Train control method, device and controller based on maglev train control system

Abstract

The present application provides a train control method, device and controller based on a maglev train control system, wherein the controller obtains the set physical parameters of the maglev system, the gap data between the track and the car body and the pre-established control signal model; wherein the gap data is collected through a gap sensor; the control signal model is established by constructing a nonlinear dynamic model of the suspension frame based on the suspension mechanism of the maglev train and introducing a disturbance signal; then the physical parameters of the maglev system and the gap data are input into the control signal model to output a control signal; finally, the control signal drives the suspension electromagnet to move to the target position through a chopper. The train control method in the embodiment of the present application takes disturbance into consideration and utilizes the positive effect of disturbance to greatly reduce control energy consumption; and has strong anti-interference capability.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a train control method, device and controller based on a magnetic levitation train control system. Background Art

[0002] The maglev train is a modern mode of transportation with a non-contact electromagnetic suspension, guidance and drive system. It achieves the suspension of the train by means of electromagnetic attraction or repulsion, thereby avoiding mechanical contact between the train and the track, and is driven by a linear motor. The maglev train has the characteristics of high speed, low energy consumption, comfortable ride and low noise, and is a theoretical means of transportation. According to the suspension principle and method, maglev trains can be divided into two types: electric suspension (Electrodynamicsuspension) and electromagnetic suspension (Electromagnetic suspension). The electric suspension type maglev system uses electromagnetic repulsion to achieve the suspension of the train above the track, while the electromagnetic suspension type maglev system uses the attraction generated by the electromagnets located under the track to achieve the suspension of the train.

[0003] In terms of suspension control, unlike the electric suspension type maglev system, the currently operating electromagnetic suspension type maglev train needs to apply active control to achieve stable suspension of the system. Therefore, achieving stable suspension of the maglev system is the key and core of the current safe operation of the maglev train. The electromagnetic suspension type suspension system has strong nonlinearity and open-loop instability, and will also be affected by the disturbance factors of the external environment during operation, resulting in extremely high uncertainty in the system parameters. Most of the current control strategies such as PID control, optimal control, fuzzy control, etc. usually directly eliminate external disturbances in the control items to achieve the control purpose. In fact, disturbances have both positive and negative effects on the system. How to use disturbances to produce positive effects is particularly important. Summary of the invention

[0004] In view of this, embodiments of the present application provide a train control method, device, magnetic levitation controller and computer-readable storage medium based on a magnetic levitation train control system.

[0005] In a first aspect, an embodiment of the present application provides a train control method based on a maglev train control system, wherein the maglev train control system includes a gap sensor, a maglev controller, a chopper, and a suspension electromagnet; the suspension electromagnet is installed on the body of the maglev train, and the gap sensor is installed on the suspension electromagnet; the gap sensor is connected to the maglev controller, and the suspension electromagnet is connected to the maglev controller through the chopper. The method is applied to the maglev controller, and the method includes:

[0006] Acquire the set physical parameters of the magnetic levitation system, the gap data between the track and the vehicle body, and the pre-established control signal model; wherein the gap data is collected by a gap sensor; the control signal model is established by constructing a nonlinear dynamic model of the suspension frame based on the suspension mechanism of the magnetic levitation train and introducing a disturbance signal;

[0007] The physical parameters of the magnetic levitation system and the gap data are input into the control signal model to output a control signal; wherein the control signal is used to drive the levitation electromagnet to move to a target position through the chopper.

