Tracked vehicle sideslip state estimation method, equipment, medium and product

Abstract

The invention discloses a tracked vehicle sideslip state estimation method and device, a medium and a product, and relates to the technical field of tracked vehicle intelligent driving, and the method comprises the steps: obtaining vehicle test data; establishing a track empirical model, and performing model calibration on empirical parameters of the track empirical model based on the vehicle test data; establishing a three-degree-of-freedom tracked vehicle dynamic model based on the track empirical model; respectively establishing a state transition equation and a measurement equation of a state estimation system and a parameter estimation system in a discrete form based on a three-degree-of-freedom tracked vehicle dynamic model; estimating driving state parameters of the vehicle by using a dual-extended Kalman filtering algorithm to obtain a state estimation value and a parameter estimation value of the vehicle at each moment; and calculating the sideslip state estimation of the vehicle based on the state estimation value and the parameter estimation value of the vehicle at each moment. According to the invention, the precision of sideslip angle estimation can be improved, so that the driving stability and the driving safety can be improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent driving technology for tracked vehicles, and in particular to a method, device, medium and product for estimating the sideslip state of a tracked vehicle. Background Art

[0002] The intelligence of tracked vehicles can improve the ability of special vehicles to operate sustainably and withstand harsh environments. The key to intelligence is to achieve accurate and safe driving control by determining various driving state variables. Although with the development of sensing and testing technology, the vehicle motion state can be measured by various sensors, the sensors are expensive and require specific installation and fixing methods. In addition, in off-road environments where GPS signals are missing, the inertial navigation commonly used by tracked vehicles has cumulative errors and sensor noise problems. Therefore, it is necessary to use state estimation methods to accurately calculate the driving state. In vehicle stability control, severe side slip can cause the vehicle to stall or even roll over, so it is necessary to estimate the side slip angle.

[0003] Kalman filtering is a model-based state estimation method that combines the state transition information of the model with the sensor measurement information for state estimation. It is widely used in linear systems with Gaussian noise characteristics. However, most actual systems are nonlinear, so the extended Kalman filter is derived. The nonlinear function is linearized at the current state point using Taylor expansion, and the Jacobian matrix is ​​solved to determine the system matrix and the observation matrix, and then the prediction covariance matrix and Kalman gain are obtained. This improvement improves the state estimation effect of nonlinear systems, but the tracked vehicle dynamics model parameters used for state estimation are usually not constants. If the model is regarded as a nonlinear steady-state system, the accuracy of the sideslip angle estimation will be affected, which will in turn affect the driving stability control and driving safety. Summary of the invention

[0004] The purpose of the present invention is to provide a method, device, medium and product for estimating the sideslip state of a tracked vehicle, which can improve the accuracy of sideslip angle estimation and further improve driving stability and driving safety.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] In a first aspect, the present invention provides a method for estimating a sideslip state of a tracked vehicle, the method comprising:

[0007] Acquire vehicle test data; the vehicle test data includes: inherent parameters of the tracked vehicle, vehicle motion state and control input under typical driving conditions; the vehicle motion state includes: longitudinal and lateral acceleration and yaw angular velocity of the vehicle; the control input includes the driving torque and rotation speed of the driving wheels on both sides.

[0008] A track empirical model is established, and based on the vehicle test data, the empirical parameters of the track empirical model are calibrated; the track empirical model is a model that describes the longitudinal force, lateral force and steering resistance torque on the tracks on both sides.

[0009] Based on the track empirical model, a three-degree-of-freedom tracked vehicle dynamics model is established; the three-degree-of-freedom tracked vehicle dynamics model is a model established based on the movement of the tracked vehicle in three directions: longitudinal, lateral and yaw on horizontal ground.

[0010] Based on the three-degree-of-freedom tracked vehicle dynamics model, the state transfer equation and the measurement equation of the state estimation system and the parameter estimation system in discrete form are established respectively.

