Method, device, medium and product for estimating sideslip state of tracked vehicle

Abstract

The present invention discloses a method, device, medium, and product for estimating the sideslip state of a tracked vehicle, relating to the field of intelligent driving technology for tracked vehicles. The method comprises: obtaining vehicle test data; establishing a track empirical model and calibrating the empirical parameters of the track empirical model based on the vehicle test data; establishing a three-degree-of-freedom tracked vehicle dynamics model based on the track empirical model; establishing discrete state transition equations and measurement equations for a state estimation system and a parameter estimation system based on the three-degree-of-freedom tracked vehicle dynamics model; and estimating vehicle driving state parameters using a double extended Kalman filter algorithm to obtain vehicle state and parameter estimates at each moment. Based on the vehicle state and parameter estimates at each moment, the vehicle sideslip state estimate is calculated. The present invention can improve the accuracy of sideslip angle estimation, thereby improving driving stability and safety.

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 intelligentization of tracked vehicles can improve their ability to operate sustainably and withstand harsh environments. Key to this is the ability to accurately and safely control driving by determining various driving state variables. While advances in sensing and testing technologies have enabled various sensors to measure vehicle motion, these sensors are expensive and require specialized mounting methods. Furthermore, in off-road environments where GPS signals are absent, the inertial navigation commonly used by tracked vehicles suffers from cumulative errors and sensor noise, necessitating the use of state estimation methods to accurately calculate driving states. Furthermore, in vehicle stability control, severe sideslip can cause the vehicle to stall or even roll over, necessitating the estimation of the sideslip angle.

[0003] The Kalman filter is a model-based state estimation method that combines the model's state transition information with sensor measurements for state estimation. It is widely used in linear systems with Gaussian noise characteristics. However, most real-world systems are nonlinear. Therefore, the extended Kalman filter was developed. This linearizes the nonlinear function using a Taylor expansion at the current state point, solves the Jacobian matrix, and determines the system matrix and observation matrix. This then yields the predicted covariance matrix and the Kalman gain. This improvement improves state estimation for nonlinear systems. However, the parameters of the tracked vehicle dynamics model used for state estimation are typically not constant. Treating the model as a nonlinear, steady-state system can compromise the accuracy of sideslip angle estimation, which in turn affects driving stability control and 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 thus 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 under typical driving conditions and control input; the vehicle motion state includes: longitudinal and lateral accelerations and yaw angular velocity of the vehicle; the control input includes the driving torque and 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 the longitudinal, lateral and yaw directions on horizontal ground.

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

[0011] Based on the state transfer equations and measurement equations of the discrete state estimation system and parameter estimation system, the vehicle's driving state parameters are estimated using the Dual Extended Kalman Filter (DEKF) algorithm to obtain the vehicle's state estimation value and parameter estimation value at each moment.

[0012] The vehicle sideslip state estimation is calculated based on the vehicle state estimation value at each moment and the parameter estimation value.

[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 one 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-mentioned 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 one 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 sideslip state of a tracked vehicle. This method establishes an empirical track model describing the longitudinal force, lateral force, and steering resistance torque on both tracks, calibrates the empirical parameters of the empirical track model, and establishes a three-degree-of-freedom tracked vehicle dynamics model based on the tracked vehicle's motion in the longitudinal, lateral, and yaw directions on horizontal ground. The method then obtains state transition equations and measurement equations for the discrete state estimation system and parameter estimation system. The method then uses a double extended Kalman filter algorithm to estimate the vehicle's driving state parameters, obtaining state and parameter estimates at each moment. Based on the state and parameter estimates at each moment, the method calculates the vehicle's sideslip state estimate. The method can be applied to steering stability control of tracked vehicles and offers advantages such as high estimation accuracy, good real-time performance, and 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 following briefly introduces the drawings required for use in the embodiments. 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 any creative work.

[0019] Figure 1 FIG2 is a diagram showing an application environment of a method for estimating sideslip state of a tracked vehicle according to an 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 track empirical formula provided by an embodiment of the present invention.

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

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

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

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts 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 primary source of nonlinearity in the tracked vehicle dynamics system. It must account for the slip rate of multiple track shoes, the integration of the tangential and normal components of the contact force, and the ground pressure concentrated under the roadwheel. Furthermore, during acceleration, braking, and steering, centrifugal forces affect the ground pressure distribution. Consequently, establishing a high-precision track model is complex and highly nonlinear, hindering real-time state estimation. Therefore, it is necessary to simplify the track model to improve state estimation efficiency.

[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 the 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 calibrates the empirical parameters of the track experience model based on the vehicle test data; 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 based on the longitudinal, lateral, and yaw motion of a tracked vehicle on horizontal ground. Based on the three-degree-of-freedom tracked vehicle dynamic model, state transition equations and measurement equations are established for a discrete state estimation system and a discrete parameter estimation system. Based on the state transition equations and measurement equations of the discrete state estimation system and the discrete parameter estimation system, a double extended Kalman filter algorithm is used to estimate the vehicle's driving state parameters, obtaining estimated vehicle state values ​​and parameter values ​​at each moment. Based on the estimated vehicle state values ​​and parameter values ​​at each moment, a vehicle sideslip state estimate is calculated. The server 104 can provide the obtained vehicle sideslip state estimate as feedback to the terminal 102. Furthermore, in some embodiments, the tracked vehicle sideslip state estimation method can be implemented solely by the server 104 or the terminal 102. For example, the terminal 102 can directly perform sideslip state estimation based on acquired vehicle test data, or the server 104 can obtain vehicle test data from a data storage system and perform sideslip state estimation based 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 a cloud server.

