Subway train control method, device and system

Abstract

The application provides a subway train control method, device and system, the method comprises the following steps: in the case that the sliding surface tends to zero and the running time of the subway train tends to infinity, a measurement state vector is solved; based on the measurement state vector, a measurement equation of the subway train is solved to obtain a state vector of the subway train; based on the state vector and each state variable of the subway train, a control model of the subway train is solved to obtain a control input corresponding to each state variable; and based on the control input corresponding to each state variable, the subway train is controlled. The application can ensure that the state of the subway train is stably operated near the equilibrium point, and can more accurately, quickly and stably control the operation of the subway train under complex road conditions and control requirements, and has high robustness.

Description

Technical Field

[0001] This invention relates to the field of train control technology, and in particular to a subway train control method, device and system. Background Technology

[0002] Subway train operation is affected by a variety of factors, including track conditions and the underground environment, and the safety of train operation is highly dependent on the stability of train operation.

[0003] When subway trains are operating at high speeds or undergoing emergency braking, they are often affected by environmental factors such as wind and curves, which can easily lead to safety issues such as sideslip and derailment. Existing stability control technologies mainly include traditional PID control and fuzzy control. However, these methods suffer from slow response speed and poor anti-interference capabilities in practical applications, and cannot meet the requirements of rapid stability control for subway trains. Summary of the Invention

[0004] This invention provides a subway train control method, device, and system to address the shortcomings of existing technologies that cannot quickly and stably control subway train operation.

[0005] This invention provides a subway train control method, comprising:

[0006] When the sliding surface approaches zero and the travel time of the subway train approaches infinity, the measurement state vector is solved; the sliding surface is constructed based on the measurement state vector and the sliding parameters corresponding to the travel time.

[0007] Based on the measured state vector, the measurement equation of the subway train is solved to obtain the state vector of the subway train; the measurement equation is used to characterize the functional relationship between the measured state vector and the state vector.

[0008] Based on the state vector and the state variables of the subway train, the control model of the subway train is solved to obtain the control input corresponding to each state variable; the control model is used to characterize the relationship between the state vector and the control input corresponding to each state variable, and the control input is less than or equal to a threshold.

[0009] The subway train is controlled based on the control inputs corresponding to each state variable.

[0010] According to a subway train control method provided by the present invention, the control model is constructed based on the relationship between each state variable, the relationship between each state variable and the corresponding control input, and the state vector.

[0011] According to a subway train control method provided by the present invention, the measurement equation is constructed based on the state vector, the mapping relationship between the state vector and the actual measurement state, and the measurement state vector.

[0012] According to a subway train control method provided by the present invention, the sliding mode parameter corresponding to the current travel time is determined based on the sliding mode parameter corresponding to the previous travel time and the state vector error corresponding to the previous travel time. The state vector error corresponding to the previous travel time is determined based on the measured state vector of the previous travel time and the estimated value of the measured state vector of the previous travel time.

[0013] A subway train control method provided by the present invention further includes:

[0014] Update each state variable based on the control error;

[0015] Based on the updated state variables, the control model is corrected;

[0016] Based on the updated state variables, the corrected control model is solved to obtain the corrected control inputs corresponding to the updated state variables;

[0017] The control error refers to the error between the output of the reference control model and the output of the control model. When the control error approaches zero and the driving time approaches infinity, the control input is less than or equal to the threshold. The reference control model is constructed based on the relationship between each preset state variable, the relationship between each preset state variable and the corresponding preset control input, and the preset state vector.

[0018] According to a subway train control method provided by the present invention, each state variable is updated based on a parameter update model, which is as follows:

[0019]

[0020]

[0021] Where, θ i Let s represent the state variables, α represent the parameter update rate coefficient, and s represent the state variables. i This indicates the control error. denoted by , m represents the filter output, T represents the sampling interval, Λ represents the gain coefficient of the nth-order Butterworth filter, and t represents the driving time.

[0022] According to a subway train control method provided by the present invention, each state variable includes at least one of attitude angle variable, acceleration variable and angular momentum variable.

[0023] The present invention also provides a subway train control device, comprising:

[0024] The first solving unit is used to solve the measurement state vector when the sliding surface approaches zero and the travel time of the subway train approaches infinity; the sliding surface is constructed based on the measurement state vector and the sliding parameters corresponding to the travel time.