[0008] In a second aspect, an embodiment of the present application provides a train control device based on a maglev train control system, wherein the maglev train control system includes a gap sensor, a maglev controller, a chopper, and a suspension electromagnet; the suspension electromagnet is installed on the body of the maglev train, and the gap sensor is installed on the suspension electromagnet; the gap sensor is connected to the maglev controller, and the suspension electromagnet is connected to the maglev controller through the chopper. The device is applied to the maglev controller, and the device includes:

[0009] An information acquisition module is used to acquire the set physical parameters of the magnetic levitation system, the gap data between the track and the vehicle body, and a pre-established control signal model; wherein the gap data is collected by a gap sensor; and the control signal model is established by constructing a nonlinear dynamic model of the suspension frame based on the suspension mechanism of the magnetic levitation train and introducing a disturbance signal;

[0010] A control signal output module is used to input the physical parameters of the magnetic suspension system and the gap data into the control signal model to output a control signal; wherein the control signal is used to drive the suspension electromagnet to move to a target position through the chopper.

[0011] In a third aspect, an embodiment of the present application provides a magnetic levitation controller, comprising: a memory; one or more processors coupled to the memory; one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, and the one or more applications are configured to execute the train control method based on the magnetic levitation train control system provided in the first aspect above.

[0012] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which program code is stored. The program code can be called by a processor to execute the train control method based on the magnetic levitation train control system provided in the first aspect above.

[0013] The embodiments of the present application provide a train control method, device, magnetic levitation controller and computer-readable storage medium based on a magnetic levitation train control system. The magnetic levitation controller obtains the set physical parameters of the magnetic levitation system, the gap data between the track and the vehicle body and a pre-established control signal model; wherein the gap data is collected by a gap sensor; the control signal model is established by constructing a nonlinear dynamic model of the suspension frame based on the suspension mechanism of the magnetic levitation train and introducing a disturbance signal; then the physical parameters of the magnetic levitation system and the gap data are input into the control signal model to output a control signal; finally, the control signal drives the suspension electromagnet to move to the target position through a chopper.

[0014] The train control method in the embodiment of the present application takes disturbance into consideration and utilizes the positive effects of disturbance to greatly reduce control energy consumption; and has strong anti-interference capability. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0016] Figure 1 A schematic diagram of an application scenario of a train control method based on a maglev train control system provided in an embodiment of the present application (i.e., a schematic diagram of the structure of a maglev train control system);

[0017] Figure 2 A schematic diagram of the working principle of a maglev train provided in one embodiment of the present application;

[0018] Figure 3 A flow chart of a train control method based on a magnetic levitation train control system provided in one embodiment of the present application;

[0019] Figure 4 A schematic diagram of a control signal model provided for an embodiment of the present application;

[0020] Figure 5 A schematic diagram of the structure of a train control device based on a magnetic levitation train control system provided in one embodiment of the present application;

[0021] Figure 6 A schematic diagram of the structure of a terminal device provided in one embodiment of the present application;

[0022] Figure 7 A schematic diagram of the structure of a computer-readable storage medium provided in one embodiment of the present application. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present application are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0024] In order to explain the present application in more detail, a train control method, apparatus, terminal device and computer-readable storage medium based on a magnetic levitation train control system provided by the present application will be specifically described below in conjunction with the accompanying drawings.

[0025] Please refer to Figure 1 , Figure 1 A schematic diagram of an application scenario of a train control method based on a maglev train control system provided in an embodiment of the present application (i.e., a maglev train control system) is shown, and the application scenario includes a track 1, a gap sensor 2, a maglev controller 3, a chopper 4, a car body 5, and a suspension electromagnet 6 provided in an embodiment of the present application. The gap sensor 2 is connected to the maglev controller 3, and the maglev controller 3 is connected to the suspension electromagnet 6 through the chopper 4, and the gap sensor 2 is installed on the suspension electromagnet 6. The suspension electromagnet 6 is installed on the car body 5, and the car body 5 is suspended on the track 1. The maglev controller 3 includes computer hardware and algorithm software, wherein the algorithm software is stored in the computer hardware.

[0026] Also, please refer to Figure 2 , Figure 2 301 represents a track, 302 represents a coil arranged between the track and the suspension electromagnet, and 303 represents the suspension electromagnet; when voltage and current are applied to the coil, the suspension electromagnet moves relative to the coil to generate a magnetic field, and the magnetic field is acted on by the vehicle body to drive the vehicle to move.

