Control method suitable for self-adaptive tracking operation of city domain train group

Abstract

The invention discloses a control method suitable for self-adaptive tracking operation of a city domain train group. The control method comprises the following steps: constructing a single-train kinematics model and a multi-train cooperative control model; with accuracy and real-time performance as targets, a self-adaptive sliding mode controller of vehicle group distributed control based on self-adaptive sliding mode control is constructed; establishing a safety protection model between the trains; acquiring a safety tracking interval between the trains; and the control force of each train is calculated based on the adaptive sliding mode controller, and adaptive tracking operation control of the city domain train group is completed. According to the method, the high-density public transportation service characteristic of urban trains is fully considered, the train operation control problem is mainly researched, and multi-train small-interval cooperative operation control is achieved on the premise that safe operation of the trains is guaranteed.

Description

Technical Field

[0001] The invention belongs to the field of train operation control, and in particular relates to a control method suitable for adaptive tracking operation of urban train groups. Background Art

[0002] Intercity railways and urban (suburban) railways are important components of the comprehensive transportation system. Urban rails adopt a "high-density, small-scale, bus-like" transportation organization model, and undertake a fast passenger rail transit system for medium- and long-distance passenger transportation within the city, mainly for commuter passenger flow. Urban rails can provide high-density bus-like services for a large number of commuter passenger flows during peak hours in the morning and evening. The train operation density during peak hours is significantly higher than that during off-peak hours. The increase in operation density also poses new challenges to train operation safety.

[0003] In the prior art, in order to minimize the minimum safe interval between trains, the dynamic information of the train ahead is used and the train safety protection is performed based on the principle of relative braking distance. However, this method has not been put into practical application. This is because the train models running in the actual operation scenario are not the same, resulting in differences in the braking behavior of the train during braking. This difference has a considerable impact on trains running at high density. In the train safety protection control based on relative braking distance, only the spatial separation relationship is considered, that is, only the safety of the position relationship between the two vehicles at the parking point is considered, and the position relationship between the front and rear vehicles during parking is not considered. When the braking performance of the front and rear vehicles is different, there is a situation where the distance between the two vehicles decreases first and then increases during braking. In this case, although the position relationship between the two vehicles at the parking point meets the safety protection requirements, collision problems may occur during braking. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a control method suitable for adaptive tracking operation of urban train groups, which solves the problem of possible collision during braking due to only considering the safety protection of train stopping points under moving block.

[0005] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0006] A control method for adaptive tracking operation of a city train group is provided, which comprises the following steps:

[0007] S1. Based on the operation process of the train, a single-vehicle kinematic model and a multi-train cooperative control model are constructed;

[0008] S2. Based on the single-vehicle kinematic model and the multi-train cooperative control model, an adaptive sliding mode controller for distributed control of vehicle groups based on adaptive sliding mode control is constructed with the goal of accuracy and real-time performance.

[0009] S3. Establish a safety protection model between trains;

[0010] S4. Based on the safety protection model between trains, obtain the safety tracking interval between trains;

[0011] S5. Obtain the real-time status information of each train and the current safe tracking interval, calculate the control force of each train based on the adaptive sliding mode controller, and complete the adaptive tracking operation control of the urban train group.

[0012] Furthermore, the expression of the bicycle kinematic model in step S1 is:

[0013]

[0014] The expression of the multi-train cooperative control model is:

[0015]

[0016] in is the derivative of the train's real-time position with respect to time, that is, the train's running speed v(t); is the real-time acceleration of the train; u(t) is the traction or braking force per unit mass of the train; g is the acceleration of gravity; w0 is the resistance during the train operation, w0 = δ a a+δ b bv(t)+δ c cv(t) 2 , a, b and c are the coefficients of Davis formula, δ a , δ b and δ c are the rates of change of a, b and c respectively; γ is the train rotation mass coefficient; d(t) is the unknown bounded disturbance, w i Add resistance to the line slope, w c Add resistance to the line curve; x k (t+1) represents the position of the kth train at time t+1; x k (t) represents the position of the kth train at time t; T s is the sampling period; v k (t) represents the speed of the kth train at time t; u k (t) represents the traction or braking force per unit mass of the kth train at time t; v k (t+1) represents the speed of the kth train at time t+1.

