Self-adaptive method and system for transverse control parameters of unmanned mining truck

Abstract

The invention discloses an unmanned mining truck transverse control parameter self-adaption method and system, and belongs to the technical field of intelligent driving. The method comprises the steps of collecting vehicle dynamic data; inputting the dynamic data of the vehicle into a pre-constructed dynamic state equation of the vehicle, and carrying out online identification by adopting an extended Kalman filtering algorithm to obtain cornering stiffness information of the vehicle; constructing a vehicle transverse LQR controller according to the vehicle dynamic state equation; based on a vehicle transverse LQR controller, a controller parameter curve is fitted by adopting a least square method according to the vehicle dynamic data and the cornering stiffness information, and self-adaptive controller parameters of the vehicle are obtained; according to the dynamic data of the vehicle, the cornering stiffness information and the parameters of the self-adaptive controller, the front wheel turning angle of the vehicle is controlled through an LQR controller; according to the method, the unmanned mining truck transverse controller can adaptively adjust the control parameters according to different vehicle dynamic data, and the accuracy and stability of vehicle transverse control are improved.

Description

Technical Field

[0001] The invention relates to a method and system for self-adapting lateral control parameters of an unmanned mining truck, belonging to the technical field of intelligent driving. Background Art

[0002] Mining Truck is the main equipment for large-scale open-pit mining operations. It is responsible for transporting mineral materials and earthwork, and transporting all kinds of mined mineral materials to crushing stations and dumping sites. Mining trucks have the advantages of large load capacity and high efficiency, which can effectively improve the efficiency of mining operations. At the same time, the professional and high-reliability design of mining trucks can meet the requirements of complex and harsh mining road environments and adapt to the smooth transportation needs in multiple scenarios.

[0003] Due to the harsh working environment in mining areas, 24-hour all-weather operations are required, and there are many pits and high mining intensity, mining areas are prone to safety accidents and serious safety hazards. The application of unmanned driving technology can reduce the number of mining workers, improve the efficiency and safety of mining transportation, thereby reducing personnel costs and accident rates, and improving the economic and social benefits of enterprises.

[0004] The present invention aims at unmanned mining truck transportation operations. In order to achieve accurate trajectory tracking control when driving at high speed, it is necessary to accurately identify the parameters of the vehicle dynamics model according to the vehicle state information and adaptively adjust the control parameters. An unmanned mining truck lateral control parameter adaptive system and method are proposed. Through vehicle dynamic data collection, dynamic model construction, lateral stiffness online identification and controller parameter adaptation, it provides strong support for the accurate trajectory tracking control of unmanned mining trucks.

[0005] Patent 202210344574.5 discloses a parameter identification method for a motion model of an autonomous driving vehicle. The method determines the state equation of the motion model of the autonomous driving vehicle; constructs an identification system based on the state equation; performs offline parameter identification equation analysis based on the identification system equation, and performs online recursive parameter identification equation group analysis; identifies a preset parameter identification mode; in offline identification mode, uses the first offline identification equation for offline parameter identification, and updates the identification system equation; in online identification mode, uses the first online recursive identification equation group for online parameter identification, and updates the identification system equation. Through the present invention, the problem of not updating model parameters in conventional optimization estimation can be solved, and both offline and online parameter identification processing methods are provided for autonomous driving vehicles.

[0006] Patent 202211071272.1 discloses an online adaptive method and system for LQR lateral controller parameters. The method sets conditions that will affect the control effect of the controller; uses the normalized control error as an evaluation function, and uses an improved genetic algorithm to optimize the LQR lateral controller parameters under the condition; uses the optimized LQR lateral controller parameters as a data set for training a neural network; and uses the trained neural network in the LQR controller to achieve online adaptive LQR lateral controller parameters under different working conditions.

[0007] Patent 201910864594.3 discloses a parameter-adaptive lateral motion LQR control method for an autonomous vehicle. The method collects the target path information, vehicle position information, and vehicle status information required by the lateral motion control system in real time; processes and converts the collected data; determines the controller parameters in the current state according to the LQR control parameter adjustment strategy based on the path tracking error and the vehicle-road position relationship based on the collected data information; calculates the steering control amount of the autonomous vehicle based on the determined controller parameters and passes it to the steering actuator for execution. This invention is based on the autonomous driving lateral LQR controller, and adds an LQR controller parameter calculation adjustment strategy based on the path tracking error and the vehicle-road position relationship, so as to improve the path tracking accuracy and the controller adaptability.

