Mining articulated vehicle path tracking method and system based on linear model prediction

Abstract

The invention belongs to the technical field of path tracking, particularly relates to a mining articulated vehicle path tracking method and system based on linear model prediction, and aims to solve the problem that an existing control scheme is difficult to consider accuracy and real-time performance at the same time. The method comprises the following steps: acquiring a kinematic model of the mining articulated vehicle; performing simplification processing on the kinematic model; performing solidification processing on the nominal axle distance, performing piecewise linearization processing on the hinge angle, further simplifying to obtain a linearized state quantity model, and performing discretization processing on the linearized state quantity model to obtain a prediction model; constructing an initial function according to the prediction model and the reference path point column under the local coordinate system, performing standard quadratic transformation on the initial function to obtain a target function, and determining the optimal control input of the mining articulated vehicle at the current moment by solving the target function; and controlling the mining articulated vehicle according to the optimal control input. According to the method, both accuracy and real-time performance can be considered, and the path tracking effect is effectively improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of path tracking, and particularly relates to a mine articulated vehicle path tracking method and system based on linear model prediction. BACKGROUND

[0002] The mine articulated vehicle adopts a front and rear vehicle body active knee folding steering form, which can effectively reduce the minimum turning radius, but the motion characteristics are relatively complex, resulting in high difficulty in path tracking control. Model predictive control (MPC) as an advanced control strategy has gradually emerged in the field of path tracking control of mine articulated vehicles in recent years.

[0003] Among various MPC methods, linear MPC (LMPC) has the advantage of high calculation efficiency, can quickly respond to control requirements, and is suitable for scenarios with high real-time requirements. However, most existing LMPCs use error model linearization methods or Jacobian model linearization methods, which are difficult to fully utilize detailed information of the reference path in front of the mine articulated vehicle, and thus have poor accuracy in complex path tracking tasks. Although LMPC based on nonlinear compensation Jacobian linearization can introduce multiple preview points in front of the vehicle, the model error generated in the linearization process is not easy to obtain, and nonlinear model iteration needs to be implemented in the controller, and a large amount of data transmission is obtained through iteration, so the real-time performance is poor. Therefore, the existing LMPC has the problem of not being able to balance accuracy and real-time performance, that is, the traditional LMPC has poor accuracy, and the LMPC based on nonlinear compensation has poor real-time performance. SUMMARY

[0004] In order to solve the above problems in the prior art, that is, the existing path control scheme based on linear model predictive control cannot balance accuracy and real-time performance when applied to mine articulated vehicles, affecting the application effect, in a first aspect, the application provides a mine articulated vehicle path tracking method based on linear model prediction, the method comprising:

[0005] obtaining a kinematic model of the mine articulated vehicle;

[0006] based on the physical law that the motion of the mine articulated vehicle relative to the current vehicle body coordinate system in the prediction time domain conforms to the small angle assumption, simplifying the kinematic model to obtain a preliminary simplified model;

[0007] based on the physical law that the articulation angle of the mine articulated vehicle changes within a limited range in the prediction time domain, performing solidification processing on the nominal wheelbase in the prediction time domain, performing piecewise linearization processing on the articulation angle in the prediction time domain, and further simplifying the preliminary simplified model to obtain a linear state quantity model of the mine articulated vehicle;

[0008] discretize the linearized state quantity model to obtain a prediction model based on the linearized state quantity model, an initial value of the prediction model being [0 0 γ] T , γ being a hinged angle;

[0009] construct an initial function according to the prediction model and a reference path point series of the mining articulated vehicle in a local coordinate system, and perform a quadratic form conversion on the initial function to obtain a target function, and determine an optimal control input of the mining articulated vehicle at a current time by solving the target function, the reference path point series of the mining articulated vehicle in the local coordinate system being obtained by converting a reference path point series corresponding to the global coordinate system to the local coordinate system after selecting a reference path point series corresponding to a prediction time domain on a preset reference path based on a reference path reference point of the mining articulated vehicle in the global coordinate system;

[0010] control the mining articulated vehicle according to the optimal control input.