[0017] Furthermore, the expression of the adaptive sliding mode controller in step S2 is:

[0018]

[0019] Where U is the controller configured for each train, U=[u1,u2,...,u n ] T , the superscript T indicates the transpose of the matrix, u n The controller configured for the nth train; K3 is a constant; is a positive constant; S is the sliding surface configured for each train, K2 is the controller parameter, K2=[k2 1 ,k2 2 ,...,k2 n ], k2 n is the integral coefficient of the nth train; S = [s1, s2, ..., s n ] T ,s n is the sliding surface configured for the nth train; sgn represents the sign function, when S is greater than 0, sgn(S) is 1, when S is less than 0, sgn(S) is -1, and when S is equal to 0, sgn(S) is 0; E is the error term configured for each train, E=[e1,e2,...,e n ] T , e n is the error term configured for the nth train; δ1=Λ1P+Γ, δ2=Λ2V+Ι, P = [p r ,p1,p2,...,p n ] T , P is the train position matrix, p n is the position of the nth train, V = [v r ,v1,v2,...,v n ] T , V is the train speed matrix, v n is the speed of the nth train, L is the safety tracking interval; The first state quantity output by the auxiliary system of the nth train; is the second state quantity output by the auxiliary system of the nth train; W1 and W2 are the outputs of the auxiliary second-order system, , c n,1 is the first positive parameter to be designed for the nth train; λ n The error coefficient designed for the nth train; is the output weight vector matrix of the neural network in each train controller, is the output weight vector of the neural network in the nth train controller; Ψ(X) is the Gaussian function vector, Ψ(X) = [Ψ1(X), Ψ2(X), ..., Ψ n (X)] T ,Ψ n (X) is the Gaussian function vector of the nth train; D is the interference received by each train, D = [D1, D2, ..., D n ] T , D n is the interference suffered by the nth train; c n,2 is the second positive parameter to be designed for the nth train; c n,3 is the third positive parameter to be designed for the nth train; ΔU is the difference between the ideal control force and the actual control force.

[0020] Furthermore, the expression of the safety protection model between trains in step S3 is:

[0021]

[0022] Where L j is the minimum safe tracking interval of the following vehicle; st represents the constraint condition; is the front position of the rear vehicle at time t; is the rear position of the preceding vehicle at time t; S m is the safety margin; t js is the stop time of the following vehicle; is the front position of the rear vehicle at the time of departure; je represents the ranging error of the following vehicle; v j (t) represents the speed of the following vehicle at time t; v j (0) indicates the speed of the following vehicle at the time of departure; v je Indicates the speed measurement error of the following vehicle; represents the acceleration of the rear car at time t; Indicates the rear position of the front vehicle at the time of departure; s ie Indicates the distance measurement error of the preceding vehicle; v i (t) represents the speed of the preceding vehicle at time t; v i (0) indicates the speed of the preceding vehicle at the time of departure; v ie Indicates the speed measurement error of the preceding vehicle; represents the acceleration of the preceding vehicle at time t.

[0023] Furthermore, the specific method for obtaining the safe tracking interval between trains in step S4 includes the following steps:

[0024] S4-1, obtain the speed, real-time position and initial acceleration of the front and rear vehicles through sensors;

[0025] S4-2, calculate the emergency braking curves of the front and rear vehicles;

[0026] S4-3, obtaining the rear position of the front vehicle and the front position of the rear vehicle, and calculating the minimum distance between the front and rear vehicles;

[0027] S4-4, determine whether the minimum distance between the front and rear vehicles is equal to the safety margin. If so, output the initial positions of the front and rear vehicles as the minimum safe tracking interval; otherwise, change the initial position of the rear vehicle and return to step S4-2.

[0028] Furthermore, the specific method of calculating the control force of each train based on the adaptive sliding mode controller in step S5 includes calculating the control force of the lead train and calculating the control force of the following train.

[0029] Furthermore, the specific method for calculating the control force of the pilot train includes the following steps:

[0030] A1. Obtaining real-time status information of the pilot train through sensors; the real-time status information includes real-time position and real-time speed;

[0031] A2. Compare the acquired real-time status information with the tracking trajectory curve of the following train to calculate the error of the pilot train; the error of the pilot train is expressed as:

[0032] e1=δ 1,1 +λ1δ 1,2

[0033] Where e1 is the error of the pilot train; δ 1,1 is the deviation between the actual position of the pilot train and the position of the train tracking trajectory curve at the same time; λ1 is a positive constant; δ 1,2 It is the deviation between the actual speed of the pilot train and the speed of the train following the track curve at the same time;