[0008] 202210344574.5 is a vehicle dynamics parameter identification method based on a two-wheel dynamics model. The method is characterized by rewriting the conventional dynamics model and the tire side slip model based on the road curvature. is the observed quantity, steering wheel angle is the state equation of the state quantity; the continuous transfer function is converted into a discrete transfer function through z-transformation, and the equation group with the minimum mean square error as the objective function is constructed to calculate the value of the identification parameter matrix. Different identification equation groups and processing procedures are set for offline parameter identification and online parameter identification scenarios. This method constructs a specific dynamic state equation, and the identification parameters are particular, so the method has poor universality.

[0009] 202211071272.1 is an adaptive method for LQR control parameters based on vehicle kinematic model. The method is characterized in that the optimal control parameters of the LQR controller under different lateral errors and heading errors are calculated by adaptive genetic algorithm; the neural network is trained by the neural network training unit using the optimized LQR lateral controller parameters; finally, the trained neural network is used in the LQR controller to achieve online parameter adaptation under different working conditions. This method adopts the vehicle kinematic model. When the vehicle is driving at high speed, the control accuracy of the kinematic model is insufficient; based on the neural network training model, the data demand is large.

[0010] 201910864594.3 is a LQR control parameter adaptive method based on vehicle dynamics model. The method is characterized by adjusting the Q and R matrices of the LQR controller according to the path tracking error, wherein the parameter gain is set by the lateral error and the heading angle error respectively, and the Q matrix is ​​adjusted. , The value of the parameter is obtained, and then the feedback control amount of the steering angle is calculated based on the dynamic model and the LQR algorithm. This method constructs an LQR control parameter adaptive method based on path tracking error, in which the setting basis and calculation method of the adjustment parameter gain are unclear. Summary of the invention

[0011] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a method and system for self-adapting lateral control parameters of unmanned mining trucks. In view of the problem that the accuracy and stability of fixed parameter control of the controller are poor when the driving speed, loading materials and mass fluctuate during the transportation operation of the unmanned mining truck, the vehicle dynamic data acquisition module, the dynamic model construction module, the lateral stiffness online identification module and the controller parameter self-adaptation module are used to realize the self-adapting control parameters of the unmanned mining truck lateral controller according to different vehicle dynamic data, thereby improving the accuracy and stability of the vehicle lateral control.

[0012] In order to achieve the above purpose / solve the above technical problems, the present invention is implemented by adopting the following technical solutions:

[0013] A first aspect: a method for adaptively controlling lateral control parameters of an unmanned mining truck, the method comprising:

[0014] Collect vehicle dynamic data;

[0015] The vehicle dynamic data is input into the pre-built vehicle dynamics state equation, and the extended Kalman filter algorithm is used to obtain the vehicle's cornering stiffness information online;

[0016] Construct the vehicle lateral LQR controller based on the vehicle dynamics state equation;

[0017] Based on the vehicle lateral LQR controller, the controller parameter curve is fitted by the least square method according to the vehicle dynamic data and cornering stiffness information to obtain the vehicle's adaptive controller parameters;

[0018] The vehicle front wheel steering angle is controlled by the vehicle lateral LQR controller based on vehicle dynamic data, cornering stiffness information and adaptive controller parameters.

[0019] Optionally, the vehicle dynamic data includes vehicle load information, vehicle lateral acceleration information and vehicle yaw rate information, and the specific collection method includes:

[0020] According to the load sensors installed on each tire, the load information on each tire of the vehicle is obtained, which are , , and ;

[0021] The lateral acceleration of the vehicle is obtained based on the inertial measurement unit installed on the vehicle body and the vehicle yaw rate information;

[0022] in, is the load information on the left front wheel of the vehicle, is the load information on the right front wheel of the vehicle, is the load information on the left rear wheel of the vehicle, The load information on the right rear wheel of the vehicle;

[0023] Vehicle quality It can be expressed as:

[0024] .

