Train control method and related device based on obstacle function and coupler constraint

Abstract

The present application discloses a train control method and related devices based on barrier function and coupler constraint, which relates to the field of train control technology. In the scheme, a nonlinear multi-particle dynamic model of the train is constructed by analyzing the coupling force in the train, and then the uncertainty of the train operation parameters is considered and linearized to obtain the dynamic equation with coupler displacement in the train equilibrium state; then it is converted into the train state space equation, and based on this and the state constraint of the coupler, an adaptive robust controller is constructed; after verifying that the Lyapunov function based on the barrier function can make the train coupler displacement strictly within the safety range, the output of the train controller can be adaptively adjusted according to the train expected speed and displacement curve. The scheme proposed in the present application can effectively ensure that the train coupler displacement meets the safety constraint conditions, and can also achieve high-precision tracking operation under uncertain environmental factors.

Description

Technical Field

[0001] The present application relates to the field of train control technology, and in particular to a train control method and related devices based on barrier functions and coupler constraints. Background Art

[0002] When marshaling a high-speed train, the carriages are connected through coupling devices to transmit traction and braking forces, so that the EMU can accelerate, decelerate and stop according to unified instructions. In addition, some coupling devices also have signal transmission functions, which can ensure smooth communication between vehicles and realize real-time monitoring and scheduling of train operation status. These coupling devices play a vital role in EMU trains, ensuring the safety and stability of the overall operation of the train.

[0003] The coupling devices between EMUs mainly include two categories: mechanical coupling devices and electrical coupling devices. Mechanical coupling devices include couplers and buffer devices, and electrical coupling devices include electrical connectors and network control systems. Generally speaking, a buffer device is provided in the coupler, which can be used to absorb and alleviate the impact and vibration during the operation of the train and protect the vehicle structure and equipment from damage. However, for the actual train coupler system, during the operation of high-speed EMUs, the displacement constraints between the couplers are not only restricted by the physical hardware of the coupler raw materials themselves, but more importantly, to ensure the safe and stable operation of the train. Once the coupler displacement exceeds the safety limit, the performance of the train system is likely to be seriously damaged, and even cause immeasurable safety accidents.

[0004] At present, some control strategies have been introduced to solve the constraint problem of train coupling system, including the use of artificial potential function to ensure that the displacement of the coupler is strictly limited within the safety limit. However, this method also has certain limitations in actual operation, especially the position fluctuation of local minimum points and unnecessary reciprocating vibration, which not only affects the passenger comfort, but also may cause mechanical wear and damage to the coupler. In addition, since the train runs in a complex environment and the interference changes of the external environment are highly random and uncertain, it is sometimes very difficult to obtain accurate operating parameters, which is also a key issue facing the current high-speed train automatic driving. Therefore, it is necessary to design an advanced control technology to ensure that the displacement between the coupling devices during the operation of the train is strictly within the safety limit. Summary of the invention

[0005] The purpose of this application is to provide a train control method and related devices based on barrier functions and coupler constraints, which can effectively ensure that the train coupler operates within a safe range and effectively ensure the safety of train operation.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a train control method based on a barrier function and a coupler constraint, comprising the following steps:

[0008] According to the analysis results of the coupling forces within the train, a nonlinear multi-particle dynamic model of the train is constructed.

[0009] According to the uncertainty and error variables of train operation parameters, the nonlinear multi-particle dynamic model of the train is linearized, and the dynamic equation with coupler displacement in the equilibrium state of the train is obtained.

[0010] The dynamic equations are transformed into train state space equations, and an adaptive robust controller is constructed based on the train state space equations and the state constraints of the train coupler.

[0011] The Lyapunov function is constructed based on the barrier function, and the Lyapunov function is used to verify whether the adaptive robust controller can make the train coupler displacement strictly within the safety range, and the first judgment result is obtained.

[0012] If the first judgment result is yes, the output of the train controller is adjusted using an adaptive robust controller according to the desired train speed and displacement curve.

[0013] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the train control method based on barrier functions and coupler constraints described above.

[0014] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the train control method based on barrier functions and coupler constraints described above.

[0015] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the train control method based on barrier functions and coupler constraints described above.