[0025] The second solving unit is used to solve the measurement equation of the subway train based on the measurement state vector to obtain the state vector of the subway train; the measurement equation is used to characterize the functional relationship between the measurement state vector and the state vector.

[0026] The third solving unit is used to solve the control model of the subway train based on the state vector and the state variables of the subway train, and obtain the control input corresponding to each state variable; the control model is used to characterize the relationship between the state vector and each state variable, and the control input is less than or equal to a threshold.

[0027] The train control unit is used to control the subway train based on the control inputs corresponding to each state variable.

[0028] The present invention also provides a subway train control system, comprising: a subway train and the subway train control device as described above.

[0029] The present invention also provides an electronic device, including 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 subway train control method described above.

[0030] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the subway train control method as described above.

[0031] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the subway train control method as described above.

[0032] The subway train control method, device, and system provided by this invention can solve for the measurement state vector when the sliding surface approaches zero and the subway train's travel time approaches infinity. This ensures that the control inputs corresponding to each state variable obtained by the final solution based on the measurement state vector can guarantee that the subway train's state operates stably near the equilibrium point. This enables more accurate, faster, and more stable control of subway train operation under complex road conditions and control requirements, and exhibits high robustness. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0034] Figure 1 This is a flowchart illustrating the subway train control method provided by the present invention;

[0035] Figure 2 This is a schematic diagram of the structure of the subway train control device provided by the present invention;

[0036] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0038] Subway trains typically operate underground in complex conditions, and their operation is highly dependent on the stability and safety of the trains.

[0039] Currently, existing methods for subway train stability control mainly include motion control according to a preset trajectory, trajectory correction based on sensor feedback signals, and closed-loop control strategies. However, these control methods usually need to be customized for different road conditions and control requirements, resulting in poor versatility and high requirements for system robustness.

[0040] Therefore, the present invention aims to provide an effective subway train control method to ensure the stable operation of subway trains under various road conditions, while improving the maneuverability and safety of subway trains.

[0041] In response, this invention provides a subway train control method. Figure 1 This is a flowchart illustrating the subway train control method provided by the present invention, as shown below. Figure 1 As shown, the method includes the following steps:

[0042] Step 110: When the sliding surface approaches zero and the travel time of the subway train approaches infinity, solve for the measurement state vector; the sliding surface is constructed based on the measurement state vector and the sliding parameters corresponding to the travel time.

[0043] Specifically, the measured state vector is used to characterize the state of the subway vehicle as measured by sensors. The goal of the sliding surface is to force the system state to be pushed in the non-sliding region, bringing the system state near the equilibrium point, thereby achieving stable control of the subway train's state.

[0044] As an optional embodiment, the sliding surface S i It can be constructed in the following way:

[0045]

[0046] Among them, c 1i and c 2i This represents the sliding mode parameter corresponding to the driving time; that is, the sliding mode parameter may be different for different driving times. i Represents the measurement state vector. Indicates y i The estimated value, which can be a relation to y i Values ​​obtained through estimation or prediction, such as It can be an estimate obtained through a filter algorithm or other algorithms.

[0047] Stable control of the vehicle's attitude is achieved by adjusting the sliding surface. The control input u... i The following requirements should be met:

[0048] S i As t approaches 0, |u i |≤u max

[0049] Where t represents the travel time, u max This indicates the maximum control input.

[0050] Step 120: Based on the measurement state vector, solve the measurement equation of the subway train to obtain the state vector of the subway train; the measurement equation is used to characterize the functional relationship between the measurement state vector and the state vector.

[0051] Specifically, the measurement equation is used to characterize the functional relationship between the measurement state vector and the state vector, where the state vector can be understood as the state of the subway train represented by its various state variables. State variables may include attitude angle variables, acceleration variables, angular momentum variables, etc.

[0052] After the measurement equation is constructed, since the measurement equation is used to characterize the functional relationship between the measurement state vector and the state vector, the measurement equation can be solved based on the measurement state vector to obtain the state vector.

[0053] Step 130: Based on the state vector and the state variables of the subway train, solve the control model of the subway train to obtain the control input corresponding to each state variable; the control model is used to characterize the relationship between the state vector and the control input corresponding to each state variable, and the control input is less than or equal to the threshold.

