Wheel vehicle design parameter and control parameter collaborative optimization method and related device

Abstract

The application discloses a wheel vehicle design parameter and control parameter collaborative optimization method and related device, relates to the wheel vehicle design development technical field, and first establishes a dynamics model of a wheel vehicle, and then establishes a collaborative optimization model of the wheel vehicle based on the dynamics model; optimization variables of the collaborative optimization model include state parameters, design parameters and control parameters; the collaborative optimization model includes a cost function and constraint conditions; the constraint conditions include dynamics constraints, variable value upper and lower bound constraints, path constraints and boundary constraints; the dynamics constraints are constraints established based on the dynamics model; finally, the collaborative optimization model is optimized and solved to obtain optimal design parameters and optimal control parameters of the wheel vehicle. The application performs collaborative optimization on the design parameters and control parameters of the wheel vehicle based on the dynamics model, so as to realize optimal performance of the vehicle.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wheeled armored vehicle design and development, and particularly relates to a method for collaborative optimization of design parameters and control parameters of an 8x8 wheeled armored vehicle based on a dynamic model and a related device. BACKGROUND

[0002] In the all-electric land platform, wheeled vehicles have higher driving speed than tracked vehicles, and have faster mobility on flat and hardened roads, which makes wheeled vehicles more suitable for tasks such as rapid mobility, rapid deployment and rapid response. And its structure is relatively simple, easier to maintain and repair, reducing the cost and complexity of maintenance and cost. The structure is lighter, relatively easy to air transport, which may be an important consideration in the case of the need to quickly deploy military forces. The performance of the electric drive system as an important source of power for the all-electric land platform directly determines the level of vehicle performance that can be achieved. Among various types of electric drive systems, the hub motor drive system has multiple advantages, simplifying mechanical transmission devices and realizing direct transmission of driving force to the wheels. On the other hand, each electric wheel can be independently driven and braked, increasing the redundancy of the system and providing more degrees of freedom for dynamic control. Each hub can be controlled independently to achieve faster and more accurate turning and vehicle stability. And it improves the level of modularization and generalization design of the vehicle. Vehicles with different tactical functions can use the same chassis system. The electric wheels in it have universality and interchangeability, which can effectively reduce the development and maintenance costs in the early stage, and also reduce the pressure of battlefield logistics support. In summary, distributed hub motor drive armored vehicles are one of the important development directions of new generation military vehicles. It is necessary to develop new equipment for wheeled armored vehicles.

[0003] Current wheeled armored vehicle development still relies heavily on empirical design and development methods. Design parameters are calculated based on requirements, then optimized based on static models. After optimization, control is applied to specific operating conditions. This results in a limited optimization space and poor dynamic performance. Furthermore, each subsystem is designed independently, limiting overall vehicle performance. Existing optimization methods based on evolutionary algorithms can consider the vehicle's dynamic model, but their lack of gradient information results in inefficient parameter optimization. Existing optimization algorithms based on dynamic models, such as dynamic programming or indirect methods, are extremely sensitive to the problem size and initial solution, making rapid optimization of multiple parameters difficult. Furthermore, vehicle design parameters often couple with control parameters to influence overall vehicle performance. However, existing research focuses solely on optimizing design or control parameters, significantly narrowing the global optimization space and limiting the potential for vehicle performance improvement. Four-wheeled vehicles have relatively simple operating conditions, resulting in rapid optimization solutions and the practicality of statically optimized design parameters. However, 8×8 wheeled vehicles have complex operating conditions, and their performance is dependent on both design and control parameters. The coupling of these two factors impacts vehicle maneuverability and mobility, making it difficult to achieve optimal performance by separating design and control. Summary of the Invention

[0004] The purpose of this application is to provide a method and device for collaborative optimization of design parameters and control parameters of a wheeled vehicle, which can collaboratively optimize the design parameters and control parameters to achieve optimal performance of the wheeled vehicle.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for collaboratively optimizing design parameters and control parameters of a wheeled vehicle, comprising:

[0007] Establish a dynamic model of wheeled vehicles;

[0008] A collaborative optimization model for the wheeled vehicle is established based on the dynamic model; the optimization variables of the collaborative optimization model include state parameters, design parameters, and control parameters; the collaborative optimization model includes a cost function and constraints, the constraints including dynamic constraints, upper and lower bound constraints on variable values, path constraints, and boundary constraints, and the dynamic constraints are constraints established based on the dynamic model;

[0009] The collaborative optimization model is optimized and solved to obtain the optimal design parameters and optimal control parameters of the wheeled vehicle.