[0027] Among them, the working principle of the maglev train control system is: the gap sensor 2 measures and collects the air gap data in real time, at a high speed and uninterruptedly, and after analog-to-digital conversion, filtering and modulation (realized by the analog-to-digital conversion module and the filtering and modulation module), the gap data is transmitted to the maglev controller 3 through the communication line. The maglev controller 3 calculates the control quantity (also called the control signal) and outputs the control quantity to the chopper 4, which converts the control quantity into a current signal to drive the suspension electromagnet 6 to work. With the real-time and uninterrupted operation of the gap sensor 2, the maglev controller 3, and the chopper 4, the train will move to the target position within a limited time and remain within the position error limit range, achieving a stable and reliable suspension control effect.

[0028] Optionally, the magnetic levitation controller may be a terminal device, and the terminal device may be various electronic devices with a display screen, including but not limited to a smart phone and a computer device, wherein the computer device may be at least one of a desktop computer, a portable computer, a laptop computer, a tablet computer, and the like. A terminal device may generally refer to one of a plurality of terminal devices, and this embodiment is only illustrated by a terminal device. Those skilled in the art may know that the number of the above-mentioned terminal devices may be more or less. For example, the above-mentioned terminal devices may be only a few, or the above-mentioned terminal devices may be dozens or hundreds, or a greater number, and the embodiment of the present application does not limit the number and type of the terminal devices. The terminal device may be used to execute a train control method based on a magnetic levitation train control system provided in an embodiment of the present application.

[0029] Based on this, an embodiment of the present application provides a train control method based on a magnetic levitation train control system. Figure 3 , Figure 3 A flow chart of a train control method based on a magnetic levitation train control system provided in an embodiment of the present application is shown. Figure 1 The magnetic levitation controller in the example is used to illustrate, including the following steps:

[0030] Step S110, obtaining the set physical parameters of the magnetic suspension system, the gap data between the track and the vehicle body, and the pre-established control signal model.

[0031] Among them, the gap data is collected through the gap sensor; the control signal model is established by constructing a nonlinear dynamic model of the suspension frame based on the suspension mechanism of the maglev train and introducing a disturbance signal.

[0032] Step S120: inputting the physical parameters of the magnetic suspension system and the gap data into the control signal model to output a control signal.

[0033] The control signal is used to drive the suspension electromagnet to move to the target position through the chopper.

[0034] Specifically, the physical parameters of the magnetic levitation system include but are not limited to the load and self-gravity of the levitation magnet, the cross-sectional area of ​​the electromagnet, the number of windings of the electromagnet coil, etc. The gap data between the track and the vehicle body refers to the gap value between the track and the vehicle body at a certain moment, which is mainly collected by the gap sensor, where the gap sensor can collect gap data in real time or at a fixed time.

[0035] The control signal model is used to calculate the control signal based on the input physical parameters and gap data of the magnetic levitation system, and then use the control signal to drive the levitation electromagnet to move to the target position. Please refer to the schematic diagram of the control signal model Figure 4 As shown; Figure 4The chopper, suspension electromagnet and gap sensor are hardware devices, while the nonlinear disturbance observer, switching logic saturation tracking controller, auxiliary function, error signal, disturbance indicator, logic equation, etc. are all algorithm modules. These algorithm modules combined together can represent the control signal model.

[0036] The control signal model is established by constructing a nonlinear dynamic model of the suspension frame based on the suspension mechanism of the maglev train and introducing a disturbance signal; the specific process is: the auxiliary function is constructed by the error between the actual gap value and the target gap value observed by the gap sensor; then the nonlinear disturbance observer is used to obtain the logic equation of the disturbance indicator item according to the control quantity (also called the control signal) and the auxiliary function, and finally the control signal model is determined. That is, the control quantity can be calculated by the observation value, the logic equation value, and the auxiliary function item observed by the nonlinear disturbance observer. The control quantity can be processed by the chopper and act on the suspension electromagnet to perform suspension control.