[0054] Step 140: Control the subway train based on the control inputs corresponding to each state variable.

[0055] Specifically, the state vector includes all state variables; that is, the state vector can be understood as representing all state variables. The control input can include the front and rear wheel traction forces of the subway train, and the control input u... i It can be represented as u i =[T fi ,T ri ] T T fi T represents the front wheel traction force. ri This represents the rear wheel traction force. The threshold can be a preset threshold or the maximum control input.

[0056] The control inputs corresponding to each state variable are used to control the subway train to operate according to the attitude corresponding to the state variable. In turn, the control inputs corresponding to each state variable are used to control the subway train to operate according to the attitude corresponding to the state vector. The state vector is obtained based on the measured state vector, and the measured state vector is obtained when the sliding surface approaches zero and the subway train's travel time approaches infinity. That is, the measured state vector is used to ensure that the subway train's state operates stably near the equilibrium point. Thus, the control inputs corresponding to each state variable can ensure that the subway train's state operates stably near the equilibrium point, enabling more accurate, faster, and more stable control of the subway train's operation under complex road conditions and control requirements, with high robustness.

[0057] The subway train control method provided in this invention solves for the measurement state vector when the sliding surface approaches zero and the subway train's travel time approaches infinity. This ensures that the control inputs corresponding to each state variable obtained from the final solution based on the measurement state vector can guarantee that the subway train's state operates stably near the equilibrium point. This enables more accurate, faster, and more stable control of the subway train under complex road conditions and control requirements, and demonstrates high robustness.

[0058] Based on the above embodiments, the control model is constructed based on the relationships between state variables, the relationships between state variables and their corresponding control inputs, and the state vector.

[0059] Specifically, each state variable can be acquired through sensors. The relationships between these state variables describe the dynamic relationships between the state variables of the subway train, which can be understood as the correlation coefficient or degree of influence between the state variables. The relationships between each state variable and its corresponding control input characterize the impact of the control input on the corresponding state variable.

[0060] As an optional implementation, the control model is as follows:

[0061]

[0062] x i =[y 1i ,y 2i ,…,y ni ,y n+i ,y n+i+1 ,…,y 2n+i ] T

[0063] Where, x i This represents the state vector, where n represents the number of state variables, and y represents the number of state variables. 1i ,y 2i ,…,y ni ,y n+i ,y n+i+1 ,…,y 2n+i Both represent state variables, where y 1i This represents the first state variable, which can be understood as a position variable of the subway train or a position-related variable. In the control model of this embodiment, y 1i It can be any variable describing the location of a subway train or location-related information, and the specific variable can be determined based on control requirements and system characteristics. For example, if the control requirement is to control the longitudinal position of the subway train, then y 1i It can be the vertical position variable of the subway train; if the control requirement is to control the lateral position of the subway train, then y 1i It can be the lateral position variable of the subway train. Indicates y 1i The derivative of y, which represents y 1i The rate of change with respect to time, i.e., the state variable y 1i The rate of change or velocity. Optionally, It can represent the rate of change of variables related to a certain location of a subway train, such as the longitudinal speed and lateral speed of the subway train. By introducing state variables and their derivatives (rates of change) in the control model, the motion state of the subway train can be described and controlled more comprehensively.

[0064] Furthermore, A represents the system state matrix, used to describe the relationships between the various state variables of the subway train. Each element in A represents the correlation coefficient or degree of influence between state variables. Through A, the mutual influence between different state variables can be understood, thereby allowing for the prediction and inference of changes in the subway train's state. B represents the input matrix, where each element describes the relationship between each state variable and its corresponding control input, i.e., the influence of the control input on each state variable. Through B, it can be understood how the control input affects the state changes of the subway train. i This represents the model disturbance term, which is used to characterize external disturbances or uncertainties in the system.

[0065] In the control model, Ax i Bu represents the internal dynamics of state variables. i This represents the contribution of the control input to the state variables. Based on Ax i and Bu i It can predict and control the state variables of subway trains.