[0010] In a second aspect, the present application 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 the above-mentioned method for collaborative optimization of design parameters and control parameters of a wheeled vehicle.

[0011] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for collaborative optimization of design parameters and control parameters of a wheeled vehicle.

[0012] In a fourth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the above-mentioned method for collaborative optimization of design parameters and control parameters of a wheeled vehicle.

[0013] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0014] This application provides a method and related device for collaborative optimization of design and control parameters of a wheeled vehicle. The method first establishes a dynamic model of the wheeled vehicle, then establishes a collaborative optimization model for the wheeled vehicle based on the dynamic model. The optimization variables of the collaborative optimization model include state parameters, design parameters, and control parameters. Finally, the collaborative optimization model is optimized and solved to obtain the optimal design and control parameters of the wheeled vehicle. This application collaboratively optimizes the design and control parameters of a wheeled vehicle based on the dynamic model, achieving optimal vehicle performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present application 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 application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 A schematic flow chart of a method for collaboratively optimizing design parameters and control parameters of a wheeled vehicle provided in Example 1 of the present application;

[0017] Figure 2 A schematic diagram illustrating the principle of a method for collaboratively optimizing design parameters and control parameters of a wheeled vehicle provided in Example 1 of the present application;

[0018] Figure 3 A schematic diagram of the wheeled vehicle dynamics model framework provided in Example 1 of the present application;

[0019] Figure 4 Schematic diagram of solving the Jacobian matrix provided in Example 1 of the present application. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0021] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0022] Example 1

[0023] like Figure 1 and Figure 2 As shown, this embodiment provides a method for collaborative optimization of design parameters and control parameters of a wheeled vehicle, taking an 8×8 wheeled armored vehicle as an example, including:

[0024] S1: Establish a dynamic model of wheeled armored vehicles.

[0025] S2: A collaborative optimization model of the wheeled armored vehicle is established based on the dynamic model; the optimization variables of the collaborative optimization model include state parameters, design parameters and control parameters; the collaborative optimization model includes a cost function and constraints, and the constraints include dynamic constraints, upper and lower bound constraints on variable values, path constraints and boundary constraints, and the dynamic constraints are constraints established based on the dynamic model.

[0026] S3: Optimize and solve the collaborative optimization model to obtain optimal design parameters and optimal control parameters of the wheeled armored vehicle.

[0027] By implementing the above steps S1 to S3, it is possible to simultaneously optimize the design parameters and control parameters of the wheeled armored vehicle based on its dynamic model. The optimal design parameters can be used for vehicle design, and the optimal control parameters can be used as a reference for the subsequent formulation of online control strategies. This solves the problem that existing research only optimizes design parameters or control parameters, greatly reducing the global optimization space and limiting the space for improving vehicle performance.

[0028] The structure of the electric transmission device of the wheeled armored vehicle is different from that of the tracked armored vehicle, and one prominent feature of the wheeled armored vehicle is that there are many axles and many types of vehicles. According to the number of axles, there are many types of vehicles such as 6x6, 8x8, 10x10, etc. As a main member of military vehicles, the 8x8 wheeled armored vehicle is mainly responsible for military reconnaissance, personnel transportation, logistical support and other tactical functions. The multifunctionality and adaptability of the 8x8 wheeled armored vehicle in combat make it an important part of modern armies and can play a key role in various tasks and battlefield environments. Therefore, it is necessary to develop new equipment for the 8x8 wheeled armored vehicle. The 8x8 wheeled armored vehicle has complex driving conditions, and the performance is related to the design parameters and control parameters, and the coupling of the two affects the passability and maneuverability of the vehicle. It is difficult to achieve optimal performance by separating design and control. Therefore, the embodiment aims to provide a wheeled armored vehicle design parameter and control parameter collaborative optimization method to collaboratively optimize the design parameters and control parameters to achieve the optimal performance of the wheeled armored vehicle.