[0016] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0017] The present application provides a train control method and related devices based on obstacle functions and coupler constraints. In the method, a nonlinear multi-particle dynamic model of the train is constructed by analyzing the coupling forces within the train. Subsequently, the nonlinear multi-particle dynamic model of the train is linearized considering the uncertainty and error variables of the train operation parameters to obtain a dynamic equation with a coupler displacement in the train equilibrium state. The dynamic equation is then converted into a train state space equation, and an adaptive robust controller is constructed based on this and the state constraints of the train coupler. After verifying that the adaptive robust controller can keep the train coupler displacement strictly within a safe range using the Lyapunov function constructed based on the obstacle function, the output of the train controller can be adjusted using the adaptive robust controller according to the train desired speed and displacement curve. The solution proposed in this application can effectively ensure that the train coupler displacement meets the safety constraints. Even if the coupler is stretched and compressed at the initial moment, as the train runs for a long time, under the proposed adaptive robust control method, the coupler displacement can eventually converge to near the zero area. The train can adjust the displacement of the carriage according to the actual operating conditions and environmental parameters to ensure the coordination and consistency of the overall operation of the train, and can achieve high-precision tracking operation under uncertain environmental factors, providing passengers with a smoother and more comfortable riding experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 A flowchart of a train control method based on barrier function and coupler constraints provided in one embodiment of the present application.

[0020] Figure 2 A force analysis diagram of adjacent carriages of a high-speed train in a train control method based on a barrier function and a coupler constraint provided in one embodiment of the present application.

[0021] Figure 3 A block diagram of a high-speed train adaptive robust control structure in a train control method based on barrier functions and coupler constraints provided in one embodiment of the present application.

[0022] Figure 4 A high-speed train desired speed tracking trajectory diagram in a train control method based on barrier function and coupler constraints is provided in a specific embodiment of the present application.

[0023] Figure 5A high-speed train expected displacement tracking trajectory diagram in a train control method based on barrier function and coupler constraints is provided in a specific embodiment of the present application.

[0024] Figure 6 A coupler variation curve diagram under a high-speed train adaptive control strategy in a train control method based on a barrier function and coupler constraints provided in a specific embodiment of the present application.

[0025] Figure 7 A displacement tracking error curve diagram of a high-speed train adaptive control strategy in a train control method based on a barrier function and a coupler constraint provided in a specific embodiment of the present application.

[0026] Figure 8 A speed tracking error curve diagram of a high-speed train under an adaptive control strategy in a train control method based on a barrier function and a coupler constraint provided in a specific embodiment of the present application.

[0027] Fig. 9 A system state trajectory diagram under a high-speed train adaptive control strategy in a train control method based on barrier functions and coupler constraints provided in a specific embodiment of the present application.

[0028] Fig.10 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0030] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0031] In an exemplary embodiment, Figure 1 The flowchart shown provides a train control method based on barrier function and coupler constraint, comprising the following steps:

[0032] S1. Based on the analysis results of the coupling forces within the train, a nonlinear multi-particle dynamic model of the train is constructed. Specifically in this embodiment, the nonlinear multi-particle dynamic model of the train is shown in the following formula (1):

[0033] (1)

[0034] in, For the i The displacement of the carriages, For the i The speed of the carriage, For the i The acceleration of the car, , and are the Davis coefficients obtained through wind tunnel tests, is the elastic coupling coefficient, is the damping coupling coefficient, For the i The quality of the carriage, For the i The control input of each car, It is j The lumped disturbance of each carriage is composed of additional resistance and unknown external disturbance. The additional resistance includes curve resistance, slope resistance and tunnel resistance. i and j The carriage number, j =2,…, n -1, n is the number of carriages in the train, l is the fixed length of the carriage.

[0035] S2. According to the uncertainty and error variables of the train operation parameters, the nonlinear multi-particle dynamic model of the train is linearized to obtain the dynamic equation with coupler displacement in the equilibrium state of the train. Specifically, in this embodiment, the displacement tracking error variable, the speed tracking error variable and the control input error variable are first defined, and considering the uncertainty of the train operation parameters, the nonlinear model of the train is linearized to obtain the dynamic equation with coupler displacement in the equilibrium state of the train.