[0011] In some preferred embodiments, the physical law that the motion of the mining articulated vehicle relative to the current vehicle body coordinate system conforms to a small angle assumption is used to simplify the kinematics model, including:

[0012] The kinematics model is simplified based on the small angle assumption, and the preliminary simplified model after the simplification satisfies: ;

[0013] In the formula, is a transverse coordinate change rate of a front axle center of the mining articulated vehicle, is a longitudinal coordinate change rate of the front axle center of the mining articulated vehicle, and v is a driving speed of the mining articulated vehicle, is a heading angle of the mining articulated vehicle, is a heading angle change rate of the mining articulated vehicle, is a distance between a front axle and a hinged point of the mining articulated vehicle, is a distance between a rear axle and the hinged point of the mining articulated vehicle, is a hinged angle, is a hinged angle change rate, and ω is a hinged angle speed.

[0014] In some preferred embodiments, the physical law that the hinged angle of the mining articulated vehicle has a limited change range in the prediction time domain is used to perform a solidification processing on a nominal wheelbase in the prediction time domain, perform a piecewise linearization processing on the hinged angle in the prediction time domain, and further simplify the preliminary simplified model, including:

[0015] determine a solidified nominal wheelbase, the solidified nominal wheelbase satisfying: ;

[0016] wherein m is a constant value corresponding to the nominal wheelbase, is the distance from the front axle to the hinge point of the mining articulated vehicle, is the distance from the rear axle to the hinge point of the mining articulated vehicle, and γ is the hinge angle;

[0017] According to the nominal wheelbase, the hinge angle in the prediction horizon is processed by piecewise linearization, and the preliminary simplified model is further simplified, and the simplified preliminary simplified model satisfies: ;

[0018] wherein m is the nominal wheelbase, and n is a proportional value;

[0019] The proportional value n is calculated according to the current hinge angle and the sine value of the mining articulated vehicle, and the calculation process of the proportional value n satisfies: .

[0020] In some preferred embodiments, the discretization processing of the linearized state quantity model comprises:

[0021] determining the vector form of the linearized state quantity model;

[0022] performing Euler method discretization processing on the linearized state quantity model based on the vector form of the linearized state quantity model, satisfying: ;

[0023] wherein is all the predicted states of the mining articulated vehicle in the prediction horizon, is a state transition matrix, is a discretized state transition matrix, is a discretized control input matrix, is a control input matrix, is a control sequence, t is a time, c is a control step number, and p is a prediction step number, is a state vector, the value of is [0 0 γ]T.

[0024] In some preferred embodiments, the vector form of the linearized state quantity model satisfies: ;

[0025] wherein is a state vector, is an input vector, is a state transition matrix, is a control input matrix, and y is the longitudinal coordinate of the center of the front axle of the mining articulated vehicle.

[0026] In some preferred embodiments, the reference path point list of the mining articulated vehicle in the preset local coordinate system is obtained by the following method:

[0027] A reference point is determined, which is the point closest to the front axle center of the mining articulated vehicle in the preset reference path;

[0028] Based on the reference point, a plurality of target points are determined according to the preset reference path to obtain the reference path point list of the mining articulated vehicle, which satisfies: ;

[0029] In the formula, is global reference path information, is a state vector of the reference path point in the global coordinate system, are respectively a horizontal coordinate, a vertical coordinate and a heading angle of the reference path point in the global coordinate system, is a time, is is a value corresponding to the i-th step prediction at the time, is a prediction step number;

[0030] The reference path point list is subjected to coordinate conversion to obtain the reference path point list of the mining articulated vehicle in the preset local coordinate system.

[0031] In some preferred embodiments, the coordinate conversion of the reference path point list satisfies: ;

[0032] In the formula, 、 are respectively a vertical coordinate and a heading angle of the reference path point in the local coordinate system, are respectively a horizontal coordinate, a vertical coordinate and a heading angle of the front axle center of the mining articulated vehicle in the global coordinate system at the time t.