[0029] Firstly, the present application applies the relevant knowledge of Lagrange dynamics to model the wheeled armored vehicle with high precision and vectorization, including the vehicle upper gun platform, suspension model, tire model, etc. The specific process of the wheeled armored vehicle dynamics modeling based on Lagrange dynamics is as follows:

[0030] The input of the multi-degree-of-freedom vehicle model is the steering wheel angle and the wheel torque, and the tire rotation is driven by the wheel torque and the ground longitudinal force. The input of the tire model module is the wheel rotational speed and the wheel center speed, and the output is the tire force and torque. The vertical motion of the unsprung mass is generated by the tire vertical force and the suspension force, and the motion of the vehicle body is generated by the longitudinal and lateral tire forces, the aerodynamic force and the suspension force. The input and output interfaces of each sub-module of the vehicle dynamics model are as shown in Figure 3 .

[0031] In order to improve the calculation efficiency, the vectorized programming method is used to establish a vehicle dynamics model supporting vectorized calculation. Figure 3 The whole vehicle dynamics model architecture of the wheeled armored vehicle includes the interaction between the vehicle body, the suspension, the unsprung mass and the tire model.

[0032] The multi-degree-of-freedom vehicle model represents the dynamic behavior of a simplified vehicle composed of multiple rigid components. The vehicle body has longitudinal, lateral and vertical movements, as well as rotations around the xyz axes. There are 8 rotational degrees of freedom for the 8 tires, and there are 8 vertical motion degrees of freedom for the 8 tires due to the presence of the suspension system.

[0033] In the modeling process, O-XYZ represents the earth coordinate system, O1-X1Y1Z1 represents the vehicle body coordinate system, and the multi-degree-of-freedom generalized coordinates are represented by a vector as follows:

[0034]

[0035] Where: q represents the generalized coordinates of the vehicle model; q bv Represents the position vector of the vehicle body’s center of mass, X A ,Y A ,Z A is the absolute displacement of the center of mass along the global coordinate system; q bω represents the rotation angle of the vehicle body’s center of mass, φ, ψ are the rotation angles of the vehicle body around the three axes of the global coordinate system; q uθ represents the angular velocity sequence generated by the rotation of the eight tires on both sides, θ i represents the angular velocity of tire rotation; q uz represents the vertical displacement sequence of the eight tires on both sides, z i Indicates the vertical displacement of the tire.

[0036] The xy coordinates of the eight tire centers in the vehicle coordinate system in the XY plane are:

[0037]

[0038] Where: l represents the longitudinal distance between the four axles of the vehicle and the center of mass, w represents the wheelbase of the four axles, l and w are the distances in the vehicle coordinate system, the subscript f represents the first axle, s represents the second axle, t represents the third axle, and r represents the fourth axle.

[0039] Unsprung mass, i.e. the relative vertical position z of the tire in the vehicle coordinate system w It can be represented by its absolute coordinates, vehicle roll angle, pitch angle and xy coordinates in the vehicle coordinate system, as shown below:

[0040]

[0041] Where z u Indicates the vertical position of the tire's center of mass.

[0042] The vehicle's 22-degree-of-freedom dynamic equation is derived based on Lagrangian dynamics, as shown below:

[0043]

[0044] Where: T represents the kinetic energy of the system, Q b represents the generalized force on the sprung mass, Q u represents the generalized force on the unsprung mass, q b represents the generalized coordinates of the vehicle body, i.e., the sprung mass, q u Represents the generalized coordinates of the eight tires, i.e., the unsprung masses, including the tire angles and vertical positions.

[0045] When calculating the kinetic energy of the sprung mass, the kinetic energy of the tire's lateral and longitudinal motion is taken into account, as shown in the following formula:

[0046]

[0047] Where: V b 、V ui are the components of the vehicle body generalized velocity and unsprung mass velocity in the vehicle body coordinate system, M b 、M ui are the mass matrices of the vehicle body and the unsprung mass when calculating the lateral and longitudinal kinetic energy. ui This is the aforementioned parameter V u,i Parameter M ui This is the aforementioned parameter M u,i .

[0048] There is a coordinate transformation between the vehicle coordinate system and the earth coordinate system, and the transformation matrix is:

[0049]

[0050] From the above formula, we can get the vehicle speed V b In the vehicle coordinate system:

[0051]

[0052] The lateral and longitudinal velocities of the eight wheels in the vehicle coordinate system can be determined by their relative positions h in the vehicle coordinate system. b,u,i The following vehicle speed is expressed as follows:

[0053]

[0054] Among them, the subscript i represents the sequential number of the 8 tires, which are the right wheel of the first axle, the left wheel of the first axle, the right wheel of the second axle, the left wheel of the second axle, the right wheel of the third axle, the left wheel of the third axle, the right wheel of the fourth axle, and the left wheel of the fourth axle.