[0036] like Figure 2 The force analysis diagram of adjacent carriages of a high-speed train is shown, and the coupler displacement can be re-expressed as: .

[0037] The high-speed train has the desired speed when it reaches equilibrium: .

[0038] Define the displacement error variable as: .

[0039] Speed ​​error variable: .

[0040] Control error variable: .

[0041] Furthermore, the dynamic equation of the high-speed train under equilibrium state containing the coupler displacement state and parameter uncertainty can be obtained as shown in equation (2):

[0042] (2)

[0043] in, For the i The first carriage and the i +1 displacement of the coupler between carriages, For the i The displacement error variable of each carriage, For the i The desired position of the carriages, For the i The speed error variable of each carriage, For the i The expected speed of each car, For the i The control error variable of each car is For the i The desired control variables for each car are: and Respectively represent parameters and uncertainty, For the j The quality of the carriage, For the j The mass of passengers and cargo in the carriage; the displacement of the coupler satisfies the constraints ,in Indicates the maximum amount by which the coupler can be stretched or compressed.

[0044] S3. The dynamic equation is converted into a train state space equation, and an adaptive robust controller is constructed based on the train state space equation and the state constraints of the train coupler; the designed adaptive robust controller can make the train coupler meet the displacement constraints, and as the running time goes by, the coupler displacement and tracking error converge to the origin asymptotically. Specifically in this embodiment, the train state space equation is shown in the following formula (3):

[0045] (3)

[0046] in, is the train system state variable including the coupler displacement, is the output variable of the train controller, represents the continuous bounded unknown external aggregate disturbance to which the train is subjected, A , , B and is the train system matrix with appropriate dimensions.

[0047] Train System Matrix is bounded, that is , train system matrix As shown in the following formula (4):

[0048] (4)

[0049] in, express Real Euclidean space.

[0050] Train System Matrix A As shown in the following formula (5):

[0051] (5)

[0052] Among them, 0 n×n for n × n Zero matrix of size, intermediate matrix A 12 , intermediate matrix A 21 and the intermediate matrix A 22 They are shown in the following equations (6) to (8):

[0053] (6)

[0054] (7)

[0055] (8)

[0056] Train System Matrix As shown in the following formula (9):

[0057] (9)

[0058] Among them, the intermediate matrix and the intermediate matrix They are shown in the following formulas (10) and (11) respectively:

[0059] (10)

[0060] (11)

[0061] Among them, the intermediate matrix Δh As shown in the following formula (12):

[0062] (12)

[0063] Train System Matrix B As shown in the following formula (13):

[0064] (13)

[0065] Among them, the intermediate matrix B 21 As shown in the following formula (14):

[0066] (14)

[0067] The state constraint of the train coupler is shown in equation (15):

[0068] (15)

[0069] Among them, the train system state variable set , express n Real Euclidean space, Indicates the maximum amount by which the coupler can be stretched or compressed.

[0070] The adaptive robust controller is shown in equation (16):

[0071] (16)

[0072] in, and They are all auxiliary control variables.

[0073] Auxiliary control variables As shown in the following formula (17):

[0074] (17)

[0075] in, is an unknown parameter, for The corresponding estimated value is , R and P are positive definite matrices, R -1 is a positive definite matrix R The inverse matrix of is the minimum eigenvalue of the corresponding matrix, sec is the secant function, To meet the conditions Any positive bounded and uniformly continuous function under , represents the set of positive real numbers.

[0076] Auxiliary control variables As shown in the following formula (18):

[0077] (18)

[0078] in, is an unknown parameter, for The corresponding estimated value.

[0079] S4. A Lyapunov function is constructed based on the barrier function, and the Lyapunov function is used to verify whether the adaptive robust controller can make the train coupler displacement strictly within a safe range, and a first judgment result is obtained; if the first judgment result is yes, step S5 is executed.

[0080] Specifically, after the adaptive robust controller is constructed through step S3, in this embodiment, the high-speed train closed-loop system and the error system are defined; in the designed Lyapunov function, the barrier function is set as the first term of the function, and when the train system state containing the coupler displacement is in an ellipsoidal domain, the Lyapunov function value is positive; by deriving and scaling the Lyapunov function, it is proved that the train system state converges to the ellipsoidal domain, and it is verified that the adaptive robust controller designed in this scheme controls the train coupler displacement to achieve the expected effect, thereby achieving strict limitation of the train coupler displacement within a safe range, ensuring the safety of train operation. The Lyapunov function is shown in the following formula (19):

[0081] (19)

[0082] in, and is any normal number, and They are and The corresponding error value.

