disasters is inevitable, which will cause changes in speed limit conditions and train operation status along the line, directly affecting the stable operation of high-speed railway virtual coupled train groups, causing trains to deviate from the stable state, and having a negative impact on the safety and stability of virtual coupled train groups. Summary of the invention

[0003] In view of the above-mentioned deficiencies in the prior art, the present invention provides an intelligent control method based on the operation of a virtual coupled train group in a harsh environment. By considering the influencing factors of the harsh environment, the insecurity and stability of the operation of the virtual coupled train group under the influence of the harsh environmental factors are solved, the influence of the harsh environmental factors is effectively dealt with, the interference is clearly identified and the disturbance is minimized through intelligent control methods, so as to ensure the safety of train operation and maintain the coordinated and stable operation of the trains.

[0004] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

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

[0006] S1. Analyze the impact of harsh environment on the running resistance of virtual coupled high-speed railway trains, and calculate the running resistance of virtual coupled high-speed railway trains in harsh environment;

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

[0008] S2. Analyze the impact of harsh environment on the safe operation speed of virtual coupled high-speed railway trains, and obtain the safe operation index and speed limit status of virtual coupled high-speed railway trains in harsh environment;

[0009] S3. Analyze the single-particle force condition of the virtual coupled high-speed railway train under harsh environment, and construct a dynamic model of the virtual coupled high-speed railway train under harsh environment according to the running resistance of the virtual coupled high-speed railway train under harsh environment;

[0010] S4. According to the dynamic model of the virtual coupled high-speed railway train in the harsh 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;

[0011] S5. According to the target state of each following virtual coupled high-speed railway train, the state quantity of the virtual coupled high-speed railway train is obtained, and the unit control quantity of the virtual coupled high-speed railway train is added 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;

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

[0013] S7. According to the predicted output, the safe operation index and speed limit status of the virtual coupled high-speed railway train in harsh environment, the intelligent control optimization objective and optimization objective constraint conditions of the coordinated operation of the virtual coupled high-speed railway train group in harsh environment are constructed, and the optimization objective constraint conditions are converted into a standard quadratic programming problem with constraints and then solved to realize the safe and stable operation of the virtual coupled high-speed railway train group in harsh environment.

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

[0015] The intelligent control method based on the operation of a virtual coupled train group in a harsh environment proposed in the present invention can complete the intelligent control of the operation of a virtual coupled high-speed railway train in a 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 the high-speed railway line. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of an intelligent control method based on virtual coupled train group operation in a harsh environment proposed by the present invention;

[0017] Figure 2 It is a schematic diagram of the force condition of a single mass point of a virtual coupled high-speed railway train;

[0018] Figure 3 A schematic diagram of the position relationship between each following virtual coupled high-speed railway train and the preceding train;

[0019] Figure 4 It is a schematic diagram of safety brake restraint;

[0020] Figure 5 It is a schematic diagram of the line speed limit conditions and the target speed curve of the pilot train under harsh environment;

[0021] Figure 6 It is a schematic diagram of the coordinated operation control effect of virtual coupled high-speed railway trains;

[0022] Figure 7 This is a schematic diagram of the running interval between adjacent trains under virtual coupled train groups and moving blocks. DETAILED DESCRIPTION

[0023] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0024] like Figure 1 As shown, an intelligent control method based on virtual coupled train group operation in a harsh environment includes the following steps S1-S7:

[0025] S1. Analyze the impact of harsh environment on the running resistance of virtual coupled high-speed railway trains, and calculate the running resistance of virtual coupled high-speed railway trains in harsh environment; the running resistance includes unit basic resistance, other additional resistance and unit line additional resistance.

[0026] In this embodiment, the purpose of analyzing the impact of harsh environment on the running resistance of virtual coupled high-speed railway train is to explore the impact of harsh environment on the running resistance of virtual coupled high-speed railway train, to analyze the expression of the resistance experienced by the virtual coupled train in various harsh environment scenarios, and to lay a theoretical foundation for the construction of subsequent dynamic models.