[0034] A3. According to the calculated error of the pilot train, based on the basic idea of ​​integral sliding mode control, the integral sliding mode function of the pilot train is defined, and its expression is:

[0035] s1=c1e1+c2∫e1d(t)

[0036] Where s1 is the integral sliding mode function of the pilot train; c1 is the proportional coefficient; c2 is the integral coefficient;

[0037] A4. The control force of the pilot train is calculated based on the integral sliding mode function of the pilot train, and its expression is:

[0038]

[0039] Where u1 is the control force of the pilot train; k3 is a positive constant; α is the power approach rate; sat(·) is a saturation function, when s1 is greater than Δ, sat(s1) is 1, when s1 is less than Δ, sat(s1) is -1, and when |s1| is less than or equal to Δ, sat(s1) is Where Δ is the custom boundary layer; v1 is the speed of the pilot train; v r The speed at which the pilot train follows the curve of the track; is the time-varying resistance of the pilot train; D(t) is the resistance disturbance of the pilot train; a r The acceleration of the pilot train following the trajectory curve.

[0040] Furthermore, the specific method for calculating the control force of the following train includes the following steps:

[0041] B1. Obtain the real-time status of the following train through sensors; the real-time status includes real-time position and real-time speed;

[0042] B2. Calculate the error of the following train based on the real-time position and real-time speed of the front and rear vehicles. The expression is:

[0043]

[0044]

[0045] e j,1 =δ j,1 +λ j δ j,2

[0046] where e j,1 is the error of the jth following train; j,1 is the position deviation of the jth train after the second-order auxiliary system feedback; j is a positive constant; j,2 is the speed deviation of the jth following train after the second-order auxiliary system feedback; p j is the position of the jth following train; p j-1 is the position of the j-1th following train; L is the current safe tracking interval; is the position output by the j-th train following auxiliary system, for The first derivative of ; is the speed output by the auxiliary system of the j-th following train, for The first derivative of u j is the actual output control force of the jth following train; is the ideal output control force of the jth following train; v jis the speed of the jth following train; v j-1 is the speed of the j-1th following train; c j,1 、c j,2 and c j,3 All are normal numbers;

[0047] B3. According to the calculated error of the following train, based on the basic idea of ​​integral sliding mode control, the integral sliding mode function of the following train is defined, and its expression is:

[0048]

[0049] where s j is the integral sliding mode function of the following train; c 1,j and c 2,j are the proportional coefficient and the integral coefficient respectively;

[0050] B4. The control force of the following train is calculated based on the integral sliding mode function of the following train, and its expression is:

[0051]

[0052] where u j k3 is the control force of the following train; j is a positive constant; α is the power approach rate; sat(·) is the saturation function, when s j Greater than Δ j Time sat(s j ) is 1, when s j Less than Δ j Time sat(s j ) is -1, when |s j |Less than or equal to Δ j Time sat(s j )for Where Δ j is a custom boundary layer; e j is the position and speed deviation of the jth following train; is the adaptation rate; j (X) is the Gaussian function vector corresponding to the jth train following the train; D j (t) is the resistance disturbance of the jth following train; a j-1 is the acceleration of the j-1th following train.

[0053] The beneficial effects of the present invention are as follows: the adaptive sliding mode controller constructed by this method can realize train operation control under uncertain systems and external interference conditions, ensure the robustness and stability of the system, ensure that the system state converges to the desired trajectory within a limited time, reduce the high-frequency jitter phenomenon in the sliding mode control, and improve the control performance in practical applications. The calculation of the minimum safe tracking interval in this method further considers the safety protection of anti-collision during train operation during train braking to ensure the safety of the train during operation. This method fully considers the high-density public transportation service characteristics of urban trains, focuses on the study of train operation control issues, and realizes the coordinated operation control of multiple vehicles with small spacing under the premise of ensuring the safe operation of the train. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 is a schematic diagram of the process of this method;

[0055] Figure 2 It is a schematic diagram of the framework of the adaptive sliding mode controller;

[0056] Figure 3 It is a schematic diagram of the control scheme of the second-order auxiliary system;

[0057] Figure 4 The spacing deviation-time curve in the embodiment;

[0058] Figure 5 It is the speed deviation-time curve in the embodiment. DETAILED DESCRIPTION

[0059] 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.