[0066] Optionally, the control model is used to describe the state estimation and control process of the subway train. The various parameter variables in the control model can be determined by the corresponding control algorithm according to the actual situation and system state requirements. The control algorithm may include PID control, model predictive control, state feedback control, etc.

[0067] Based on any of the above embodiments, the measurement equation is constructed based on the state vector, the mapping relationship between the state vector and the actual measurement state, and the measurement state vector.

[0068] As an optional embodiment, the measurement equation is as follows:

[0069] y i =Cx i +n i

[0070] Among them, y i Let represent the measurement state vector, which is the sensor's measurement output state vector, used to characterize the vehicle state of the subway train as measured by the sensor; C represents the output matrix, used to describe the mapping relationship between the state vector and the measurement state vector, x i Represents the state vector, n i This represents the measurement noise term, used to indicate the noise or error in sensor measurements.

[0071] Based on any of the above embodiments, the sliding mode parameter corresponding to the current driving time is determined based on the sliding mode parameter corresponding to the previous driving time and the state vector error corresponding to the previous driving time. The state vector error corresponding to the previous driving time is determined based on the measured state vector of the previous driving time and the estimated value of the measured state vector of the previous driving time.

[0072] To improve the robustness of the system, embodiments of the present invention improve the system's robustness to unknown disturbances and noise by adaptively adjusting sliding mode parameters (i.e., different sliding mode parameters corresponding to different driving durations).

[0073] Specifically, the steps for adaptively adjusting sliding mode parameters are as follows:

[0074] First, the parameter update model is established as follows:

[0075]

[0076]

[0077]

[0078] in, and All are sliding mode parameters, where γ1 and γ2 represent parameter update rate coefficients. The initial values ​​of γ1 and γ2 are usually selected based on specific application and design requirements. Generally, the initial values ​​of γ1 and γ2 can be obtained based on experience or system modeling. Common methods include initialization using system characteristics, engineering experience, or experimental data.

[0079] Next, based on the parameter update model, a parameter update law is designed, and the sliding mode parameters are updated based on the parameter update law. The parameter update law is as follows:

[0080] c 1i (t+Δt)=c 1i (t)-γ1S i y ei (t)Δt

[0081] c 2i (t+Δt)=c 2i (t)-γ2y ei (t)Δt

[0082] Where t represents the previous travel time, and t+Δt represents the current travel time.

[0083] Based on any of the above embodiments, the method further includes:

[0084] Update each state variable based on the control error;

[0085] Based on the updated state variables, the control model is corrected;

[0086] Based on the updated state variables, the corrected control model is solved to obtain the corrected control inputs corresponding to the updated state variables;

[0087] Among them, the control error refers to the error between the output of the reference control model and the output of the control model. When the control error approaches zero and the driving time approaches infinity, the control input is less than or equal to the threshold. The reference control model is constructed based on the relationship between each preset state variable, the relationship between each preset state variable and the corresponding preset control input, and the preset state vector.

[0088] Specifically, the reference control model can be understood as an ideal model, whose main function is to set the desired trajectory and robustness requirements of the system. In this embodiment of the invention, the reference control model can be designed based on the stability and control requirements of the subway train to ensure the stability of the subway train during operation. The reference control model can be a mathematical model describing the desired system response, or a specific trajectory, such as a function of required speed and position over time, or a series of target points or requirements. In this embodiment of the invention, the reference control model is the subway train stability index requirement, which can be derived from relevant subway operation standards. That is, the reference control model is constructed based on the relationships between preset state variables, the relationships between preset state variables and corresponding preset control inputs, and preset state vectors, where preset state variables, preset control inputs, and preset state vectors can be determined based on relevant subway operation standards. In other words, the reference control model is used to control the operation of the subway train under ideal conditions, that is, to control the subway train's operating state to be ideal, while the control model is used to control the subway train's operation under actual conditions.

[0089] Control error refers to the error between the output of the reference control model and the output of the control model. The smaller the control error, the smaller the difference between the reference control model and the control model, which means that the current operating state of the subway train is closer to the ideal operating state and the stability is better.

[0090] To obtain a more robust control model, the control input can satisfy the following condition:

[0091] e i As t approaches 0, |u i |≤u max

[0092] Among them, e i The value represents the control error, t represents the driving time, and u represents the control error. max This indicates the maximum control input.