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

[0028] S11. Acquire adverse environmental factors including strong wind factor, rainfall factor, lightning factor, visibility factor, earthquake factor and debris flow factor.

[0029] S12. If the adverse environmental factor is a 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] Where r represents the unit basic resistance of 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 coefficients 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 of 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 the wind direction is the same as the running direction of the virtual coupled high-speed railway train, C2 represents the wind direction is opposite to the running direction of the virtual coupled high-speed railway train, C3 represents 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, α represents the wind direction angle, and β represents the sideslip angle.

[0032] In this embodiment, the unit basic resistance r includes friction resistance and air resistance, with the unit of KN / t; speed v, with the unit of km / h; rolling mechanical damping coefficient c0, with the unit of KN / t; other mechanical resistance coefficient c1, with the unit of KN / (km / h·t); external air damping coefficient c2, with the unit of KN / (km 2 / h 2 ·t); air density ρ, unit is kg / m 3 ; Cross-sectional area S w , unit is m 2 ; Total speed v h , unit is km / h; instantaneous wind speed v w , unit is km / h; in addition, wind direction angle α refers to the angle between wind direction and line direction; sideslip angle β refers to the angle between the combined speed of the virtual coupled high-speed railway train's reverse speed and wind speed and the line direction.

[0033] S13. If the adverse environmental factor is a factor other than the strong wind factor, then other additional resistances experienced by the virtual coupled high-speed railway train under the factors other than the strong wind factor are updated in real time through sampling.

[0034] In this embodiment, due to the large uncertainty and strong nonlinearity of the impact of other harsh environmental factors on the train running resistance, other additional resistance ω is expressed, and its value is updated in real time through sampling. Among them, in the actual train operation process, the value of other additional resistance ω can be calculated in real time and updated online, that is, it can be known from the train acceleration expression: a = urg-ω, where u is the unit control amount of the train (traction / braking). By setting a reasonable sampling frequency, the control amount u at the sampling moment is known, and the acceleration a is known through the train acceleration sensor, then the other additional resistance ω can be calculated and obtained, and then approximately used as the value of the next moment, and updated in real time through 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 harsh environmental factors, that is, the unit line additional resistance of the virtual coupled high-speed railway train under the harsh 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 It represents the additional resistance per unit curve of the virtual coupled high-speed railway train, g L represents the additional resistance of the unit tunnel 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 in thousandths, 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, L tn Indicates 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 , unit is KN / t; unit curve additional resistance g r , unit is KN / t; unit tunnel additional resistance g L , unit is KN / t; length L of the tunnel where the virtual coupled high-speed railway train is located tn , unit is m; the empirical curve resistance coefficient e1 is 600, and the empirical tunnel resistance coefficient L1 is 0.00013.

[0039] S2. Analyze the impact of harsh environment on the safe operating speed of virtual coupled high-speed railway trains, and obtain the safe operating indicators and speed limit status of virtual coupled high-speed railway trains in harsh environment.

[0040] In this embodiment, the purpose of analyzing the impact of harsh environments on the safe operating speed of virtual coupled high-speed railway trains is to obtain the safe operating indicators and speed limit status of virtual coupled high-speed railway trains under various harsh environment scenarios, so as to determine the safe operating speed constraints of trains at each time in the optimal control model.

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

[0042] S21. If the severe environmental factor is a strong wind factor, the instantaneous wind speed is selected as the safe operation index of the virtual coupled high-speed railway train, and according to the instantaneous wind speed standard, the speed limit condition of the virtual coupled high-speed railway train under the instantaneous wind speed standard is obtained.

[0043] S22. If the adverse environmental factor is a rainfall factor, the rainfall is selected as the safe operation index of the virtual coupled high-speed railway train, and according to the rainfall standard, the speed limit condition of the virtual coupled high-speed railway train under the rainfall standard is obtained.

[0044] S23. If the severe environmental factor is a lightning factor, lightning is used as a safe operation indicator of the virtual coupled high-speed railway train, and the speed limit conditions of the virtual coupled high-speed railway train under the lightning standard are obtained based on the lightning standard.