[0011] Based on the state transfer equation and measurement equation of the discrete state estimation system and parameter estimation system, the driving state parameters of the vehicle are estimated using a dual extended Kalman filter (DEKF) algorithm to obtain the state estimation value and parameter estimation value of the vehicle at each moment.

[0012] Based on the estimated values ​​of the vehicle state at each moment and the estimated values ​​of the parameters, an estimated side slip state of the vehicle is calculated.

[0013] In a second aspect, the present invention provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described methods for estimating the sideslip state of a tracked vehicle.

[0014] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described methods for estimating the sideslip state of a tracked vehicle.

[0015] In a fourth aspect, the present invention provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-mentioned methods for estimating the sideslip state of a tracked vehicle.

[0016] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0017] The present invention provides a method, device, medium and product for estimating the side slip state of a tracked vehicle. By establishing a track empirical model describing the longitudinal force, lateral force and steering resistance torque on the tracks on both sides, the empirical parameters of the track empirical model are calibrated, and a three-degree-of-freedom tracked vehicle dynamic model based on the movement of the tracked vehicle in the longitudinal, lateral and yaw directions on the horizontal ground is established; the state transfer equation and measurement equation of the state estimation system and parameter estimation system in discrete form are obtained; the vehicle driving state parameters are estimated using a double extended Kalman filter algorithm to obtain the state estimation value and parameter estimation value of the vehicle at each moment; based on the state estimation value and the parameter estimation value of the vehicle at each moment, the vehicle side slip state estimation is calculated. The present invention can be applied to the steering stability control of tracked vehicles, and has the advantages of high estimation accuracy, good real-time performance and good robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 This is a diagram of the application environment of a method for estimating the sideslip state of a tracked vehicle in one embodiment of the present invention.

[0020] Figure 2 A schematic flow chart of a method for estimating the sideslip state of a tracked vehicle provided in one embodiment of the present invention.

[0021] Figure 3 A schematic diagram of the fitting results of the crawler empirical formula provided by an embodiment of the present invention.

[0022] Figure 4 A principle block diagram of a dual extended Kalman filter algorithm provided in one embodiment of the present invention.

[0023] Figure 5 A schematic diagram of test results of a method for estimating the sideslip state of a tracked vehicle provided in one embodiment of the present invention.

[0024] Figure 6 A schematic diagram of the structure of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0027] The track model is the main nonlinear source of the track vehicle dynamics system. Since it is necessary to consider the slip rate of multiple track plates, the integration of the tangential and normal components of the contact force, the ground pressure is concentrated under the road wheel, and the centrifugal force will affect the ground pressure distribution during acceleration, braking and steering. Therefore, the high-precision track model established is relatively complex and highly nonlinear, which is not conducive to real-time state estimation. Therefore, it is necessary to simplify the track model to improve the efficiency of state estimation.

[0028] The method for estimating the sideslip state of a tracked vehicle provided by the embodiment of the present invention can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the acquired vehicle test data to the server 104, and the vehicle test data include: inherent parameters of the tracked vehicle, vehicle motion state under typical driving conditions, and control input; after the server 104 receives the vehicle test data, the server 104 establishes a track experience model for the vehicle test data, and based on the vehicle test data, performs model calibration on the experience parameters of the track experience model; the track experience model is a model that describes the longitudinal force, lateral force and steering resistance torque on the tracks on both sides; based on the track experience model, a three-degree-of-freedom tracked vehicle dynamics model is established; the three-degree-of-freedom tracked vehicle The dynamic model is a model established based on the movement of the tracked vehicle in the longitudinal, lateral and yaw directions on the horizontal ground; based on the three-degree-of-freedom tracked vehicle dynamic model, the state transfer equations and measurement equations of the discrete state estimation system and parameter estimation system are established respectively; based on the state transfer equations and measurement equations of the discrete state estimation system and parameter estimation system, the vehicle driving state parameters are estimated using the double extended Kalman filter algorithm to obtain the state estimation value and parameter estimation value of the vehicle at each moment; based on the state estimation value and parameter estimation value of the vehicle at each moment, the vehicle side slip state estimation is calculated. The server 104 can feed back the obtained vehicle side slip state estimation to the terminal 102. In addition, in some embodiments, the tracked vehicle side slip state estimation method can also be implemented by the server 104 or the terminal 102 alone, such as the terminal 102 can directly perform side slip state estimation on the acquired vehicle test data, or the server 104 can obtain the vehicle test data from the data storage system and perform side slip state estimation on the vehicle test data.

