Underground scraper truck control method and device, underground scraper truck and storage medium
By generating the predicted state equation and optimization matrix of error state variables, the time-variability problem of path curvature in unmanned underground shoveling machine control is solved, and the control efficiency and safety are achieved with high precision and high stability are improved.
Patent Information
- Application Number
- CN202510729472.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-03
- Publication Date
- 2025-08-26
AI Technical Summary
The existing control method of unmanned underground shovelers ignores the time-varying curvature of the underground shovel truck, resulting in a significant reduction in control effect, making it difficult to achieve high-precision and high-stability control.
By obtaining the operating status information of the underground shovel truck, a predicted state equation of the error state variable is generated, the optimization matrix is determined based on the curvature of the driving path, and the objective function is constructed to calculate the optimal control increment, so as to achieve high-precision and high stability control of the underground shovel truck.
In complex underground operation scenarios, high-precision and high-stability control of underground shovel trucks are realized, and control efficiency and safety are improved.
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Figure CN120540319A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of vehicle control, and in particular to a method and device for controlling an underground scraper, an underground scraper, and a storage medium. Background Art
[0002] With the continuous advancement of industrial production technology, improving transportation efficiency and reducing transportation costs have become crucial factors in business development. Traditional underground loaders not only require a significant amount of manpower but are also prone to underground accidents. Unmanned underground loaders, however, utilize intelligent sensing and control technologies to significantly improve operational efficiency and safety. Summary of the Invention
[0003] The inventors noted that in related technologies, unmanned underground LHDs typically feature front-to-rear articulated structures. Compared to unmanned passenger vehicles, these vehicles have more complex vehicle kinematic models and operate in a wider range of underground scenarios. Effectively controlling these vehicles presents a technical challenge. Currently proposed control methods for underground LHDs mostly ignore the time-varying curvature of the LHD's travel path, significantly reducing their control effectiveness.
[0004] Accordingly, the present disclosure provides an underground scraper control method that can achieve high-precision and high-stability control of the underground scraper in complex underground operation scenarios.
[0005] According to a first aspect of an embodiment of the present disclosure, a method for controlling an underground shovel loader is provided, comprising: obtaining operating status information of the underground shovel loader at a current moment; generating a predicted state equation of an error state variable based on the operating status information; determining a first optimization matrix and a second optimization matrix based on the curvature of the current driving path of the underground shovel loader; inputting the first optimization matrix and the second optimization matrix into an objective function to obtain an optimal incremental value of a control increment in the predicted state equation at the next moment, wherein the objective function is constructed by the predicted state equation; and controlling the motion state of the underground shovel loader at the next moment based on the optimal incremental value.
[0006] In some embodiments, determining the first optimization matrix and the second optimization matrix based on the curvature of the current driving path of the underground loader includes: determining the curvature sub-interval in which the curvature is located in the curvature interval; using the first parameter matrix of the curvature sub-interval as the first optimization matrix, and using the second parameter matrix of the curvature sub-interval as the second optimization matrix.
[0007] In some embodiments, the curvature interval is divided into N curvature subintervals, wherein the lower limit value and the upper limit value of each curvature subinterval are both the weighted sum of a predetermined maximum curvature and a predetermined minimum curvature, and N is a natural number greater than 1.
[0008] In some embodiments, when calculating the lower limit value of the i-th curvature subinterval, the weight of the predetermined maximum curvature is , the weight of the predetermined minimum curvature is , ; In the case of calculating the upper limit value of the i-th curvature subinterval, the weight of the predetermined maximum curvature is , the weight of the predetermined minimum curvature is .
[0009] In some embodiments, the control increment includes a longitudinal velocity increment and an articulated chain steering angle increment of the underground haul truck.
[0010] In some embodiments, the objective function is determined by the difference between the error state variable and the predetermined desired state variable, the first optimization matrix, the second optimization matrix, and the control variable.
[0011] In some embodiments, the objective function for:
[0012]
[0013] in, is the error state variable, is the predetermined expected state variable, is the first optimization matrix, is the second optimization matrix, To control the amount, Represents a transpose operation.
[0014] In some embodiments, the controlled variables include the longitudinal speed of the underground loader and the articulated chain steering angle.