[0045] S24. If the adverse environment factor is a visibility factor, select the visibility distance as the safe operation index of the virtual coupled high-speed railway train, and obtain the speed limit condition of the virtual coupled high-speed railway train under the visibility distance standard according to the visibility distance standard.

[0046] S25. If the severe environmental factor is an earthquake factor, the predicted or measured peak seismic acceleration is selected as the safe operation index of the virtual coupled high-speed railway train, and according to the peak seismic acceleration standard, the speed limit condition of the virtual coupled high-speed railway train under the peak seismic acceleration standard is obtained.

[0047] S26. If the severe environmental factor is a debris flow, the debris flow is selected as the safe operation indicator of the virtual coupled high-speed railway train, and according to the debris flow standard, the speed limit condition of the virtual coupled high-speed railway train under the debris flow standard is obtained.

[0048] S27. If there are multiple severe environmental factors at the same time, the minimum speed under the severe environmental factor standard is selected as the speed limit condition of the virtual coupled high-speed railway train.

[0049] In this embodiment, according to the above steps S21-S26, the safe operation index and speed limit status of the virtual coupled high-speed railway train in a harsh environment are specifically shown in Table 1:

[0050] Table 1 Safety operation index and speed limit status table

[0051]

[0052]

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

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

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

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

[0057] In this embodiment, the force condition of a single point of a virtual coupled high-speed railway train in a harsh environment is as follows: Figure 2 As shown, it includes traction force F, running resistance f and braking force B, wherein traction force F and braking force B are collectively referred to as control quantity. When the virtual coupled high-speed railway train needs to accelerate, it is subject to traction force F, and when the train decelerates, it is subject to braking force B.

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

[0059]

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

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

[0062] In this embodiment, according to the train speed limit conditions in various harsh environments, with the help of the constructed dynamic model of the virtual coupled high-speed railway train in harsh environments, the target speed curve of the pilot train can be obtained through the dynamic programming method, such as Figure 5 As shown, 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, providing a target for train operation control.

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

[0064] S41. According to the dynamic model of the virtual coupled high-speed railway train in harsh environment, the dynamic programming method is adopted to obtain the position, speed, unit control amount and acceleration of the leading virtual coupled high-speed railway train.

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

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

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

[0068]

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

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

[0071] The target position of each following virtual coupled high-speed railway train is:

[0072]

[0073] in, represents the target position of the ith virtual coupled high-speed railway train at time t, s i-1 (t) represents the position of the i-1th virtual coupled high-speed railway train at time t, d des 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 position relationship between each following virtual coupled high-speed railway train and the preceding train is as follows: Figure 3 As shown, where Δs i is the difference between the distance between the virtual coupled high-speed railway train i and the preceding train and the expected distance. According to the control target, we have: Δs i = 0, so the target position of each following virtual coupled high-speed railway train is

[0075] S5. According to the target state of each following virtual coupled high-speed railway train, the state quantity of the virtual coupled high-speed railway train is obtained, and the unit control quantity of the virtual coupled high-speed railway train is added 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.

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

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

[0078] S51. Based on the target state of each following virtual coupled high-speed railway train, the state quantity of the virtual coupled high-speed railway train is obtained, that is:

[0079]

[0080] Among them, x i (t) represents the state of the i-th virtual coupled high-speed railway train at time t, Δs i (t) represents the deviation between the position of the i-th virtual coupled high-speed railway train at time t and the target position, Δv i (t) represents the deviation between the speed of the ith virtual coupled high-speed railway train at the tth time and the target speed, and T represents the transposition.

[0081] S52. According to the state quantity of the virtual coupled high-speed railway train, the forward Euler method is used for discretization processing, and the 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] in, represents the new state of the i-th virtual coupled high-speed railway train at time t+1, represents the new state of the ith virtual coupled high-speed railway train at time t, A, B, C, and D represent different coefficient matrices, and y i (t) represents the control output variable of the ith virtual coupled high-speed railway train at time t, u i (t-1) represents the unit control quantity of the i-th virtual coupled high-speed railway train at time t-1, Δu i (t) represents the difference between the unit control amount of the ith virtual coupled high-speed railway train at time t and the unit control amount of the ith virtual coupled high-speed railway train at time t-1, a i-1 (t) represents the acceleration of the i-1th virtual coupled high-speed railway train at the tth time, and E represents the control output coefficient matrix.