[0093] Since the control model corresponding to the actual system often deviates from the reference model, it is necessary to correct the control model in real time to improve control accuracy and robustness.

[0094] Specifically, the real-time correction control model steps include the following steps:

[0095] First, the parameter update model is established as follows:

[0096]

[0097]

[0098] Where, θ i Let s represent the state variables, α represent the parameter update rate coefficient, and s represent the state variables. i Indicates control error. denoted by , m represents the filter output, T represents the sampling interval, Λ represents the gain coefficient of the nth-order Butterworth filter, and t represents the travel time.

[0099] Next, the state variables are updated based on the parameter update model, and the system state matrix A and input matrix B in the control model are corrected based on the updated state variables:

[0100]

[0101]

[0102] in, This represents the system state matrix of the initial control model. Let ΔA represent the input matrix of the initial control model. i The correction matrix ΔB represents the system state matrix. i A represents the correction matrix of the input matrix. i B represents the corrected system state matrix. i This represents the corrected input matrix.

[0103] Optionally, ΔA i and ΔB i It can be determined in the following way:

[0104]

[0105]

[0106] Within each time step Δt, the model is updated based on the parameters, using the calculated... The system state matrix A and input matrix B are corrected, and the control model is corrected based on the corrected system state matrix A and input matrix B. This enables the control model to adaptively adjust according to the control error and filter output, thereby improving the performance and robustness of the control system.

[0107] Based on any of the above embodiments, each state variable includes at least one of attitude angle variable, acceleration variable, and angular momentum variable.

[0108] Specifically, each state variable can be acquired through sensors, which may include angle sensors, accelerometers, gyroscopes, etc.

[0109] For angle sensors, the attitude angle variable θ of the subway train is obtained by measuring the lateral sway angle, heading angle, pitch angle, etc. i :

[0110]

[0111] Among them, v i β represents the speed of a subway train. i The angle of sideslip of the subway train is represented by g, and the acceleration due to gravity is θ. i This represents the attitude angle variable of the subway train.

[0112] For acceleration sensors, the acceleration variable 'a' of the subway train is obtained by measuring its longitudinal acceleration, lateral acceleration, and vertical acceleration. i :

[0113]

[0114] Among them, a xi a represents lateral acceleration. yi a represents longitudinal acceleration. zi This represents vertical acceleration.

[0115] For gyroscopes, the angular momentum of the subway train is obtained by measuring its angular velocity. Typically, the gyroscope outputs discrete sampled values ​​of the angular velocity. Integrating these sampled values ​​over a period of time yields the angular displacement of the subway train during that time period. Dividing this displacement by the time interval gives the angular velocity, as shown in the formula below:

[0116]

[0117] Where, Δθ i ω represents the change in angle within Δr, Δt represents the time step, and ω i It represents angular velocity.

[0118] The change of angular velocity ω(t) over a time step Δt can be estimated using the following Euler integral formula:

[0119] θ(t)=θ(t-1)+ω(t)×Δt

[0120] Where θ(t) represents the angular displacement at time t, θ(t-1) represents the angular displacement at the previous time step, ω(t) represents the gyroscope angular velocity at time t, and Δt represents the time step.

[0121] The subway train control device provided by the present invention is described below. The subway train control device described below and the subway train control method described above can be referred to in correspondence.

[0122] Based on any of the above embodiments, the present invention also provides a subway train control device, such as... Figure 2 As shown, the device includes:

[0123] The first solving unit 210 is used to solve the measurement state vector when the sliding surface approaches zero and the travel time of the subway train approaches infinity; the sliding surface is constructed based on the measurement state vector and the sliding parameters corresponding to the travel time.

[0124] The second solving unit 220 is used to solve the measurement equations of the subway train based on the measurement state vector to obtain the state vector of the subway train; the measurement equations are used to characterize the functional relationship between the measurement state vector and the state vector.

[0125] The third solving unit 230 is used to solve the control model of the subway train based on the state vector and the state variables of the subway train, and obtain the control input corresponding to each state variable; the control model is used to characterize the relationship between the state vector and each state variable, and the control input is less than or equal to the threshold.

[0126] The train control unit 240 is used to control the subway train based on the control inputs corresponding to each state variable.