[0025] Optionally, the method for constructing the vehicle dynamics state equation includes:

[0026] Based on the y-axis of the vehicle coordinate system, Newton's second law equation, The yaw dynamics equation of the axis moment balance is constructed as follows:

[0027]

[0028] In the formula, is the vehicle lateral speed, is the vehicle front wheel cornering stiffness, is the vehicle rear wheel cornering stiffness, For around The vehicle moment of inertia about the axle, is the distance from the center of the front axle to the center of mass of the vehicle, The distance from the center of the vehicle's rear axle to the center of mass, is the longitudinal velocity at the vehicle's center of mass, : Vehicle heading angle; : The front wheel turning angle of the vehicle;

[0029] In the vehicle dynamics state equation, according to the vehicle load information and vehicle mass , vehicle lateral acceleration and the vehicle yaw rate Calculate the centroid position ( , ) and vehicle rotational torque , and will be input into the vehicle dynamics state equation as a fixed parameter.

[0030] Optionally, the calculation of the centroid position ( , ) methods include:

[0031] Based on the bicycle model, the two front wheels and two rear wheels of the vehicle are assumed to be a wheel at the center of the two axles, and the masses of the front and rear wheels are:

[0032]

[0033]

[0034] In the formula, : The mass of the vehicle at the front wheel; : The mass of the vehicle at the rear wheel;

[0035] Combined with the measured vehicle wheelbase value, the distance between the vehicle's center of gravity and the center of the vehicle's front axle and rear axle can be calculated:

[0036]

[0037]

[0038] based on , And the vehicle positioning information is used to calculate the centroid position;

[0039] In the formula, is the distance between the center of mass of the vehicle and the center of the front axle, is the distance between the center of mass of the vehicle and the center of the rear axle, is the vehicle wheelbase.

[0040] Optionally, the vehicle rotation torque The calculation formula is:

[0041] .

[0042] Optionally, the step of inputting the vehicle dynamic data into a pre-constructed vehicle dynamics state equation and using an extended Kalman filter algorithm to online identify the vehicle's cornering stiffness information includes:

[0043] Discretize the vehicle dynamics state equation and establish the state equation and measurement equation of the extended Kalman filter:

[0044]

[0045]

[0046] In the formula, for The state quantity at the moment, here ; for Observable quantity at a moment; is the process noise; is the system observation noise, process noise and system observation noise are represented as mutually independent Gaussian white noise;

[0047] The parameters to be identified in the vehicle dynamics state equation Set as a state variable, Expand to , where the state variable is the front wheel cornering stiffness , rear wheel cornering stiffness ;

[0048] Calculate the predicted state and the forecast error covariance , calculate the Kalman gain , update state quantity and the posterior covariance ;

[0049] Continuous iteration and update to achieve vehicle front wheel lateral stiffness , rear wheel cornering stiffness Online identification of the cornering stiffness information obtained

[0050] Optionally, the vehicle lateral LQR controller is based on the least square method to fit the controller parameter curve according to the vehicle dynamic data and the cornering stiffness information to obtain the vehicle's adaptive controller parameters, including:

[0051] The cost function and state equation of the vehicle lateral LQR controller under discrete system are expressed as:

[0052]

[0053]

[0054] In the formula, is the state cost matrix, is the control cost matrix, the state variable Including lateral position error , lateral velocity error , yaw angle error , yaw rate error , control variables Including front wheel angle , the control parameters in the vehicle lateral LQR controller are: , ;

[0055] According to the importance of the control parameters corresponding to the variables, in multiple groups of different quality ,speed In the calibration experiment, , Set to a fixed value, for , , Carry out calibration;

[0056] When obtaining multiple sets of different vehicle masses ,speed Under the experiment , , After the data is obtained, the least square method is used to fit the three-dimensional parameter curve of each control parameter to obtain the adaptive controller parameters.