[0037] In one embodiment, the expression of the control signal model is:

[0038]

[0039] Wherein, u(t) represents the control signal at time t; arctan(·) and sgn(·) are sign functions, H(·) is a logic function (0 or 1), Γ is a disturbance signal, s is an auxiliary function that represents the error between the actual suspension gap and the target suspension gap and is introduced as a filter signal. is the theoretical mass of the vehicle, g is the acceleration due to gravity, k p , k s , λ is the control gain parameter, is the disturbance estimate.

[0040] The train control method in the embodiment of the present application takes disturbance into consideration and utilizes the positive effects of disturbance to greatly reduce control energy consumption; and has strong anti-interference capability.

[0041] Furthermore, an implementation method for establishing a control signal model is provided, which is described in detail as follows:

[0042] In one embodiment, establishing a control signal model includes:

[0043] S1: Construct a nonlinear dynamic model of the suspension frame based on the suspension mechanism of the maglev train.

[0044] In one embodiment, a nonlinear dynamic model of a suspension frame is constructed based on the suspension mechanism of a maglev train, including: establishing an initial nonlinear dynamic model of the suspension frame based on the suspension mechanism of the maglev train; simplifying the initial nonlinear dynamic model based on the current control principle of the maglev train to form a nonlinear dynamic model.

[0045] Specifically, the suspension controllers of the maglev train can be considered independent of each other within a certain range, so the single-point suspension system can be regarded as a minimum dynamic model for controller design. The initial nonlinear dynamic model of the single-point suspension system is expressed as:

[0046]

[0047] in, y(t) is the output of the dynamics model, and They correspond to the first-order derivatives of x1(t), x2(t), and x3(t), respectively. t represents time, and z(t) is the gap between the train track and the train body at time t. is the first-order derivative of the gap between the train track and the train body at time t, i(t) is the current of the electromagnet coil at time t, u1(t) is the excitation voltage across the electromagnet at time t, m is the load and self-gravity of the electromagnet, g is the acceleration of gravity, A is the cross-sectional area of ​​the electromagnet, N is the number of windings of the electromagnet coil, R is the resistance of the electromagnet coil winding, μ0 is the vacuum permeability, and f d is the external disturbance of the suspension system.

[0048] Since the actual control of the maglev train is carried out by current, the initial nonlinear dynamic model can be simplified to obtain the expression of the nonlinear dynamic model:

[0049]

[0050] in, y(t) is the output of the dynamics model, is the first-order derivative of x1(t), is the first-order derivative of x2(t), z(t) is the gap between the train track and the train body at time t, is the first-order derivative of the gap between the train track and the train body at time t, i(t) is the current of the electromagnet coil at time t, m is the load and self-weight of the electromagnet, g is the acceleration of gravity, A is the cross-sectional area of ​​the electromagnet, N is the number of windings of the electromagnet coil, R is the resistance of the electromagnet coil winding, μ0 is the vacuum permeability, and f d is the external disturbance of the suspension system.

[0051] The relationship between the current signal and the suspension gap is used as the control quantity, that is, The nonlinear dynamic model can be further simplified as:

[0052] in,

[0053]

[0054]

[0055] z is the suspension gap, A is the cross-sectional area of ​​the electromagnet, N is the number of electromagnet coil windings, μ0 is the vacuum permeability, and in order to ensure that the proposed control model always provides a control value greater than 0 and works within the amplitude saturation, the control gain term k p , k s The value of must be satisfied.

[0056] Since the suspension gap z(t) can be measured in real time by the sensor, the control current required by the system can be obtained as long as the control quantity is obtained:

[0057]

[0058] S2: defines the error signal between the actual suspension gap and the target suspension gap.