[0015] In some embodiments, the error state variables include a horizontal displacement error of the underground loader in a first direction, a vertical displacement error in a second direction perpendicular to the first direction, and a heading angle error.
[0016] In some embodiments, obtaining the operating status information of the underground scraper at the current moment includes: obtaining the lateral speed, longitudinal speed, lateral displacement, longitudinal displacement, heading angle, and articulated chain steering angle of the underground scraper at the current moment.
[0017] In some embodiments, the predicted state equation is:
[0018]
[0019] in, is the prediction dimension parameter, is the error state variable, To control the increment, is the first state matrix, is the second state matrix.
[0020] In some embodiments, the first state matrix is determined by the longitudinal speed of the underground loader, the steering angle of the centerline point of the vehicle head, and the time parameter; the second state matrix is determined by the longitudinal speed, the steering angle of the centerline point of the vehicle head, the time parameter, the steering angle of the articulated chain, and the distance from the rear body tire to the articulated chain.
[0021] In some embodiments, the first state matrix for:
[0022]
[0023] in, for:
[0024]
[0025] in, is the longitudinal speed of the underground LHD, is the steering angle of the centerline point of the vehicle head, and T is the time parameter.
[0026] In some embodiments, the second state matrix for:
[0027]
[0028] in, for:
[0029]
[0030] in, is the articulated chain steering angle, It is the distance from the rear tire to the articulation chain.
[0031] According to a second aspect of an embodiment of the present disclosure, an underground shovel loader control device is provided, comprising: a memory; a processor coupled to the memory, the processor being configured to execute an underground shovel loader control method as described in any of the above embodiments based on instructions stored in the memory.
[0032] According to a third aspect of an embodiment of the present disclosure, an underground shovel loader is provided, comprising: an underground shovel loader control device as involved in the above-mentioned embodiment; a sensor configured to collect operating status information of the underground shovel loader in real time and send the operating status information to the underground shovel loader control device.
[0033] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and when the instructions are executed by a processor, the underground shovel loader control method involved in any of the above embodiments is implemented.
[0034] According to a fifth aspect of an embodiment of the present disclosure, a computer program product is provided, comprising computer instructions, wherein when the computer instructions are executed by a processor, the underground shovel loader control method as described in any of the above embodiments is implemented.
[0035] Other features and advantages of the present disclosure will become apparent from the following detailed description of exemplary embodiments of the present disclosure with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS
[0036] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.
[0037] Figure 1 This is a flow chart of a method for controlling an underground scraper according to an embodiment of the present disclosure;
[0038] Figure 2 This is a schematic structural diagram of an underground scraper according to an embodiment of the present disclosure;
[0039] Figure 3 This is a schematic structural diagram of an underground scraper control device according to an embodiment of the present disclosure;
[0040] Figure 4 This is a schematic structural diagram of an underground scraper according to another embodiment of the present disclosure. DETAILED DESCRIPTION
[0041] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is in no way intended to limit the present disclosure and its application or use. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.
[0042] Unless specifically stated otherwise, the relative arrangement of components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present disclosure.
[0043] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.
[0044] Technologies, methods and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, such technologies, methods and equipment should be considered part of the authorization specification.
[0045] In all examples shown and discussed herein, any specific values should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.
[0046] It should be noted that like reference numerals and letters refer to like items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.
[0047] Figure 1 The flowchart of the underground scraper control method according to one embodiment of the present disclosure is shown in FIG. In some embodiments, the underground scraper control method is executed by an underground scraper control device, including steps 11-15.
[0048] In step 11, the operating status information of the underground scraper at the current moment is obtained.
[0049] In some embodiments, the lateral speed, longitudinal speed, lateral displacement, longitudinal displacement, heading angle, and articulated chain steering angle of the underground scraper at the current moment are obtained.
[0050] It should be noted here that the operating status information can be obtained through a variety of on-board sensors of the underground scraper. Among them, the lateral speed, longitudinal speed, lateral displacement, longitudinal displacement and heading angle information can be obtained through the combined navigation system installed on the underground scraper, and the articulated chain steering angle can be obtained through the steering angle sensor installed on the articulated chain of the underground scraper.