[0057] A second aspect: A lateral control parameter adaptive system for an unmanned mining truck, the system comprising:

[0058] Data acquisition module, used to collect vehicle dynamic data;

[0059] The cornering stiffness online identification module is used to input the vehicle dynamic data into the pre-built vehicle dynamics state equation and use the extended Kalman filter algorithm to obtain the vehicle's cornering stiffness information online;

[0060] A dynamic model building module is used to build a vehicle lateral LQR controller based on the vehicle dynamics state equation;

[0061] A controller parameter calculation module is used to obtain the vehicle's adaptive controller parameters by fitting the controller parameter curve using the least square method based on the vehicle's lateral LQR controller and according to the vehicle's dynamic data and cornering stiffness information;

[0062] The controller parameter adaptive module is used to control the front wheel steering angle of the vehicle through the LQR controller according to the vehicle dynamic data, the cornering stiffness information and the adaptive controller parameters.

[0063] A third aspect: A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for adaptively adapting lateral control parameters of an unmanned mining truck as described in the first aspect.

[0064] A fourth aspect: a device comprising:

[0065] A memory for storing instructions;

[0066] The processor is used to execute the instructions so that the device performs operations to implement the method for adaptively controlling lateral control parameters of the unmanned mining truck described in the first aspect.

[0067] Compared with the prior art, the present invention has the following beneficial effects:

[0068] The present invention optimizes the relationship between the accuracy and complexity of the balance model by constructing a vehicle dynamics model based on a single vehicle model, and adapts to the requirements of existing vehicles and working conditions;

[0069] The present invention can obtain accurate model parameters under different vehicle states through online identification of vehicle dynamic equation parameters;

[0070] The present invention obtains controller parameters under different vehicle masses and speeds through controller parameter curve fitting, which can adapt to changes in loading mass and transportation speed in mine transportation scenarios and realize precise control of unmanned mining trucks. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 Shown is a flow chart of an embodiment of the method of the present invention;

[0072] Figure 2 Shown is a diagram of an embodiment of an unmanned mining truck of the present invention;

[0073] Figure 3 Shown is a schematic diagram of a vehicle dynamics single-vehicle model of the present invention;

[0074] Figure 4 Shown is the extended Kalman filter flow chart of the present invention. DETAILED DESCRIPTION

[0075] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0076] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and the like are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", and the like may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0077] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood by specific circumstances.

[0078] Embodiment 1: The purpose of the present invention is to solve the problem of poor accuracy and stability of fixed parameter control of controller when the driving speed, loading materials and mass fluctuate during unmanned mining truck transportation operations. By means of a vehicle dynamic data acquisition module, a dynamic model building module, a lateral stiffness online identification module and a controller parameter adaptive module, the lateral controller of the unmanned mining truck can adaptively adjust the control parameters according to different vehicle dynamic data, thereby improving the accuracy and stability of the lateral control of the vehicle. A method for adaptive lateral control parameters of an unmanned mining truck is provided, and the method comprises:

[0079] Step 1, collecting vehicle dynamic data;

[0080] Step 2, inputting the vehicle dynamic data into the pre-constructed vehicle dynamic state equation, and using the extended Kalman filter algorithm to obtain the vehicle's lateral stiffness information online;

[0081] Step 3, constructing a vehicle lateral LQR controller according to the vehicle dynamics state equation;

[0082] Step 4, based on the vehicle lateral LQR controller, the controller parameter curve is fitted by the least square method according to the vehicle dynamic data and the cornering stiffness information to obtain the vehicle's adaptive controller parameters;

[0083] Step 5, controlling the front wheel steering angle of the vehicle through the vehicle lateral LQR controller according to the vehicle dynamic data, cornering stiffness information and adaptive controller parameters.

[0084] In the specific implementation process of this embodiment, for step 1, the vehicle dynamic data includes vehicle load information, vehicle lateral acceleration information and vehicle yaw rate information, and the specific collection method includes:

[0085] Step S11: Reference Figure 2 , according to the load sensors installed on each tire, the load information on each tire of the vehicle is obtained, which are , , and ;

[0086] The lateral acceleration of the vehicle is obtained based on the inertial measurement unit installed on the vehicle body and the vehicle yaw rate information;

[0087] in, is the load information on the left front wheel of the vehicle, is the load information on the right front wheel of the vehicle, is the load information on the left rear wheel of the vehicle, The load information on the right rear wheel of the vehicle;

[0088] Vehicle quality It can be expressed as:

[0089] .