[0059] Since the control goal of the maglev train is to keep the suspension gap z(t) at the target suspension gap, the error signal can be defined as: e(t) = z(t) - z d (3)

[0060] Where, e(t) is the error signal at time t; z d is the target suspension gap, which is usually a fixed value.

[0061] S3: Define an auxiliary function based on the error signal.

[0062] Specifically, an auxiliary function s(t) can be defined based on the error signal as follows:

[0063]

[0064] in and are the first-order derivative and second-order derivative of the error signal at time t, is the first-order derivative of the auxiliary function s(t)t at time t, and They are the first-order derivative and second-order derivative of z(t) at time t respectively.

[0065] Substituting formula (4) into (1), we can obtain:

[0066]

[0067] S4: Establish the disturbance signal function.

[0068] Specifically, in actual operation, the load of the electromagnet will change according to the passengers getting on and off the bus or other loads such as wind load. m is usually uncertain, so we introduce Let (5) be rewritten as:

[0069]

[0070] in, Represents theoretical mass, and its value may be different at different times.

[0071] By combining the disturbance terms in (6), we can obtain equation (7): (7); where the disturbance merging term Δ in (7) is:

[0072]

[0073] Where Δ represents the disturbance signal function. is the first-order derivative of Δ, λ p With λ d are Δ and The upper bound of ; Δ is the disturbance signal function, is the first-order derivative of e(t), s(t) is an auxiliary function, is the first-order derivative of s(t).

[0074] S5: Substitute the auxiliary function, theoretical mass and disturbance signal function into the nonlinear dynamic model to form a control signal model.

[0075] Since the disturbance observer has errors, it is defined as follows:

[0076]

[0077] In formula (10), is the observed value of the disturbance observer. The disturbance observer constructed in the present invention is as follows:

[0078]

[0079]

[0080]

[0081] in is the first-order derivative of q1, L is the positive observation gain, q1 and q2 are two auxiliary equations. Based on the disturbance observer involved, the estimated disturbance term And the range of observer error is:

[0082]

[0083] in and They are and The upper bound of and They are and Initial value.

[0084] The range of formula (12) is proved as follows:

[0085] Derivative both sides of equation (11a) and substitute into (7), (11b) and (11c):

[0086]

[0087] in for The first-order derivative of is the first-order derivative of q2.

[0088] Solving the differential equation (13), we can obtain:

[0089]

[0090]

[0091] According to (13), we can get:

[0092]

[0093] in for The first-order derivative of .

[0094] Similarly, solving the differential equation (15) yields:

[0095]

[0096]

[0097] In order to use disturbances that are beneficial to control, a disturbance indicator (also called disturbance signal) is introduced here:

[0098]

[0099] The following definitions are made: when Γ=1, the disturbance is not conducive to control; when Γ=0, the disturbance has no effect on control; when Γ=-1, the disturbance is conducive to control.

[0100] In order to prove the stability, a Lyapunov equation is constructed:

[0101]

[0102] Deriving (18) and substituting it into (7), we obtain:

[0103]

[0104] in is the first-order derivative of the Lyapunov equation V.

[0105] Based on (19), the following switching logic saturation tracking controller (i.e., control signal model) is designed:

[0106]

[0107] where k p With k s is a positive control gain term, and H(Γ) is a logistic equation with a value of 0 or 1, which is defined as:

[0108]

[0109] Therefore, when the disturbance term is not conducive to control, the disturbance term can be removed in the controller. Substituting (20) into (19), we can get:

[0110]

[0111]

[0112] (12) and Substitute the upper bound of s >ε d +ε p , we can get the following inequality:

[0113] Since V is positive definite, If is negative definite, it can be obtained that the controller designed in (20) is Lyapunov asymptotically stable, and the auxiliary function s used in the present invention eventually converges to 0. The proof is as follows:

[0114]

[0115] Where c1 is determined according to the initial conditions (does not affect convergence).