[0051] Figure 2 The figure is a schematic structural diagram of an underground scraper according to an embodiment of the present invention.
[0052] like Figure 2 As shown, the underground scraper truck includes a front body 21, a rear body 22 and an articulated chain 23. The front body 21 and the rear body 22 are connected by the articulated chain 23.
[0053] In some embodiments, the kinematic model of the underground LHD is shown in equations (1), (2), and (3).
[0054] (1)
[0055] (2)
[0056] (3)
[0057] like Figure 2 As shown, represents the longitudinal velocity of the underground LHD, Indicates the steering angle of the centerline point of the vehicle head, Indicates the distance from the front tire to the steering angle of the articulated chain. Indicates the distance from the rear tire to the steering angle of the articulated chain. represents the articulated chain steering angle of the underground LHD, and They represent the horizontal displacement in the x direction and the vertical displacement in the y direction of the rear axle center point, represents the lateral speed of the underground LHD, Indicates the steering angle of the rear centerline point of the vehicle.
[0058] In step 12, a prediction state equation of the error state variable is generated based on the operating state information.
[0059] In some embodiments, the error state variables include a horizontal displacement error of the underground loader in a first direction, a vertical displacement error in a second direction perpendicular to the first direction, and a heading angle error.
[0060] For example, the first direction is the x-axis direction in the Cartesian coordinate system, and the second direction is the y-axis direction in the Cartesian coordinate system.
[0061] For example, select the horizontal displacement error in the x direction , vertical displacement error in y direction and heading angle error , then the error state variable As shown in formula (4).
[0062] (4)
[0063] Accordingly, the state space equation is shown in formula (5).
[0064] (5)
[0065] In formula (5), the matrix parameters As shown in formula (6), the matrix parameters As shown in formula (7), the control quantity As shown in formula (8).
[0066] (6)
[0067] (7)
[0068] (8)
[0069] It should be noted here that the control amount Includes the underground loader's longitudinal speed and articulated chain steering angle.
[0070] In some embodiments, based on the above state-space equation, a discrete state-space equation is obtained, as shown in formula (9).
[0071] (9)
[0072] Transform formula (9) to obtain formula (10).
[0073]
[0074] (10)
[0075] In formula (10), k represents the kth moment, is the identity matrix, and the matrix parameters As shown in formula (11), the matrix parameters As shown in formula (12).
[0076] (11)
[0077] (12)
[0078] In formula (11) and formula (12), T is a time parameter.
[0079] In some embodiments, a predicted state equation for the error state variable is generated based on a discrete state space equation.
[0080] For example, suppose the prediction dimension of the prediction state equation is , the current moment is k, the control increment of the longitudinal speed and articulated chain steering angle of the underground LHD is , then according to the discrete state space equation shown in formula (10), the error state variable is generated The predicted state equation is shown in formula (13).
[0081] (13)
[0082] It should be noted here that the prediction state equation shown in formula (13) can be expressed in matrix form, as shown in formula (14).
[0083] (14)
[0084] In formula (14), is the prediction dimension parameter, is the error state variable, To control the increment, is the first state matrix, is the second state matrix.
[0085] In some embodiments, the first state matrix is determined by the longitudinal velocity of the underground loader, the steering angle of the centerline of the vehicle head, and a time parameter. The second state matrix is determined by the longitudinal velocity, the steering angle of the centerline of the vehicle head, a time parameter, the steering angle of the articulated chain, and the distance from the rear tire to the articulated chain.
[0086] For example, the first state matrix As shown in formula (15).
[0087] (15)
[0088] In formula (15), each parameter As shown in formula (11).
[0089] For example, the second state matrix As shown in formula (16).
[0090] (16)
[0091] In formula (16), each parameter As shown in formula (11), the parameters As shown in formula (12).
[0092] In step 13, a first optimization matrix and a second optimization matrix are determined according to the curvature of the current driving path of the underground LHD.
[0093] In some embodiments, determining the first optimization matrix and the second optimization matrix according to the curvature of the current driving path of the underground scraper includes the following steps S1-S2.
[0094] S1) determining a curvature subinterval in which the curvature is located within the curvature interval.