[0033] In some preferred embodiments, the construction process of the target function includes:

[0034] An initial function is constructed, which satisfies: ;

[0035] In the formula, Q and R are preset weight matrices,

[0036] The initial function is subjected to quadratic transformation to obtain the target function, which satisfies: ;

[0037] In the formula, H and G are quadratic parameters, Ω is a dynamic matrix, and N(t) is a compensation term.

[0038] In some preferred embodiments, the determining the optimal control input of the mining articulated vehicle at the current time instant by solving the objective function comprises:

[0039] solving the objective function based on the prediction model to determine the input value sequence that minimizes the objective function;

[0040] determining the first input value in the input value sequence as the optimal control input of the mining articulated vehicle at the current time instant.

[0041] In a second aspect, the present application provides a mining articulated vehicle path tracking system based on linear model prediction, the system comprising:

[0042] a data acquisition module configured to acquire a kinematic model of the mining articulated vehicle;

[0043] a first simplification module configured to simplify the kinematic model based on a physical law that the movement of the mining articulated vehicle relative to a current vehicle body coordinate system in a prediction time domain conforms to a small angle assumption, to obtain a preliminary simplified model;

[0044] a second simplification module configured to simplify the preliminary simplified model based on a physical law that the articulation angle of the mining articulated vehicle has a limited variation range in the prediction time domain, by performing solidification processing on the nominal wheelbase in the prediction time domain, segmenting and linearizing the articulation angle in the prediction time domain, and further simplifying the preliminary simplified model, to obtain a linearized state quantity model of the mining articulated vehicle;

[0045] a model construction module configured to discretize the linearized state quantity model to obtain a prediction model based on the linearized state quantity model, the initial value of the prediction model being [0 0 γ]T, and γ being the articulation angle;

[0046] a constraint solving module configured to construct an initial function according to the prediction model and a reference path point sequence of the mining articulated vehicle in a local coordinate system, to perform a quadratic form transformation on the initial function to obtain an objective function, and to determine the optimal control input of the mining articulated vehicle at the current time instant by solving the objective function, the reference path point sequence of the mining articulated vehicle in the local coordinate system being obtained by converting the reference path point sequence corresponding to the global coordinate system to the local coordinate system based on the reference path reference point of the mining articulated vehicle in the global coordinate system, and selecting a reference path point sequence corresponding to the prediction time domain on the preset reference path;

[0047] a vehicle control module configured to control the mining articulated vehicle according to the optimal control input.

[0048] The present application has the following advantages:

[0049] Based on the method provided in the embodiment of the present application, by adopting a small-angle assumption model linearization method different from the general linear model predictive control, a linearization model of the articulated vehicle is constructed, then a prediction model based on the linearization model is constructed, by a reference path coordinate conversion method in a prediction time domain different from the general linear model predictive control method, a reference path point column in a local coordinate system is obtained, and an initial form similar to the nonlinear model predictive control is constructed, but finally a standard quadratic optimization objective function specific to the linear model predictive control can be converted, finally relative to the linear model predictive control, the nonlinear model predictive control and other control methods, the purpose of guaranteeing the path tracking control accuracy and real-time of the mine vehicle is realized. BRIEF DESCRIPTION OF DRAWINGS

[0050] Other features, objects and advantages of the present application will become more apparent from the following detailed description of non-limiting embodiments made with reference to the attached drawings:

[0051] Figure 1 is a flowchart of a mine articulated vehicle path tracking method based on linear model prediction provided in the embodiment of the present application;

[0052] Figure 2 is a structural schematic diagram of a mine articulated vehicle path tracking system based on linear model prediction provided in the embodiment of the present application;

[0053] Figure 3 is a structural schematic diagram of a computer system of a server for implementing the method and system embodiments of the present application. DETAILED DESCRIPTION

[0054] The present application will be further described in detail below with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the related application, and not to limit the application. In addition, it should be noted that, for the convenience of description, only the parts related to the application are shown in the drawings.