[0116] In addition, the designed control signal model needs to be subject to upper limit control:

[0117]

[0118] u max is the maximum control quantity that can be achieved. And consider that the value of the controller is always positive:

[0119]

[0120]

[0121] It should be understood that although Figure 2 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Figure 2 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0122] The above-mentioned embodiment disclosed in the present application describes in detail a train control method based on a maglev train control system. The above-mentioned method disclosed in the present application can be implemented by various forms of equipment. Therefore, the present application also discloses a train control device based on a maglev train control system corresponding to the above-mentioned method. Specific embodiments are given below for detailed description.

[0123] See also Figure 5 , is a train control device based on a maglev train control system disclosed in an embodiment of the present application, the maglev train control system includes a gap sensor, a maglev controller, a chopper and a suspension electromagnet; the suspension electromagnet is installed on the body of the maglev train, and the gap sensor is installed on the suspension electromagnet; the gap sensor is connected to the maglev controller, and the suspension electromagnet is connected to the maglev controller through the chopper, the device is applied to the maglev controller, and the device includes:

[0124] The information acquisition module 510 is used to acquire the set physical parameters of the magnetic levitation system, the gap data between the track and the vehicle body, and the pre-established control signal model; wherein the gap data is collected by the gap sensor; the control signal model is established by constructing a nonlinear dynamic model of the suspension frame based on the suspension mechanism of the magnetic levitation train and introducing a disturbance signal;

[0125] The control signal output module 520 is used to input the physical parameters and gap data of the magnetic suspension system into the control signal model to output a control signal; wherein the control signal is used to drive the suspension electromagnet to move to the target position through the chopper.

[0126] In one embodiment, the expression of the control signal model is:

[0127]

[0128] Wherein, u(t) represents the control signal at time t; arctan(·) and sgn(·) are sign functions, H(·) is a logic function (0 or 1), Γ is a disturbance signal, s is an auxiliary function that represents the error between the actual suspension gap and the target suspension gap and is introduced as a filter signal. is the theoretical mass of the vehicle, g is the acceleration due to gravity, k p , k s , λ is the control gain parameter, is the disturbance estimate.

[0129] In one embodiment, the apparatus further comprises:

[0130] The nonlinear model building module is used to build a nonlinear dynamic model of the suspension frame based on the suspension mechanism of the maglev train.

[0131] The error signal definition module is used to define the error signal between the actual suspension gap and the target suspension gap.

[0132] The auxiliary function definition module is used to define the auxiliary function based on the error signal.

[0133] The disturbance signal function establishment module is used to establish the disturbance signal function.

[0134] The control signal model building module is used to substitute the auxiliary function, theoretical mass and disturbance signal function into the nonlinear dynamic model to form a control signal model.

[0135] In one embodiment, the nonlinear model building module is used to build an initial nonlinear dynamic model of the suspension frame based on the suspension mechanism of the maglev train; and simplify the initial nonlinear dynamic model based on the current control principle of the maglev train to form a nonlinear dynamic model.

[0136] In one embodiment, the expression of the nonlinear dynamic model is:

[0137]

[0138] in, y(t) is the output of the dynamics model, is the first-order derivative of x1(t), is the first-order derivative of x2(t), Z(t) is the gap between the train track and the train body at time t, is the first-order derivative of the gap between the train track and the train body at time t, i(t) is the current of the electromagnet coil at time t, m is the load and self-weight of the electromagnet, g is the acceleration of gravity, A is the cross-sectional area of ​​the electromagnet, N is the number of windings of the electromagnet coil, R is the resistance of the electromagnet coil winding, μ0 is the vacuum permeability, and f d is the external disturbance of the suspension system.

[0139] In one embodiment, the error signal is expressed as: e(t)=z(t)-z d , where e(t) is the error signal at time t, z d is the target suspension gap.