[0060] like Figure 1 As shown, the control method applicable to the adaptive tracking operation of urban train groups includes the following steps:

[0061] S1. Based on the operation process of the train, a single-vehicle kinematic model and a multi-train cooperative control model are constructed;

[0062] S2. Based on the single-vehicle kinematic model and the multi-train cooperative control model, an adaptive sliding mode controller for distributed control of vehicle groups based on adaptive sliding mode control is constructed with the goal of accuracy and real-time performance.

[0063] S3. Establish a safety protection model between trains;

[0064] S4. Based on the safety protection model between trains, obtain the safety tracking interval between trains;

[0065] S5. Obtain the real-time status information of each train and the current safe tracking interval, calculate the control force of each train based on the adaptive sliding mode controller, and complete the adaptive tracking operation control of the urban train group.

[0066] In this embodiment, considering that the actual operation of the train may be affected by external weather and terrain changes, the basic resistance model of the actual operation of the train is expressed as:

[0067] w0=δ a a+δ b bv(t)+δ c cv(t) 2

[0068] Where a, b and c are the coefficients of Davis formula, δ a , δ b and δ c are the rates of change of a, b and c, respectively, which are related to the train operation environment. They are constant values ​​within a certain time range and belong to slow time-varying parameters; v(t) is the running speed of the train;

[0069] In order to ensure the universality of the controller under different line conditions, the additional resistance of train operation affected by line conditions is regarded as an unknown bounded disturbance and expressed as:

[0070]

[0071] Where d(t) is an unknown bounded disturbance, w i Add resistance to the line slope, w c Add resistance to line curves;

[0072] Therefore, the expression of the bicycle kinematic model in step S1 is:

[0073]

[0074] The expression of the multi-train cooperative control model is:

[0075]

[0076] in is the derivative of the train's real-time position with respect to time, that is, the train's running speed v(t); is the real-time acceleration of the train; u(t) is the traction or braking force per unit mass of the train; g is the acceleration of gravity; w0 is the resistance during the train operation; γ is the train rotation mass coefficient; d(t) is the unknown bounded disturbance, w iAdd resistance to the line slope, w c Add resistance to the line curve; x k (t+1) represents the position of the kth train at time t+1; x k (t) represents the position of the kth train at time t; T s is the sampling period; v k (t) represents the speed of the kth train at time t; u k (t) represents the traction or braking force per unit mass of the kth train at time t; v k (t+1) represents the speed of the kth train at time t+1.

[0077] Based on the single-vehicle kinematic model and the multi-train cooperative control model, the actual output signal of the controller is considered to be saturated when the controller input occurs. with u k The relationship between (t) can be expressed as:

[0078]

[0079] Among them, u max 、u min are the maximum and minimum values ​​of the actual output of the controller respectively.

[0080] In the specific implementation process, Figure 2 As shown, the adaptive sliding mode controller first inputs the control target as the desired speed and desired position of the train, then establishes an integral sliding mode model, determines the sliding surface of the desired speed and position, and then designs the controller considering the saturation of the control input, and applies the controller to the train system with disturbances, so that the train can run according to the desired speed and position sliding surface under some disturbances such as signal delay, motor overheating efficiency instability, etc., and then the train system outputs the actual speed and position, and compares them with the desired speed and position. The resulting position and speed deviations are adaptively compensated using a neural network algorithm and transmitted back to the controller. At the same time, based on the real-time information of the preceding vehicle, it is determined whether the position and speed output by the train system have reached the minimum safe tracking interval with the preceding vehicle, and the control target, i.e., the desired speed and position, needs to be adjusted in real time. Therefore, the expression of the adaptive sliding mode controller in step S2 is:

[0081]

