Reverse solution method, device and equipment for vehicle motion model

Abstract

The invention provides a reverse solution method, device and equipment for a vehicle motion model, relates to the technical field of dynamics and control, and aims to solve the problems that in the prior art, a forward solution model and a reverse solution model cannot be shared, and reverse solution universality is poor. The method comprises the following steps: acquiring dynamic data of a vehicle motion model, and establishing a vehicle motion model equation set according to the dynamic data; based on a to-be-solved target, target parameters are added into the vehicle motion model equation set, and an implicit differential algebraic equation is added for the vehicle motion model equation set according to the target parameters and the dynamic data; and performing reverse solution on the vehicle motion model by using the vehicle motion model equation set after the target parameter and the implicit differential algebraic equation are added.

Description

Technical Field

[0001] The present application relates to the fields of dynamics technology and vehicle model technology, and in particular to a method, device and equipment for inverse solution of a vehicle motion model. Background Art

[0002] Inverse solution of multi-body dynamics system plays an important role in vehicle simulation and adjustment in the field of vehicle motion model. The inverse solution refers to the calculation of certain system parameters so that the multi-body dynamics system can move or remain stationary in a specified manner, including inverse dynamics solution, inverse kinematics solution and inverse statics solution. It is mainly used in the fields of robot motion control, vehicle control, etc., including two categories: analytical method and numerical method.

[0003] In related technologies, the forward solution model and the inverse solution model cannot be used together. Usually, an additional model and an additional algorithm code need to be established for the inverse solution. In addition, in order to achieve the inverse solution, the physical system often needs to be modified to meet the conditions for the existence of the inverse solution, which limits the creation of models and applications and cannot be widely used.

[0004] Therefore, it is necessary to provide a reverse solution method for the vehicle motion model to solve the above problems. Summary of the invention

[0005] In order to solve the above technical problems, the present application provides a method, device and equipment for inverse solution of a vehicle motion model to solve the problem in the prior art that the forward solution model and the inverse solution model cannot be shared and the inverse solution has poor versatility.

[0006] To achieve the above technical objectives, this application provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present specification provides a method for inversely solving a vehicle motion model, including:

[0008] Acquire dynamic data of the vehicle motion model, and establish a vehicle motion model equation group based on the dynamic data;

[0009] Based on the target to be solved, target parameters are added to the vehicle motion model equations, and implicit differential algebraic equations are added to the vehicle motion model equations according to the target parameters and dynamics data;

[0010] The vehicle motion model is reversely solved using the vehicle motion model equations after adding target parameters and implicit differential algebraic equations.

[0011] In one embodiment, the dynamics data includes a system generalized coordinate vector, a system generalized velocity vector, a system generalized acceleration vector, a system mass matrix, a Jacobian matrix of a system constraint equation, a system constraint multiplier vector, and a system time;

[0012] The vehicle motion model equations are established based on the dynamics data, including:

[0013] Constructing the vehicle motion model equation using system generalized coordinate vector, system generalized velocity vector, system generalized acceleration vector, system mass matrix, system constraint equation Jacobian matrix, system constraint multiplier vector and system time;

[0014] The constraint equations of the vehicle motion model are constructed using the system generalized coordinate vector and system time.

[0015] In one embodiment, the vehicle motion model equation is constructed using the system generalized coordinate vector, the system generalized velocity vector, the system generalized acceleration vector, the system mass matrix, the system constraint equation Jacobian matrix, the system constraint multiplier vector and the system time, including:

[0016] The first function is constructed using the system mass matrix and the system generalized acceleration vector;

[0017] Construct the system load vector using the system generalized velocity vector, system generalized coordinate vector and system time;

[0018] constructing a second function using the Jacobian matrix of the system constraint equation, the system constraint multiplier vector and the system time;

[0019] The vehicle motion model equation is obtained using the first function, the second function and the system load vector, that is, the sum of the second function and the system load vector is equal to the first function.

[0020] In one embodiment, adding target parameters to the vehicle motion model equations includes:

[0021] Include target parameters in the system load vector.

[0022] In one embodiment, implicit differential algebraic equations are added to the vehicle motion model equations according to the target parameters and the dynamics data, including:

[0023] Using the system generalized coordinate vector, system generalized velocity vector, system generalized acceleration vector, system time and target parameters, the inverse solution condition is constructed;

[0024] Based on the inverse solution of the boundary conditions, the implicit differential algebraic equation is obtained and added into the vehicle motion model equation group.

