High-speed train multi-particle cooperative control method, system, equipment and medium

Abstract

The invention discloses a high-speed train multi-mass-point cooperative control method, system, equipment and medium, and relates to the field of high-speed train operation control, the method comprises the following steps: dividing carriages of a high-speed train into multiple groups, and constructing a discretization kinetic equation of the high-speed train; performing multi-objective optimization on the control gain and the sliding mode surface parameters according to the displacement tracking error and the coupler force of the high-speed train in the set time period; based on the optimal control gain, the inter-group equivalent coupler force and composite resistance of the high-speed train are estimated in real time; determining a nonsingular fast terminal sliding mode surface based on the optimal sliding mode surface parameter and a displacement tracking error of the high-speed train; and according to the nonsingular fast terminal sliding mode surface, the inter-group equivalent coupler force estimated value, the composite resistance estimated value and the discretization kinetic equation, solving control input in real time so as to control the traction braking force of the front drive group and the traction braking force of the rear drive group in real time. The control precision, the anti-interference capability and the safety of the coupler force of the high-speed train are improved.

Description

Technical Field

[0001] This application relates to the field of high - speed train operation control, and particularly to a multi - particle collaborative control method, system, device and medium for high - speed trains. Background Art

[0002] As the core equipment of modern rail transit, the operation safety, ride comfort and energy - efficiency optimization of high - speed trains are the key points of technical research. Taking the CRH380B - type multiple unit train as an example, this type of train adopts an alternating formation design of motor cars and trailers. Although it can improve the utilization rate of traction power, the non - symmetric distribution of power will cause the middle trailers to bear significantly higher dynamic coupler forces than those at both ends. Test data shows that under emergency braking or variable - slope conditions, the peak coupler force of the middle car can reach 156.3 kN, far exceeding the 80 - kN safety threshold specified in the "Railway Vehicle Strength Design Specification". This will exacerbate the mechanical fatigue of the coupler buffer device, and may even lead to the train uncoupling accident in severe cases. Currently, solutions such as locally strengthening the coupler buffer device or optimizing the driving curve are usually adopted to alleviate such problems. However, these methods only stay at the level of mechanical structure improvement and operation strategy, and fail to solve the problem of unbalanced force - chain transmission under the coupling action of multiple power units from the perspective of dynamic control, resulting in the inability to achieve the global optimum of the dynamic distribution of coupler forces.

[0003] In the field of train - whole - vehicle collaborative control, sliding - mode control is widely used in the coordinated control of multiple power units due to its strong robustness. However, the sampling delay and quantization error in the discretization process will exacerbate the discontinuous switching characteristics of the control signal, resulting in severe oscillations in the output of traction or braking force, making it difficult to meet the ride - comfort adjustment requirements of high - speed trains. Moreover, the unmodeled dynamic coupling of coupler forces and the spatio - temporal distribution characteristics of resistance will lead to a large deviation in the mean - square error of train disturbance estimation.

[0004] Therefore, the current solutions are difficult to achieve collaborative optimization among multiple conflicting objectives such as speed - tracking accuracy, coupler - force balance and energy - consumption efficiency. Especially under complex line conditions, they cannot meet the high - safety, high - ride - comfort and long - lifespan maintenance requirements of high - speed trains. Summary of the Invention

[0005] The purpose of this application is to provide a multi - particle collaborative control method, system, device and medium for high - speed trains, which can improve the control accuracy, anti - interference ability and safety of coupler forces of high - speed trains.

[0006] To achieve the above - mentioned purpose, this application provides the following solutions: In the first aspect, this application provides a multi - particle collaborative control method for high - speed trains, including: Divide the carriages of a high-speed train into multiple groups, and construct a discretized dynamic equation of the high-speed train; the multiple groups are respectively a front drive group, an intermediate group, and a rear drive group; the state vector of the discretized dynamic equation includes the displacement and velocity of each group; the control inputs of the discretized dynamic equation include the traction braking force of the front drive group and the traction braking force of the rear drive group; Perform multi-objective optimization on the control gain and sliding mode surface parameters according to the displacement tracking error and coupler force of the high-speed train within a set time period to obtain the optimal control gain and the optimal sliding mode surface parameters; Based on the optimal control gain, estimate the equivalent coupler force and composite resistance between groups of the high-speed train in real time to obtain the estimated value of the equivalent coupler force between groups and the estimated value of the composite resistance; Determine a non-singular fast terminal sliding mode surface based on the optimal sliding mode surface parameters and the displacement tracking error of the high-speed train; According to the non-singular fast terminal sliding mode surface, the estimated value of the equivalent coupler force between groups, the estimated value of the composite resistance, and the discretized dynamic equation, solve the control input in real time to control the traction braking force of the front drive group and the traction braking force of the rear drive group in real time.