[0084] In this embodiment,

[0085]

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

[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 to obtain the prediction state matrix and obtain the prediction output quantity.

[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 prediction time domain N at time t. p The estimated value of the new state of the i-th virtual coupled high-speed railway train at each moment.

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

[0090]

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

[0092] In this embodiment, according to the state space model of the virtual coupled high-speed railway train in a harsh environment, a model predictive control method is used to obtain an 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 measurement 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] Among them, X i (t) represents the predicted state matrix of the ith virtual coupled high-speed railway train at time t, ψ, δ, φ represent the recursive coefficient matrices of each new state variable, ΔU i Represents the predicted time from t to t+N at time t c The unit control quantity increment matrix of the i-th virtual coupled high-speed railway train at time -1, Indicates that the i-1th virtual coupled high-speed railway train is in the range of t to t+N p The acceleration matrix at time -1.

[0096] In this embodiment, Where n represents the lower bound of the accumulated sign.

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

[0098]

[0099] Among them, Y i (t) represents the predicted output of the ith virtual coupled high-speed railway train at time t, y i (t+1|t) represents the state output of the i-th virtual coupled high-speed railway train at time t+1 predicted at time t, that is, the position deviation and speed deviation at time t+1, y i (t+N p |t) represents the t+Nth predicted at time t p The state output of the i-th virtual coupled high-speed railway train at time t+N p The position deviation and speed deviation at the moment, y i (t+j1|t) represents the state output of the i-th virtual coupled high-speed railway train at time t+j1 predicted at time t, that is, the position deviation and speed deviation at time t+j1, represents the state of the i-th virtual coupled high-speed railway train at the time t+j1 predicted at the time t, and ψ′, φ′, δ′, and φ′ respectively represent the recursive coefficient matrices of the predicted output.

[0100] S7. According to the predicted output, the safe operation index and speed limit status of the virtual coupled high-speed railway train in harsh environment, the intelligent control optimization objective and optimization objective constraint conditions of the coordinated operation of the virtual coupled high-speed railway train group in harsh environment are constructed, and the optimization objective constraint conditions are converted into a standard quadratic programming problem with constraints and then solved to realize the safe and stable operation of the virtual coupled high-speed railway train group in harsh environment.

[0101] In this embodiment, the cooperative control target 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, avoid frequent switching between traction and braking as much as possible to reduce energy consumption; therefore, the weighted sum of the three indicators of position error, speed error and control increment can be used to construct the optimization target of the cooperative operation intelligent control of the virtual coupled high-speed railway train group in a harsh environment, and at the same time, a scalar relaxation factor is added to the optimization target to enhance the ability to solve the optimization target. In addition, during the operation of the virtual coupled high-speed railway train group, the train is limited by its own performance and the operating environment. The optimization target constraints should be considered in the operation intelligent control to ensure the safety and performance of the train. At the same time, the standard quadratic programming problem with constraints is solved, and the unit control quantity increment at time t and time t-1 can be obtained, so as to obtain the unit control quantity at each moment.

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

[0103] S71. According to the predicted output and the safe operation index and speed limit status of the virtual coupled high-speed railway train in the harsh environment, the intelligent control optimization target of the coordinated operation of the virtual coupled high-speed railway train group in the harsh environment is constructed, namely:

[0104]

[0105] Among them, J i (t) represents the intelligent control optimization target of the i-th virtual coupled high-speed railway train at time t, Δu i (t+j2-1|t) represents the t+Nth predicted value at time t. c The unit control increment of the ith virtual coupled high-speed railway train at time -1, ε represents the relaxation factor, q1 represents the intermediate variable, diag represents the diagonal matrix, q2 and ρ′ represent the weight coefficients of position error, velocity error, control increment and relaxation factor respectively.