[0083] S5. According to the train's expected speed and displacement curve, the output of the train controller is adjusted using an adaptive robust controller. Specifically, given the expected speed and displacement curve of the high-speed train system, the state variables inside the system are obtained through the two designed adaptive rates and the auxiliary control function part to obtain the output of the controller. In the case of external disturbances, the system state can also adjust the output of the controller by itself through the designed adaptive controller, so that the displacement of the train coupler is strictly under the safety constraint condition.

[0084] The control method proposed in this embodiment can effectively ensure that the displacement of the high-speed train coupler meets the safety constraint conditions and can ensure the safe operation of the train. Even if the coupler may be stretched or compressed at the initial moment, as the train runs over time, under the adaptive control method proposed in this application, the coupler displacement can eventually converge to the vicinity of the zero area. The designed adaptive control strategy enables the train to adjust the displacement between the carriages according to the actual operation conditions and environmental parameters to ensure the coordination and consistency of the overall operation of the train, and can also achieve high-precision tracking operation under uncertain environmental factors, while also providing passengers with a more stable and comfortable riding experience. For a given target speed, under the proposed control method, all carriages of the train can achieve accurate tracking and converge to the vicinity of the zero area in a relatively short time. In addition, the maximum speed tracking error is also much smaller than the actual train tracking error allowable value, and has a high tracking control accuracy. Under the proposed control method, the speed tracking error fluctuation amplitude is very small, which can ensure the stability of the train during the driving process to a certain extent, and has a strong anti-interference ability. This control method strategy has a high application value.

[0085] In another exemplary embodiment of the present application, the train control method based on the barrier function and the coupler constraint provided in the present application is divided into two parts, the design of the adaptive robust controller and the design of the Lyapunov function based on the barrier function, respectively including the following steps:

[0086] A1. Assume that there is a unique matrix , so that the Algebraic Riccati Equation (20) is established:

[0087] (20)

[0088] Further, there is a definition as shown in formula (21):

[0089] (twenty one)

[0090] Therefore, the above Riccati equation can be rewritten as equation (22):

[0091] (twenty two)

[0092] In the formula, P , Q , R is a positive definite matrix of appropriate dimension, I is a unit matrix of appropriate dimension, and η is any scalar greater than zero, which can facilitate the subsequent analysis of the controller stability.

[0093] A2. Considering the train state space equation (3) and equation (21), there is always an unknown number This makes equation (23) valid, which is convenient for scaling the derivative of the Lyapunov function:

[0094] (twenty three)

[0095] In order to achieve the control target of high-speed trains, an adaptive robust controller of form (16) is constructed. The state space equation shown in equation (24) is obtained from equation (3):

[0096] (twenty four)

[0097] Under the assumption that there are unknown constants Make Under the condition that So that formula (25) holds true:

[0098] (25)

[0099] Then let the unknown parameters , so as to design the adaptive robust controller (16) and As shown in formula (26) and formula (27) respectively:

[0100] (26)

[0101] (27)

[0102] in, To meet the conditions Any positive bounded and uniformly continuous function under , and is an unknown parameter, and They correspond to their estimated values ​​respectively, and satisfy the adaptation rates shown in equations (28) and (29):

[0103] (28)

[0104] (29)

[0105] In the formula, and is any positive constant.

[0106] A3. Set the parameters and The initial values ​​of are all set to positive numbers. From equations (28) and (29), we can get the parameter and Always positive.

[0107] To obtain the error system, define and , then we have the following equations (30) and (31):

[0108] (30)

[0109] (31)

[0110] After completing the design of the adaptive robust controller, given the expected speed and displacement curve of the high-speed train system, the state variables inside the system are obtained through the two designed adaptive rates and the auxiliary control function part to obtain the output of the controller. In the case of external disturbances, the system state can also adjust the output of the controller by itself through the designed adaptive controller, so that the displacement of the train coupler is strictly under the safety constraint conditions. The specific adaptive robust control structure block diagram is shown in the figure. Figure 3 shown.

