Modeling method and system for vehicle dynamics of concrete mixing truck

Abstract

The invention discloses a dynamic modeling method and a dynamic modeling system for a concrete mixing truck, which are characterized in that the dynamics of the concrete mixing truck is modeled through a data driving method, and the model does not need vehicle parameters and also does not need to model tire-road interaction. The modeling efficiency, the control precision and the like are remarkably improved, the follow-up movement control of the unmanned concrete mixing carrier vehicle is promoted, and the operation safety performance of the unmanned concrete mixing carrier vehicle can be further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of civil engineering, and particularly relates to a method and system for modeling the vehicle dynamics of a concrete mixer truck driven by data. Background Art

[0002] For the driverless concrete mixer truck in the precast beam yard, due to the particularity of its working process, the characteristics of the vehicle system are in a long-term dynamic change, which poses a severe challenge to the accurate modeling and subsequent control of the system. The existing technology usually describes the dynamic relationship of vehicle motion based on Newton's laws of motion and the moment balance equation. The main problems are: (1) A large amount of calibration work needs to be carried out on vehicle parameters such as the mass and the position of the center of mass of the concrete mixer truck, which is time-consuming and laborious, and cannot adapt to the dynamic operation scenario of the vehicle. (2) It is necessary to model the tire-ground, which has strong nonlinearity, resulting in a complex model and being not conducive to the subsequent design of the controller.

[0003] Therefore, the existing dynamic modeling method for the concrete mixer truck has the technical problem of low reliability due to the above problems. It can be seen that how to improve the reliability of the dynamic modeling method for the concrete mixer truck is a problem to be solved in this field. Summary of the Invention

[0004] Aiming at the technical problem of low reliability existing in the existing dynamic modeling method for the concrete mixer truck, the purpose of the present invention is to provide a method for modeling the vehicle dynamics of a concrete mixer truck, which is applicable to various operating conditions of the vehicle, such as low-speed, high-speed, straight-line, turning and other operating scenarios, greatly reducing the process of calibrating the parameters of the traditional dynamic model. On this basis, a system for modeling the vehicle dynamics of a concrete mixer truck capable of implementing this method is further provided.

[0005] In order to achieve the above purpose, the present invention provides a method for modeling the vehicle dynamics of a concrete mixer truck, which models the vehicle dynamics of the concrete mixer truck by a data-driven method. The modeling method includes the following steps:

[0006] Step 1: Data acquisition

[0007] Collect the state data of the vehicle to be modeled during the movement process.

[0008] Step 2: Establishment of the traditional dynamic model

[0009] Establish a traditional dynamic model through dynamic relationship formulas according to the theoretical characteristics and vehicle states of the concrete mixer truck system dynamics.

[0010] Step 3: Setting of the states and inputs in the vehicle dynamic model

[0011] Set the vehicle data collected in step 1 as the state and input in the vehicle dynamics model;

[0012] Step 4: Establishment of the non - linear state - space model

[0013] Convert the dynamic relationship in step 2 into a state - space equation in general form;

[0014] Step 5: Dimension - elevation of the non - linear state - space model

[0015] Output and elevate the dimension of the non - linear state - space model in step 3 for linearization;

[0016] Step 6: Optimize each coefficient matrix of the linear state - space model and complete the establishment of the linear state - space model;

[0017] Step 7: Effect verification

[0018] Collect data by adopting a set of complex working conditions, and verify the linear state model established in step 6 through the collected data.

[0019] Furthermore, in step 1, collect the longitudinal speed, yaw rate, lateral acceleration signal, wheel rotation angle or steering wheel rotation angle signal during the vehicle movement process.

[0020] Furthermore, in step 3, use the longitudinal speed of the vehicle, the lateral acceleration of the vehicle and the yaw rate signal of the vehicle collected in step 1 as state variables, and the wheel rotation angle or steering wheel rotation angle signal as the input variable.

[0021] Furthermore, elevate the dimension of the non - linear state - space model by using the dimension - elevation function in the Koopman operator theory.

[0022] Furthermore, collect multiple groups of data of the concrete mixer truck and define them as a data matrix.

[0023] Furthermore, construct a least - squares algorithm to identify each matrix of the linear model, and finally obtain each coefficient matrix by using an optimization algorithm.