[0106] S72, establishing optimization target constraint conditions including control performance constraint, safety braking constraint and safe running speed constraint, specifically:

[0107] The control performance constraints are:

[0108] uMin ≤u i (t)≤u Max

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

[0110] In this embodiment, during the operation of the train, its traction and braking performance are subject to the inherent constraints of the train design. This constraint is specifically reflected in the unit control quantity of the train, that is, u Min ≤u i (t)≤u Max .

[0111] The safety brake constraints are:

[0112]

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

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

[0115] The safe operating speed constraint is:

[0116] 0≤v i (t)≤v lim (s i (t))

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

[0118] In this embodiment, affected by the line conditions and various uncertain environmental factors (harsh environmental factors), the running speed of the virtual coupled train must meet the safe running speed, which is less than the speed limit in the line, to reduce the safety risk of train operation. lim It is closely related to the harsh environmental factors, and the specific value can be expressed using a piecewise function according to the speed limit conditions under each harsh environmental factor.

[0119] S73. Convert the optimization objective and optimization objective constraint conditions of the intelligent control of the coordinated operation of virtual coupled high-speed railway train groups under harsh environments into a vector-form constrained standard quadratic programming problem and solve it to achieve safe and stable operation of virtual coupled high-speed railway train groups under harsh environments.

[0120] In order to verify the effectiveness of the intelligent control method based on virtual coupled train group operation in a harsh environment proposed by the present invention, based on the harsh environment conditions shown in Table 2, namely:

[0121] Table 2 Virtual coupling high-speed railway train position and speed limit conditions under harsh environment

[0122] Location (km) Harsh environmental factors Speed ​​limit conditions (km / h) 22-32 <![CDATA[Headwind, v w = 20 m / s]]> 300 45-60 Rainfall, R = 200 mm / s 200 110-135 Low visibility, L = 150m 250 190-200 There is thunder and lightning 200 265-285 <![CDATA[Wind direction angle 45°, v w = 20 m / s]]> 300

[0123] Based on the data in Table 2, the method proposed in the present invention is used to simulate the running state of the following train using MATLAB. According to the train speed limit conditions under various harsh environments, the train dynamics model and dynamic programming method are used to obtain the target speed curve of the leading train, as shown in the following figure: Figure 5 As shown. Then, the state space model can be used, based on the model predictive control method, with the help of the constructed control model, to solve the control effect of the coordinated operation of virtual coupled high-speed railway trains as shown in Figure 6 As shown, from Figure 6 It can be seen that the method proposed in the present invention can effectively deal with the interference of harsh environmental factors on the operation of virtual coupled trains, control the virtual coupled train group to quickly recover and maintain a stable coordinated state, control the train group to operate in coordination at the same speed and expected interval to the greatest extent, keep the train position error and speed error at a small value, and ensure the safe operation of the train group.

[0124] At the same time, based on the method proposed in the present invention, compared with the traditional moving block method, the virtual coupling technology effectively shortens the running interval between trains, such as Figure 7As shown. According to calculation, in this embodiment, the minimum train interval under the moving block mode is 2429 meters on average, while the train interval of VCTS is mostly maintained at 150 meters, which is equal to the set expected interval plus the safety margin. The average interval of each adjacent train in virtual coupling 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 invention, on the basis of ensuring train safety, the expected interval simulation value can be further reduced to achieve a smaller train running interval.

[0125] To sum up, the intelligent control method based on the operation of a virtual coupled train group in a harsh environment proposed in the present invention can effectively cope with the interference of the harsh environment on the train operation, control the virtual coupled high-speed railway train group to restore and maintain a coordinated and stable operation state in the harsh environment, ensure the safe operation of the train, and effectively reduce the running distance between trains and improve the line transportation capacity.