[0055] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0056] In order to more clearly explain the method provided in the present application, the following will be combined with Figure 1 The steps in the embodiment of the present application will be described in detail. Please refer to Figure 1 As shown in Figure 1 The first embodiment of the present application provides a mine articulated vehicle path tracking method based on linear model prediction, comprising:

[0057] Step S10, obtaining a kinematic model of the mine articulated vehicle;

[0058] Step S20, based on the physical law that the motion of the mining articulated vehicle relative to the current vehicle body coordinate system in the prediction time domain conforms to the small angle assumption, the kinematic model is simplified to obtain a preliminary simplified model;

[0059] Step S30, based on the physical law that the articulation angle of the mining articulated vehicle changes within a limited range in the prediction time domain, the nominal wheelbase in the prediction time domain is solidified, the articulation angle in the prediction time domain is linearized in segments, and the preliminary simplified model is further simplified to obtain a linearized state quantity model of the mining articulated vehicle;

[0060] Step S40, the linearized state quantity model is discretized to obtain a prediction model based on the linearized state quantity model, and the initial value of the prediction model is [0 0 γ] T , γ is the articulation angle;

[0061] Step S50, according to the prediction model and the reference path point list of the mining articulated vehicle in the local coordinate system, an initial function is constructed, and a target function is obtained by standard quadratic transformation of the initial function, and the optimal control input of the mining articulated vehicle at the current time is determined by solving the target function, and the reference path point list of the mining articulated vehicle in the local coordinate system is obtained by converting the reference path point list corresponding to the global coordinate system to the local coordinate system based on the reference path reference point of the mining articulated vehicle in the global coordinate system, and selecting the reference path point list corresponding to the prediction time domain on the preset reference path;

[0062] Step S60, according to the optimal control input, the mining articulated vehicle is controlled.

[0063] Those skilled in the art can understand that, since the running speed of the mining articulated vehicle is low and the prediction time domain of the controller is short, in the local coordinate system of the vehicle body, the initial value of the heading angle θ is 0 and the change range is very small, which fully meets the small angle assumption, and based on this, it can be assumed that in the vehicle body coordinate system, it satisfies: ;

[0064] Therefore, the kinematic model of the articulated vehicle can be first simplified as: ;

[0065] In the formula, is the change rate of the lateral coordinate of the center of the front axle of the mining articulated vehicle, is the change rate of the longitudinal coordinate of the center of the front axle of the mining articulated vehicle, and v is the driving speed of the mining articulated vehicle, is the heading angle of the mining articulated vehicle, is the change rate of the heading angle of the mining articulated vehicle, is the distance between the front axle and the articulation point of the mining articulated vehicle, The distance between the rear axle and the hinge point of the articulated vehicle, The articulation angle, The rate of change of the articulation angle, and ω is the articulation angle velocity.

[0066] For , considering that γ changes less in a shorter prediction horizon, The overall value also changes less, so it can be regarded as a constant value in each control cycle, and let:

[0067] where m is the nominal wheelbase.

[0068] For , the value changes greatly, and if the upper limit of the articulation angle of the partial articulated vehicle is 50°, it does not perfectly meet the small angle assumption, so the following assumption can be made: ;

[0069] where n is a proportionality constant, and its value is calculated from the current articulation angle and its sine value: ;

[0070] In this way, the articulated vehicle model can be simplified as: .

[0071] In some embodiments, for path tracking control, the velocity can be regarded as a constant, so the linearized model can be simplified by one dimension, and the final representation is: .

[0072] In some embodiments, the aforementioned linearized model can be abstracted as a vector form as follows: ;

[0073] where is the state vector, is the input vector, is the state transition matrix, is the control input matrix, and y is the longitudinal coordinate of the center of the front axle of the mine articulated vehicle.

[0074] Based on this, through Euler method discretization processing, all predicted states of the mine articulated vehicle in the prediction horizon can be obtained, and finally converted into a matrix form, which can be expressed as: ;

[0075] where is all predicted states of the mine articulated vehicle in the prediction horizon, is the state transition matrix, is the state transition matrix after discretization processing, is the control input matrix after discretization processing, is the control input matrix, and y is the longitudinal coordinate of the center of the front axle of the mine articulated vehicle. is the control sequence, t is the time, c is the control step number, p is the prediction step number, is the state vector, The value of is [0 0 γ]T.