[0082] Where U is the controller configured for each train, U=[u1,u2,...,u n ] T , the superscript T indicates the transpose of the matrix, u n The controller configured for the nth train; K3 is a constant; is a positive constant; S is the sliding surface configured for each train, K2 is the controller parameter, K2=[k2 1 ,k2 2 ,...,k2 n ], k2 n is the integral coefficient of the nth train; S = [s1, s2, ..., s n ] T ,s n is the sliding surface configured for the nth train; sgn represents the sign function, when S is greater than 0, sgn(S) is 1, when S is less than 0, sgn(S) is -1, and when S is equal to 0, sgn(S) is 0; E is the error term configured for each train, E=[e1,e2,...,e n ] T , e n is the error term configured for the nth train; δ1=Λ1P+Γ, δ2=Λ2V+Ι, P = [p r ,p1,p2,...,p n ] T , P is the train position matrix, p n is the position of the nth train, V = [v r ,v1,v2,...,v n ] T , V is the train speed matrix, v n is the speed of the nth train, L is the safety tracking interval; The first state quantity output by the auxiliary system of the nth train; is the second state quantity output by the auxiliary system of the nth train; W1 and W2 are the outputs of the auxiliary second-order system, , c n,1 is the first positive parameter to be designed for the nth train; λ n The error coefficient designed for the nth train; is the output weight vector matrix of the neural network in each train controller, is the output weight vector of the neural network in the nth train controller; Ψ(X) is the Gaussian function vector, Ψ(X) = [Ψ1(X), Ψ2(X), ..., Ψ n (X)] T ,Ψ n (X) is the Gaussian function vector of the nth train; D is the interference received by each train, D = [D1, D2, ..., D n ] T , D nis the interference suffered by the nth train; c n,2 is the second positive parameter to be designed for the nth train; c n,3 is the third positive parameter to be designed for the nth train; ΔU is the difference between the ideal control force and the actual control force.

[0083] The expression of the safety protection model between trains in step S3 is:

[0084]

[0085] Where L j is the minimum safe tracking interval of the following vehicle; st represents the constraint condition; is the front position of the rear vehicle at time t; is the rear position of the preceding vehicle at time t; S m is the safety margin; t js is the stop time of the following vehicle; is the front position of the rear vehicle at the time of departure; je represents the ranging error of the following vehicle; v j (t) represents the speed of the following vehicle at time t; v j (0) indicates the speed of the following vehicle at the time of departure; v je Indicates the speed measurement error of the following vehicle; represents the acceleration of the rear car at time t; Indicates the rear position of the front vehicle at the time of departure; s ie Indicates the distance measurement error of the preceding vehicle; v i (t) represents the speed of the preceding vehicle at time t; v i (0) indicates the speed of the preceding vehicle at the time of departure; v ie Indicates the speed measurement error of the preceding vehicle; represents the acceleration of the preceding vehicle at time t.

[0086] The specific method for obtaining the safe tracking interval between trains in step S4 includes the following steps:

[0087] S4-1, obtain the speed, real-time position and initial acceleration of the front and rear vehicles through sensors;

[0088] S4-2, calculate the emergency braking curves of the front and rear vehicles;

[0089] S4-3, obtaining the rear position of the front vehicle and the front position of the rear vehicle, and calculating the minimum distance between the front and rear vehicles;

[0090] S4-4, determine whether the minimum distance between the front and rear vehicles is equal to the safety margin. If so, output the initial positions of the front and rear vehicles as the minimum safe tracking interval; otherwise, change the initial position of the rear vehicle and return to step S4-2.

[0091] The specific method of calculating the control force of each train based on the adaptive sliding mode controller in step S5 includes calculating the control force of the lead train and calculating the control force of the following train.

[0092] The specific method for calculating the control force of the pilot train includes the following steps:

[0093] A1. Obtaining real-time status information of the pilot train through sensors; the real-time status information includes real-time position and real-time speed;

[0094] A2. Compare the acquired real-time status information with the tracking trajectory curve of the following train to calculate the error of the pilot train; the error of the pilot train is expressed as:

[0095] e1=δ 1,1 +λ1δ 1,2

[0096] Where e1 is the error of the pilot train; δ 1,1 is the deviation between the actual position of the pilot train and the position of the train tracking trajectory curve at the same time; λ1 is a positive constant; δ 1,2 It is the deviation between the actual speed of the pilot train and the speed of the train following the track curve at the same time;

[0097] A3. According to the calculated error of the pilot train, based on the basic idea of ​​integral sliding mode control, the integral sliding mode function of the pilot train is defined, and its expression is:

[0098] s1=c1e1+c2∫e1d(t)

[0099] Where s1 is the integral sliding mode function of the pilot train; c1 is the proportional coefficient; c2 is the integral coefficient;

[0100] A4. The control force of the pilot train is calculated based on the integral sliding mode function of the pilot train, and its expression is:

[0101]

[0102] Where u1 is the control force of the pilot train; k3 is a positive constant; α is the power approach rate; sat(·) is a saturation function, when s1 is greater than Δ, sat(s1) is 1, when s1 is less than Δ, sat(s1) is -1, and when |s1| is less than or equal to Δ, sat(s1) is Where Δ is the custom boundary layer; v1 is the speed of the pilot train; v r The speed at which the pilot train follows the curve of the track; is the time-varying resistance of the pilot train; D(t) is the resistance disturbance of the pilot train; a r The acceleration of the pilot train following the trajectory curve.