[0024] To achieve the above - mentioned purpose, the present invention provides a modeling system for the vehicle dynamics of a concrete mixer truck, and the modeling system for the vehicle dynamics of the concrete mixer truck includes:

[0025] A data acquisition module, which is used to collect the state data of the vehicle to be modeled during the movement process;

[0026] The establishment module of the traditional dynamics model, which establishes the traditional dynamics model according to the theoretical characteristics and vehicle state of the concrete mixer truck system dynamics through dynamic relational expressions;

[0027] The state and input setting module of the vehicle dynamics model, which interacts with the data acquisition module. It sets the state and input in the vehicle dynamics model based on the signals collected by the above data acquisition module;

[0028] The establishment module of the nonlinear state space model, which interacts with the establishment module of the traditional dynamics model and converts the dynamic relational expressions in the establishment module of the traditional dynamics model into a state space equation in general form.

[0029] The optimization and establishment module of the linear state space model, which interacts with the establishment module of the nonlinear state space model. It elevates the dimension of the nonlinear state space model established by the establishment module of the nonlinear state space model through the dimension-raising function of the Koopman operator for linearization.

[0030] Furthermore, the modeling method of the concrete mixer truck vehicle dynamics further includes an effect verification module, which interacts with the optimization and establishment module of the linear state space model. It collects data by adopting a set of complex working conditions and verifies the established linear state model through the collected data.

[0031] For the vehicle dynamics modeling method and system of the concrete mixer truck provided by the present invention, in the modeling method proposed by this solution, configuration parameters such as the mass, centroid position, and moment of inertia of the vehicle are not required, nor is it necessary to establish a complex vehicle tire-ground contact mechanics model. Only by measuring the motion state of the vehicle can the dynamics characteristics of the vehicle be described, improving the reliability of the concrete mixer truck vehicle dynamics modeling method. Brief Description of the Drawings

[0032] The following further illustrates the present invention in conjunction with the drawings and specific embodiments.

[0033] Figure 1 It is a schematic flowchart of the vehicle dynamics modeling method for this concrete mixer truck;

[0034] Figure 2 It is a schematic diagram of the time history curves of the vehicle steering wheel angle, longitudinal acceleration, and lateral acceleration;

[0035] Figure 3 It is a schematic diagram for verifying the running path of the vehicle in the data-driven concrete mixer truck vehicle dynamics model;

[0036] Figure 4 This is a verification schematic diagram of the tire slip ratio of the vehicle in the vehicle dynamics model of the data-driven concrete mixer truck;

[0037] Figure 5 This is a verification schematic diagram of the vehicle dynamics model of the data-driven concrete mixer truck. Specific implementation manner

[0038] In order to make the technical means, creative features, achieved purposes and effects realized by the present invention easy to understand, the present invention will be further described below with reference to specific drawings.

[0039] Aiming at the technical problem of low reliability existing in the existing vehicle dynamics modeling method of concrete mixer trucks, considering the characteristics of time-varying vehicle parameters caused by the dynamic operation characteristics of the concrete mixer truck system and the complexity of the tire-road contact force model, the present invention proposes a data-driven method to model the vehicle dynamics of the concrete mixer truck. This model does not require vehicle parameters and does not require modeling of the tire-road interaction, and has significant improvements in modeling efficiency, control accuracy, etc., which has a promoting effect on the subsequent motion control of unmanned concrete mixer trucks and can further improve the operation safety performance of unmanned concrete mixer trucks.

[0040] From Figure 1 it can be seen that the vehicle dynamics modeling method of the concrete mixer truck can be carried out through the following steps:

[0041] Step 1: Data acquisition:

[0042] This step is used to collect the state data of the vehicle to be modeled during the movement process, which is convenient for the establishment of the subsequent data-driven dynamics model.

[0043] First, RTK and IMU can be used to record the vehicle's running three-axis trajectory, speed, acceleration, inclination angle, and angular velocity. And make the vehicle start from the origin, go through a straight line, turn, turn with variable curvature, and when continuously making multiple small-curvature turns, increase the vehicle's steering angle to make the tire run in the non-linear region.

[0044] Collect signals such as the longitudinal speed, yaw angular velocity, and lateral acceleration during the vehicle movement process, and calculate the lateral speed of the vehicle through calculation.

[0045] Calculate the corresponding vehicle running speed through the vehicle running trajectory and the corresponding time v = s / t, calculate the corresponding vehicle running acceleration through the running speed and the corresponding time a = v / t, and calculate the corresponding vehicle running angular velocity through the three-axis inclination angle and the corresponding time v` = theta / t.