[0076] In the above embodiment, the point closest to the front axle center of the mining articulated vehicle on the reference path of the mining articulated vehicle is selected as the reference reference point, and the specific selection process is not limited in the embodiment.

[0077] It is easy to understand that in the embodiment, the reference path in the prediction time domain is converted into the local coordinate system corresponding to the vehicle body. On the one hand, in the vehicle body coordinate system, the small angle assumption can be established. On the other hand, since the path tracking control technology belongs to the absolute navigation technology, the reference path is generally discrete point column information in the global coordinate system. Only the conversion of the reference path in the prediction time domain can ensure the real-time performance.

[0078] The steps specifically include: first, selecting a reference path reference point in the global coordinate system, i.e. the point on the reference path closest to the articulated vehicle; then selecting a reference path point column in the prediction time domain on the reference path in the global coordinate system according to the point and the equal interval point principle; and then converting the reference path point column in the prediction time domain into the time-varying local coordinate system.

[0079] On the basis of the reference point, points are taken forward from the reference path, the distance between each point should be equal to the product of the vehicle speed and the iteration period, a total of p points are taken to form a reference path point column:

[0080] In the formula, the subscript ref represents reference information, the subscript G represents information in the global coordinate system, and correspondingly, is the global reference path information, is the state vector of the reference path point in the global coordinate system, are the horizontal coordinate, the vertical coordinate and the heading angle of the reference path point in the global coordinate system respectively, is the time, is the value corresponding to the ith step prediction at the time, is the prediction step number;

[0081] Finally, the reference point column is converted in coordinates:

[0082] In the formula, , are the vertical coordinate and the heading angle of the reference path point in the local coordinate system respectively, are the horizontal coordinate, the vertical coordinate and the heading angle of the front axle center of the mining articulated vehicle in the global coordinate system at the time t.

[0083] ​​Finally, the reference path point series in the local coordinate system of the vehicle body can be obtained: ;

[0084] In this embodiment, the initial form of the optimization objective function (i.e., the initial function) is the same as the optimization objective function of a general nonlinear model predictive control: ;

[0085] In the formula, Q and R are weight matrices, is a state tracking error term, is a control input penalty term, is a vehicle state sequence in a prediction horizon (future p steps), is a target state sequence (future p steps) of the reference path in the local coordinate system;

[0086] The optimization objective function can be converted into a standard quadratic form specific to linear model predictive control: ;

[0087] In the formula, H and G are quadratic form parameters, is a dynamic matrix, and N(t) is a compensation term.

[0088] Under the preset constraint condition, the input value sequence that can minimize the optimization objective function is solved, wherein the first value is the optimal control input generated by the mine vehicle path tracking controller based on linear model predictive control, and based on the optimal control input, the control strategy of the mine articulated vehicle at the moment is determined, so as to realize the control process of the mine articulated vehicle.

[0089] Although the steps in the above embodiment are described in the above order, those skilled in the art can understand that, in order to achieve the effect of the embodiment, the different steps do not have to be executed in such an order, and they can be executed simultaneously (in parallel) or in a reversed order, and these simple changes are within the protection scope of the present application.

[0090] The second embodiment of the present application provides a path tracking system for articulated mine vehicle based on linear model prediction. The system can be a software module including a plurality of instructions stored in a memory accessible by a processor to invoke the instructions for execution to complete the path tracking method for articulated mine vehicle based on linear model prediction described in the above embodiments. In some embodiments, the path tracking system for articulated mine vehicle based on linear model prediction can also be built by hardware devices, for example, the path tracking system for articulated mine vehicle based on linear model prediction can be built by one or more chips, each chip can work with each other to complete the path tracking method for articulated mine vehicle based on linear model prediction described in the above embodiments. For another example, the path tracking system for articulated mine vehicle based on linear model prediction can also be built by various logic devices, such as general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), single-chip microcomputers, ARM (Acorn RISC Machine) or other programmable logic devices, discrete gates or transistor logic, discrete hardware components or any combination of these components.