[0029] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, and tablet computers. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0030] In an exemplary embodiment, Figure 2 As shown, a method for estimating the side slip state of a tracked vehicle is provided. The method is executed by a computer device, and specifically can be executed by a computer device such as a terminal or a server alone, or can be executed by a terminal and a server together. In an embodiment of the present invention, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate the method, which includes the following steps S1 to S6. Among them:

[0031] S1: Acquire vehicle test data; the vehicle test data includes: inherent parameters of the tracked vehicle, vehicle motion state under typical driving conditions, and control input.

[0032] S2: Establishing an empirical track model, and calibrating the empirical parameters of the empirical track model based on the vehicle test data; the empirical track model is a model that describes the longitudinal force, lateral force and steering resistance torque on the tracks on both sides.

[0033] S3: Based on the track empirical model, a three-degree-of-freedom tracked vehicle dynamics model is established; the three-degree-of-freedom tracked vehicle dynamics model is a model established based on the movement of the tracked vehicle in three directions: longitudinal, lateral and yaw on horizontal ground.

[0034] S4: Based on the three-degree-of-freedom tracked vehicle dynamics model, the state transfer equation and the measurement equation of the discrete state estimation system and the parameter estimation system are established respectively.

[0035] S5: Based on the state transfer equation and measurement equation of the discrete state estimation system and parameter estimation system, the vehicle driving state parameters are estimated using a double extended Kalman filter algorithm to obtain the state estimation value and parameter estimation value of the vehicle at each moment.

[0036] S6: Based on the estimated values ​​of the vehicle state at each moment and the estimated values ​​of the parameters, the estimated sideslip state of the vehicle is calculated.

[0037] Implementing the above steps S1 to S6 can reduce the computational complexity and improve the efficiency of state estimation. At the same time, the driving state and parameters are estimated based on the double extended Kalman filter algorithm. The parameter estimation and state estimation are coupled with each other, which improves the applicability of the model and the accuracy of state estimation.

[0038] In an exemplary embodiment, in step S1, it specifically includes:

[0039] S11: Obtain design values ​​of inherent parameters of the tracked vehicle.

[0040] S12: Use an accelerometer to obtain the longitudinal and lateral accelerations of the vehicle.

[0041] S13: Using a gyroscope to obtain the yaw angular velocity of the vehicle when turning.

[0042] S14: Use torque and speed sensors to obtain the driving torque and speed of the driving wheels on both sides.

[0043] In step S2, when establishing the track empirical model, the ground forces and moments on both sides of the track are only related to the current motion state of the vehicle, and i=1,2 represents the left and right tracks respectively. Under different longitudinal vehicle speeds and yaw angular velocities, the high-precision track force simulation model is used to calculate the longitudinal force, lateral force and steering resistance moment on both sides of the track, and the following magic formula is used to fit the results:

[0044]

[0045] Fzi=μF N iZ 1 sin{Ez 2 arctan[Ez 3 Szi-Ez 4 (Ez 3 Szi-arctan(Ez 3 Szi))]}(2);

[0046]