[0007] In a second aspect, the present application provides a multi-particle cooperative control system for a high-speed train, including: A dynamic equation construction module for dividing the carriages of a high-speed train into multiple groups and constructing a discretized dynamic equation of the high-speed train; wherein, the multiple groups are respectively a front drive group, an intermediate group, and a rear drive group; the state vector of the discretized dynamic equation includes the displacement and velocity of each group; the control inputs of the discretized dynamic equation include the traction braking force of the front drive group and the traction braking force of the rear drive group; A parameter optimization module for performing multi-objective optimization on the control gain and sliding mode surface parameters according to the displacement tracking error and coupler force of the high-speed train within a set time period to obtain the optimal control gain and the optimal sliding mode surface parameters; A data estimation module for estimating the equivalent coupler force and composite resistance between groups of the high-speed train in real time based on the optimal control gain to obtain the estimated value of the equivalent coupler force between groups and the estimated value of the composite resistance; A sliding mode surface determination module for determining a non-singular fast terminal sliding mode surface based on the optimal sliding mode surface parameters and the displacement tracking error of the high-speed train; A control module for solving the control input in real time according to the non-singular fast terminal sliding mode surface, the estimated value of the equivalent coupler force between groups, the estimated value of the composite resistance, and the discretized dynamic equation to control the traction braking force of the front drive group and the traction braking force of the rear drive group in real time.

[0008] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the above-mentioned multi-particle cooperative control method for high-speed trains.

[0009] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned multi-particle cooperative control method for high-speed trains is implemented.

[0010] According to the specific embodiments provided by the present application, the present application has the following technical effects: The present application provides a multi-particle cooperative control method, system, device and medium for high-speed trains. By dividing the carriages of a high-speed train into a front drive group, an intermediate group and a rear drive group, and constructing a discretized dynamic equation of the high-speed train to characterize the dynamic constraints of the coupler force, according to the displacement tracking error and coupler force of the high-speed train within a set time period, multi-objective optimization is performed on the control gain and sliding mode surface parameters to balance the tracking accuracy of the train and the coupler force of the carriages. A non-singular fast terminal sliding mode surface is designed to eliminate the singularity of traditional sliding mode control and accelerate the convergence speed. During the solution process of the control input, dynamic compensation is performed on the disturbance through the estimated value of the equivalent coupler force between groups and the estimated value of the composite resistance, improving the control accuracy, anti-interference ability of the high-speed train and the safety of the coupler force. Description of the Drawings

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0012] Figure 1 It is an application environment diagram of a multi-particle cooperative control method for high-speed trains in an embodiment of the present application.

[0013] Figure 2 It is a schematic flowchart of a multi-particle cooperative control method for high-speed trains provided in an embodiment of the present application.

[0014] Figure 3 It is a block diagram of a multi-particle cooperative control method for high-speed trains provided in an embodiment of the present application.

[0015] Figure 4 It is a schematic diagram of a motor car-trailer chain-type discrete multi-particle model constructed in an embodiment of the present application.

[0016] Figure 5This is a technical roadmap of a multi-particle collaborative control method for high-speed trains provided by an embodiment of the present application.

[0017] Figure 6 This is a schematic diagram of the functional modules of a multi-particle collaborative control system for high-speed trains provided by an embodiment of the present application. Detailed implementation manners

[0018] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0019] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0020] The multi-particle collaborative control method for high-speed trains provided by the embodiments of the present application can be applied to an application environment as Figure 1 shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. In addition, in some embodiments, the multi-particle collaborative control method for high-speed trains can also be implemented independently by the server 104 or the terminal 102.