[0127] Based on any of the above embodiments, the control model is constructed based on the relationships between state variables, the relationships between state variables and corresponding control inputs, and the state vector.

[0128] Based on any of the above embodiments, the measurement equation is constructed based on the state vector, the mapping relationship between the state vector and the actual measurement state, and the measurement state vector.

[0129] Based on any of the above embodiments, the sliding mode parameter corresponding to the current driving time is determined based on the sliding mode parameter corresponding to the previous driving time and the state vector error corresponding to the previous driving time. The state vector error corresponding to the previous driving time is determined based on the measured state vector of the previous driving time and the estimated value of the measured state vector of the previous driving time.

[0130] Based on any of the above embodiments, a correction unit is further included, for:

[0131] Update each state variable based on the control error;

[0132] Based on the updated state variables, the control model is corrected;

[0133] Based on the updated state variables, the corrected control model is solved to obtain the corrected control inputs corresponding to the updated state variables;

[0134] Among them, the control error refers to the error between the output of the reference control model and the output of the control model. When the control error approaches zero and the driving time approaches infinity, the control input is less than or equal to the threshold. The reference control model is constructed based on the relationship between each preset state variable, the relationship between each preset state variable and the corresponding preset control input, and the preset state vector.

[0135] Based on any of the above embodiments, each state variable is updated based on a parameter update model, which is as follows:

[0136]

[0137]

[0138] Where, θ i Let s represent the state variables, α represent the parameter update rate coefficient, and s represent the state variables. i Indicates control error. denoted by , m represents the filter output, T represents the sampling interval, Λ represents the gain coefficient of the nth-order Butterworth filter, and t represents the travel time.

[0139] Based on any of the above embodiments, the state variables include at least one of attitude angle variables, acceleration variables, and angular momentum variables.

[0140] Based on any of the above embodiments, the present invention also provides a subway train control system, including: a subway train and a subway train control device as described in any of the above embodiments, wherein the subway train control device can be installed on the subway train or on the train control center platform, and the embodiments of the present invention do not specifically limit this.

[0141] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention, such as... Figure 3As shown, the electronic device may include a processor 310, a memory 320, a communications interface 330, and a communications bus 340, wherein the processor 310, the memory 320, and the communications interface 330 communicate with each other through the communications bus 340. The processor 310 can call logic instructions in the memory 320 to execute a subway train control method, which includes: solving for a measurement state vector when the sliding surface tends to zero and the travel time of the subway train tends to infinity; the sliding surface is constructed based on the measurement state vector and the sliding parameters corresponding to the travel time; solving the measurement equation of the subway train based on the measurement state vector to obtain the state vector of the subway train; the measurement equation is used to characterize the functional relationship between the measurement state vector and the state vector; solving the control model of the subway train based on the state vector and each state variable of the subway train to obtain the control input corresponding to each state variable; the control model is used to characterize the relationship between the state vector and the control input corresponding to each state variable, wherein the control input is less than or equal to a threshold; and controlling the subway train based on the control input corresponding to each state variable.

[0142] Furthermore, the logical instructions in the aforementioned memory 320 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0143] On the other hand, the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by the computer, the computer can execute the subway train control method provided by the above methods. The method includes: solving for a measurement state vector when the sliding surface tends to zero and the travel time of the subway train tends to infinity; the sliding surface is constructed based on the measurement state vector and the sliding parameters corresponding to the travel time; solving the measurement equation of the subway train based on the measurement state vector to obtain the state vector of the subway train; the measurement equation is used to characterize the functional relationship between the measurement state vector and the state vector; solving the control model of the subway train based on the state vector and each state variable of the subway train to obtain the control input corresponding to each state variable; the control model is used to characterize the relationship between the state vector and the control input corresponding to each state variable, wherein the control input is less than or equal to a threshold; and controlling the subway train based on the control input corresponding to each state variable.

[0144] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the aforementioned subway train control methods. The method includes: solving for a measurement state vector when the sliding surface tends to zero and the travel time of the subway train tends to infinity; the sliding surface is constructed based on the measurement state vector and sliding parameters corresponding to the travel time; solving the measurement equation of the subway train based on the measurement state vector to obtain the state vector of the subway train; the measurement equation characterizes the functional relationship between the measurement state vector and the state vector; solving the control model of the subway train based on the state vector and each state variable of the subway train to obtain the control input corresponding to each state variable; the control model characterizes the relationship between the state vector and the control input corresponding to each state variable, wherein the control input is less than or equal to a threshold; and controlling the subway train based on the control input corresponding to each state variable.