[0047] Among them, F xi is the fitting result of the longitudinal force acting on the tracks on both sides by the ground, F yi is the fitting result of the lateral force acting on the tracks on both sides by the ground, T ri is the fitting result of the steering resistance torque on the tracks on both sides, F zi is the ground normal reaction force fitting result, F Ni is the track ground pressure, S xi is the track slip rate, S zi is the comprehensive slip coefficient, β is the sideslip angle, B is the track center distance, ω is the yaw angular velocity, v x is the longitudinal velocity, μ is the friction coefficient between the track and the ground, L is the track contact length, v c is the vehicle center of mass velocity, v spi is the speed of the track relative to the vehicle body, E z1 、E z2 、E z3 、E z4 、E r1 、E r2 、E r3 and E r4 are empirical parameters respectively. The empirical parameters are obtained by calibrating the model using the obtained vehicle test data. The model calibration uses the vehicle's motion state and the driving wheel drive torque signal, and uses the particle swarm optimization algorithm for calibration. The driving wheel speed is used as the model input, and the prediction error of the yaw angular velocity and the driving wheel torque is used as the evaluation index of the model accuracy.

[0048] Since the vehicle test data has high accuracy but covers a small range of vehicle speed and yaw rate, a high-precision track force model is first built to calculate the longitudinal force, lateral force and steering resistance torque on both sides of the track at different longitudinal speeds and yaw rates to provide sufficient simulation data, and then the track empirical formula is used to fit the simulation calculation results. The results are as follows: Figure 3 shown.

[0049] In step S3, a three-degree-of-freedom tracked vehicle dynamics model is first established to describe the movement of the tracked vehicle in the longitudinal, lateral and yaw directions on the horizontal ground. The nonlinear tracked vehicle dynamics model is established as follows:

[0050]

[0051] Where m is the vehicle weight, I z is the turning inertia of the vehicle, B is the track center distance, v x is the longitudinal velocity, v y is the lateral velocity, ω is the yaw angular velocity, F x1 and F x2 are the longitudinal forces acting on the tracks on both sides, F yi is the fitting result of the lateral force acting on the tracks on both sides by the ground, T ri is the fitting result of the steering resistance torque on both sides of the track, g is the acceleration of gravity, f r is the ground resistance coefficient, F N1 and F N2 are the ground pressures of the tracks on both sides respectively.

[0052] Then, according to the requirements of nonlinear state space and double extended Kalman filtering, the state transfer equation and measurement equation of the discrete state estimation system are established as follows:

[0053] In the state transfer equation and measurement equation of the state estimation system, for each variable of the double extended Kalman filter, the superscript s indicates that it is related to state estimation, and the superscript p indicates that it is related to parameter estimation.

[0054]

[0055] in, is the state variable at the current moment, is the state variable at the next moment, is the control input at the current moment, is the parameter variable at the current moment, and the time-varying parameters are designed to be the ground resistance coefficient and the track-ground friction coefficient, that is, is the output variable of the state estimation system at the next moment, f(·) is the state transfer function, h(·) is the measurement function, is the process noise of the state estimation system, is the noise covariance matrix of the state estimation process at the current moment, is the measurement noise of the state estimation system, is the measurement noise covariance matrix, and δ is the sampling time interval.

[0056] The expressions of the state transfer equation and the measurement equation of the parameter estimation system are:

[0057]

[0058] in, is the parameter variable at the current moment, is the parameter variable at the next moment, is the state variable at the current moment, is the control input at the current moment, is the output variable of the parameter estimation system at the next moment, h(·) is the measurement function, is the process noise of the parameter estimation system, is the noise covariance matrix of the parameter estimation process at the current moment, is the measurement noise of the parameter estimation system, is the measurement noise covariance matrix.