[0021] Among them, the terminal 102 can be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0022] In an exemplary embodiment, as Figure 2 and Figure 3 shown, a multi-particle collaborative control method for high-speed trains is provided. This method is executed by a computer device, and can be specifically executed independently by a computer device such as a terminal or a server, or jointly executed by the terminal and the server. In the embodiments of the present application, taking this method applied to Figure 1 the server 104 as an example for illustration, it includes the following steps 201 to step 205.

[0023] Step 201: Divide the carriages of the high-speed train into multiple groups, and construct the discretized dynamic equation of the high-speed train. The multiple groups are the leading group, the intermediate group, and the trailing group respectively. The state vector of the discretized dynamic equation includes the displacement and velocity of each group. The control inputs of the discretized dynamic equation include the traction and braking forces of the leading group and the traction and braking forces of the trailing group.

[0024] This application analyzes the problems of uneven coupler force distribution, underactuated characteristics, and disturbance sensitivity of high-speed multiple units in view of existing research. Considering an 8-car formation CRH380B high-speed train, where the motor drive units are located in carriages No. 1, 3, 6, and 8, and the trailer units are located in carriages No. 2, 4, 5, and 7, the motion of each carriage satisfies: ; where, is the mass of the th carriage, is the acceleration of the th carriage, is the traction and braking force of the th carriage, is the coupler force of the th carriage, , is the coupler stiffness coefficient of the th carriage, is the coupler damping coefficient of the th carriage, is the displacement of the th carriage, is the displacement of the th carriage, is the velocity of the th carriage, is the velocity of the th carriage, is the compound resistance of the th carriage, , is the unmodeled disturbance of the th carriage, , , are the basic resistance coefficients of the th carriage.

[0025] Define the state vector , the input vector , and the linearized model can be obtained: .

[0026] where, is the system matrix, the input matrix , .

[0027] Input matrix The rank of the matrix represents the number of independent control channels. For CRH380B trains: , while the degree of freedom of the system is 16. Obviously, , so in the multi-particle modeling of common high-speed trains, the dimension of the control input < the dimension of the system state, thus presenting an underactuated characteristic, resulting in excessive coupler forces between the 3rd and 5th carriages of the train.

[0028] In view of the underactuated characteristic presented by the CRH380B high-speed train during operation, this application divides the high-speed train into a front drive group, an intermediate group, and a rear drive group, and establishes a discretized dynamic equation including the dynamic constraint of the coupler force.

[0029] As Figure 4 shown, first, a motor-car-trailer chain-type discrete multi-particle model is constructed. The motor carriages are represented by solid wheels, and the trailer carriages are represented by hollow wheels. That is, in the CRH380B high-speed train, the 1st, 3rd, 6th, and 8th carriages are motor carriages, and the 2nd, 4th, 5th, and 7th carriages are trailer carriages. The first three carriages are taken as the front drive group , the middle two carriages are taken as the intermediate group , and the last three carriages are taken as the rear drive group for modeling. To are 8 carriages, is the resistance suffered by the high-speed multiple unit, is the coupler force between carriages, is the output control force, v is the train forward direction.

[0030] Based on the analysis of the underactuated characteristic in traditional high-speed trains, the train front drive group is designed, which includes the 1st - 3rd carriages with a mass , and the control input is , that is, k the traction and braking force of the front drive group at time, the intermediate group includes the 4th - 5th carriages with a mass , and has no direct control input. The rear drive group includes the 6th - 8th carriages with a mass , and the control input is , that is, k the traction and braking force of the rear drive group at time. The coupler force of the intermediate group is used as a virtual control quantity and mapped to the control inputs of the front drive group and the rear drive group through the dynamic coupling relationship. , , where is the control input of the front drive group after mapping, is the control input for the rear drive group after mapping, is k the displacement of the front drive group at time and the intermediate group the coupler force between them. The specific calculation formula is , d is the coupler stiffness coefficient, is the damping coefficient, is k the displacement of the front drive group at time is k the displacement of the intermediate group at time is k the velocity difference form of the front drive group at time is k the velocity difference form of the intermediate group at time is k the displacement of the intermediate group at time and the rear drive group the coupler force between them. The specific calculation formula is , c is the basic resistance coefficient, is the velocity difference form of the rear drive group.