[0103] The specific method for calculating the control force of the following train includes the following steps:

[0104] B0, for following trains, to solve the input saturation problem, such as Figure 3 As shown ( Figure 3 The output state quantity W and Δu correspond to W j and Δu j ), the second-order auxiliary system is introduced and expressed as:

[0105]

[0106]

[0107]

[0108] in is the output state quantity of the jth following train auxiliary system; C = [c j,1 ,c j,2 , c j,3 ] is the positive parameter to be designed; for The first derivative of ; is the position output by the j-th train following auxiliary system; is the speed output by the auxiliary system of the j-th following train; for The first derivative of j is the actual output control force of the jth following train; is the ideal output control force of the jth following train;

[0109] B1. Obtain the real-time status of the following train through sensors; the real-time status includes real-time position and real-time speed;

[0110] B2. Calculate the error of the following train based on the real-time position and real-time speed of the front and rear vehicles. The expression is:

[0111]

[0112]

[0113] e j,1 =δ j,1 +λ j δ j,2

[0114] where e j,1 is the error of the jth following train; j,1 is the position deviation of the jth train after the second-order auxiliary system feedback; j is a positive constant; j,2is the speed deviation of the jth following train after the second-order auxiliary system feedback; p j is the position of the jth following train; p j-1 is the position of the j-1th following train; L is the current safe tracking interval; v j is the speed of the jth following train; v j-1 is the speed of the j-1th following train; c j,1 、c j,2 and c j,3 All are normal numbers;

[0115] B3. According to the calculated error of the following train, based on the basic idea of ​​integral sliding mode control, the integral sliding mode function of the following train is defined, and its expression is:

[0116] s j =c 1,j e 1,j +c 2,j ∫e 1,j d(t)

[0117] where s j is the integral sliding mode function of the following train; c 1,j and c 2,j are the proportional coefficient and the integral coefficient respectively;

[0118] B4. The control force of the following train is calculated based on the integral sliding mode function of the following train, and its expression is:

[0119]

[0120] where u j k3 is the control force of the following train; j is a positive constant; α is the power approach rate; sat(·) is the saturation function, when s j Greater than Δ j Time sat(s j ) is 1, when s j Less than Δ j Time sat(s j ) is -1, when |s j |Less than or equal to Δ j Time sat(s j )for where Δ j For custom boundary layers; is the adaptation rate; j (X) is the Gaussian function vector corresponding to the jth train following the train; D j (t) is the resistance disturbance of the jth following train; a j-1 is the acceleration of the j-1th following train.

[0121] In one embodiment of the present invention, Figure 4 The variation of train spacing deviation over time after using this method is shown in Figure 2. Figure 5 The train speed deviation changes with time after using this method. There is a leading train and three following trains. The following trains have an initial speed disturbance and the speed deviation is 4km / h. Figure 4 , the speed disturbance causes all trains to deviate from the equilibrium state, but the disturbance is eliminated after about 5 seconds. Figure 5 In the process, the spacing between adjacent trains fluctuates with the speed fluctuation, and the error is gradually eliminated.

[0122] In summary, the present invention fully considers the high-density public transportation service characteristics of urban trains, focuses on the study of train operation control issues, and realizes the coordinated operation control of multiple vehicles with small spacing under the premise of ensuring the safe operation of trains.

Claims

1. A control method for adaptive tracking operation of urban train groups, characterized in that: The following steps are involved: S1. Based on the operation process of the train, a single-vehicle kinematic model and a multi-train cooperative control model are constructed; S2. Based on the single-vehicle kinematic model and the multi-train cooperative control model, an adaptive sliding mode controller for distributed control of vehicle groups based on adaptive sliding mode control is constructed with the goal of accuracy and real-time performance. S3. Establish a safety protection model between trains; S4. Based on the safety protection model between trains, obtain the safety tracking interval between trains; S5. Obtain the real-time status information of each train and the current safe tracking interval, calculate the control force of each train based on the adaptive sliding mode controller, and complete the adaptive tracking operation control of the urban train group.