[0111] Since there is no guarantee that the collection is an invariant set, so we need to find a subset , so that any initial condition , the state of the high-speed train system can always remain in the subset , and can converge to the origin asymptotically. Therefore, it is necessary to design a Lyapunov function to find the above-mentioned ellipsoidal domain For the designed adaptive robust controller, the system stability needs to be analyzed, so it is necessary to design a Lyapunov function and introduce a barrier function for verification. The steps for Lyapunov function design are as follows:

[0112] B1. Define the high-speed train closed-loop system and error system as , and then design the Lyapunov function as shown in formula (32):

[0113] (32)

[0114] In the formula, the first term on the right side of the equal sign is the barrier function.

[0115] B2. Combining the train system (3) and the adaptive robust controller (16), in order to analyze the system stability, the Lyapunov function is derived as shown in equation (33):

[0116] (33)

[0117] B3. Consider equation (33), and scaling it to obtain the inequalities shown in equations (34) and (35):

[0118] (34)

[0119] (35)

[0120] B4. Further scaling it, equation (33) can be rewritten as the inequality shown in equation (36):

[0121] (36)

[0122] in, , is an unknown constant, .

[0123] B5. In order to verify the convergence of the state variables, select any positive constant c and define the set shown in equations (37) and (38):

[0124] (37)

[0125] (38)

[0126] In formula (37) and formula (38), is a state variable Departure assembly, is a state variable Arrived at the set, and it is known , as well as .

[0127] B6. To obtain Uniformly bounded, the system state variables converge, in the interval Integrating equation (36) above yields equation (39):

[0128] (39)

[0129] We can further obtain equation (40) and equation (41):

[0130] (40)

[0131] (41)

[0132] B7. According to formula (40), the error system is uniformly bounded, which can ensure the stability of the system and obtain The formula is uniformly continuous. Combining formula (41) and Barbalat's lemma, we can get formula (42):

[0133] (42)

[0134] Obviously, .

[0135] So far, the initial conditions are satisfied , then the solution is uniformly bounded, and the train system state satisfies the constraints , , the train state converges asymptotically to the origin. Therefore, the designed adaptive robust controller can effectively control the train so that the coupler displacement is within the safety constraint condition.

[0136] In order to verify the effectiveness of the high-speed train tracking control method under the coupler constraint condition based on the barrier function proposed in this embodiment, a simulation experiment is conducted on the performance of the proposed adaptive robust controller and the results of the simulation experiment are analyzed as follows.

[0137] The basic parameters of the simulated high-speed train are shown in Table 1.

[0138] Table 1 Basic parameters of high-speed trains

[0139]

[0140] Assume that the total disturbance to which the high-speed train is subjected during operation is It has the form shown in formula (43):

[0141] (43)

[0142] in, , and They represent the slope resistance, tunnel resistance and curve resistance of the train, respectively, and , as well as ; The external unknown disturbance is set to ; " rand "express Random value between; gravitational acceleration g=9.8N / kg.

[0143] Select , select , use MATLAB to solve the Riccati equation and solve the matrix P and K .

[0144] Assume that the initial state of the coupler is , which means that each coupler is stretched or compressed to a certain extent. In addition, the parameter , , , , , , , .

[0145] Through simulation, the expected velocity curve and expected displacement curve are given as follows Figure 4 and Figure 5 As shown in the figure, the displacement, velocity tracking error and system state trajectory change curves of each coupler under the above adaptive control method are obtained as follows Figure 6~Figure 9 As shown. Figure 6 and Figure 7 It can be seen that the proposed control method can effectively ensure that the displacement of the train coupler meets the safety constraints. Even if the coupler is stretched and compressed at the initial moment, as the train runs for a long time, under the proposed adaptive control method, the coupler displacement can eventually converge to the vicinity of the zero region. The train can adjust the displacement of the carriage according to the actual operating conditions and environmental parameters to ensure the coordination and consistency of the overall operation of the train, and can achieve high-precision tracking operation under uncertain environmental factors, which also provides passengers with a smoother and more comfortable riding experience.