[0095] In some embodiments, the curvature interval is divided into N curvature subintervals, wherein the lower limit value and the upper limit value of each curvature subinterval are both the weighted sum of a predetermined maximum curvature and a predetermined minimum curvature, and N is a natural number greater than 1.
[0096] It should be noted that the predetermined maximum curvature and the predetermined minimum curvature are respectively the maximum curvature and the minimum curvature of the path traveled by the underground LHD. By dividing the curvature interval into N curvature subintervals, the curvature of the path traveled by the underground LHD is divided into N dimensions.
[0097] For example, when calculating the lower limit of the i-th curvature subinterval, the weight of the predetermined maximum curvature is , the weight of the predetermined minimum curvature is , When calculating the upper limit of the i-th curvature subinterval, the weight of the predetermined maximum curvature is , the weight of the predetermined minimum curvature is .
[0098] For example, suppose the predetermined maximum curvature of the curvature interval is , the predetermined minimum curvature is , the curvature interval is divided into 5 curvature subintervals, that is, N is 5, then the lower limit and upper limit of the i-th curvature subinterval are shown in formula (17).
[0099] (17)
[0100] S2) using the first parameter matrix of the curvature subinterval as the first optimization matrix and using the second parameter matrix of the curvature subinterval as the second optimization matrix.
[0101] It should be noted here that for each divided curvature sub-interval, a corresponding first parameter matrix and second parameter matrix are preset.
[0102] For example, suppose the predetermined maximum curvature of the curvature interval is , the predetermined minimum curvature is , divide the curvature interval into 5 curvature subintervals, that is, N is 5, and the first parameter matrix of the i-th curvature subinterval is , the second parameter matrix is , then the first optimization matrix and the second optimization matrix As shown in formula (18).
[0103] (18)
[0104] It should be noted that the curvature of the path of an underground LHD is a significant factor in controlling the LHD. Different optimization parameters are selected for different path curvatures. By using the first and second parameter matrices corresponding to the curvature subinterval of the current path as the first and second optimization matrices, respectively, adaptively adjusting the optimization parameters improves the efficiency and robustness of the LHD control method.
[0105] In step 14, the first optimization matrix and the second optimization matrix are input into the objective function to obtain the optimal incremental value of the control increment in the prediction state equation at the next moment. The objective function is constructed by the prediction state equation.
[0106] In some embodiments, the objective function is determined by the difference between the error state variable and the predetermined desired state variable, the first optimization matrix, the second optimization matrix, and the control variable.
[0107] For example, the objective function As shown in formula (19).
[0108] (19)
[0109] in, is the error state variable, is the predetermined expected state variable, is the first optimization matrix, is the second optimization matrix, is the control quantity, including the above multiple control quantities vector, Represents a transpose operation.
[0110] For example, the predetermined desired state variable As shown in formula (20).
[0111] (20)
[0112] In some embodiments, the control amount Includes the underground loader's longitudinal speed and articulated chain steering angle.
[0113] For example, the first optimization matrix determined according to formula (18) and the second optimization matrix Enter the objective function , the optimal incremental value of the control increment at time t is .
[0114] In some embodiments, the objective function The constraints are shown in formulas (21) and (22).
[0115] (twenty one)
[0116] (twenty two)
[0117] in, and Represent the minimum and maximum values of the control quantity, and Represent the minimum and maximum values of the control increment respectively.
[0118] It should be noted here that the above objective function can be transformed into a quadratic programming problem. By minimizing the objective function, the optimal control strategy can be found.
[0119] For example, to facilitate calculation, the objective function The transformation is performed as shown in formula (23).
[0120] (twenty three)
[0121] In step 15, the motion state of the underground scraper at the next moment is controlled according to the optimal increment value.
[0122] For example, suppose the control quantity at time t-1 is , the optimal increment value is , the optimal control quantity of underground LHD at time t As shown in formula (24).
[0123] (twenty four)
[0124] Through the underground shovel loader control method provided by the above embodiment, the prediction state equation and objective function of the underground shovel loader are constructed, and the corresponding optimization parameters are selected according to the curvature of the driving path to obtain the optimal control increment of the underground shovel loader, thereby achieving high-precision and high-stability control of the underground shovel loader in complex underground operation scenarios.