[0059] Based on the above three-degree-of-freedom tracked vehicle dynamics model, the expansion state F is introduced. y The vehicle model is approximated as a nonlinear dynamic model with four state variables as shown in the following equation. The model state variables are defined as X s =[v x ,v y ,ω,F y ] T , the control input is u s =[F x1 ,F x2 ] T , the output variable is Y s =[a x ,a y ,ω,ω sp1 ,ω sp2 ] T , where a x ,a y are the longitudinal and lateral accelerations of the vehicle, ω is the yaw rate of the vehicle, ω sp1 ,ω sp2 is the speed of the left and right driving wheels; ignoring the influence of the rotational inertia of the crawler travel device, the longitudinal force F x1 ,Fx2 According to the driving wheel torque, F xi =T spi / r z Get, where T spi is the torque of the driving wheels on both sides, r z is the radius of the driving wheel; the output variable is measured by the accelerometer, gyroscope and speed sensor, k y >0, is related to the lateral force F y Constants related to dynamic response.

[0060] Therefore, the state transfer function is:

[0061]

[0062] According to the vehicle dynamics model, the measurement function is obtained as follows:

[0063]

[0064] The track slip rate is calculated as: When the slip rate is not large, the track longitudinal force is further approximated as F according to the track empirical model. xi =μF Ni E z1 E z2 E z3 S xi , and then get

[0065] In an exemplary embodiment, in step S5, it specifically includes:

[0066] S51: Parameter prediction: input the parameter estimation value at the current moment and the state covariance matrix estimation value of the parameter estimation system into the state transfer equation of the parameter estimation system, and calculate the parameter prediction value at the next moment and the state covariance matrix prediction value of the parameter estimation system.

[0067] S52: State prediction: The state estimation value at the current moment, the state covariance matrix estimation value of the state estimation system, the control input at the current moment and the parameter prediction value at the next moment are input into the state transfer equation of the state estimation system, and the state prediction value at the next moment and the state covariance matrix prediction value of the state estimation system are calculated.

[0068] S53: State correction: input the state prediction value at the next moment, the state covariance matrix prediction value of the state estimation system and the parameter prediction value at the next moment into the measurement equation of the state estimation system to obtain the state estimation value at the next moment; the measurement equation of the state estimation system is an equation obtained based on the state space equation of the state estimation system in discrete form, the state transfer function of the tracked vehicle steering and the measurement function.

[0069] S54: Parameter correction: input the parameter prediction value at the next moment, the state covariance matrix prediction value of the parameter estimation system and the state prediction value at the next moment into the measurement equation of the parameter estimation system to obtain the parameter estimation value at the next moment; the measurement equation of the parameter estimation system is an equation obtained based on the state space equation of the parameter estimation system in discrete form, the state transfer function of the tracked vehicle steering and the measurement function.

[0070] S55: Determine whether the next moment is the predicted moment and obtain the determination result.

[0071] S56: If the judgment result is yes, the state estimation value and parameter estimation value of the vehicle at each moment are obtained.

[0072] S57: If the judgment result is no, the next moment is taken as the current moment and the process returns to step S51.

[0073] In this embodiment, a double extended Kalman filter is used to couple the system state estimation and the model parameter estimation, and the numerical transfer process can be divided into four links: parameter prediction, state prediction, parameter correction and state correction. In each time step, the double extended Kalman filter first uses the prior estimation results of the model parameters to support the prior estimation results of the state, and then uses the prior estimation results of the system state to calculate the output variable, compare it with the measurement signal, and use it to correct the state and model parameters. Specifically including:

[0074] A1: Based on the state space equations of the discrete form of the state estimation system and the parameter estimation system, the state transfer function and measurement function of the tracked vehicle steering, the measurement equations of the state estimation system and the parameter estimation system are obtained.

[0075] A2: Parameter prediction: The parameter estimate at time k And the state covariance matrix estimate of the parameter estimation system Input into the state transfer equation of the parameter estimation system to calculate the parameter prediction value at time k+1 and the state covariance matrix of the parameter estimation system predicted value

[0076]

[0077] A3: State prediction: The estimated state value at time k State covariance matrix estimate of the state estimation system and the system control input variables at time k and the parameter prediction values ​​of the parameter estimation system Input into the state transfer equation of the state estimation system to calculate the state prediction value at time k+1 And the state covariance matrix prediction value of the state estimation system

[0078]

[0079] Among them, A k is the first Jacobian matrix, f k is the state transition value at the current moment.