[0126] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

[0127] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. An intelligent control method based on virtual coupled train group operation in harsh environment, characterized in that: The following steps are involved: S1. Analyze the impact of harsh environment on the running resistance of virtual coupled high-speed railway trains, and calculate the running resistance of virtual coupled high-speed railway trains in harsh environment; Among them, the operating resistance includes unit basic resistance, other additional resistance and unit line additional resistance; S2. Analyze the impact of harsh environment on the safe operation speed of virtual coupled high-speed railway trains, and obtain the safe operation index and speed limit status of virtual coupled high-speed railway trains in harsh environment; S3. Analyze the single-particle force condition of the virtual coupled high-speed railway train under harsh environment, and construct a dynamic model of the virtual coupled high-speed railway train under harsh environment according to the running resistance of the virtual coupled high-speed railway train under harsh environment; S4. According to the dynamic model of the virtual coupled high-speed railway train in the harsh 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; S5. According to the target state of each following virtual coupled high-speed railway train, the state quantity of the virtual coupled high-speed railway train is obtained, and the unit control quantity of the virtual coupled high-speed railway train is added 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; S6. According to the state space model of the virtual coupled high-speed railway train in the harsh environment, an estimated value of a new state quantity in the prediction time domain is obtained and simplified into a matrix form to obtain a prediction state matrix and obtain a prediction output quantity; S7. According to the predicted output, the safe operation index and speed limit status of the virtual coupled high-speed railway train in harsh environment, the intelligent control optimization objective and optimization objective constraint conditions of the coordinated operation of the virtual coupled high-speed railway train group in harsh environment are constructed, and the optimization objective constraint conditions are converted into a standard quadratic programming problem with constraints and then solved to realize the safe and stable operation of the virtual coupled high-speed railway train group in harsh environment.

2. The intelligent control method based on virtual coupled train group operation in harsh environment according to claim 1 is characterized in that: Step S1 specifically includes: S11. Obtaining adverse environmental factors including strong wind factor, rainfall factor, lightning factor, visibility factor, earthquake factor and debris flow factor; S12. If the adverse environmental factor is a strong wind factor, the unit basic resistance of the virtual coupled high-speed railway train under the strong wind factor is calculated, that is: Where r represents the unit basic resistance of 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 coefficients 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 of 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 the wind direction is the same as the running direction of the virtual coupled high-speed railway train, C2 represents the wind direction is opposite to the running direction of the virtual coupled high-speed railway train, C3 represents 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, α represents the wind direction angle, and β represents the side slip angle; S13. If the adverse environmental factor is a factor other than the strong wind factor, then other additional resistances suffered by the virtual coupled high-speed railway train under the factors other than the strong wind factor are updated in real time by sampling; S14. Calculate the additional resistance of the virtual coupled high-speed railway train under different line conditions under the harsh environmental factors, that is, the unit line additional resistance of the virtual coupled high-speed railway train under the harsh environmental factors, that is: 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 It represents the additional resistance per unit curve of the virtual coupled high-speed railway train, g L represents the additional resistance of the unit tunnel 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 in thousandths, 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, L tn Indicates the length of the tunnel where the virtual coupled high-speed railway train is located.

3. The intelligent control method based on virtual coupled train group operation in harsh environment according to claim 2 is characterized in that: Step S2 specifically includes: S21. If the adverse environmental factor is a strong wind factor, the instantaneous wind speed is selected as the safe operation index of the virtual coupled high-speed railway train, and according to the instantaneous wind speed standard, the speed limit condition of the virtual coupled high-speed railway train under the instantaneous wind speed standard is obtained; S22. If the adverse environmental factor is a rainfall factor, the rainfall is selected as the safe operation index of the virtual coupled high-speed railway train, and according to the rainfall standard, the speed limit condition of the virtual coupled high-speed railway train under the rainfall standard is obtained; S23. If the severe environmental factor is a lightning factor, lightning is used as a safe operation indicator of the virtual coupled high-speed railway train, and according to the lightning standard, the speed limit condition of the virtual coupled high-speed railway train under the lightning standard is obtained; S24. If the adverse environment factor is a visibility factor, select the visibility distance as the safe operation index of the virtual coupled high-speed railway train, and obtain the speed limit condition of the virtual coupled high-speed railway train under the visibility distance standard according to the visibility distance standard; S25. If the severe environmental factor is an earthquake factor, the predicted or measured peak seismic acceleration is selected as the safe operation index of the virtual coupled high-speed railway train, and according to the peak seismic acceleration standard, the speed limit condition of the virtual coupled high-speed railway train under the peak seismic acceleration standard is obtained; S26. If the severe environmental factor is a debris flow, the debris flow is selected as the safe operation index of the virtual coupled high-speed railway train, and according to the debris flow standard, the speed limit condition of the virtual coupled high-speed railway train under the debris flow standard is obtained; S27. If there are multiple severe environmental factors at the same time, the minimum speed under the severe environmental factor standard is selected as the speed limit condition of the virtual coupled high-speed railway train.