2. The control method for adaptive tracking operation of urban train groups according to claim 1 is characterized in that: The expression of the bicycle kinematic model in step S1 is: The expression of the multi-train cooperative control model is: Where x(t) is the derivative of the train's real-time position with respect to time, that is, the train's running speed v(t); is the real-time acceleration of the train; u(t) is the traction or braking force per unit mass of the train; g is the acceleration of gravity; w0 is the resistance during the train operation, w0 = δ a a+δ b bv(t)+δ c cv(t) 2 , a, b and c are the coefficients of Davis formula, δ a , δ b and δ c are the rates of change of a, b and c respectively; γ is the train rotation mass coefficient; d(t) is the unknown bounded disturbance, w i Add resistance to the line slope, w c Add resistance to the line curve; x k (t+1) represents the position of the kth train at time t+1; x k (t) represents the position of the kth train at time t; T s is the sampling period; v k (t) represents the speed of the kth train at time t; u k (t) represents the traction or braking force per unit mass of the kth train at time t; v k (t+1) represents the speed of the kth train at time t+1.

3. The control method for adaptive tracking operation of urban train groups according to claim 1 is characterized in that: The expression of the adaptive sliding mode controller in step S2 is: Where U is the controller configured for each train, U=[u1,u2,...,u n ] T , the superscript T indicates the transpose of the matrix, u n The controller configured for the nth train; K3 is a constant; is a positive constant; S is the sliding surface configured for each train, K2 is the controller parameter, K2=[k2 1 ,k2 2 ,...,k2 n ], k2 n is the integral coefficient of the nth train; S = [s1, s2, ..., s n ] T ,s n The sliding surface configured for the nth train; sgn represents the sign function. When S is greater than 0, sgn(S) is 1, when S is less than 0, sgn(S) is -1, and when S is equal to 0, sgn(S) is 0. E is the error term configured for each train. E=[e1,e2,...,e n ] T , e n is the error term configured for the nth train; δ1=Λ1P+Γ, δ2=Λ2V+Ι, P=[p r ,p1,p2,...,p n ] T , P is the train position matrix, p n is the position of the nth train, V = [v r ,v1,v2,...,v n ] T , V is the train speed matrix, v n is the speed of the nth train, L is the safety tracking interval; The first state quantity output by the auxiliary system of the nth train; is the second state quantity output by the auxiliary system of the nth train; W1 and W2 are the outputs of the auxiliary second-order system, c n,1 is the first positive parameter to be designed for the nth train; λ n The error coefficient designed for the nth train; is the output weight vector matrix of the neural network in each train controller, is the output weight vector of the neural network in the nth train controller; Ψ(X) is the Gaussian function vector, Ψ(X) = [Ψ1(X), Ψ2(X), ..., Ψ n (X)] T ,Ψ n (X) is the Gaussian function vector of the nth train; D is the interference received by each train, D = [D1, D2, ..., D n ] T , D n is the interference suffered by the nth train; c n,2 is the second positive parameter to be designed for the nth train; c n,3 is the third positive parameter to be designed for the nth train; ΔU is the difference between the ideal control force and the actual control force.

4. The control method for adaptive tracking operation of urban train groups according to claim 1, characterized in that: The expression of the safety protection model between trains in step S3 is: Where L j is the minimum safe tracking interval of the following vehicle; st represents the constraint condition; is the front position of the rear vehicle at time t; is the rear position of the preceding vehicle at time t; S m is the safety margin; t js is the stop time of the following vehicle; is the front position of the rear vehicle at the time of departure; je represents the ranging error of the following vehicle; v j (t) represents the speed of the following vehicle at time t; v j (0) indicates the speed of the following vehicle at the time of departure; v je Indicates the speed measurement error of the following vehicle; represents the acceleration of the rear car at time t; Indicates the rear position of the front vehicle at the time of departure; s ie Indicates the distance measurement error of the front vehicle; v i (t) represents the speed of the preceding vehicle at time t; v i (0) indicates the speed of the preceding vehicle at the time of departure; v ie Indicates the speed measurement error of the preceding vehicle; represents the acceleration of the preceding vehicle at time t.