[0055] Through coordinate transformation and matrix calculation, the kinetic energy of the sprung mass can be expressed as:

[0056]

[0057] Where M gb =[h A,b ] T [M b ][h A,b ]+Σ[h A,b ] T [h b,u,i ] T [M ui ][h b,u,i ][h A,b ].

[0058] The kinetic energy of the unsprung mass is composed of the rotation and vertical motion of the tires, as shown in the following equation:

[0059]

[0060] where V uz and ω u are the vertical velocity and angular velocity of the eight tires under the suspension, respectively, and M u and J u are the mass matrix and the moment of inertia matrix for the calculation of the vertical kinetic energy of the unsprung mass. According to the matrix calculation, the equation can be converted into the form of generalized coordinates and generalized mass matrix:

[0061]

[0062]

[0063] where m u,fr ,…,m u,rl represent the mass of the eight tires, and j u,fr ,…j u,rl represent the moment of inertia of the eight tires.

[0064] The vertical force acting on the sprung mass (vehicle body) includes the vehicle body weight, suspension force, and vertical air resistance, and the longitudinal force includes the lateral force and longitudinal force generated between the tire and the ground, as well as the component forces of the air resistance in the global coordinate system. The lateral force includes the component forces of the lateral and longitudinal forces received by the tire. The three-axis torque received includes the torque on the center of mass point, tire righting torque, air resistance torque, and the reaction torque received by the driving tire.

[0065] The vertical force received by the tire is composed of the tire's own weight m u,i g and the suspension force F bs,i :

[0066] F z,i = F bs,i + m u,i g (13) ;

[0067] The longitudinal slip ratio of the tire contact point with the ground is calculated as follows:

[0068]

[0069] where κ i is the tire slip ratio, ω i is the tire rotation angular velocity, R e,i is the effective radius of the wheel, v wx,i , and v wy,i are the wheel center longitudinal and lateral velocities, respectively, and are calculated as shown in the following equation:

[0070]

[0071] Where: v ux,i , v uy,i The lateral and longitudinal speeds of the tire in the vehicle coordinate system, δ i For tire corners.

[0072] Apply the magic formula to calculate the tire longitudinal force F x,i , lateral force F y,i , return torque M z,i , rolling resistance moment M y,i , yaw resistance moment M x,i .

[0073] The suspension force is composed of spring force and damping force. When the stiffness and damping ratio are constant, the suspension force can be expressed as:

[0074]

[0075] Where, F bss,i is the suspension force, k bs,i is the suspension stiffness coefficient, Δl s.i is the suspension deformation, c d,i is the suspension damping coefficient, is the suspension deformation speed.

[0076] To calculate the air resistance, first calculate the air resistance slip angle. Assuming the air is still and the wind speed is 0, the air resistance slip angle is the vehicle's center of mass slip angle. The calculation method is as follows:

[0077]

[0078] Among them, α air is the air resistance slip angle, is the longitudinal velocity of the vehicle, is the lateral velocity of the vehicle body, and ψ is the yaw angle of the vehicle body.

[0079] Then, the air resistance coefficient is calculated by linear interpolation and cubic spline interpolation according to the air resistance side slip angle:

[0080]

[0081] C Fx ,C Fy , C Fz , C Mx , C My , C Mz Respectively represent the longitudinal, lateral, vertical air resistance and the air resistance moment around the x-axis, y-axis, and z-axis air resistance coefficients, interp represents the interpolation function, α air,refRepresents the interpolated air resistance side slip angle reference value, C Fx,ref ,C Fy,ref , C Fz,ref , C Mx,ref , C My,ref , C Mz,ref They represent the interpolated longitudinal, lateral, and vertical air resistance values, as well as the air resistance moments around the x-axis, y-axis, and z-axis, respectively. method: linear represents the application of the linear interpolation method, and method: spline represents the application of the cubic spline interpolation method.

[0082] Calculate the air resistance factor Q:

[0083]

[0084] Where D is the air density.

[0085] In summary, the above results are brought in to calculate the air resistance:

[0086]

[0087] Where: A air is the frontal area of ​​the vehicle, L air Calculate the length for the vehicle's air resistance.

[0088] Based on the calculation of the tire force and suspension model above, the generalized force matrix acting on the sprung mass is as follows:

[0089]

[0090] Among them, δ i is the tire angle input, T d,i is the tire torque input.