[0046] Through this calculation method, the speeds, accelerations, etc. on the three axes of the vehicle can be calculated, namely the longitudinal speed, yaw angular velocity, lateral acceleration, and lateral speed.

[0047] Collect the wheel rotation angle or steering wheel rotation angle signal during the vehicle movement.

[0048] As an example, an encoder can be installed on the steering wheel to collect the steering angle of the steering wheel during vehicle driving, and the angular velocity of the steering wheel rotation can be calculated through the corresponding time.

[0049] The time history curves of the vehicle steering wheel rotation angle, longitudinal acceleration, and lateral acceleration obtained can be seen in Figure 2 .

[0050] Step 2: Establishment of the traditional dynamics model

[0051] This step is the basis of the data-driven vehicle dynamics model. Establishing the traditional dynamics model is to clarify the theoretical characteristics of the concrete mixer truck system dynamics, vehicle states, etc., and can form a comparison with the subsequent data-driven model (that is, the data-driven method does not require vehicle parameters).

[0052] For the concrete mixer truck, the traditional vehicle dynamics model usually considers the longitudinal, lateral, and yaw motions of the vehicle, and its dynamic relationship can be described in the following form:

[0053]

[0054] Among them, m is the vehicle mass; v x is the longitudinal speed of the vehicle; v y is the lateral speed of the vehicle; r represents the yaw angular velocity of the vehicle; δ f is the front wheel rotation angle of the vehicle; represents the force on the tire, i = x, y represents longitudinal or lateral; j = f, r represents front wheel or rear wheel; I z is the moment of inertia of the vehicle about the vertical axis; L f represents the distance from the vehicle center of mass position to the center position of the front axle; L r represents the distance from the vehicle center of mass position to the center position of the rear axle.

[0055] Step 3: State and input settings in the vehicle dynamics model.

[0056] In this step, the longitudinal speed of the vehicle, the lateral acceleration of the vehicle, and the yaw angular velocity signal collected in Step 1 are used as state variables, and the wheel rotation angle or steering wheel rotation angle signal is used as the input variable, and the above collected signals are set as the state and input in the vehicle dynamics model through formula (2):

[0057] x k =[x1 x 2 x 3 T

[0058] =[v x v y r] T (2)

[0059] u k =δ f

[0060] where represents the state vector of the system, is the external

[0061] input of the system.

[0062] Step 4: Establishment of the nonlinear state - space model:

[0063] Based on the above definitions, the system in form (1) can be converted into the state - space equation in general form

[0064] x k+1 =f(x k , u k ) (3)

[0065] Step 5: Dimension - elevation of the nonlinear state - space model.

[0066] In this step, the output of the nonlinear state - space model in Step 3 is output and its dimension is elevated for linearization.

[0067] For the above - mentioned system, considering the system output dynamics, the system can be further written as

[0068] x k +1 =f(x k , u k ) (4)

[0069] y k =g(x k , u k )

[0070] where represents the output of the system.

[0071] Define the dimension - elevation function for the Koopman operator as According to the definition of the Koopman operator,

[0072]

[0073] Let ​Then the controlled system can be transformed into the following form through the Koopman operator

[0074] s k+1 =As k +Bu k (6)

[0075] y k =Cs k +Du k

[0076] where A, B, C, and D are linear model matrices to be trained.

[0077] Step 6: Optimize the coefficient matrices of the linear state-space model and complete the establishment of the linear state-space model.

[0078] In this step, multiple sets of data of the concrete mixer truck are collected and substituted into the linear state-space model constructed in Step 4 to optimize the coefficient matrices of the linear state-space model.

[0079] Specifically, multiple sets of data of the concrete mixer truck are collected, and the following data matrices are defined:

[0080] S k =[s 0 s 1 …s n-1

[0081] S k+1 =[s 1 s 2 …s n (7)

[0082] U k =[u 0 u 1 …u n-1

[0083] Y k =[y 0 y 1 …y n-1

[0084] After collecting multiple sets of data of the concrete mixer truck, the coefficient matrices of the linear state-space model are then optimized. Specifically, by constructing a least squares algorithm, the parameters collected in Step 5 are used to identify the matrices of the linear model, and the coefficient matrices are finally obtained using an optimization algorithm.

[0085]

[0086] Step 7: Effect verification

[0087] ​​​In this step, a set of complex working conditions are adopted for data acquisition, and the established linear state model is verified by the collected data. Specifically, refer to Figures 3 - 4 .