[0025] In one embodiment, the method further comprises:

[0026] Based on the inverse solution results of the target to be solved, the vehicle motion model is corrected.

[0027] In a second aspect, an embodiment of the present specification provides a reverse solution device for a vehicle motion model, comprising:

[0028] An acquisition unit, used for acquiring dynamic data of a vehicle motion model and establishing a vehicle motion model equation group according to the dynamic data;

[0029] A processing unit, for adding target parameters to the vehicle motion model equations based on the target to be solved, and adding implicit differential algebraic equations to the vehicle motion model equations according to the target parameters and dynamics data;

[0030] The solving unit is used for inversely solving the vehicle motion model by using the vehicle motion model equation group after adding target parameters and implicit differential algebraic equations.

[0031] In one embodiment, the dynamics data includes a system generalized coordinate vector, a system generalized velocity vector, a system generalized acceleration vector, a system mass matrix, a Jacobian matrix of a system constraint equation, a system constraint multiplier vector, and a system time;

[0032] The acquisition unit is specifically used for:

[0033] Constructing the vehicle motion model equation using system generalized coordinate vector, system generalized velocity vector, system generalized acceleration vector, system mass matrix, system constraint equation Jacobian matrix, system constraint multiplier vector and system time;

[0034] The constraint equations of the vehicle motion model are constructed using the system generalized coordinate vector and system time.

[0035] In one implementation, the acquisition unit is specifically configured to:

[0036] The first function is constructed using the system mass matrix and the system generalized acceleration vector;

[0037] Construct the system load vector using the system generalized velocity vector, system generalized coordinate vector and system time;

[0038] constructing a second function using the Jacobian matrix of the system constraint equation, the system constraint multiplier vector and the system time;

[0039] The vehicle motion model equation is obtained using the first function, the second function and the system load vector, that is, the sum of the second function and the system load vector is equal to the first function.

[0040] In one embodiment, the processing unit is specifically configured to:

[0041] Include target parameters in the system load vector.

[0042] In one embodiment, the processing unit is specifically configured to:

[0043] Using the system generalized coordinate vector, system generalized velocity vector, system generalized acceleration vector, system time and target parameters, the inverse solution condition is constructed;

[0044] Based on the inverse solution of the boundary conditions, the implicit differential algebraic equation is obtained and added into the vehicle motion model equation group.

[0045] In one embodiment, the processing unit is further configured to:

[0046] Based on the inverse solution results of the target to be solved, the vehicle motion model is corrected.

[0047] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the method for determining the action parameter value of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0048] In a fourth aspect, an embodiment of the present specification provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the inverse solution method of the vehicle motion model as described in any one of the above items is implemented.

[0049] In a fifth aspect, an embodiment of the present specification provides a computer program product or a computer program, wherein the computer program product includes a computer program, and the computer program is stored in a computer-readable storage medium; the processor of the computer device reads the computer program from the computer-readable storage medium, and when the processor executes the computer program, it implements the inverse solution method of the vehicle motion model as described in any one of the above items.

[0050] It can be seen from the above technical solution that the present application provides a method, device and equipment for inverse solution of a vehicle motion model. The method first obtains the dynamic data of the vehicle motion model, and establishes a vehicle motion model equation group according to the dynamic data, then adds the target parameter to the vehicle motion model equation group, and adds implicit differential algebraic equations to the vehicle motion model equation group according to the target parameter and the dynamic data, and finally uses the vehicle motion model equation group after adding the target parameter and the implicit differential algebraic equation to reversely solve the vehicle motion model. By adding the target parameter to the vehicle motion model equation group when constructing the model, and adding implicit differential algebraic equations to the vehicle motion model equation group through the target parameter, the vehicle motion model equation group supports forward and reverse solution, and realizes that forward solution and reverse solution share a set of models and a set of algorithm codes, and reverse solution does not require additional modification of the physical model, thereby improving the versatility of reverse solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are merely embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0052] Figure 1 A schematic flow chart of a method for inversely solving a vehicle motion model provided for an embodiment of this specification;

[0053] Figure 2 A schematic diagram of a suspension system provided for an embodiment of this specification;

[0054] Figure 3 A schematic diagram of a principle tire lifting force provided for an embodiment of this specification;

[0055] Figure 4 A schematic diagram of the structure of a reverse solution device for a vehicle motion model provided in an embodiment of this specification;

[0056] Figure 5 A schematic diagram of the structure of an electronic device provided for an implementation of this specification. DETAILED DESCRIPTION

[0057] Unless otherwise defined, the technical terms or scientific terms used in the embodiments of this specification shall have the common meanings understood by persons skilled in the field to which this specification belongs. The words "first", "second" and similar words used in the embodiments of this specification do not indicate any order, quantity or importance, but are only used to avoid confusion of constituent elements.