[0125] Figure 3 FIG. 1 is a schematic diagram of the structure of an underground scraper control device according to an embodiment of the present disclosure. Figure 3 As shown, the underground loader control device 30 includes a memory 31, a processor 32, and a bus 33 connecting various system components.
[0126] The memory 31 may include, for example, system memory, non-volatile storage media, and the like. The system memory may store, for example, an operating system, application programs, a boot loader, and other programs. The system memory may include volatile storage media, such as random access memory (RAM) and / or cache memory. The non-volatile storage media may store, for example, instructions corresponding to at least one embodiment of the underground LHD control method being executed. Non-volatile storage media include, but are not limited to, disk storage, optical storage, and flash memory.
[0127] The processor 32 may be implemented using a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, or discrete hardware components such as discrete gates or transistors. Accordingly, the method in any of the above embodiments may be implemented by a central processing unit (CPU) executing instructions in a memory that execute the corresponding steps, or by dedicated circuits that execute the corresponding steps.
[0128] For example, the processor 32 is configured to execute instructions stored in the memory to implement the following Figure 1 The method according to any one of the embodiments.
[0129] The bus 33 may use any of a variety of bus architectures, including, but not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, and a Peripheral Component Interconnect (PCI) bus.
[0130] These interfaces 34, 35, and 36 of the underground loader control device 30, as well as the memory 31 and processor 32, can be connected via a bus 33. The input / output interface 34 provides a connection interface for input / output devices such as a display, mouse, and keyboard. The network interface 35 provides a connection interface for various networked devices. The storage interface 36 provides a connection interface for external storage devices such as floppy disks, USB flash drives, and SD cards.
[0131] Here, various aspects of the present disclosure are described with reference to flowcharts and / or block diagrams of methods, devices, and computer program products according to embodiments of the present disclosure. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks, can be implemented by computer-readable program instructions.
[0132] These computer-readable program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable device to produce a machine, so that the processor executes the instructions to produce means for implementing the functions specified in one or more blocks in the flowcharts and / or block diagrams.
[0133] These computer-readable program instructions may also be stored in a computer-readable memory, which cause the computer to operate in a specific manner to produce an article of manufacture, including instructions for implementing the functions specified in one or more blocks in the flowcharts and / or block diagrams.
[0134] The present disclosure can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects.
[0135] The present disclosure also provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, which, when executed by a processor, implement the following Figure 1 The method according to any one of the embodiments.
[0136] The present disclosure also provides a computer program product, including computer instructions, wherein when the computer instructions are executed by a processor, the following is achieved: Figure 1 The method according to any one of the embodiments.
[0137] Figure 4 FIG. 1 is a schematic structural diagram of an underground scraper according to another embodiment of the present disclosure. Figure 4As shown, the underground loader 40 includes an underground loader control device 41 and a sensor 42 .
[0138] The underground scraper control device 41 is as follows Figure 3 The underground scraper control device shown in any embodiment.
[0139] The sensor 42 is configured to collect operating status information of the underground loader 40 in real time and send the operating status information to the underground loader control device 41 .
[0140] By implementing the above-mentioned embodiments of the present disclosure, corresponding optimization parameters are selected according to the curvature of the driving path, and the optimal control increment of the underground shovel loader is obtained, thereby achieving high-precision and high-stability control of the underground shovel loader in complex underground operation scenarios.
[0141] In some embodiments, the functional units described above may be implemented as general-purpose processors, programmable logic controllers (PLCs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or any appropriate combination thereof, for performing the functions described in the present disclosure.
[0142] Those skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware, or by a program to instruct the relevant hardware, and the program may be stored in a computer-readable storage medium, which may be a read-only memory, a disk, or an optical disk, etc.
[0143] The description of the present disclosure is provided for purposes of illustration and description and is not intended to be exhaustive or to limit the disclosure to the disclosed form. Many modifications and variations will be apparent to those skilled in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present disclosure and to enable those skilled in the art to understand the present disclosure and design various embodiments with various modifications suitable for specific applications.