[0090] In the specific implementation process of this embodiment, for step 2, the method for constructing the vehicle dynamics state equation includes:

[0091] Step S21, based on the Newton's second law equation of the vehicle body coordinate system y axis, The yaw dynamics equation of the axis moment balance is constructed as follows:

[0092]

[0093] In the formula, is the vehicle lateral speed, is the vehicle front wheel cornering stiffness, is the vehicle rear wheel cornering stiffness, For around The vehicle moment of inertia about the axle, is the distance from the center of the front axle to the center of mass of the vehicle, The distance from the center of the vehicle's rear axle to the center of mass, is the longitudinal velocity at the vehicle's center of mass, : Vehicle heading angle; : The front wheel turning angle of the vehicle;

[0094] In the vehicle dynamics state equation, according to the vehicle load information and vehicle mass , vehicle lateral acceleration and the vehicle yaw rate Calculate the centroid position ( , ) and vehicle rotational torque , and will be input into the vehicle dynamics state equation as a fixed parameter.

[0095] Step S211, the calculation of the centroid position ( , ) methods include:

[0096] Based on the bicycle model, the two front wheels and two rear wheels of the vehicle are assumed to be a wheel at the center of the two axles, and the masses of the front and rear wheels are:

[0097]

[0098]

[0099] In the formula, : The mass of the vehicle at the front wheel; : The mass of the vehicle at the rear wheel;

[0100] Combined with the measured vehicle wheelbase value, the distance between the vehicle's center of gravity and the center of the vehicle's front axle and rear axle can be calculated:

[0101]

[0102]

[0103] based on , The center of mass position is calculated based on the vehicle positioning information; the center of mass position calculation output here is not the xy coordinate, but the distance from the center of mass to the center of the vehicle's front axle. , the distance from the center of mass to the center of the rear axle .based on , The center of mass position can be calculated by the vehicle positioning information, but in our model and subsequent calculation process, it is not necessary to calculate the xy coordinates of the center of mass. , In the formula, is the distance between the center of mass of the vehicle and the center of the front axle, is the distance between the center of mass of the vehicle and the center of the rear axle, is the vehicle wheelbase.

[0104] Step S212: The moment of inertia of the vehicle is approximately equal to two point masses connected by a massless rod, so the vehicle rotation moment The calculation formula is:

[0105] .

[0106] In the specific implementation process of this embodiment, with respect to step S3, the vehicle dynamic data is input into the pre-constructed vehicle dynamic state equation, and the extended Kalman filter algorithm is used to online identify the vehicle's cornering stiffness information, including:

[0107] Step S31: Discretize the vehicle dynamics state equation in step S2 to establish the state equation and measurement equation of the extended Kalman filter:

[0108]

[0109]

[0110] In the formula, for The state quantity at the moment, here ; for Observable quantity at a moment; is the process noise; is the system observation noise, process noise and system observation noise are represented as mutually independent Gaussian white noise.

[0111] The parameters to be identified in the vehicle dynamics state equation Set as a state variable, Expand to , where the state variable is the front wheel cornering stiffness , rear wheel cornering stiffness ;

[0112] Calculate the predicted state and the forecast error covariance , calculate the Kalman gain , update state quantity and the posterior covariance ;

[0113] Continuous iteration and update to achieve vehicle front wheel lateral stiffness , rear wheel cornering stiffness Online identification of the cornering stiffness information obtained

[0114] In the specific implementation process of this embodiment, for step S4, the vehicle lateral LQR controller uses the least square method to fit the controller parameter curve according to the vehicle dynamic data and the cornering stiffness information to obtain the vehicle's adaptive controller parameters, including:

[0115] The LQR cost function and state equation for discrete systems are expressed as:

[0116]

[0117]

[0118] In the formula, is the state cost matrix, is the control cost matrix, the state variable Including lateral position error , lateral velocity error , yaw angle error , yaw rate error , control variables Including front wheel angle , the control parameters in the vehicle lateral LQR controller are: , ;

[0119] According to the importance of the control parameters corresponding to the variables, in multiple groups of different quality ,speed In the calibration experiment, , Set to a fixed value, for , , Carry out calibration;

[0120] When obtaining multiple sets of different vehicle masses ,speed Under the experiment , , After the data is obtained, the least square method is used to fit the three-dimensional parameter curve of each control parameter to obtain the adaptive controller parameters.