[0091] Please refer to Figure 2 , Figure 2 The structure of the path tracking system for articulated mine vehicle based on linear model prediction is shown in the figure, which includes:

[0092] The data acquisition module 210 is configured to acquire the kinematic model of the articulated mine vehicle.

[0093] The first simplification module 220 is configured to simplify the kinematic model based on the physical law that the motion of the articulated mine vehicle relative to the current vehicle coordinate system in the prediction time domain conforms to the small-angle assumption, to obtain a preliminary simplified model.

[0094] The second simplification module 230 is configured to simplify the preliminary simplified model based on the physical law that the articulation angle of the articulated mine vehicle changes within a limited range in the prediction time domain, by solidifying the nominal wheelbase in the prediction time domain, segmenting and linearizing the articulation angle in the prediction time domain, and further simplifying the preliminary simplified model, to obtain a linearized state quantity model of the articulated mine vehicle.

[0095] The model construction module 240 is configured to discretize the linearized state quantity model to obtain a prediction model based on the linearized state quantity model, and the initial value of the prediction model is [0 0 γ] T , and γ is the articulation angle.

[0096] The constraint solving module 250 is configured to construct an initial function according to the prediction model and the reference path point series of the articulated mining vehicle in the local coordinate system, and perform standard quadratic transformation on the initial function to obtain a target function, and determine the optimal control input of the articulated mining vehicle at the current time by solving the target function, wherein the reference path point series of the articulated mining vehicle in the local coordinate system is obtained by converting the reference path point series corresponding to the global coordinate system to the local coordinate system, based on the reference path reference point of the articulated mining vehicle in the global coordinate system, and selecting the reference path point series corresponding to the prediction time domain on the preset reference path;

[0097] The vehicle control module 260 is configured to control the articulated mining vehicle according to the optimal control input.

[0098] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process and related description of the system described above can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0099] Reference will be made to the following description of the drawings Figure 3 which shows the structural schematic diagram of a computer system of a server for implementing the method and system embodiments of the present application. Figure 3 The server shown is merely an example, and should not bring any limitation to the functions and use range of the embodiments of the present application.

[0100] As shown in Figure 3 the computer system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 302 or programs loaded from a storage portion 308 to a random access memory (RAM) 303. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0101] The following components are connected to the I / O interface 305: an input section 306 including a keyboard, a mouse, etc.; an output section 307 including a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as necessary. A removable medium 311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is attached to the drive 310 as necessary, so that a computer program read out therefrom is installed in the storage section 308 as necessary.

[0102] In particular, the processes described above with reference to the flowcharts can be implemented as a computer software program according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer readable medium, the computer program comprising program code for performing the methods illustrated by the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via the communication section 309, and / or installed from the removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, the above-described functions defined in the methods of the present application are performed. It should be noted that the computer readable medium of the present application can be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium may, for example, be, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or apparatus or device, or any suitable combination of the foregoing. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, the computer readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, system, or apparatus. In the present application, the computer readable signal medium can include a data signal that is propagated in baseband or that is propagated as part of a carrier wave, in which the computer readable program code is carried. Such a propagated data signal can take a variety of forms, including but not limited to electro-magnetic, optical, or any suitable combination thereof. The computer readable signal medium can also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, system, or apparatus. Program code contained on a computer readable medium can be transmitted by any suitable medium, including but not limited to wireless, wireline, optical fiber, RF, etc., or any suitable combination of the foregoing.

[0103] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0104] The computer program instructions can also be loaded onto a computer or other programmable information processing apparatus to cause a series of operations to be performed on the computer or other programmable information processing apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable information processing apparatus implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0105] The terms "first", "second", etc. are used to distinguish between similar objects, and are not used to describe or indicate a particular order or sequence. The term "comprises" or any other similar term is intended to encompass the non-exclusive inclusion such that a process, method, article, or apparatus that comprises a list of elements does not necessarily include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0106] So far, the technical solutions of the present application have been described in combination with the preferred embodiments shown in the drawings, but those skilled in the art can easily understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to the related technical features without departing from the principles of the present application, and the technical solutions after the changes or replacements will all fall within the protection scope of the present application.