5. The control method for adaptive tracking operation of urban train groups according to claim 4 is characterized in that: The specific method for obtaining the safe tracking interval between trains in step S4 includes the following steps: S4-1, obtain the speed, real-time position and initial acceleration of the front and rear vehicles through sensors; S4-2, calculate the emergency braking curves of the front and rear vehicles; S4-3, obtaining the rear position of the front vehicle and the front position of the rear vehicle, and calculating the minimum distance between the front and rear vehicles; S4-4, determine whether the minimum distance between the front and rear vehicles is equal to the safety margin. If so, output the initial positions of the front and rear vehicles as the minimum safe tracking interval; otherwise, change the initial position of the rear vehicle and return to step S4-2.

6. The control method for adaptive tracking operation of urban train groups according to claim 1, characterized in that: The specific method of calculating the control force of each train based on the adaptive sliding mode controller in step S5 includes calculating the control force of the lead train and calculating the control force of the following train.

7. The control method for adaptive tracking operation of urban train groups according to claim 6 is characterized in that: The specific method for calculating the control force of the pilot train includes the following steps: A1. Obtaining real-time status information of the pilot train through sensors; the real-time status information includes real-time position and real-time speed; A2. Compare the acquired real-time status information with the tracking trajectory curve of the following train to calculate the error of the pilot train; the error of the pilot train is expressed as: e1=δ 1,1 +λ1δ 1,2 Where e1 is the error of the pilot train; δ 1,1 is the deviation between the actual position of the pilot train and the position of the train tracking trajectory curve at the same time; λ1 is a positive constant; δ 1,2 It is the deviation between the actual speed of the pilot train and the speed of the train following the track curve at the same time; A3. According to the calculated error of the pilot train, based on the basic idea of ​​integral sliding mode control, the integral sliding mode function of the pilot train is defined, and its expression is: s1=c1e1+c2∫e1d(t) Where s1 is the integral sliding mode function of the pilot train; c1 is the proportional coefficient; c2 is the integral coefficient; A4. The control force of the pilot train is calculated based on the integral sliding mode function of the pilot train, and its expression is: Where u1 is the control force of the pilot train; k3 is a positive constant; α is the power approach rate; sat(·) is a saturation function, when s1 is greater than Δ, sat(s1) is 1, when s1 is less than Δ, sat(s1) is -1, and when |s1| is less than or equal to Δ, sat(s1) is Where Δ is the custom boundary layer; v1 is the speed of the pilot train; v r The speed at which the pilot train follows the curve of the track; is the time-varying resistance of the pilot train; D(t) is the resistance disturbance of the pilot train; a r The acceleration of the pilot train following the trajectory curve.

8. The control method for adaptive tracking operation of urban train groups according to claim 6 is characterized in that: The specific method for calculating the control force of the following train includes the following steps: B1. Obtain the real-time status of the following train through sensors; the real-time status includes real-time position and real-time speed; B2. Calculate the error of the following train based on the real-time position and real-time speed of the front and rear vehicles. The expression is: e j,1 =d j,1 +λ j d j,2 where e j,1 is the error of the jth following train; j,1 is the position deviation of the jth train after the second-order auxiliary system feedback; j is a positive constant; j,2 is the speed deviation of the jth following train after the second-order auxiliary system feedback; p j is the position of the jth following train; p j-1 is the position of the j-1th following train; L is the current safe tracking interval; is the position output by the j-th train following auxiliary system, for The first derivative of ; is the speed output by the auxiliary system of the j-th following train, for The first derivative of u j is the actual output control force of the jth following train; is the ideal output control force of the jth following train; v j is the speed of the jth following train; v j-1 is the speed of the j-1th following train; c j,1 、c j,2 and c j,3 All are normal numbers; B3. According to the calculated error of the following train, based on the basic idea of ​​integral sliding mode control, the integral sliding mode function of the following train is defined, and its expression is: s j =c 1,j e 1,j +c 2,j ∫e 1,j d(t) where s j is the integral sliding mode function of the following train; c 1,j and c 2,j are the proportional coefficient and the integral coefficient respectively; B4. The control force of the following train is calculated based on the integral sliding mode function of the following train, and its expression is: where u j k3 is the control force of the following train; j is a positive constant; α is the power approach rate; sat(·) is the saturation function, when s j Greater than Δ j Time sat(s j ) is 1, when s j Less than Δ j Time sat(s j ) is -1, when |s j |Less than or equal to Δ j Time sat(s j )for Where Δ j is a custom boundary layer; e j is the position and speed deviation of the jth following train; is the adaptation rate; j (X) is the Gaussian function vector corresponding to the jth train following the train; D j (t) is the resistance disturbance of the jth following train; a j-1 is the acceleration of the j-1th following train.

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