[0080] A4: State correction: The state prediction value of the state estimation system at time k+1 is converted to State covariance matrix prediction value and the parameter prediction value at time k+1 Input into the measurement equation of the state estimation system to obtain the state estimation value at time k+1

[0081]

[0082] in, is the Kalman gain of the state estimation system at time k+1, h(·) is the measurement function, is the second Jacobian matrix and I is the identity matrix.

[0083] A5: Parameter correction: The parameter prediction value of the parameter estimation system at time k+1 is State covariance matrix prediction value And the state prediction value at time k+1 Input into the measurement equation of the parameter estimation system to obtain the parameter estimation value at time k+1

[0084]

[0085] in, is the Kalman gain of the parameter estimation system at time k+1, h(·) is the measurement function, is the third Jacobian matrix and I is the identity matrix.

[0086] A6: Repeat steps A2 to A5 to obtain the state estimation value and parameter estimation value of the vehicle at each time, and then calculate the tracked vehicle side slip state estimation based on the double extended Kalman filter. In each time step, the tracked vehicle side slip angle β at the current time is calculated by the state estimation value of the longitudinal speed and the lateral speed at the current time, and the formula is as follows:

[0087]

[0088] In steps A2 to A5, the Jacobian matrix A used in the state transfer equation and measurement equation of the state estimation system is k and Calculated by the following formula:

[0089]

[0090] Among them, h k is the measurement value at the current moment.

[0091] Jacobian matrix used in the measurement equation of the parameter estimation system Calculated by the following formula:

[0092]

[0093] The principle block diagram of the double extended Kalman filter algorithm is as follows Figure 4 As shown. Specifically, the double extended Kalman filter is used to couple the system state estimation and model parameter estimation of the tracked vehicle. The numerical transfer process can be divided into four steps: parameter prediction, state prediction, parameter correction and state correction. In one time step, the double extended Kalman filter first uses the prior estimation results of the model parameters Prior estimation results of the unsupported state Then, the output variable is calculated using the prior estimation result of the system state and compared with the measured signal Compare and use to correct the status and model parameters

[0094] Figure 5 is a test result of a tracked vehicle sideslip state estimation method in this embodiment, specifically: state estimation result (yaw angular velocity and sideslip angle), parameter estimation result (track-ground friction coefficient), by Figure 5 It can be seen that the tracked vehicle sideslip state estimation method based on double extended Kalman filtering has high estimation accuracy and good applicability.

[0095] In summary, the present invention establishes a track empirical model based on the magic formula, reduces the computational complexity, and improves the efficiency of state estimation. At the same time, based on the double extended Kalman filter algorithm (driving state and parameters are estimated, and parameter estimation and state estimation are coupled with each other, the model applicability and the accuracy of state estimation are improved.

[0096] The present invention also provides an application scenario, which applies the above-mentioned tracked vehicle sideslip state estimation method. Specifically: the tracked vehicle sideslip state estimation method provided in this embodiment can be applied in the tracked vehicle intelligent driving scenario. The tracked vehicle intelligent driving scenario includes: data acquisition link, tracked experience model establishment link, three-degree-of-freedom tracked vehicle dynamics model establishment link, state transfer equation and measurement equation establishment link, driving state parameter estimation link and sideslip state estimation link; based on the acquired vehicle test data, the empirical parameters of the established tracked experience model are calibrated; based on the tracked experience model, a three-degree-of-freedom tracked vehicle dynamics model is established; based on the three-degree-of-freedom tracked vehicle dynamics model, the state transfer equation and measurement equation of the discrete state estimation system and parameter estimation system are respectively established; based on the state transfer equation and measurement equation of the discrete state estimation system and parameter estimation system, the vehicle driving state parameters are estimated using the double extended Kalman filter algorithm to obtain the state estimation value and parameter estimation value of the vehicle at each moment; based on the state estimation value and the parameter estimation value of the vehicle at each moment, the vehicle sideslip state estimation can be calculated.