[0031] Define the state vector , is the displacement of the front drive group, is the velocity of the front drive group, is the displacement of the intermediate group, is the velocity of the intermediate group, is the displacement of the rear drive group, is the velocity of the rear drive group. The control input is extended to , and the input matrix is adjusted to: .

[0032] Since , the newly established model is completely controllable and can solve the underactuated characteristics existing in traditional modeling. Thus, it can be obtained that at the sampling period , the discretized train dynamics equation is: ; where, is the mass of the front drive group, is the mass of the intermediate group, is the mass of the rear drive group, is k the acceleration difference form of the front drive group at time , is the position at time T is the sampling period, is Acceleration difference form of the intermediate group at a moment is Acceleration difference form of the rear-wheel drive group at a moment is Tractive braking force of the front-wheel drive group at a moment is Tractive braking force of the rear-wheel drive group at a moment is k Composite resistance of the front-wheel drive group at a moment is k Composite resistance of the intermediate group at a moment is k Composite resistance of the rear-wheel drive group at a moment , i =1 represents the front-wheel drive group, i =2 represents the intermediate group, i =3 represents the rear-wheel drive group.

[0033] In addition, the discretized train dynamics equation can also be expressed by the following formula: .

[0034] Step 202: According to the displacement tracking error and coupler force of the high-speed train within the set time period, perform multi-objective optimization on the control gain and sliding mode surface parameters to obtain the optimal control gain and optimal sliding mode surface parameters.

[0035] In a specific application example, the black-winged kite algorithm is used to perform multi-objective optimization on the control gain and sliding mode surface parameters to balance the speed tracking accuracy of the train and the coupler force between carriages, and achieve the multi-objective balance of tracking accuracy and suppression of coupler force. The optimization objective function of the black-winged kite algorithm is: ; Among them, is the objective function value, is k The first weight coefficient at the moment is k The second weight coefficient at the moment is k The third weight coefficient at the moment is the number of moments within the set time period, is k At the moment, the i Displacement tracking error of the is k At the moment, the i Equivalent coupler force between groups of the is k Actual coupler force value at the moment, is the target coupler force value to be achieved. The dynamic adjustment rule of the weight coefficient is: , Determine decay rate of as the standard time scale to ensure that the weight adjustment is completed within a finite time; update the search for the optimal parameter set .

[0036] Step 203, based on the optimal control gain, estimate the equivalent coupler force and composite resistance between groups of high-speed trains in real time, and obtain the estimated value of the equivalent coupler force between groups and the estimated value of the composite resistance.

[0037] In a specific application example, the Kalman filter algorithm is used to estimate the equivalent coupler force and composite resistance between groups of high-speed trains in real time. To solve the problem of dynamic estimation of coupler force and composite resistance in the high-speed train dynamics equation, an adaptive extended Kalman filter algorithm is proposed to achieve accurate modeling of dynamic compensation for high-speed train disturbances. The acceleration of the train is estimated in real time through the Kalman filter algorithm, and based on this acceleration value, the air resistance and external disturbances are estimated, and the estimated values are fed back to the control input for feedforward compensation.

[0038] For each group, define a four-dimensional state vector (expanded state vector) including displacement, velocity, equivalent coupler force, and composite resistance ): , and the discrete state equation based on the dynamic characteristics of high-speed trains is obtained as follows: .

[0039] Among them, is the error state transition matrix, , is the coupler force dynamic decay coefficient, reflecting the energy dissipation characteristics of the coupler buffer device, is the input matrix, , is k the control matrix at time , is k the process noise at time

[0040] For the displacement and velocity of each group, the observation equation is: , where is the observation matrix, is the observation noise, is the observation result.

[0041] Update the state transition matrix and the observation matrix according to the dynamics equation, and iteratively output the disturbance estimation value through time update and measurement update. The state prediction and update steps of the adaptive Kalman filter algorithm include: (1) State prediction: ; Among them, is the state prediction result. is k the state estimate value at time - 1.

[0042] (2) Covariance prediction: ; where is the covariance prediction result, is k the state error covariance at time - 1, is the process noise covariance.