[0058] Unless the context requires otherwise, throughout the specification, "plurality" means "at least two", and "including" is interpreted as an open, inclusive meaning, that is, "including, but not limited to". In the description of the specification, the terms "one embodiment", "some embodiments", "exemplary embodiments", "examples", "specific examples" or "some examples" are intended to indicate that a particular feature, structure, material or characteristic associated with the embodiment or example is included in at least one embodiment or example of the specification. The schematic representation of the above terms does not necessarily refer to the same embodiment or example.

[0059] The following will be combined with the drawings in the embodiments of this specification to clearly and completely describe the technical solutions in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this specification.

[0060] Overview

[0061] As mentioned in the background technology, the inverse solution of multi-body dynamics system plays an important role in the simulation and adjustment of vehicles in the field of vehicle motion models. The inverse solution refers to the calculation of certain system parameters so that the multi-body dynamics system can move or remain stationary in a specified manner, including inverse dynamics solution, inverse kinematics solution and inverse statics solution. For example, if you want an autonomous driving vehicle to travel along a specific trajectory at a given speed, you need to reversely solve the steering wheel angle or torque; if you want to control the robot to complete certain designed actions, you need to reversely solve the angle or torque of each joint. One of the important uses of the inverse solution of multi-body dynamics system is as a feedforward of the control system. Because the feedforward conforms to the physical properties of the system itself, it can obtain better control accuracy and control stability.

[0062] However, in related technologies, inverse kinematics solutions are mainly used in robot motion control, including two major categories: analytical methods and numerical methods.

[0063] The sufficient condition for a serial robot to have an analytical solution for inverse kinematics is to satisfy the Pieper criterion. That is, if the robot satisfies one of the following two sufficient conditions, a closed solution will be obtained: ① The three adjacent joint axes intersect at one point; ② The three adjacent joint axes are parallel to each other. Most commercial industrial robots now try to satisfy the Pieper criterion when designing their configurations, because analytical solutions can be quickly solved using less computing power and a lower-cost controller.

[0064] Except for some special manipulator configurations, the inverse kinematics problem of the manipulator is difficult to solve analytically, so numerical solutions are used in many cases. Usually, an optimization objective function is set to transform the inverse solution problem into an optimization problem for numerical solution. The NR method (Newton-Raphson, NR) is a method for numerical solution. It requires a basic Jacobian matrix. However, the NR method will succeed if and only if the function of the original equation has an inverse function and the original equation is solvable. From a kinematic point of view, the former condition means that the robot needs to be non-redundant and the robot does not pass through singular points during the movement from the initial configuration to the final configuration. The latter condition means that the desired position and orientation of the manipulator need to be within the robot's workspace and be solvable. Due to these restrictions, the NR method cannot guarantee global convergence, so it depends heavily on the initial values.

[0065] In summary, the forward solution model and the inverse solution model cannot be used together. Usually, an additional model and an additional algorithm code need to be established for the inverse solution. In addition, in order to achieve the inverse solution, the physical system often needs to be modified to meet the conditions for the existence of an inverse solution, which limits the creation of models and applications and cannot be widely used.

[0066] Therefore, it is necessary to provide a reverse solution method for the vehicle motion model to avoid the above problems.

[0067] In order to solve the problem that the forward solution model and the reverse solution model cannot be shared in the existing calculation, and the versatility of the reverse solution is poor, in the technical solution of the present application, the dynamic data of the vehicle motion model is first obtained, and the vehicle motion model equation group is established according to the dynamic data, and then the target parameter is added to the vehicle motion model equation group, and the implicit differential algebraic equation is added to the vehicle motion model equation group according to the target parameter and the dynamic data, and finally the vehicle motion model is reversely solved by using the vehicle motion model equation group after adding the target parameter and the implicit differential algebraic equation. By adding the target parameter to the vehicle motion model equation group when constructing the model, and adding the implicit differential algebraic equation to the vehicle motion model equation group through the target parameter, the constraint relationship is added to the variables in the vehicle motion model, so that the equation group supports both forward and reverse solutions, and realizes that the forward solution and the reverse solution share a set of models and a set of algorithm codes, and the reverse solution does not require additional modification of the physical model, thereby improving the versatility of the reverse solution.