[0088] Combined with the above modeling theory, without the need for any vehicle configuration parameters, such as Figure 5 As shown, it is the modeling effect of the vehicle dynamics of the concrete mixer truck. It can be seen from the figure that this method accurately reconstructs the evolution of vehicle dynamics and has a high modeling accuracy.

[0089] The data here includes but is not limited to straight running, variable curvature curves, large slip conditions, etc.

[0090] According to the above, the traditional model established in Step 1 above requires vehicle parameters such as vehicle mass, moment of inertia, and centroid position, as well as a tire force model. In the modeling method proposed in this solution, vehicle configuration parameters such as vehicle mass, centroid position, and moment of inertia are not required, nor is it necessary to establish a complex vehicle tire-ground contact mechanics model. Only by measuring the vehicle's motion state can the vehicle's dynamic characteristics be described.

[0091] For the vehicle dynamics modeling method of the concrete mixer truck given in this example solution, in specific applications, a corresponding software program can be formed to form a vehicle dynamics modeling system for the concrete mixer truck. When this software program runs, it will execute the above vehicle dynamics modeling method for the concrete mixer truck and be stored in a corresponding storage medium for the processor to retrieve and execute.

[0092] The vehicle dynamics modeling system formed thereby mainly includes, in terms of function: a data acquisition module, a traditional dynamics model establishment module, a vehicle dynamics model state and input setting module, a nonlinear state space model establishment module, a nonlinear state space model dimension elevation module, a linear state space model optimization and establishment module, and an effect verification module.

[0093] Among them, the data acquisition module in this system is used to collect the state data of the vehicle to be modeled during the movement process, facilitating the establishment of a data-driven dynamics model in the later stage.

[0094] The data includes but is not limited to the vehicle's running three-axis trajectory, speed, acceleration, inclination angle, angular velocity, straight running, turning, variable curvature turning, steering angle, longitudinal speed, yaw angular velocity, lateral acceleration, wheel rotation angle, or steering wheel rotation angle.

[0095] This data acquisition module is configured to execute the above data acquisition steps to achieve the corresponding functions.

[0096] The establishment module of the traditional dynamics model in this system forms a traditional dynamics model through dynamic relationships. This model is the basis of the data-driven vehicle dynamics model. The traditional dynamics model is established to clarify the theoretical characteristics of the concrete mixer truck system dynamics, vehicle states, etc., and can be compared with the subsequent data-driven model.

[0097] The establishment module of the traditional dynamics model is configured to execute the above steps for establishing the traditional dynamics model to achieve the corresponding functions.

[0098] The state and input setting module in the vehicle dynamics model of this system interacts with the data acquisition module, and sets the states and inputs in the vehicle dynamics model based on the signals collected by the above data acquisition module.

[0099] The state and input setting module in the vehicle dynamics model is configured to execute the above steps for setting the states and inputs in the vehicle dynamics model to achieve the corresponding functions.

[0100] The establishment module of the nonlinear state space model in this system interacts with the establishment module of the traditional dynamics model, and converts the dynamic relationship in the establishment module of the traditional dynamics model into a state space equation in general form.

[0101] The establishment module of the nonlinear state space model is configured to execute the above steps for establishing the nonlinear state space model to achieve the corresponding functions.

[0102] The dimension-raising module of the nonlinear state space model in this system interacts with the establishment module of the nonlinear state space model, and raises the dimension of the nonlinear state space model established by the establishment module of the nonlinear state space model through the dimension-raising function of the Koopman operator for linearization.

[0103] The dimension-raising module of the nonlinear state space model is configured to execute the above steps of the dimension-raising module of the nonlinear state space model to achieve the corresponding functions.

[0104] The optimization and establishment module of the linear state space model in this system interacts with the dimension-raising module of the nonlinear state space model. By collecting multiple groups of data of the concrete mixer truck and substituting them into the linear state space model after dimension-raising in the dimension-raising module of the nonlinear state space model, the coefficient matrices of the linear state space model are optimized and the final establishment of the linear state space model is completed.

[0105] The optimization and establishment module of the linear state space model is configured to execute the above steps for optimizing and establishing the linear state space model to achieve the corresponding functions.

[0106] The effect verification module in this system interacts with the optimization and establishment module of the linear state space model. It collects data using a set of complex working conditions and verifies the established linear state model through the collected data.

[0107] The effect verification module is configured to execute the steps of the above effect verification module to achieve corresponding functions.