[0121] Embodiment 2: A lateral control parameter adaptive system for unmanned mining trucks, the system comprising:

[0122] Data acquisition module, used to collect vehicle dynamic data;

[0123] The cornering stiffness online identification module is used to input the vehicle dynamic data into the pre-built vehicle dynamics state equation and use the extended Kalman filter algorithm to obtain the vehicle's cornering stiffness information online;

[0124] A dynamic model building module is used to build a vehicle lateral LQR controller based on the vehicle dynamics state equation;

[0125] A controller parameter calculation module is used to obtain the vehicle's adaptive controller parameters by fitting the controller parameter curve using the least square method based on the vehicle's lateral LQR controller and according to the vehicle's dynamic data and cornering stiffness information;

[0126] The controller parameter adaptive module is used to control the front wheel steering angle of the vehicle through the LQR controller according to the vehicle dynamic data, the cornering stiffness information and the adaptive controller parameters.

[0127] Embodiment 3: A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the method for adaptively controlling lateral control parameters of an unmanned mining truck as described in Embodiment 1.

[0128] Embodiment 4: A device comprising:

[0129] A memory for storing instructions;

[0130] The processor is used to execute the instructions so that the device performs the operations of implementing the method for adaptively controlling lateral control parameters of the unmanned mining truck described in Example 1.

[0131] The present invention optimizes the relationship between the accuracy and complexity of the balance model by constructing a vehicle dynamics model based on a single vehicle model, and adapts to the requirements of existing vehicles and working conditions;

[0132] The present invention can obtain accurate model parameters under different vehicle states through online identification of vehicle dynamic equation parameters;

[0133] The present invention obtains controller parameters under different vehicle masses and speeds through controller parameter curve fitting, which can adapt to changes in loading mass and transportation speed in mine transportation scenarios and realize precise control of unmanned mining trucks.

[0134] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0135] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0136] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0137] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0138] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for adaptively controlling lateral control parameters of an unmanned mining truck, characterized in that: The method comprises: Collect vehicle dynamic data; The vehicle dynamic data is input into the pre-built vehicle dynamics state equation, and the extended Kalman filter algorithm is used to obtain the vehicle's cornering stiffness information online; Construct the vehicle lateral LQR controller based on the vehicle dynamics state equation; Based on the vehicle lateral LQR controller, the controller parameter curve is fitted by the least square method according to the vehicle dynamic data and cornering stiffness information to obtain the vehicle's adaptive controller parameters; The vehicle front wheel steering angle is controlled by the vehicle lateral LQR controller based on vehicle dynamic data, cornering stiffness information and adaptive controller parameters.

2. The method for adaptively controlling lateral control parameters of an unmanned mining truck according to claim 1, characterized in that: The vehicle dynamic data includes vehicle load information, vehicle lateral acceleration information and vehicle yaw rate information, and the specific collection method includes: According to the load sensors installed on each tire, the load information on each tire of the vehicle is obtained, which are , , and ; The lateral acceleration of the vehicle is obtained based on the inertial measurement unit installed on the vehicle body and the vehicle yaw rate information; in, is the load information on the left front wheel of the vehicle, is the load information on the right front wheel of the vehicle, is the load information on the left rear wheel of the vehicle, The load information on the right rear wheel of the vehicle; Vehicle quality It can be expressed as: 。 3. The method for adaptively controlling lateral control parameters of an unmanned mining truck according to claim 2, characterized in that: The method for constructing the vehicle dynamics state equation includes: Based on the y-axis of the vehicle coordinate system, Newton's second law equation, The yaw dynamics equation of the axis moment balance is constructed as follows: ； In the formula, is the vehicle lateral speed, is the vehicle front wheel cornering stiffness, is the vehicle rear wheel cornering stiffness, For around The vehicle moment of inertia about the axle, is the distance from the center of the front axle to the center of mass of the vehicle, The distance from the center of the vehicle's rear axle to the center of mass, is the longitudinal velocity at the vehicle's center of mass, : Vehicle heading angle; : The front wheel turning angle of the vehicle; In the vehicle dynamics state equation, according to the vehicle load information and vehicle mass , vehicle lateral acceleration and the vehicle yaw rate Calculate the centroid position ( , ) and vehicle rotation torque , and will be input into the vehicle dynamics state equation as a fixed parameter.