[0068] Based on the above-mentioned inventive concept, the inverse solution method of the vehicle motion model provided in the embodiment of this specification is exemplarily described below.

[0069] Exemplary Methods

[0070] The embodiment of this specification provides a method for inverse solution of a vehicle motion model, such as Figure 1 As shown, including:

[0071] S101, obtaining dynamic data of a vehicle motion model, and establishing a vehicle motion model equation group according to the dynamic data.

[0072] In specific implementation, the dynamic data includes the system generalized coordinate vector, the system generalized velocity vector, the system generalized acceleration vector, the system mass matrix, the Jacobian matrix of the system constraint equation, the system constraint multiplier vector and the system time. X is the system generalized coordinate vector. Generalized coordinates are variables that describe the state of the system. They are usually used to express the position information of the system. In a multi-body system, generalized coordinates are independent coordinates used to define the spatial position of each object. They can be a combination of the position, angle or other geometric quantities of a particle. The choice of generalized coordinates usually depends on the symmetry and constraints of the problem. is the system generalized velocity vector. Generalized velocities are the time derivatives of generalized coordinates. They describe the rate at which generalized coordinates change over time and represent information such as the velocity or angular velocity of an object. Generalized velocities and generalized coordinates are usually used together to construct the Lagrange equations. is the system generalized acceleration vector. Generalized acceleration is the derivative of generalized velocity with respect to time. It represents the acceleration of an object in generalized coordinates and is one of the core quantities used to describe motion in the vehicle motion model. M is the system mass matrix. The mass matrix (MM) is a matrix that describes the mass distribution of the system. It is usually a symmetric positive definite matrix that contains the mass information of each object in the system and the coupling relationship between them. In Lagrangian dynamics, the mass matrix appears in the motion equations together with the generalized acceleration. Q is the system load vector. The load vector F represents the external or internal force acting on the system. It includes all external forces applied to the system (such as gravity, resistance, etc.) and possible internal forces (such as spring force, connection force, etc.). The dimension of the load vector is usually the same as the generalized coordinate vector. C is the system constraint equation vector. The constraint equations represent the constraints existing in the system. These constraints can be geometric constraints (such as rigid body connections, motion restrictions) or mechanical constraints (such as maintaining certain force balances, etc.). They are usually expressed as a set of equations that restrict the possible values ​​of the generalized coordinates and generalized velocities. is the Jacobian matrix of the system constraint equation. The Jacobian Matrix is ​​the first-order partial derivative matrix of the constraint equation with respect to the generalized coordinates. It describes the rate of change of the constraint conditions in the generalized coordinate space and is usually used to linearize the constraint equations so that the Lagrange multiplier method or other numerical methods can be applied to solve the system's equations of motion. λ is the system constraint multiplier vector. The constraint multipliers (LagrangeMultipliers) are variables introduced into the constraint equations to consider the constraint forces. They are used in the Lagrange multiplier method to solve optimization problems containing constraints. The dimension of this vector is usually the same as the number of constraint equations, representing the corresponding "force" of each constraint equation. t is the system time, which is the basic variable of the dynamic evolution of the system and represents the process of the object state changing over time. In the vehicle motion model, all generalized coordinates, velocities, accelerations, forces and other quantities are related to time, and the behavior of the system is usually predicted by solving the time evolution equation.

[0073] The vehicle motion model equations are established based on the dynamics data, including:

[0074] The vehicle motion model equation is constructed using the system generalized coordinate vector, the system generalized velocity vector, the system generalized acceleration vector, the system mass matrix, the system constraint equation Jacobian matrix, the system constraint multiplier vector and the system time. Specifically, when constructing the vehicle motion model equation, the first function is constructed using the system mass matrix and the system generalized acceleration vector. Construct the system load vector using the system generalized velocity vector, system generalized coordinate vector and system time Construct the second function using the Jacobian matrix of the system constraint equation, the system constraint multiplier vector and the system time The vehicle motion model equation is obtained by using the first function, the second function and the system load vector, that is, the sum of the second function and the system load vector is equal to the first function. The final vehicle motion model equation is as follows:

[0075]

[0076] The vehicle motion model constraint equations are constructed using the system generalized coordinate vector and system time as shown below:

[0077] C(X,t)=0.