[0043] (3) Kalman gain calculation: ; where is the Kalman gain prediction result, is the measurement noise covariance matrix.

[0044] (4) State correction: .

[0045] (5) Covariance correction: ; I is the identity matrix.

[0046] (6) To cope with the disturbances of variable working conditions of high - speed trains, an adaptive dynamic update rule for the process noise covariance is designed as: ; where is the initial process noise, is the adaptive update adjustment factor.

[0047] Step 204: Based on the optimal sliding mode surface parameters and the displacement tracking error of the high - speed train, determine the non - singular fast terminal sliding mode surface.

[0048] In a specific application example, by defining a composite sliding mode surface containing the discrete accumulation term of velocity error and the non - linear power term, solve the control input to eliminate the singularity of traditional sliding mode control and accelerate the system convergence speed. The non - singular fast terminal sliding mode surface is: ; where is k the non - singular fast terminal sliding mode surface of the i th group at time is k the displacement tracking error of the i th group at time , is k the displacement of the i th group at time is k the target displacement at time is m the iThe displacement tracking error of the group, is the sampling period, is the sliding mode surface parameter, representing the i integral term coefficient of the group, which is used to adjust the weight of the integral term of the sliding mode surface, and are the exponential parameters in the sliding mode surface design, which are used to characterize the sensitivity of the accumulation term to the error, , ensuring that when the sliding mode surface is at , the singular phenomenon is avoided.

[0049] Step 205, according to the non-singular fast terminal sliding mode surface, the estimated value of the equivalent coupler force between groups, the estimated value of the composite resistance, and the discretized dynamic equation, solve the control input in real time to control the traction and braking forces of the front driving group and the rear driving group in real time.

[0050] In a specific application example, the following formula is used to solve the control input: ; where, is the k traction and braking force of the i group at time is the mass of the front driving group, is the sampling period, is the k target speed at time is the k speed difference form of the front driving group at time is the k non-singular fast terminal sliding mode surface of the i group at time and are the control gains, , represents the proportional gain of the i group, represents the switching gain of the i group, which are respectively used to accelerate the convergence speed of the sliding mode surface and suppress the unmodeled disturbances and errors of the system, is the saturation function, is the boundary layer thickness, , by replacing the traditional function with the saturation function to suppress high-frequency chattering, is the k estimated value of the equivalent coupler force between groups of the i group at time is the k estimated value of the composite resistance of the i group at time

[0051] In addition, to verify the global stability of this application, a composite energy function including the sliding mode surface error, coupler force deviation, and disturbance estimation error is constructed based on the discrete Lyapunov function theory, and the stability constraint conditions are derived to ensure the convergence and robustness under the optimal control law and disturbance compensation.

[0052] For the front-wheel drive group , the middle group and the rear-wheel drive group , define the global energy function: ; where is the global energy value, the sliding mode surface energy term characterizes the dynamic convergence of the tracking error, is the state estimation error energy term, is k the state estimation error at time , is a positive definite matrix used to quantify the estimation deviations of displacement, velocity, coupler force, and resistance, is the coupler force tracking error term, .

[0053] The stability condition is: ; is the difference in the global energy values between the k +1 time and the k time, is a scalar, is a positive scalar used to adjust the weight of the state estimation error term in the Lyapunov stability analysis.

[0054] According to the global asymptotic stability condition of the Lyapunov function, if there exist a symmetric positive definite matrix and a scalar , the matrix inequality needs to be satisfied; where is the sliding mode coupling term.

[0055] The difference calculation formula of the energy function is: .

[0056] where is k the difference in the nonsingular fast terminal sliding mode surface between the k +1 time and the

[0057] According to the formula of the nonsingular fast terminal sliding mode surface in step 204, we can get: , substituting it into the above difference equation and amplifying, we get: .

[0058] According to the adaptive covariance update rule in step 203, ensure that .

[0059] Combining the optimal control gain and the optimal sliding mode surface parameters in step 202, we can obtain: ; That is, when and , , global asymptotic stability can be achieved.