[0091] The forces acting on the unsprung mass include the suspension force, the vertical force from the ground, and the weight of the tire itself, while the torques acting on it include the driving torque and the rolling resistance torque of the tire. The generalized force matrix is ​​expressed as:

[0092]

[0093] Among them, δ i is the tire angle input, T d,i is the tire torque input, and g is the acceleration due to gravity.

[0094] Based on force analysis and the principle of virtual work, the generalized force can be derived as shown in the following equation:

[0095]

[0096] According to Lagrangian mechanics and D'Alembert's principle, the generalized equation of motion for the sprung mass is as follows:

[0097]

[0098] The generalized equation of motion for the unsprung mass is as follows:

[0099]

[0100] The second-order differentials of all state variables can be obtained by equations (20) and (21), and the first-order differentials of the state variables can be obtained by integration. The values ​​of all state variables can be obtained by calculation.

[0101] After establishing the dynamic model of the wheeled armored vehicle, we further construct the collaborative optimal design and optimal control problem. According to the needs, we consider multiple objectives such as maneuverability and vertical impact, establish a cost function, establish dynamic constraints (residual constraints) based on the dynamic model, extract the upper and lower bounds of the vehicle state according to the vehicle driving conditions, establish boundary constraints, and design path constraints and boundary constraints according to the needs. The path constraint is to limit the dynamic state of the vehicle such as displacement and speed when driving on the path to ensure that the vehicle follows a specific trajectory. The boundary constraint is to limit the state parameters (such as position, speed, acceleration, etc.) of the vehicle at the starting and ending times to ensure that the initial and final states of the vehicle meet specific requirements. The collaborative optimal design and optimal control problem is established by combining the design parameters and control parameters. Complete the objective function, upper and lower bounds of the constraints, and guess the initial value as needed. It is also necessary to set some parameters related to optimization, such as the transformation method m trans (The transformation method is a method for calculating residual constraints, Hermite-Simpson see formula (23)), differential method m diff , allocation point number N n , zoom settings scal, etc.

[0102] According to the established vehicle dynamics model, the input of the problem is provided, including the initial guess value x0 of the state quantity, the initial guess value u0 of the control quantity, and the terminal time t f0 And the design variable p0. Design variables include suspension stiffness, tire radius, etc.

[0103]

[0104] Among them, N x,usr is the number of discrete points of the state variable provided by the user. Each discrete point can be considered as a moment, and the value of the state parameter of each discrete point needs to be determined; t is the time from 0 to t f0 Discretize into N x,usr Time series of points, N u,usr is the number of discrete points of the control variable provided by the user, n x is the number of state variables of the dynamic equation; nu is the number of control variables. If the terminal time is also a parameter to be optimized, then n tf is 1, otherwise it is 0, n p is the number of design parameters, Represents a two-dimensional matrix with i rows and j columns.

[0105] The above state variables, control variables, terminal time, upper and lower bounds of design variables and inequality constraints g are given by the following formula, n g is the number of path constraints:

[0106]

[0107] Among them, x min is the lower bound of the state parameter; x max is the upper bound of the state parameter; u min is the lower bound of the control parameter; u max is the upper bound of the control parameter; p min is the lower bound of the design parameter; p max is the upper bound of the design parameters; t fmin is the lower bound of the termination time; g min is the lower bound of the path constraint function; g max is the upper bound of the path constraint function.

[0108] In addition to the above initial values ​​and upper and lower bounds, the problem input also includes the Lagrangian term of the objective function. Meyer The first-order dynamic constraint function f, the path constraint function g, and the boundary constraint function b, the input and output dimensions of the above functions are shown in the following formula:

[0109]

[0110] A collaborative optimization model of wheeled vehicles is established based on the dynamic model. The optimization variables of the collaborative optimization model include state parameters, design parameters, and control parameters. The collaborative optimization model includes cost functions and constraints. The constraints include dynamic constraints, upper and lower bounds of variable values, path constraints, and boundary constraints. Dynamic constraints are constraints established based on the dynamic model. The collaborative optimization model is:

[0111]