[0078] S102. Based on the target to be solved, add target parameters to the vehicle motion model equations, and add implicit differential algebraic equations to the vehicle motion model equations according to the target parameters and dynamics data.

[0079] In specific implementation, the system load vector Adding the target parameter α makes the system load vector become

[0080] Then, the inverse solution condition E is constructed using the system generalized coordinate vector, system generalized velocity vector, system generalized acceleration vector, system time and target parameter. The number of equations should be the same as the dimension of the target parameter α. The inverse solution condition is:

[0081] Specifically, when constructing, the target parameter α is an unknown vector. It is a mathematical parameter, not a generalized coordinate. There is no virtual displacement, so there is no need to consider its generalized force and constraint reaction force. In actual software applications, the diff modeling element of ADAMS can be directly applied to establish the inverse solution conditions and unknown vectors. The diff modeling element of ADAMS is a univariate differential equation, expressed as:

[0082]

[0083] For example, given a velocity curve of an object over time, you can use the diff element to perform an integration to get the position curve of the object over time. This modeling element supports implicit definition, that is, In this way, the diff element can be used to introduce the reverse solution conditions and unknown variables at the same time. The diff modeling element can be used to quickly introduce the reverse solution conditions directly in the software, and is fully compatible with the original modeling method, which improves the user's modeling efficiency.

[0084] It should be noted that the equation The explicit differential equation represented by is complete, that is, the number of equations is the same as the number of unknowns, and it can be solved. The equation represented is incomplete, it adds an extra equation, but no unknowns. In order to make the equation complete, it is necessary to define an additional external load, whose expression appears in the unknown, such as an sforce, that is, an externally applied force, whose expression usually involves some unknowns, such as position, velocity, or some system parameters, and its force expression is F = α.

[0085] Based on the inverse solution of the boundary conditions, the implicit differential algebraic equation is obtained and added to the vehicle motion model equation group, which is finally as follows:

[0086]

[0087] S103, using the vehicle motion model equation group after adding target parameters and implicit differential algebraic equations to reversely solve the vehicle motion model.

[0088] In specific implementation, since the target parameters and implicit differential algebraic equations are added to the vehicle motion model equations, the equations can be solved forward and reversely. In one example, the wheel driving force is inversely solved in ADAMS to achieve the wheel center height control of KC. Figure 2 As shown, if the tire lifting force is known and the wheel center lifting distance is calculated, it is a forward calculation. If the wheel center lifting distance is known and the tire lifting force is calculated, it is a reverse calculation. The reverse calculation is used as an example for explanation below.

[0089] Assume that the left wheel center lift distance d = 0.1m. At this time, it is necessary to establish a mark point at the left wheel center and define it as 1, that is, this is the location where the force and motion are applied. Then establish a diff element and define it as 1. Set it as implicit and the expression is: DZ(1)-step(time,0,0,1,0.1), where the tire lift force is the target parameter. The relationship between the wheel center lift distance and the tire lift force is defined in this implicit expression. Then establish a contact force between the tire and the platform, set the contact stiffness to 1e8, the contact index to 2.2, and the contact damping to 1e3. Finally, establish a unidirectional force F on the platform. z , the direction is vertically upward, the expression is: dif(1), such as Figure 3 Here, an unknown variable is introduced, which just matches the wheel center height equation. In this way, the overall number of equations and the number of degrees of freedom of the system are equal, and the solution can be determined. Then, a quasi-static simulation of the suspension system is performed, and the simulation time is set to 1 second. The output F z The time history curve of the lift is the value at 1 second, which is the lift force to be calculated. The reverse calculation yields F z Of course, the tire contact force and the forces acting on each bushing can also be output simultaneously, thus achieving the simultaneous implementation of forward and reverse solutions.

[0090] In this step, the vehicle motion model can also be adjusted in real time according to the results obtained in the forward or reverse direction, so that the various components of the model are more coordinated.