[0097] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 6 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store vehicle test data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for estimating the sideslip state of a tracked vehicle is implemented.

[0098] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0099] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the above-mentioned method embodiments are implemented when the processor executes the computer program.

[0100] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the above-mentioned method embodiments are implemented.

[0101] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the above-mentioned method embodiments are implemented.

[0102] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the present invention are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0103] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0104] The database involved in each embodiment provided by the present invention may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided by the present invention may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0105] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0106] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for estimating the sideslip state of a tracked vehicle, characterized in that: The tracked vehicle sideslip state estimation method comprises: Acquire vehicle test data; the vehicle test data includes: inherent parameters of the tracked vehicle, vehicle motion state and control input under typical driving conditions; the vehicle motion state includes: longitudinal and lateral acceleration and yaw rate of the vehicle; the control input includes the driving torque and speed of the driving wheels on both sides; Establishing a track empirical model, and calibrating the empirical parameters of the track empirical model based on the vehicle test data; the track empirical model is a model that describes the longitudinal force, lateral force and steering resistance torque on the tracks on both sides; Based on the track empirical model, a three-degree-of-freedom track vehicle dynamics model is established; the three-degree-of-freedom track vehicle dynamics model is a model established based on the movement of the track vehicle in three directions: longitudinal, lateral and yaw directions on a horizontal ground; Based on the three-degree-of-freedom tracked vehicle dynamics model, a state transfer equation and a measurement equation of a discrete state estimation system and a parameter estimation system are established respectively; Based on the state transfer equation and measurement equation of the discrete state estimation system and parameter estimation system, a double extended Kalman filter algorithm is used to estimate the driving state parameters of the vehicle to obtain the state estimation value and parameter estimation value of the vehicle at each moment; Based on the estimated values ​​of the vehicle state at each moment and the estimated values ​​of the parameters, the estimated state of the vehicle sideslip is calculated.

2. The method for estimating the sideslip state of a tracked vehicle according to claim 1, characterized in that: Obtain vehicle test data, including: Obtaining design values ​​of inherent parameters of tracked vehicles; An accelerometer is used to obtain the longitudinal and lateral acceleration of the vehicle; A gyroscope is used to obtain the yaw rate of the vehicle when it turns; Torque and speed sensors are used to obtain the driving torque and speed of the driving wheels on both sides.

3. The method for estimating the sideslip state of a tracked vehicle according to claim 1, characterized in that: The expression of the crawler empirical model is: F zi =μF Ni AND z1 sin{And z2 arctan[E z3 S zi -AND z4 (AND z3 S zi -arctan(E z3 S zi ))]}; Among them, F xi is the fitting result of the longitudinal force acting on the tracks on both sides by the ground, F yi is the fitting result of the lateral force acting on the tracks on both sides, T ri is the fitting result of the steering resistance torque on the tracks on both sides, F zi is the ground normal reaction force fitting result, F Ni is the track ground pressure, S xi is the track slip rate, S zi is the comprehensive slip coefficient, β is the sideslip angle, B is the track center distance, ω is the yaw angular velocity, v x is the longitudinal velocity, μ is the track-ground friction coefficient, L is the track contact length, v c is the vehicle center of mass velocity, v spi is the speed of the track relative to the vehicle body, E z1 、E z2 、E z3 、E z4 、E r1 、E r2 、E r3 and E r4 are empirical parameters respectively.