[0112] Among them, J is the cost function; For Meyer Xiang; is the Lagrangian term; t0 is the starting time; t f is the end time; is the dynamic constraint; is the first-order derivative of the state parameter at time t; f[.] is the dynamic constraint function; x(t) is the state parameter at time t; u(t) is the control parameter at time t; p is the design parameter; x min ≤x(t)≤x max 、u min ≤u(t)≤u max 、p min ≤p≤p max The upper and lower bounds of the variable value are constrained; x min is the lower bound of the state parameter; x max is the upper bound of the state parameter; u min is the lower bound of the control parameter; u max is the upper bound of the control parameter; p min is the lower bound of the design parameter; p max is the upper bound of the design parameters; g min ≤g[x(t),u(t),t,p]≤g max is the path constraint; g min is the lower bound of the path constraint function; g[.] is the path constraint function; g max is the upper bound of the path constraint function; b min ≤b[x(t0),t0,x(t f ),t f ,p]≤b max is the boundary constraint; b min is the lower bound of the boundary constraint function; b[.] is the boundary constraint function; x(t0) is the state parameter at the starting time t0; x(t f ) is the end time t f State parameters of b max is the upper bound of the boundary constraint function.

[0113] Among them, the state parameters are:

[0114]

[0115] The 8 tires are distinguished by subscripts. The subscript ufr represents the right tire of the first axle, ufl represents the left tire of the first axle, usr represents the right tire of the second axle, usl represents the left tire of the second axle, utr represents the right tire of the third axle, utl represents the left tire of the third axle, urr represents the right tire of the fourth axle, and url represents the left tire of the fourth axle.

[0116] Design parameters are defined according to requirements;

[0117] The control parameters are the rotation angle and torque of the eight tires;

[0118] After the optimal design and control problem is constructed, the problem is solved, i.e. the equation (29) is solved to obtain the optimal control parameters and the optimal design parameters. The problem is transformed into an NLP problem, and the problem is solved by sparse matrix operation, improved direct collocation method, automatic normalization (corresponding to the scaling part of (3) below), and high-performance cluster computing. The sparse matrix operation and high-performance cluster computing are applied in programming to improve the problem solving efficiency and complete the efficient solution of the problem. The optimal design parameters are applied to vehicle development, and the optimal control parameters are applied to guide the development of vehicle control algorithm, so as to realize the collaborative optimization control of the 8x8 wheeled vehicle.

[0119] In the problem solving part, the problem needs to be transformed into a large-scale NLP problem, and then a bottom solver such as IPOPT is called to solve the problem. In the problem transformation part, the format of the variables and functions in the problem input is transformed into the format required by the NLP solver. In this part, the local collocation method, the differential method, and the automatic scaling method are developed. In the last part, the NLP solver is applied to solve the problem.

[0120] (1) Local collocation method

[0121] (1.1) NLP solver variables

[0122] In the local collocation method, the continuous state variables and control variables in the entire time interval can be discretized into N n nodes by using the linear interpolation method, and the new variables obtained are given by the following equation:

[0123]

[0124] Then, the discretized state variables, control variables and design variables are reconstructed into NLP column vectors, as shown in the following equation.

[0125]

[0126] For the Hermite-Simpson method (local collocation method), when the control variables in each sampling interval are also selected as the parameters to be optimized, the reconstructed NLP variables are:

[0127]

[0128] When the state variables in each sampling interval are also selected as the parameters to be optimized, the reconstructed NLP variables are as follows:

[0129]

[0130] In the formula, subscripts are used to represent different moments, and the underlined variables are obtained by calculating the corresponding two variables before and after them, that is, for and Calculated control parameters. for and Calculated state parameters.

[0131] The upper and lower bounds of the variables in formula (27) must also be reconstructed based on the reconstructed NLP variables.

[0132] (1.2) Constraints (this step is to process the constraints)

[0133] 1) Residual constraints of the Hermite-Simpson method

[0134] Based on the third-order Hermite interpolation, at x k with x k+1 The state variables between and its derivatives It can be deduced as follows:

[0135]

[0136] Where h is the time interval between discrete points, that is, the time interval between two adjacent moments (i.e., two adjacent nodes).

[0137] Based on the Hermite-Simpson method, Residual constraint at It can be expressed as:

[0138]

[0139] Apply linear interpolation to calculate the control parameter u at time k k and the control parameter u at time k+1 k+1 Control variables between as follows:

[0140]

[0141] In order to reduce repeated calculations in numerical calculations, matrix calculation method is applied, so the state variables and control variables It can be expressed as:

[0142]

[0143] is the dynamic equation of the system:

[0144]

[0145] Transformation matrix T s1 and T s2 are:

[0146]

[0147] The residual constraint ζ can be expressed in matrix computation as:

[0148]

[0149] 2) Residual constraint of Trapezoidal method

[0150] Residual constraint ζ of Trapezoidal method k can be expressed as:

[0151]

[0152] in matrix computation as:

[0153]

[0154] When calculating the residual constraint, one of the residual constraint of Hermite-Simpson method in 1) above and the residual constraint of Trapezoidal method in 2) above can be selected.