[0091] In the inverse solution method of a vehicle motion model provided in this embodiment, the dynamic data of the vehicle motion model is first obtained, and the vehicle motion model equation group is established according to the dynamic data, and then the target parameter is added to the vehicle motion model equation group, and the implicit differential algebraic equation is added to the vehicle motion model equation group according to the target parameter and the dynamic data, and finally the vehicle motion model is reversely solved by using the vehicle motion model equation group after adding the target parameter and the implicit differential algebraic equation. By adding the target parameter to the vehicle motion model equation group when constructing the model, and adding the implicit differential algebraic equation to the vehicle motion model equation group through the target parameter, a constraint relationship is added to the variables in the vehicle motion model, so that the equation group supports both forward and reverse solutions, and realizes that forward solution and reverse solution share a set of models and a set of algorithm codes, and reverse solution does not require additional modification of the physical model, thereby improving the versatility of reverse solution.

[0092] Exemplary Devices

[0093] In an exemplary embodiment of the present specification, a reverse solution device 400 for a vehicle motion model is also provided, such as Figure 4 As shown, including:

[0094] An acquisition unit 401 is used to acquire dynamic data of a vehicle motion model and establish a vehicle motion model equation group according to the dynamic data;

[0095] The processing unit 402 is used to add target parameters to the vehicle motion model equations based on the target to be solved, and add implicit differential algebraic equations to the vehicle motion model equations according to the target parameters and dynamics data;

[0096] The solving unit 403 is used to perform reverse solving of the vehicle motion model by using the vehicle motion model equation group after adding the target parameters and the implicit differential algebraic equations.

[0097] In one embodiment, the dynamics data includes a system generalized coordinate vector, a system generalized velocity vector, a system generalized acceleration vector, a system mass matrix, a Jacobian matrix of a system constraint equation, a system constraint multiplier vector, and a system time;

[0098] The acquisition unit 401 is specifically used for:

[0099] Constructing the vehicle motion model equation using system generalized coordinate vector, system generalized velocity vector, system generalized acceleration vector, system mass matrix, system constraint equation Jacobian matrix, system constraint multiplier vector and system time;

[0100] The constraint equations of the vehicle motion model are constructed using the system generalized coordinate vector and system time.

[0101] In one implementation, the acquisition unit 401 is specifically configured to:

[0102] The first function is constructed using the system mass matrix and the system generalized acceleration vector;

[0103] Construct the system load vector using the system generalized velocity vector, system generalized coordinate vector and system time;

[0104] constructing a second function using the Jacobian matrix of the system constraint equation, the system constraint multiplier vector and the system time;

[0105] The vehicle motion model equation is obtained using the first function, the second function and the system load vector, that is, the sum of the second function and the system load vector is equal to the first function.

[0106] In one implementation, the processing unit 402 is specifically configured to:

[0107] Include target parameters in the system load vector.

[0108] In one implementation, the processing unit 402 is specifically configured to:

[0109] Using the system generalized coordinate vector, system generalized velocity vector, system generalized acceleration vector, system time and target parameters, the inverse solution condition is constructed;

[0110] Based on the inverse solution of the boundary conditions, the implicit differential algebraic equation is obtained and added into the vehicle motion model equation group.

[0111] In one implementation, the processing unit 402 is further configured to:

[0112] Based on the inverse solution results of the target to be solved, the vehicle motion model is corrected.

[0113] The inverse solution device for the vehicle motion model provided in this embodiment belongs to the same application concept as the inverse solution method for the vehicle motion model provided in the above embodiments of this application, and can execute the inverse solution method for the vehicle motion model provided in any of the above embodiments of this application, and has the corresponding functional modules and beneficial effects of executing the inverse solution method for the vehicle motion model. For technical details not fully described in this embodiment, please refer to the specific processing content of the inverse solution method for the vehicle motion model provided in the above embodiments of this application, and will not be repeated here.

[0114] Exemplary Devices

[0115] In an exemplary embodiment of the present specification, an electronic device is also provided, such as Figure 5As shown, the electronic device may include: a processor 510, a communication interface 520, a memory 530 and a communication bus 540, wherein the processor 510, the communication interface 520 and the memory 530 communicate with each other through the communication bus 540. The processor 510 may call the logic instructions in the memory 530 to execute the inverse solution method of the vehicle motion model, and the method includes:

[0116] Acquire dynamic data of the vehicle motion model, and establish a vehicle motion model equation group based on the dynamic data;

[0117] Adding target parameters to the vehicle motion model equations, and adding implicit differential algebraic equations to the vehicle motion model equations based on the target parameters and dynamics data;

[0118] The vehicle motion model is reversely solved using the vehicle motion model equations after adding target parameters and implicit differential algebraic equations.