[0060] In view of the problems of uneven coupler force distribution, underactuated characteristics and disturbance sensitivity existing in the existing high-speed EMUs, this application constructs a discretized train dynamics equation to characterize the dynamic constraints of the coupler force; designs a distributed non-singular fast terminal sliding mode control law to eliminate the singularity of the traditional sliding mode control and accelerate the convergence speed; uses the Kalman filtering algorithm to estimate and compensate the influence of air resistance and track disturbances in real time; adopts the black-winged kite algorithm to optimize the sliding mode parameters to balance the tracking accuracy of the train and the coupler force of the carriages; constructs a composite energy function based on the Lyapunov function theory to prove the global stability. Therefore, the present invention can effectively enhance the robustness of high-speed trains under complex disturbances and is applicable to the cooperative control scenario of multiple power units. By designing a deep fusion of the adaptive Kalman filter and the intelligent optimization algorithm, the problems of coupler force imbalance, disturbance sensitivity and insufficient control accuracy under the underactuated characteristics of high-speed trains are solved, the robustness of high-speed trains under complex disturbances is effectively enhanced, and the safety of the coupler force, control accuracy and anti-disturbance ability of high-speed trains are improved.

[0061] As Figure 5 shown, this application addresses four key problems in high-speed trains: ① An accurate control model needs to be built to meet the needs of control and monitoring; ② A safety control algorithm suitable for the high-speed train control system needs to be designed; ③ The coupler force of high-speed EMUs needs to be effectively monitored; ④ External disturbances need to be compensated to improve the control quality - and proposes solutions one by one: ① Study the discretized dynamics equation; ② Study the fast terminal sliding mode control rate; ③ Study the Kalman filtering algorithm to reconstruct the coupler force of the train; ④ Study the black-winged kite algorithm to optimize the sliding mode parameter strategy. And based on the established model, the non-singular fast terminal sliding mode control method in the discrete domain is removed, and the extended Kalman compensation model is removed to design a high-speed train safety control strategy. Further, in order to verify the feasibility of this application, the stability of the constructed energy function is analyzed based on the Lyapunov function theory, and simulation processing is carried out using matlab software to obtain experimental results.

[0062] The following provides a simulation process.

[0063] (1) Simulation model construction: Build a discretized dynamics equation based on the Simulink environment, with the input being the control force , and the output being the displacements of each group 、 Speed and the equivalent coupler force between groups , and the relevant parameters are dynamically loaded through the matlab script.

[0064] (2) Control algorithm deployment: The control rate is designed by using the MATLAB Function Block, and the inputs are 、 , and the outputs are , and are updated online 、 . The state prediction and update of the Kalman filter algorithm are realized through the Stateflow module. The inputs are and , and the outputs are and .

[0065] (3) Optimization and verification: Call the MATLAB global optimization toolbox. According to the established objective function, iteratively optimize the sliding mode parameter set , and the termination condition is , the relative change rate , automatically calculated through the script , and verify the Lyapunov stability.

[0066] Based on the same inventive concept, the embodiment of the present application also provides a multi-particle collaborative control system for a high-speed train for implementing the above-mentioned multi-particle collaborative control method for a high-speed train. The implementation solution provided by this system to solve the problem is similar to the implementation solution described in the above method. Therefore, the specific limitations in one or more embodiments of the multi-particle collaborative control system for a high-speed train provided below can refer to the limitations on the multi-particle collaborative control method for a high-speed train in the above text, and will not be repeated here.

[0067] In an exemplary embodiment, as Figure 6 shown, a multi-particle collaborative control system for a high-speed train is provided, including: a dynamic equation construction module 601, a parameter optimization module 602, a data estimation module 603, a sliding mode surface determination module 604, and a control module 605.

[0068] The dynamic equation construction module 601 is used to divide the carriages of the high-speed train into multiple groups and construct a discretized dynamic equation of the high-speed train. Among them, the multiple groups are respectively a front drive group, an intermediate group, and a rear drive group. The state vector of the discretized dynamic equation includes the displacement and speed of each group. The control inputs of the discretized dynamic equation include the traction and braking forces of the front drive group and the traction and braking forces of the rear drive group.

[0069] The parameter optimization module 602 is used to perform multi-objective optimization on the control gain and the sliding mode surface parameters according to the displacement tracking error and the coupler force of the high-speed train within a set time period, so as to obtain the optimal control gain and the optimal sliding mode surface parameters.