[0108] The dynamic modeling method and system of a concrete mixer truck vehicle composed of the above solution model the vehicle dynamics of a concrete mixer truck through a data-driven method. This model does not require vehicle parameters nor does it need to model the tire-road interaction, and has significant improvements in aspects such as modeling efficiency and control accuracy, which promotes the motion control of subsequent unmanned concrete mixer trucks and further improves the operating safety performance of unmanned concrete mixer trucks.

[0109] The above method of the present invention, or a specific system unit, or a part of its units, is a pure software architecture and can be distributed through program code on an entity medium, such as a hard disk, a CD-ROM, or any electronic device (such as a smart phone, a computer-readable storage medium). When the machine loads the program code and executes (such as a smart phone loading and executing), the machine becomes a device for implementing the present invention. The method and device of the present invention can also be in the form of program code and be transmitted through some transmission media, such as cables, optical fibers, or any transmission type. When the program code is received, loaded, and executed by a machine (such as a smart phone), the machine becomes a device for implementing the present invention.

[0110] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification only illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A modeling method for vehicle dynamics of a concrete mixer truck, characterized in that: The vehicle dynamics of a concrete mixer truck is modeled by a data-driven method, and the modeling method includes the following steps: Step 1: Data Collection Collect the state data of the vehicle to be modeled during its movement; Step 2: Establishment of traditional kinetic model According to the theoretical characteristics of the concrete mixer truck system dynamics and the vehicle status, the traditional dynamics model is established through the dynamics relationship; Step 3: State and input settings in the vehicle dynamics model By setting the vehicle data collected in step 1 as states and inputs in the vehicle dynamics model; Step 4: Establishment of nonlinear state space model Convert the dynamical relations in step 2 into a general form of state-space equations; Step 5: Dimensionality increase of nonlinear state space model Output the nonlinear state space model in step 3 and increase the dimension for linearization; Step 6: Optimize the coefficient matrices of the linear state space model and complete the establishment of the linear state space model; Step 7: Effect verification A set of complex working conditions is used to collect data, and the linear state model established in step 6 is verified by the collected data.

2. A modeling method for vehicle dynamics of a concrete mixer truck according to claim 1, characterized in that: In step 1, the longitudinal velocity, yaw angular velocity, lateral acceleration signal, wheel angle or steering wheel angle signal of the vehicle are collected during movement.

3. The modeling method of vehicle dynamics of a concrete mixer truck according to claim 1, characterized in that: In step 3, the longitudinal velocity of the vehicle, the lateral acceleration of the vehicle and the yaw rate signal of the vehicle collected in step 1 are used as state quantities, and the wheel angle or steering wheel angle signal is used as input quantity.

4. The modeling method of vehicle dynamics of a concrete mixer truck according to claim 1, characterized in that: The dimension of nonlinear state space model is increased by using the dimension-increasing function in Koopman operator theory.

5. The modeling method of vehicle dynamics of a concrete mixer truck according to claim 1, characterized in that: Collect multiple sets of data from concrete mixer trucks and define them into a data matrix.

6. The modeling method of vehicle dynamics of a concrete mixer truck according to claim 1, characterized in that: By constructing the least squares algorithm, the matrices of the linear model are identified, and the coefficient matrices are finally obtained using the optimization algorithm.

7. A modeling system for vehicle dynamics of a concrete mixer truck, characterized in that: The modeling system of the concrete mixer truck vehicle dynamics comprises: A data acquisition module, the data acquisition module is used to collect state data of the vehicle to be modeled during movement; A module for establishing a traditional dynamics model, wherein the traditional dynamics model is established through a dynamics relationship according to theoretical characteristics of the dynamics of a concrete mixer truck system and a vehicle state; A state and input setting module for the vehicle dynamics model, which interacts with the data acquisition module and is used to set the state and input in the vehicle dynamics model based on the signals collected by the data acquisition module; A module for establishing a nonlinear state space model interacts with data of a module for establishing a traditional dynamic model, and converts the dynamic relationship in the module for establishing the traditional dynamic model into a general form of state space equation. The linear state space model optimization and establishment module interacts with the nonlinear state space model establishment module data, and increases the dimension of the nonlinear state space model established by the nonlinear state space model establishment module through the Koopman operator's dimensionality increase function for linearization.

8. A method for modeling vehicle dynamics of a concrete mixer truck according to claim 7, characterized in that: The modeling method of the vehicle dynamics of the concrete mixer truck also includes an effect verification module, which interacts with the optimization and establishment module of the linear state space model. It uses a set of complex working conditions to collect data and verifies the linear state model established above through the collected data.

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