Claims

1. A mine articulated vehicle path tracking method based on linear model prediction, characterized in that, The method comprises: obtaining a kinematic model of a mining articulated vehicle; simplifying the kinematic model based on a physical law that the movement of the mining articulated vehicle relative to a current vehicle body coordinate system in a prediction time domain conforms to a small-angle assumption, to obtain a preliminary simplified model; based on a physical law that the articulation angle of the mining articulated vehicle has a limited change range in the prediction time domain, performing solidification processing on the nominal wheelbase in the prediction time domain, performing piecewise linearization processing on the articulation angle in the prediction time domain, and further simplifying the preliminary simplified model to obtain a linearized state quantity model of the mining articulated vehicle; Discretize the linearized state variable model to obtain a prediction model based on the linearized state variable model, an initial value of the prediction model being [0 0 γ] T , γ being a hinge angle; constructing an initial function according to the prediction model and a reference path point list of the mining articulated vehicle in a local coordinate system, and performing standard quadratic form conversion on the initial function to obtain a target function, and determining an optimal control input of the mining articulated vehicle at the current time by solving the target function, wherein the reference path point list of the mining articulated vehicle in the local coordinate system is obtained by converting the reference path point list corresponding to the global coordinate system to the local coordinate system after selecting the reference path point list corresponding to the prediction time domain on the preset reference path based on the reference path reference point of the mining articulated vehicle in the global coordinate system; controlling the mining articulated vehicle according to the optimal control input.

2. The path tracking method for a mining articulated vehicle based on linear model prediction according to claim 1, characterized in that, The simplification processing of the kinematic model based on the physical law that the movement of the mining articulated vehicle relative to the current vehicle body coordinate system conforms to the small-angle assumption comprises: simplifying the kinematic model based on the small-angle assumption, and the preliminary simplified model after the simplification processing satisfies: ； wherein is the rate of change of the lateral coordinate of the center of the front axle of the articulated mine vehicle, is the rate of change of the longitudinal coordinate of the center of the front axle of the articulated mine vehicle, v is the speed of travel of the articulated mine vehicle, is the heading angle of the articulated mine vehicle, is the rate of change of the heading angle of the articulated mine vehicle, is the distance between the front axle and the articulation point of the articulated mine vehicle, is the distance between the rear axle and the articulation point of the articulated mine vehicle, is the articulation angle, is the rate of change of the articulation angle, ω is the articulation angle velocity.

3. The path tracking method for articulated mining vehicles based on linear model prediction according to claim 1, characterized in that, The simplification processing of the kinematic model based on the physical law that the articulation angle of the mining articulated vehicle has a limited change range in the prediction time domain, performing solidification processing on the nominal wheelbase in the prediction time domain, performing piecewise linearization processing on the articulation angle in the prediction time domain, and further simplifying the preliminary simplified model comprises: determining the solidification processing nominal wheelbase, and the nominal wheelbase satisfies: ； In the formula, m is a constant value corresponding to a nominal wheelbase, is the distance from the front axle to the articulation point of the articulated vehicle, is the distance from the rear axle to the articulation point of the articulated vehicle, is the articulation angle; performing piecewise linearization processing on the articulation angle in the prediction time domain according to the nominal wheelbase, and further simplifying the preliminary simplified model, and the preliminary simplified model after the further simplification processing satisfies: ； wherein m is the nominal wheelbase, and n is a proportion value; the proportion value n is calculated from the current articulation angle and the sine value of the mining articulated vehicle, and the calculation process of the proportion value n satisfies: 。 4. The path tracking method of an articulated vehicle for mine based on linear model prediction according to claim 1, characterized in that, The discretization processing of the linearized state quantity model comprises: determining the vector form of the linearized state quantity model; performing Euler method discretization processing on the linearized state quantity model based on the vector form of the linearized state quantity model, and satisfying: ； wherein is the state transition matrix, is the state transition matrix, is the state transition matrix, is the state transition matrix, is the state transition matrix, is the state transition matrix, is the state transition matrix, is the state transition matrix, T .