4. The intelligent control method based on virtual coupled train group operation in harsh environment according to claim 3 is characterized in that: Step S3 specifically includes: S31, obtaining the traction force or braking force and running resistance of a virtual coupled high-speed railway train under harsh conditions; S32. According to the traction force or braking force and running resistance of the virtual coupled high-speed railway train in the harsh environment, a dynamic model of the virtual coupled high-speed railway train in the harsh environment is constructed, that is: Among them, s i (t) represents the position of the i-th virtual coupled high-speed railway train at time t, represents the first-order derivative of the position of the i-th 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, represents the first-order derivative of the speed of the ith virtual coupled high-speed railway train at time t, a i (t) represents the acceleration of the ith virtual coupled high-speed railway train at time t, u i (t) represents the unit control quantity of the ith virtual coupled high-speed railway train at time t, r i (t) represents the unit basic resistance of the ith virtual coupled high-speed railway train at time t, G i [s i (t)] represents the unit line additional resistance at the position of the i-th virtual coupled high-speed railway train at the t-th time, w i [s i (t)] represents the other additional resistance experienced by the position of the i-th virtual coupled high-speed railway train at the t-th moment.

5. The intelligent control method based on virtual coupled train group operation in harsh environment according to claim 4 is characterized in that: Step S4 specifically includes: S41. According to the dynamic model of the virtual coupled high-speed railway train in the harsh environment, a dynamic programming method is used to obtain the position, speed, unit control amount and acceleration of the pilot virtual coupled high-speed railway train; S42, according to the target speed and position relationship between adjacent trains, obtaining the target state of each following virtual coupled high-speed railway train, specifically: The target speed of each following virtual coupled high-speed railway train is: in, represents the target speed of the ith virtual coupled high-speed railway train at time t, v i-1 (t) represents the speed of the i-1th virtual coupled high-speed railway train at time t; The target position of each following virtual coupled high-speed railway train is: in, represents the target position of the ith virtual coupled high-speed railway train at time t, s i-1 (t) represents the position of the i-1th virtual coupled high-speed railway train at time t, d des 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.

6. The intelligent control method based on virtual coupled train group operation in harsh environment according to claim 5 is characterized in that: Step S5 specifically includes: S51. Based on the target state of each following virtual coupled high-speed railway train, the state quantity of the virtual coupled high-speed railway train is obtained, that is: Among them, x i (t) represents the state of the ith virtual coupled high-speed railway train at time t, Δs i (t) represents the deviation between the position of the ith virtual coupled high-speed railway train at time t and the target position, Δv i (t) represents the deviation between the speed of the ith virtual coupled high-speed railway train and the target speed at the tth time, and T represents the transposition; S52. According to the state quantity of the virtual coupled high-speed railway train, the forward Euler method is used for discretization processing, and the 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: in, represents the new state of the i-th virtual coupled high-speed railway train at time t+1, represents the new state of the ith virtual coupled high-speed railway train at time t, A, B, C, and D represent different coefficient matrices, and y i (t) represents the control output variable of the ith virtual coupled high-speed railway train at time t, u i (t-1) represents the unit control quantity of the i-th virtual coupled high-speed railway train at time t-1, Δu i (t) represents the difference between the unit control amount of the ith virtual coupled high-speed railway train at time t and the unit control amount of the ith virtual coupled high-speed railway train at time t-1, a i-1 (t) represents the acceleration of the i-1th virtual coupled high-speed railway train at the tth time, and E represents the control output coefficient matrix.