4. The method for adaptively controlling lateral control parameters of an unmanned mining truck according to claim 3, characterized in that: The calculation of the centroid position ( , ) methods include: Based on the bicycle model, the two front wheels and two rear wheels of the vehicle are assumed to be a wheel at the center of the two axles, and the masses of the front and rear wheels are: ， ； In the formula, : The mass of the vehicle at the front wheel; : The mass of the vehicle at the rear wheel; Combined with the measured vehicle wheelbase value, the distance between the vehicle's center of gravity and the center of the vehicle's front axle and rear axle can be calculated: ， ； based on , And the vehicle positioning information is used to calculate the centroid position; In the formula, is the distance between the center of mass of the vehicle and the center of the front axle, is the distance between the center of mass of the vehicle and the center of the rear axle, is the vehicle wheelbase.

5. The method for adaptively controlling lateral control parameters of unmanned mining trucks according to claim 3 is characterized in that: The vehicle rotation torque The calculation formula is: 。 6. The method for adaptively controlling lateral control parameters of an unmanned mining truck according to claim 3, characterized in that: The vehicle dynamic data is input into a pre-constructed vehicle dynamics state equation, and the extended Kalman filter algorithm is used to online identify the vehicle's cornering stiffness information, including: Discretize the vehicle dynamics state equation and establish the state equation and measurement equation of the extended Kalman filter: ， ； In the formula, for The state quantity at the moment, here ; for Observable quantity at a moment; is the process noise; is the system observation noise, process noise and system observation noise are represented as mutually independent Gaussian white noise; The parameters to be identified in the vehicle dynamics state equation Set as a state variable, Expand to , where the state variable is the front wheel cornering stiffness , rear wheel cornering stiffness ; Calculate the predicted state and the forecast error covariance , calculate the Kalman gain , update state quantity and the posterior covariance ; Continuous iteration and update to achieve vehicle front wheel lateral stiffness , rear wheel cornering stiffness Online identification of the cornering stiffness information obtained 7. The method for adaptively controlling lateral control parameters of an unmanned mining truck according to claim 1, characterized in that: The vehicle lateral LQR controller is based on the least square method to fit the controller parameter curve according to the vehicle dynamic data and the cornering stiffness information to obtain the vehicle's adaptive controller parameters, including: The cost function and state equation of the vehicle lateral LQR controller under discrete system are expressed as: ， ； In the formula, is the state cost matrix, is the control cost matrix, the state variable Including lateral position error , lateral velocity error , yaw angle error , yaw rate error , control variables Including front wheel angle , the control parameters in the vehicle lateral LQR controller are: , ; According to the importance of the control parameters corresponding to the variables, in multiple groups of different quality ,speed In the calibration experiment, , Set to a fixed value, for , , Carry out calibration; When obtaining multiple sets of different vehicle masses ,speed Under the experiment , , After the data is obtained, the least square method is used to fit the three-dimensional parameter curve of each control parameter to obtain the adaptive controller parameters.

8. An adaptive system for lateral control parameters of unmanned mining trucks, characterized in that: The system comprises: Data acquisition module, used to collect vehicle dynamic data; The cornering stiffness online identification module is used to input the vehicle dynamic data into the pre-built vehicle dynamics state equation and use the extended Kalman filter algorithm to obtain the vehicle's cornering stiffness information online; A dynamic model building module is used to build a vehicle lateral LQR controller based on the vehicle dynamics state equation; A controller parameter calculation module is used to obtain the vehicle's adaptive controller parameters by fitting the controller parameter curve using the least square method based on the vehicle's lateral LQR controller and according to the vehicle's dynamic data and cornering stiffness information; The controller parameter adaptive module is used to control the front wheel steering angle of the vehicle through the LQR controller according to the vehicle dynamic data, the cornering stiffness information and the adaptive controller parameters.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for adaptively controlling lateral control parameters of an unmanned mining truck as described in any one of claims 1 to 7 is implemented.

10. A device, characterized in that: include: A memory for storing instructions; A processor is used to execute the instruction so that the device performs the operation of implementing the method for adaptively controlling lateral control parameters of an unmanned mining truck as described in any one of claims 1 to 7.

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