[0140] In one embodiment, the expression of the disturbance signal function is:

[0141]

[0142] Where |Δ|≤λ p , is the first-order derivative of Δ, λ p With λ d are Δ and The upper bound of ; Δ is the disturbance signal function, is the first-order derivative of e(t), s(t) is an auxiliary function, is the first-order derivative of s(t).

[0143] The specific definition of the train control device based on the maglev train control system can be found in the definition of the method above, which will not be repeated here. Each module in the above device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above modules can be embedded in or independent of the processor in the terminal device in the form of hardware, or can be stored in the memory in the terminal device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0144] Please refer to Figure 6 , Figure 6 It shows a structural block diagram of a suspension controller provided in an embodiment of the present application. The suspension controller 60 may be a computer device. The suspension controller 60 in the present application may include one or more of the following components: a processor 62, a memory 64, and one or more applications, wherein the one or more applications may be stored in the memory 64 and configured to be executed by one or more processors 62, and the one or more applications are configured to execute the method described in the above method embodiment applied to the suspension controller, and may also be configured to execute the method described in the above train control method embodiment applied to a maglev train control system.

[0145] The processor 62 may include one or more processing cores. The processor 62 uses various interfaces and lines to connect the various parts of the entire suspension controller 60, and executes various functions and processes data of the suspension controller 60 by running or executing instructions, programs, code sets or instruction sets stored in the memory 64, and calling data stored in the memory 64. Optionally, the processor 62 can be implemented in at least one hardware form of digital signal processing (DSP), field programmable gate array (FPGA), and programmable logic array (PLA). The processor 62 can integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing display content; and the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 62, but may be implemented separately through a communication chip.

[0146] The memory 64 may include a random access memory (RAM) or a read-only memory (ROM). The memory 64 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 64 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the following various method embodiments, etc. The data storage area may also store data created by the suspension controller 60 during use, etc.

[0147] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the suspension controller to which the solution of the present application is applied. The specific suspension controller may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0148] In summary, the suspension controller provided in the embodiment of the present application is used to implement the corresponding train control method based on the magnetic levitation train control system in the aforementioned method embodiment, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0149] See also Figure 7 , which shows a block diagram of a computer-readable storage medium provided in an embodiment of the present application. The computer-readable storage medium 70 stores program codes, which can be called by a processor to execute the method described in the embodiment of the train control method based on the maglev train control system, or can be called by a processor to execute the method described in the embodiment of the train control method based on the maglev train control system.

[0150] The computer readable storage medium 70 can be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read-only memory), an EPROM, a hard disk, or a ROM. Optionally, the computer readable storage medium 70 includes a non-transitory computer-readable storage medium. The computer readable storage medium 70 has storage space for program code 72 that performs any method steps in the above method. These program codes can be read from or written to one or more computer program products. The program code 72 can be compressed, for example, in a suitable form.

[0151] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0152] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A train control method based on a magnetic levitation train control system, characterized in that: The maglev train control system comprises a gap sensor, a maglev controller, a chopper and a levitation electromagnet; the levitation electromagnet is installed on the body of the maglev train, and the gap sensor is installed on the levitation electromagnet; The gap sensor is connected to the magnetic suspension controller, the suspension electromagnet is connected to the magnetic suspension controller via a chopper, and the method is applied to the magnetic suspension controller, and the method includes: Acquire the set physical parameters of the magnetic levitation system, the gap data between the track and the vehicle body, and the pre-established control signal model; wherein the gap data is collected by a gap sensor; the control signal model is established by constructing a nonlinear dynamic model of the suspension frame based on the suspension mechanism of the magnetic levitation train and introducing a disturbance signal; Inputting the physical parameters of the magnetic suspension system and the gap data into the control signal model to output a control signal; wherein the control signal is used to drive the suspension electromagnet to move to a target position through the chopper; Establishing the control signal model includes: A nonlinear dynamic model of the suspension frame is constructed based on the suspension mechanism of the maglev train; An error signal defining the actual suspension gap and the target suspension gap; defining an auxiliary function based on the error signal; Establish disturbance signal function; The auxiliary function, theoretical mass and the disturbance signal function are substituted into the nonlinear dynamic model to form the control signal model.