[0070] The data estimation module 603 is used to estimate the equivalent coupler force and the composite resistance between groups of the high-speed train in real time based on the optimal control gain, so as to obtain the estimated value of the equivalent coupler force between groups and the estimated value of the composite resistance.

[0071] The sliding mode surface determination module 604 is used to determine a non-singular fast terminal sliding mode surface based on the optimal sliding mode surface parameters and the displacement tracking error of the high-speed train.

[0072] The control module 605 is used to solve the control input in real time according to the non-singular fast terminal sliding mode surface, the estimated value of the equivalent coupler force between groups, the estimated value of the composite resistance, and the discretized dynamic equation, so as to control the traction and braking forces of the front driving group and the rear driving group in real time.

[0073] In an exemplary embodiment, a computer device is provided, which includes a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0074] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0075] In an exemplary embodiment, a computer program product is provided, which includes a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0076] 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 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 need to comply with relevant regulations.

[0077] In this application, all actions of obtaining signals, information, or data are carried out on the premise of complying with the corresponding data protection regulations and policies of the country where the location is located, and with the authorization given by the owner of the corresponding device.

[0078] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. 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 methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAMs), magnetoresistive random access memories (MRAMs), ferroelectric random access memories (FRAMs), phase change memories (PCMs), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0079] The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0080] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, 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, it should be considered to be within the scope described in this specification.

[0081] Specific examples are used in this article to elaborate on the principles and implementation manners of the present application. The descriptions of the above embodiments are only used to help understand the methods and core ideas of the present application; at the same time, for those of ordinary skill in the art, according to the ideas of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A high-speed train multi-particle coordinated control method, characterized in that: include: The carriages of the high-speed train are divided into multiple groups, and a discretized dynamic equation of the high-speed train is constructed; the multiple groups are a front drive group, an intermediate group and a rear drive group; the state vector of the discretized dynamic equation includes the displacement and speed of each group; the control input of the discretized dynamic equation includes the traction braking force of the front drive group and the traction braking force of the rear drive group; According to the displacement tracking error and coupler force of the high-speed train in the set period, the control gain and sliding surface parameters are optimized by multiple objectives to obtain the optimal control gain and optimal sliding surface parameters; Based on the optimal control gain, the inter-group equivalent coupler force and the composite resistance of the high-speed train are estimated in real time to obtain an estimated value of the inter-group equivalent coupler force and the estimated value of the composite resistance; Determining a non-singular fast terminal sliding surface based on the optimal sliding surface parameters and the displacement tracking error of the high-speed train; According to the non-singular fast terminal sliding surface, the estimated value of the inter-group equivalent coupler force, the estimated value of the composite resistance and the discretized dynamic equation, the control input is solved in real time to control the traction braking force of the front drive group and the traction braking force of the rear drive group in real time.

2. The high-speed train multi-particle coordinated control method according to claim 1 is characterized in that: The discretized dynamic equation is: ； in, is the mass of the precursor group, is the mass of the middle group, is the mass of the rear drive group, for k The acceleration difference form of the front group at time, , for The location at the moment, T is the sampling period, for The acceleration difference form of the middle group at time, for The acceleration differential form of the rear drive group at time for The traction braking force of the front drive group at all times, for The traction braking force of the rear drive group at all times, for The workshop coupling force between the front drive group and the middle group at the moment, , d is the coupler stiffness coefficient, is the damping coefficient, for k The displacement of the predecessor group at time for k The displacement of the middle group at time, for k The speed difference form of the front group at the moment is: for k The velocity difference form of the middle group at the moment is, for k The workshop coupling force between the middle group and the rear drive group at the moment, , is the speed differential form of the rear drive group, c is the basic resistance coefficient, for k The composite resistance of the precursor group at the moment, for k The composite resistance of the middle group at this moment, for k The composite resistance of the rear drive group at that moment.

3. The high-speed train multi-particle coordinated control method according to claim 1, characterized in that: According to the displacement tracking error and coupler force of the high-speed train in the set period, the control gain and sliding surface parameters are optimized by multi-objective optimization to obtain the optimal control gain and optimal sliding surface parameters, including: According to the displacement tracking error and coupler force of the high-speed train within the set time period, the Black Kite algorithm is used to perform multi-objective optimization on the control gain and sliding surface parameters to obtain the optimal control gain and sliding surface parameters.