[0146] from Figure 8 and Fig. 9 It can be seen that for a given target speed, under the proposed control method, all carriages of the train can achieve accurate tracking and converge to the vicinity of the zero region in a short time. Under the proposed control method, the speed tracking error fluctuation is very small, which can ensure the stability of the train during driving to a certain extent, has strong anti-interference ability, and can effectively ensure the safety of train operation.

[0147] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Fig.10As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a train control method based on barrier functions and coupler constraints is implemented.

[0148] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0149] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0150] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0151] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0152] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0153] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0154] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0155] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A train control method based on barrier function and coupler constraint, characterized in that: include: Based on the analysis results of the coupling forces within the train, a nonlinear multi-particle dynamic model of the train is constructed; According to the uncertainty and error variables of the train operation parameters, the nonlinear multi-particle dynamic model of the train is linearized to obtain the dynamic equation with coupler displacement under the train equilibrium state; The dynamic equation is converted into a train state space equation, and an adaptive robust controller is constructed according to the train state space equation and the state constraint condition of the train coupler; A Lyapunov function is constructed based on the barrier function, and the Lyapunov function is used to verify whether the adaptive robust controller can make the displacement of the train coupler strictly within a safe range, thereby obtaining a first judgment result; If the first judgment result is yes, adjusting the output of the train controller using the adaptive robust controller according to the train desired speed and displacement curve; The nonlinear multi-particle dynamics model of the train is shown as follows: ； in, For the i The displacement of the carriages, For the i The speed of the carriage, For the i The acceleration of the car, , and are the Davis coefficients obtained through wind tunnel tests, is the elastic coupling coefficient, is the damping coupling coefficient, For the i The quality of the carriage, For the i The control input of each car, It is j The lumped disturbance of each carriage is composed of additional resistance and unknown external disturbance. The additional resistance includes curve resistance, slope resistance and tunnel resistance. i and j The carriage number, j =2,…, n -1, n is the number of carriages in the train, l is the fixed length of the carriage; The kinetic equation is shown below: ； in, For the i The first carriage and the i +1 displacement of the coupler between carriages, For the i The displacement error variable of each carriage, For the i The desired position of the carriages, For the i The speed error variable of each carriage, For the i The expected speed of each car, For the i The control error variable of each car is For the i The desired control variables for each car are: and Respectively represent parameters and uncertainty; The train state space equation is shown as follows: ； in, is the train system state variable including the coupler displacement, is the output variable of the train controller, represents the continuous bounded unknown external aggregate disturbance to which the train is subjected, A , , B and is the train system matrix with appropriate dimensions; The train system matrix is bounded, that is , train system matrix As shown below: ； in, express Real Euclidean space; The train system matrix A As shown below: ； Among them, 0 n×n for n × n Zero matrix of size, intermediate matrix A 12 , intermediate matrix A 21 and the intermediate matrix A 22 They are shown as follows: ； ； ； The train system matrix As shown below: ； Among them, the intermediate matrix and the intermediate matrix They are shown as follows: ； ； Among them, the intermediate matrix Δh As shown below: ； The train system matrix B As shown below: ； Among them, the intermediate matrix B 21 As shown below: ； The state constraints of the train coupler are as follows: ； Among them, the train system state variable set , express n Real Euclidean space, Indicates the maximum amplitude by which the coupler can be stretched or compressed; The adaptive robust controller is shown as follows: ； in, and All are auxiliary control variables; The auxiliary control variable As shown below: ； in, is an unknown parameter, for The corresponding estimated value is , R and P are positive definite matrices, R -1 is a positive definite matrix R The inverse matrix of is the minimum eigenvalue of the corresponding matrix, sec is the secant function, To meet the conditions Any positive bounded and uniformly continuous function under , represents the set of positive real numbers; The auxiliary control variable As shown below: ； in, is an unknown parameter, for The corresponding estimated value; The Lyapunov function is shown below: ； in, and They are and The corresponding error value is and is any positive constant.

2. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the train control method based on barrier functions and coupler constraints as described in claim 1.

3. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the train control method based on barrier function and coupler constraint described in claim 1 is implemented.

4. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the train control method based on barrier function and coupler constraint described in claim 1 is implemented.

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