2. The method according to claim 1, characterized in that The expression of the control signal model is: Wherein, u(t) represents the control signal at time t; arctan(·) and sgn(·) are sign functions, H(·) is a logic function (0 or 1), Γ is a disturbance signal, s is an auxiliary function representing the error between the actual suspension gap and the target suspension gap, and is the theoretical mass of the vehicle, g is the acceleration due to gravity, k p , k s , λ is the control gain parameter, is the disturbance estimate.

3. The method according to any one of claims 1 to 2, characterized in that: The nonlinear dynamic model of the suspension frame based on the suspension mechanism of the magnetic levitation train includes: The initial nonlinear dynamic model of the suspension frame is established based on the suspension mechanism of the maglev train; The initial nonlinear dynamic model is simplified based on the current control principle of the maglev train to form the nonlinear dynamic model.

4. The method according to claim 1, characterized in that: The expression of the nonlinear dynamic model is: in, y(t) is the output of the dynamics model, is the first-order derivative of x1(t), is the first-order derivative of x2(t), z(t) is the gap between the train track and the train body at time t, is the first-order derivative of the gap between the train track and the train body at time t, i(t) is the current of the electromagnet coil at time t, m is the load and self-weight of the electromagnet, g is the acceleration of gravity, A is the cross-sectional area of ​​the electromagnet, N is the number of windings of the electromagnet coil, R is the resistance of the electromagnet coil winding, μ0 is the vacuum permeability, and f d is the external disturbance of the suspension system.

5. The method according to claim 4, characterized in that The expression of the error signal is: e(t)=z(t)-z d Where e(t) is the error signal at time t, z d is the target suspension gap.

6. The method according to claim 5, characterized in that The expression of the disturbance signal function is: Where |Δ|≤λ p , is the first-order derivative of Δ, λ p With λ d are Δ and The upper bound of ; Δ is the disturbance signal function, is the first-order derivative of e(t), s(t) is an auxiliary function, is the first-order derivative of s(t).

7. A train control device based on a magnetic levitation train control system, characterized in that: The maglev train control system comprises a gap sensor, a maglev controller, a chopper and a levitation electromagnet; the levitation electromagnet is installed on the body of the maglev train, and the gap sensor is installed on the levitation electromagnet; The gap sensor is connected to the magnetic suspension controller, the suspension electromagnet is connected to the magnetic suspension controller through a chopper, and the device is applied to the magnetic suspension controller, and the device includes: An information acquisition module is used to acquire the set physical parameters of the magnetic levitation system, the gap data between the track and the vehicle body, and a pre-established control signal model; wherein the gap data is collected by a gap sensor; and the control signal model is established by constructing a nonlinear dynamic model of the suspension frame based on the suspension mechanism of the magnetic levitation train and introducing a disturbance signal; A control signal output module, used for inputting the physical parameters of the magnetic suspension system and the gap data into the control signal model to output a control signal; wherein the control signal is used for driving the suspension electromagnet to move to a target position through the chopper; Establishing the control signal model includes: A nonlinear dynamic model of the suspension frame is constructed based on the suspension mechanism of the maglev train; An error signal defining the actual suspension gap and the target suspension gap; defining an auxiliary function based on the error signal; Establish disturbance signal function; The auxiliary function, theoretical mass and the disturbance signal function are substituted into the nonlinear dynamic model to form the control signal model.

8. A magnetic suspension controller, characterized in that: include: Memory; One or more processors coupled to the memory; One or more applications, wherein the one or more applications are stored in a memory and configured to be executed by one or more processors, and the one or more applications are configured to execute the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program codes, which can be called by a processor to execute the method according to any one of claims 1 to 6.

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