[0119] In addition, the logic instructions in the above-mentioned memory 530 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0120] Exemplary computer program products and storage media

[0121] In addition to the above-mentioned methods and devices, the inverse solution method of the vehicle motion model provided in the embodiments of this specification may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the inverse solution method of the vehicle motion model according to various embodiments of this specification described in the above "Exemplary Method" section of this specification.

[0122] The computer program product can be written in any combination of one or more programming languages ​​to write program codes for executing the operations of the embodiments of this specification, and the programming languages ​​include object-oriented programming languages, such as Java, C++, etc., and also include conventional procedural programming languages, such as "C" language or similar programming languages.

[0123] In addition, an embodiment of the present specification also provides a computer-readable storage medium on which a computer program is stored, and the computer program is executed by a processor to execute the steps of the inverse solution method of the vehicle motion model according to various embodiments of the present specification described in the above "Exemplary Method" section of the present specification.

[0124] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods 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 memory, storage, database or other media used in the embodiments provided in this specification can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0125] 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.

[0126] The above-mentioned embodiments only express several implementation methods of this specification, and the descriptions are relatively specific and detailed, but they cannot be understood as limiting the scope of the solutions provided by the embodiments of this specification. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of this specification, which all belong to the protection scope of this specification. Therefore, the protection scope of the patent of this specification shall be based on the attached claims.

Claims

1. A method for inverse solution of a vehicle motion model, characterized in that: include: Acquiring dynamic data of a vehicle motion model, and establishing a vehicle motion model equation group according to the dynamic data; Based on the target to be solved, adding target parameters to the vehicle motion model equations, and adding implicit differential algebraic equations to the vehicle motion model equations according to the target parameters and the dynamics data; The target to be solved is reversely solved by using the vehicle motion model equation group after adding the target parameters and the implicit differential algebraic equation.

2. The method according to claim 1, characterized in that The dynamics data include system generalized coordinate vector, system generalized velocity vector, system generalized acceleration vector, system mass matrix, system constraint equation Jacobian matrix, system constraint multiplier vector and system time; Then the vehicle motion model equations are established according to the dynamics data, including: Constructing a vehicle motion model equation using the system generalized coordinate vector, the system generalized velocity vector, the system generalized acceleration vector, the system mass matrix, the system constraint equation Jacobian matrix, the system constraint multiplier vector and the system time; The vehicle motion model constraint equations are constructed using the system generalized coordinate vector and the system time.

3. The method according to claim 2, characterized in that The vehicle motion model equation is constructed by using the system generalized coordinate vector, the system generalized velocity vector, the system generalized acceleration vector, the system mass matrix, the system constraint equation Jacobian matrix, the system constraint multiplier vector and the system time, including: Constructing a first function using the system mass matrix and the system generalized acceleration vector; Constructing a system load vector using the system generalized velocity vector, the system generalized coordinate vector and the system time; constructing a second function using the system constraint equation Jacobian matrix, the system constraint multiplier vector and the system time; The vehicle motion model equation is obtained using the first function, the second function and the system load vector, that is, the sum of the second function and the system load vector is equal to the first function.

4. The method according to claim 3, characterized in that The adding of target parameters into the vehicle motion model equations comprises: The target parameter is added to the system load vector.

5. The method according to claim 4, characterized in that The step of adding an implicit differential algebraic equation to the vehicle motion model equation group according to the target parameter and the dynamics data includes: Using the system generalized coordinate vector, the system generalized velocity vector, the system generalized acceleration vector, the system time and the target parameter, constructing an inverse solution condition; The implicit differential algebraic equation is obtained based on the inverse solution condition and added into the vehicle motion model equation group.

6. The method according to claim 5, characterized in that The method further comprises: Based on the inverse solution result of the target to be solved, the vehicle motion model is corrected.

7. A reverse solution device for a vehicle motion model, characterized in that: include: An acquisition unit, used for acquiring dynamic data of a vehicle motion model and establishing a vehicle motion model equation group according to the dynamic data; A processing unit, configured to add a target parameter to the vehicle motion model equations based on a target to be solved, and to add an implicit differential algebraic equation to the vehicle motion model equations according to the target parameter and the dynamics data; A solving unit is used to perform reverse solving of the vehicle motion model by using the vehicle motion model equation group after adding the target parameter and the implicit differential algebraic equation.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the inverse solution method of the vehicle motion model according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the inverse solution method of the vehicle motion model according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the inverse solution method of the vehicle motion model according to any one of claims 1 to 6 is implemented.

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