[0145] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0146] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0147] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A subway train control method, characterized in that, include: Solve for the measurement state vector when the sliding surface approaches zero and the subway train's travel time approaches infinity; The sliding surface is constructed based on the measured state vector and the sliding parameters corresponding to the driving time; Based on the measured state vector, the measurement equation of the subway train is solved to obtain the state vector of the subway train; the measurement equation is used to characterize the functional relationship between the measured state vector and the state vector. Based on the state vector and the state variables of the subway train, the control model of the subway train is solved to obtain the control input corresponding to each state variable; the control model is used to characterize the relationship between each state variable and the relationship between each state variable and the corresponding control input, and the control input is less than or equal to a threshold. The subway train is controlled based on the control inputs corresponding to each state variable. Also includes: Update each state variable based on the control error; Based on the updated state variables, the control model is corrected; Based on the updated state variables, the corrected control model is solved to obtain the corrected control inputs corresponding to the updated state variables; The control error refers to the error between the output of the reference control model and the output of the control model. When the control error approaches zero and the driving time approaches infinity, the control input is less than or equal to the threshold. The reference control model is constructed based on the relationship between each preset state variable, the relationship between each preset state variable and the corresponding preset control input, and the preset state vector.

2. The subway train control method according to claim 1, characterized in that, The control model is constructed based on the relationships between state variables, the relationships between state variables and their corresponding control inputs, and the state vector.

3. The subway train control method according to claim 1, characterized in that, The measurement equation is constructed based on the state vector, the mapping relationship between the state vector and the actual measurement state, and the measurement state vector.

4. The subway train control method according to claim 1, characterized in that, The sliding mode parameters corresponding to the current driving time are determined based on the sliding mode parameters corresponding to the previous driving time and the state vector error corresponding to the previous driving time. The state vector error corresponding to the previous driving time is determined based on the measured state vector of the previous driving time and the estimated value of the measured state vector of the previous driving time.

5. The subway train control method according to claim 1, characterized in that, Each state variable is updated based on a parameter update model, which is as follows: ； ； in, Representing each state variable, This represents the parameter update rate coefficient. This indicates the control error. Indicates the filter output. Indicates the size of the filter window. Indicates the sampling interval time. express The gain coefficient of the Butterworth filter of order 1. This indicates the travel time.

6. The subway train control method according to any one of claims 1 to 4, characterized in that, Each state variable includes at least one of the following: attitude angle variable, acceleration variable, and angular momentum variable.

7. A subway train control device, characterized in that, include: The first solving unit is used to solve the measurement state vector when the sliding surface approaches zero and the travel time of the subway train approaches infinity. The sliding surface is constructed based on the measured state vector and the sliding parameters corresponding to the driving time; The second solving unit is used to solve the measurement equation of the subway train based on the measurement state vector to obtain the state vector of the subway train; the measurement equation is used to characterize the functional relationship between the measurement state vector and the state vector. The third solving unit is used to solve the control model of the subway train based on the state vector and the state variables of the subway train, and obtain the control input corresponding to each state variable; the control model is used to characterize the relationship between each state variable and the relationship between each state variable and the corresponding control input, and the control input is less than or equal to a threshold. The train control unit is used to control the subway train based on the control inputs corresponding to each state variable; Also includes: Update each state variable based on the control error; Based on the updated state variables, the control model is corrected; Based on the updated state variables, the corrected control model is solved to obtain the corrected control inputs corresponding to the updated state variables; The control error refers to the error between the output of the reference control model and the output of the control model. When the control error approaches zero and the driving time approaches infinity, the control input is less than or equal to the threshold. The reference control model is constructed based on the relationship between each preset state variable, the relationship between each preset state variable and the corresponding preset control input, and the preset state vector.

8. A subway train control system, characterized in that, include: The subway train and the subway train control device as described in claim 7.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the subway train control method as described in any one of claims 1 to 6.

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