7. The intelligent control method based on virtual coupled train group operation in harsh environment according to claim 6 is characterized in that: The estimated value of the new state quantity in the prediction time domain in step S6 is: Among them, N p represents the prediction time domain, N c represents the control time domain, represents the state of the i-th virtual coupled high-speed railway train at time t+1 predicted at time t, that is, the estimated value of the new state when the control time domain is 1, represents the state 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 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-1th virtual coupled high-speed railway train at the t+1th time, It indicates the t+Nth predicted at time t. p The state quantity of the i-th virtual coupled high-speed railway train at time, Δu i (t+N c -1|t) represents the t+Nth predicted at time t c The unit control increment of the ith virtual coupled high-speed railway train at time -1, a i-1 (t+N p -1) indicates the t+Nth p The acceleration of the i-1th virtual coupled high-speed railway train at time -1.

8. The intelligent control method based on virtual coupled train group operation in harsh environment according to claim 7 is characterized in that: The predicted state matrix in step S6 is: Among them, X i (t) represents the predicted state matrix of the ith virtual coupled high-speed railway train at time t, ψ, δ, φ represent the recursive coefficient matrices of each new state variable, ΔU i Represents the predicted time from t to t+N at time t c The unit control quantity increment matrix of the i-th virtual coupled high-speed railway train at time -1, Indicates that the i-1th virtual coupled high-speed railway train is from t to t+N p The acceleration matrix at time -1.

9. The intelligent control method based on virtual coupled train group operation in harsh environment according to claim 8 is characterized in that: The predicted output in step S6 is: Among them, Y i (t) represents the predicted output of the ith virtual coupled high-speed railway train at time t, y i (t+1|t) represents the state output of the i-th virtual coupled high-speed railway train at time t+1 predicted at time t, that is, the position deviation and speed deviation at time t+1, y i (t+N p |t) represents the t+Nth predicted at time t p The state output of the i-th virtual coupled high-speed railway train at time t+N p The position deviation and speed deviation at the moment, y i (t+j1|t) represents the state output of the i-th virtual coupled high-speed railway train at time t+j1 predicted at time t, that is, the position deviation and speed deviation at time t+j1, represents the state of the i-th virtual coupled high-speed railway train at the time t+j1 predicted at the time t, and ψ′, φ′, δ′, φ′ all represent the recursive coefficient matrix of the predicted output.

10. The intelligent control method based on virtual coupled train group operation in harsh environment according to claim 9, characterized in that: Step S7 specifically includes: S71. According to the predicted output and the safe operation index and speed limit status of the virtual coupled high-speed railway train in the harsh environment, the intelligent control optimization target of the coordinated operation of the virtual coupled high-speed railway train group in the harsh environment is constructed, namely: Among them, J i (t) represents the intelligent control optimization target of the i-th virtual coupled high-speed railway train at time t, Δu i (t+j2-1|t) represents the t+Nth predicted value at time t. c The unit control increment of the ith virtual coupled high-speed railway train at time -1, ε represents the relaxation factor, q1 represents the intermediate variable, diag represents the diagonal matrix, q2, ρ′ represent the weight coefficients of position error, velocity error, control increment and relaxation factor respectively; S72, establishing optimization target constraint conditions including control performance constraint, safety braking constraint and safe running speed constraint, specifically: The control performance constraints are: u Min ≤u i (t)≤u Max Among them, u Min 、u Max They represent the maximum braking deceleration and maximum traction acceleration of the virtual coupled train respectively; The safety brake constraints are: Among them, max means taking the maximum value, v i (t) represents the speed of the ith virtual coupled high-speed railway train at time t, v i-1 (t) represents the speed of the i-1th virtual coupled high-speed railway train at time t; The safe operating speed constraint is: 0≤v i (t)≤v lim (s i (t)) Among them, v lim Indicates safe operating speed; S73. Convert the optimization objective and optimization objective constraint conditions of the intelligent control of the coordinated operation of virtual coupled high-speed railway train groups under harsh environments into a vector-form constrained standard quadratic programming problem and solve it to achieve safe and stable operation of virtual coupled high-speed railway train groups under harsh environments.

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