4. The high-speed train multi-particle coordinated control method according to claim 3 is characterized in that: The optimization objective function of the black kite algorithm is: ； in, is the objective function value, for k The first weight coefficient at time, for k The second weight coefficient at time, for k The third weight coefficient at time, is the number of moments in the set time period, for k Moment i The displacement tracking error of the group, for k The moment i The equivalent coupler force between groups, for k The actual coupler force value at the moment, is the target value of the coupler force to be achieved, i =1 indicates the precursor group, i =2 indicates the middle group, i =3 indicates rear-wheel drive group.

5. The high-speed train multi-particle coordinated control method according to claim 1, characterized in that: Based on the optimal control gain, the inter-group equivalent coupler force and the composite resistance of the high-speed train are estimated in real time to obtain the inter-group equivalent coupler force estimation value and the composite resistance estimation value, which specifically includes: Based on the optimal control gain, the Kalman filter algorithm is used to estimate the inter-group equivalent coupler force and the composite resistance of the high-speed train in real time, and the estimated value of the inter-group equivalent coupler force and the estimated value of the composite resistance are obtained.

6. The high-speed train multi-particle coordinated control method according to claim 1, characterized in that: The non-singular fast terminal sliding surface is: ； in, for k Moment i The non-singular fast terminal sliding surface of the group, for k Moment i The displacement tracking error of the group, for m Moment i The displacement tracking error of the group, is the sampling period, is the sliding surface parameter, indicating the i The integral term coefficient of the group, and is an exponential parameter in sliding surface design, which is used to characterize the sensitivity of the cumulative term to the error. , i =1 indicates the precursor group, i =2 indicates the middle group, i =3 indicates rear-wheel drive group.

7. The high-speed train multi-particle coordinated control method according to claim 1, characterized in that: The control input is solved using the following formula: ； in, for k Moment i The traction braking force of the group, is the mass of the precursor group, is the sampling period, for k Target speed at time +1, for k The speed difference form of the front group at the moment is: for k Moment i The non-singular fast terminal sliding surface of the group, and To control the gain, Indicates i The proportional gain of the group, Indicates i The switching gain of the group, is the boundary layer thickness, for k Moment i The estimated equivalent coupler force between groups, for k Moment i Estimated value of the combined resistance of the group, i =1 indicates the precursor group, i =2 indicates the middle group, i =3 indicates rear-wheel drive group.

8. A high-speed train multi-particle coordinated control system, applied to the high-speed train multi-particle coordinated control method according to any one of claims 1 to 7, characterized in that: The high-speed train multi-particle coordinated control system comprises: A dynamic equation construction module is used to divide the carriages of the high-speed train into multiple groups and construct a discretized dynamic equation of the high-speed train; wherein the multiple groups are a front drive group, an intermediate group and a rear drive group; the state vector of the discretized dynamic equation includes the displacement and velocity of each group; the control input of the discretized dynamic equation includes the traction braking force of the front drive group and the traction braking force of the rear drive group; The parameter optimization module is used to perform multi-objective optimization on the control gain and sliding surface parameters according to the displacement tracking error and coupler force of the high-speed train within a set period of time to obtain the optimal control gain and optimal sliding surface parameters; A data estimation module, used for estimating the inter-group equivalent coupler force and the composite resistance of the high-speed train in real time based on the optimal control gain, and obtaining an estimated value of the inter-group equivalent coupler force and the estimated value of the composite resistance; A sliding surface determination module, used for determining a non-singular fast terminal sliding surface based on the optimal sliding surface parameters and the displacement tracking error of the high-speed train; The control module is used to solve the control input in real time according to the non-singular fast terminal sliding surface, the estimated value of the equivalent coupler force between the groups, the estimated value of the composite resistance and the discretized dynamic equation, so as to control the traction braking force of the front drive group and the traction braking force of the rear drive group in real time.

9. 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 high-speed train multi-particle collaborative control method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the high-speed train multi-particle coordinated control method described in any one of claims 1 to 7 is implemented.

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