5. The path tracking method for articulated mining vehicles based on linear model prediction according to claim 4, characterized in that, The vector form of the linearized state quantity model satisfies: ； wherein is the state vector, is the input vector, is the state transition matrix, is the control input matrix, and y is the longitudinal coordinate of the center of the front axle of the articulated mine vehicle.

6. The path tracking method of an articulated vehicle for mining based on linear model prediction according to claim 1, characterized in that, The reference path point list of the mining articulated vehicle in the preset local coordinate system is obtained by: determining a reference point, which is the point closest to the front axle center of the mining articulated vehicle in the preset reference path; determining a plurality of target points based on the reference point according to the preset reference path to obtain the reference path point list of the mining articulated vehicle, and satisfying: ； In the formula, is the global reference path information, is the state vector of the reference path point in the global coordinate system, is the horizontal coordinate, the vertical coordinate and the heading angle of the reference path point in the global coordinate system respectively, is the time, is the is the value corresponding to the i-th step prediction at the time, is the prediction step number; performing coordinate conversion on the reference path point list to obtain the reference path point list of the mining articulated vehicle in the preset local coordinate system.

7. The path tracking method of an articulated vehicle for mining based on linear model prediction according to claim 6, characterized in that, The coordinate conversion on the reference path point list meets: ； In the formula, , are the longitudinal coordinate and the heading angle of the reference path point in the local coordinate system, respectively, are the transverse coordinate, the longitudinal coordinate and the heading angle of the center of the front axle of the mining articulated vehicle in the global coordinate system at time t, respectively.

8. The path tracking method of an articulated vehicle for mining based on linear model prediction according to claim 1, characterized in that, The construction process of the target function includes: The initial function is constructed and meets: ； In the formula, Q and R are preset weight matrices; The initial function is subjected to quadratic form conversion to obtain the target function, and the target function meets: ； In the formula, H, G are quadratic form parameters, is a dynamic matrix, and N(t) is a compensation term.

9. The path tracking method of an articulated vehicle for mining based on linear model prediction according to claim 1, characterized in that, The optimal control input of the mining articulated vehicle at the current time is determined by solving the target function, and the optimal control input includes: Based on the prediction model, the target function is solved to determine the input value sequence that minimizes the target function; The first input value in the input value sequence is determined as the optimal control input of the mining articulated vehicle at the current time.

10. A mine articulated vehicle path tracking system based on linear model prediction, characterized by, The system includes: A data acquisition module is configured to acquire a kinematic model of a mining articulated vehicle. A first simplification module is configured to simplify the kinematic model based on a physical law that the movement of the mining articulated vehicle relative to a current vehicle body coordinate system in a prediction time domain meets a small-angle assumption, to obtain a preliminary simplified model. A second simplification module is configured to simplify the preliminary simplified model based on a physical law that the articulation angle of the mining articulated vehicle has a limited variation range in the prediction time domain, by fixing the nominal wheelbase in the prediction time domain, segmenting and linearizing the articulation angle in the prediction time domain, and further simplifying the preliminary simplified model, to obtain a linearized state quantity model of the mining articulated vehicle. A model construction module is configured to discretize the linearized state quantity model to obtain a prediction model based on the linearized state quantity model, wherein an initial value of the prediction model is [0 0 γ] T , and γ is a hinge angle. A constraint solving module is configured to construct an initial function according to the prediction model and a reference path point list of the mining articulated vehicle in a local coordinate system, to perform standard quadratic form conversion on the initial function to obtain a target function, to determine the optimal control input of the mining articulated vehicle at the current time by solving the target function, and to obtain the reference path point list of the mining articulated vehicle in the local coordinate system based on a reference path reference point of the mining articulated vehicle in a global coordinate system, by selecting a reference path point list corresponding to the prediction time domain on a preset reference path, and by converting the reference path point list corresponding to the global coordinate system to the local coordinate system. A vehicle control module is configured to control the mining articulated vehicle according to the optimal control input.

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