[0030] In an exemplary embodiment, Figure 2 As shown, a method for estimating the sideslip state of a tracked vehicle is provided. The method is executed by a computer device, specifically a computer device such as a terminal or a server, or a terminal and a server. 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.

[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 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 both sides of the track.

[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 the longitudinal, lateral and yaw directions on the 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 respectively established.

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

[0036] S6: Based on the vehicle state estimation value at each moment and the parameter estimation value, calculate and obtain the vehicle sideslip state estimation.

[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, step S1 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 empirical track model, the ground forces and moments acting on both tracks are solely dependent on the vehicle's current motion state, with i = 1 and 2 representing the left and right tracks, respectively. A high-precision track force simulation model is used to calculate the longitudinal force, lateral force, and steering torque on both tracks at varying longitudinal speeds and yaw rates. The results are then fitted using the following magic formula:

[0044]

[0045] Fzi=μF N iEz1sin{Ez2arctan[Ez3Szi-Ez4(Ez3Szi-arctan(Ez3Szi))]}(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 both sides of the track, F zi is the fitting result of the ground normal reaction force, 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 ground 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. These parameters are obtained by calibrating the model using vehicle test data. Model calibration utilizes the vehicle's motion state and the driving wheel torque signal, using a particle swarm optimization algorithm. The driving wheel speed is used as the model input, and the prediction errors of the yaw rate and driving wheel torque are used as evaluation indicators for 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. 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 motion 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 vehicle's steering inertia, 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 filter, 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 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 below. 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 vehicle yaw rate, 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 The F can be calculated based on the driving wheel torque 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 transition function is:

[0061]

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

[0063]

[0064] The track slip rate is calculated as follows: 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, step S5 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: Input 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 into the state transfer equation of the state estimation system, and calculate the state prediction value at the next moment and the state covariance matrix prediction value of the state estimation system.

[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 discrete form of the state estimation system, 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 discrete form of the parameter estimation system, 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 a 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 model parameter estimation. The numerical transfer process can be divided into four steps: 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, it includes:

[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 transition equation of the parameter estimation system to calculate the parameter prediction value at time k+1 and parameter estimation system state covariance matrix prediction 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 predicted value and the parameter prediction value at time k+1 Input 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 used State covariance matrix predicted value And the state prediction value at time k+1 Input 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 vehicle state and parameter estimates at each time step, and then calculate the tracked vehicle sideslip state estimate based on the double extended Kalman filter. At each time step, the tracked vehicle sideslip angle β at the current moment is calculated based on the state estimates of the longitudinal and lateral vehicle speeds at the current moment, using the following formula:

[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 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 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 measurement signal Compare and use to correct status and model parameters

[0094] Figure 5 is the 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), Figure 5 It can be seen that the tracked vehicle sideslip state estimation method based on double extended Kalman filter has high estimation accuracy and good applicability.

[0095] In summary, the present invention establishes an empirical track model based on the magic formula, reducing computational complexity and improving state estimation efficiency. Furthermore, the dual extended Kalman filter algorithm (DELKF) estimates driving state and parameters, coupling parameter estimation with state estimation, improving model applicability and state estimation accuracy.

[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: a data acquisition link, a tracked experience model establishment link, a three-degree-of-freedom tracked vehicle dynamics model establishment link, a state transfer equation and a measurement equation establishment link, a driving state parameter estimation link and a 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 equations and measurement equations of the discrete state estimation system and parameter estimation system are respectively established; 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 vehicle state estimation value and parameter estimation value at each moment; based on the vehicle state estimation value and the parameter estimation value at each moment, the vehicle sideslip state estimation can be obtained by calculation.

[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, abbreviated as I / O) and a communication interface. The processor, memory and 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 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 merely a block diagram of a portion 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 shown in the figure, or combine certain components, or have a different component arrangement.

[0099] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the above method embodiments when executing the computer program.

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

[0101] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the above 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 will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. 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 memory, database or other media 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), magnetic 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. Non-relational databases may include, but are not limited to, distributed databases based on blockchain. The processor involved in each embodiment provided by the present invention may be, but is not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, etc.

[0105] The technical features of the above embodiments can 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] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed 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 under typical driving conditions, and control inputs; the vehicle motion state includes: longitudinal and lateral accelerations and yaw angular velocity of the vehicle; and the control inputs include the driving torque and speed of the driving wheels on both sides; Establishing a track empirical model and calibrating 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 both sides of the track; 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 the 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 equations and measurement equations 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; Calculating a vehicle sideslip state estimate based on the vehicle state estimate at each moment and the parameter estimate; 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 vehicle state estimation value and parameter estimation value 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 estimate, the state covariance matrix estimate of the state estimation system, the current control input, and the next-moment parameter prediction into the state transfer equation of the state estimation system to calculate the next-moment state estimate and the state covariance matrix prediction 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 discrete form of the state estimation system, the state transfer function of the tracked vehicle steering, and the measurement function; Parameter correction: Inputting 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 judgment result; If the judgment result is yes, then obtaining the vehicle state estimation value and parameter estimation value at each moment; 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.

2. The method for estimating the sideslip state of a tracked vehicle according to claim 1, wherein: 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; Use a gyroscope to obtain the yaw rate of the vehicle when turning; 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, wherein: The expression of the crawler experience model is: 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 both sides of the track, F zi is the fitting result of the ground normal reaction force, 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 ground 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, wherein: The expression of the three-degree-of-freedom tracked vehicle dynamics model is: Where m is the vehicle weight, I z is the vehicle's steering inertia, 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 exerted by the ground 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 sideslip state of a tracked vehicle according to claim 1, characterized in that: The state transfer equation and measurement equation of the state estimation system are expressed as follows: 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, wherein: The expressions of the state transfer equation and 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. 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 6.

8. 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 6 is implemented.

9. 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 6 is implemented.

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