Claims
1. A method for controlling an underground scraper, executed by an underground scraper control device, comprising: Get the current running status information of the underground scraper; generating a prediction state equation of an error state variable according to the operating state information; determining a first optimization matrix and a second optimization matrix according to the curvature of the current driving path of the underground scraper; Inputting the first optimization matrix and the second optimization matrix into an objective function to obtain an optimal incremental value of the control increment in the prediction state equation at the next moment, wherein the objective function is constructed by the prediction state equation; The motion state of the underground scraper at the next moment is controlled according to the optimal increment value.
2. The method according to claim 1, wherein Determining the first optimization matrix and the second optimization matrix according to the curvature of the current driving path of the underground scraper includes: Determining a curvature subinterval in which the curvature is located within the curvature interval; The first parameter matrix of the curvature subinterval is used as the first optimization matrix, and the second parameter matrix of the curvature subinterval is used as the second optimization matrix.
3. The method according to claim 2, wherein: The curvature interval is divided into N curvature subintervals, wherein the lower limit value and the upper limit value of each curvature subinterval are both the weighted sum of a predetermined maximum curvature and a predetermined minimum curvature, and N is a natural number greater than 1.
4. The method according to claim 3, wherein: When calculating the lower limit of the i-th curvature subinterval, the weight of the predetermined maximum curvature is , the weight of the predetermined minimum curvature is , ; When calculating the upper limit of the i-th curvature subinterval, the weight of the predetermined maximum curvature is , the weight of the predetermined minimum curvature is .
5. The method according to claim 1, wherein The control increment includes a longitudinal speed increment and an articulated chain steering angle increment of the underground scraper.
6. The method according to claim 1, wherein The objective function is determined by the difference between the error state variable and a predetermined desired state variable, the first optimization matrix, the second optimization matrix, and a control variable.
7. The method according to claim 6, wherein: The objective function for: in, is the error state variable, is the predetermined desired state variable, is the first optimization matrix, For the second optimization matrix, is the control quantity, Represents a transpose operation.
8. The method according to claim 6, wherein: The control variables include the longitudinal speed and the articulated chain steering angle of the underground scraper.
9. The method according to claim 1, wherein The error state variables include a horizontal displacement error of the underground loader in a first direction, a vertical displacement error in a second direction perpendicular to the first direction, and a heading angle error.
10. The method according to claim 1, wherein The acquisition of the current operating status information of the underground scraper includes: Obtain the lateral speed, longitudinal speed, lateral displacement, longitudinal displacement, heading angle, and articulated chain steering angle of the underground LHD at the current moment.
11. The method according to any one of claims 1 to 10, wherein The predicted state equation is: in, is the prediction dimension parameter, is the error state variable, is the control increment, is the first state matrix, is the second state matrix.
12. The method according to claim 11, wherein The first state matrix is determined by the longitudinal speed of the underground scraper, the steering angle of the centerline point of the vehicle head, and time parameters; The second state matrix is determined by the longitudinal speed, the steering angle of the centerline point of the vehicle head, the time parameter, the steering angle of the articulated chain, and the distance from the rear vehicle body tire to the articulated chain.
13. The method according to claim 12, wherein: The first state matrix for: in, for: in, is the longitudinal speed of the underground LHD, is the steering angle of the centerline point of the vehicle head, and T is the time parameter.
14. The method according to claim 13, wherein The second state matrix for: in, for: in, is the articulated chain steering angle, is the distance from the rear tire to the articulated chain.
15. An underground scraper control device, comprising: Memory; A processor is coupled to the memory, and the processor is configured to execute the method according to any one of claims 1 to 14 based on instructions stored in the memory.
16. An underground scraper, comprising: The underground scraper control device according to claim 15; The sensor is configured to collect the operating status information of the underground scraper in real time and send the operating status information to the underground scraper control device.
17. A computer-readable storage medium, wherein: The computer-readable storage medium stores computer instructions, and when the instructions are executed by a processor, the method according to any one of claims 1 to 14 is implemented.
18. A computer program product comprising computer instructions, wherein when the computer instructions are executed by a processor, the method according to any one of claims 1 to 14 is implemented.