4. The method for estimating the sideslip state of a tracked vehicle according to claim 1, characterized in that: The expression of the three-degree-of-freedom tracked vehicle dynamics model is: Where m is the vehicle weight, I z is the turning inertia of the vehicle, B is the track center distance, v x is the longitudinal velocity, v y is the lateral velocity, ω is the yaw angular velocity, F x1 and F x2 are the longitudinal forces acting on the tracks on both sides, F yi is the lateral force of the ground acting on the tracks on both sides, T ri is the steering resistance torque on both sides of the track, g is the acceleration of gravity, f r is the ground resistance coefficient, F N1 and F N2 are the ground pressures of the tracks on both sides respectively.

5. The method for estimating the sideslip state of a tracked vehicle according to claim 1, characterized in that: The expressions of the state transfer equation and the measurement equation of the state estimation system are: in, is the state variable at the current moment, is the state variable at the next moment, is the control input at the current moment, is the parameter variable at the current moment, is the output variable of the state estimation system at the next moment, f(·) is the state transfer function, h(·) is the measurement function, is the process noise of the state estimation system, is the noise covariance matrix of the state estimation process at the current moment, is the measurement noise of the state estimation system, is the measurement noise covariance matrix, and δ is the sampling time interval.

6. The method for estimating the sideslip state of a tracked vehicle according to claim 1, characterized in that: The expressions of the state transfer equation and the measurement equation of the parameter estimation system are: in, is the parameter variable at the current moment, is the parameter variable at the next moment, is the state variable at the current moment, is the control input at the current moment, is the output variable of the parameter estimation system at the next moment, h(·) is the measurement function, is the process noise of the parameter estimation system, is the noise covariance matrix of the parameter estimation process at the current moment, is the measurement noise of the parameter estimation system, is the measurement noise covariance matrix.

7. The method for estimating the sideslip state of a tracked vehicle according to claim 1, characterized in that: Based on the state transfer equation and measurement equation of the discrete state estimation system and parameter estimation system, the vehicle driving state parameter estimation is performed using the double extended Kalman filter algorithm to obtain the state estimation value and parameter estimation value of the vehicle at each moment, specifically including: Parameter prediction: inputting the parameter estimation value at the current moment and the state covariance matrix estimation value of the parameter estimation system into the state transfer equation of the parameter estimation system, and calculating the parameter prediction value at the next moment and the state covariance matrix prediction value of the parameter estimation system; State prediction: Input the current state estimation value, the state covariance matrix estimation value of the state estimation system, the current control input and the parameter prediction value of the next moment into the state transfer equation of the state estimation system, and calculate the state prediction value of the next moment and the state covariance matrix prediction value of the state estimation system; State correction: inputting the state prediction value at the next moment, the state covariance matrix prediction value of the state estimation system and the parameter prediction value at the next moment into the measurement equation of the state estimation system to obtain the state estimation value at the next moment; the measurement equation of the state estimation system is an equation obtained based on the state space equation of the state estimation system in discrete form, the state transfer function of the tracked vehicle steering and the measurement function; Parameter correction: input the parameter prediction value at the next moment, the state covariance matrix prediction value of the parameter estimation system and the state prediction value at the next moment into the measurement equation of the parameter estimation system to obtain the parameter estimation value at the next moment; the measurement equation of the parameter estimation system is an equation obtained based on the state space equation of the parameter estimation system in discrete form, the state transfer function of the tracked vehicle steering and the measurement function; Determine whether the next moment is the predicted moment and obtain the determination result; If the judgment result is yes, then the state estimation value and parameter estimation value of the vehicle at each moment are obtained; If the judgment result is no, the next moment is taken as the current moment, and the parameter estimation value at the current moment and the state covariance matrix estimation value of the parameter estimation system are input into the state transfer equation of the parameter estimation system to calculate the parameter prediction value at the next moment and the state covariance matrix prediction value of the parameter estimation system.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for estimating the sideslip state of a tracked vehicle according to any one of claims 1 to 7.

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 estimating the sideslip state of a tracked vehicle according to any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for estimating the sideslip state of a tracked vehicle according to any one of claims 1 to 7 is implemented.

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