[0155] 3) Path constraint and boundary constraint

[0156] The path constraint is a function of state parameters, control parameters, design parameters and time, and the boundary constraint is a function of initial state (i.e. state parameters at initial time) x0and final state (i.e. state parameters at final time) x f The path constraint is as follows:

[0157]

[0158] Finally, after all the constraint calculations are completed, the NLP constraint is expressed as:

[0159]

[0160] All the upper and lower bounds of residual constraints are 0.

[0161] (1.3) Jacobians matrix - Jacobian matrix (this step is to calculate the derivative of the above constraints with respect to variables) 1) Jacobians matrix of residual constraint

[0162] Without considering the state parameters and control parameters at the midpoint of each discrete time interval, the residual constraint is x a , ua ,x b ,u b ,t f ,p function, such as Figure 4 As shown, x a ,u a ,x b ,u b The specific values ​​are as follows:

[0163] To avoid repeated calculations, perform matrix calculations and first calculate the derivatives of ζ with respect to x, u, and p:

[0164]

[0165] Therefore, the Jacobians matrix can be simplified to:

[0166]

[0167] Where, is a matrix with the i-th column being 1 and the rest of the elements being 0, T a ,T b It is given by:

[0168]

[0169] in,

[0170] 2) Jacobians matrix of path constraints and boundary constraints

[0171] First, calculate the derivative of the path constraint g with respect to x, u, p, t as follows:

[0172]

[0173] Then calculate the boundary constraint b for x0,x f ,p,t’s derivative is as follows:

[0174]

[0175] (1.4) Objective function (cost function)

[0176] The input objective function consists of Mayer terms and Lagrange terms, which can be expressed as:

[0177]

[0178] (1.5) Gradient of the objective function

[0179] The objective function is [x,u,t f,p], so its gradient to state parameters and control parameters is as follows:

[0180]

[0181] Then calculate the gradient of the objective function with respect to the terminal time and the design parameters, and finally obtain the NLP gradient, as follows:

[0182]

[0183] (2) Difference method (when calculating the Jacobians matrix, it is necessary to differentiate the variables, and this difference method is used for differentiation)

[0184] Most NLP solvers require constraints on the first-order derivatives of c(y), and accurate calculation of these derivatives helps achieve faster convergence. Among them, the difference methods implemented include forward finite differences, central differences, complex step differences, and analytical differences. Any of the above difference methods can be used. The forward finite difference approximation method is denoted as:

[0185]

[0186] Where x is the state quantity used to calculate the derivative; h i is the time interval.

[0187] The central finite difference approximation method is:

[0188]

[0189] The complex step-size differential approximation is based on Taylor expansion:

[0190]

[0191] Ignore h 2 and higher-order terms, the approximation error is The first-order derivative of can be approximated as:

[0192] f'(x)=Im[f(x+ih)] / h(57);

[0193] Where i is the imaginary number symbol.

[0194] Another method is the analytical method, which is to calculate through the symbolic toolbox to obtain the analytical form of the first-order derivative. This method can obtain accurate derivative values, but it may be difficult to obtain or impossible to obtain the analytical form for some complex problems.

[0195] (3) Zoom

[0196] (3.1) Variable scaling

[0197] Apply linear scaling to scale state variables, control variables, terminal times, and design variables:

[0198]

[0199]

[0200] Apply a gradient-based average norm method to scale the constraints for faster convergence:

[0201] The residual constraints are scaled as follows:

[0202]

[0203] Where N scal is the number of loops used to solve for the scaling factor; j,i is the i-th residual constraint in the j-th cycle.

[0204] The path constraint is scaled as follows:

[0205]

[0206] In formula (61), g j,i is the i-th path constraint in the j-th cycle.

[0207] The bounds constraints are scaled as follows:

[0208]

[0209] In formula (62), b j,i is the i-th boundary constraint in the j-th cycle.

[0210] The cost function is not scaled.

[0211] (3.3) Scaling of Jacobians

[0212] The relationship between scaling variables and scaling constraints is considered in the scaling of the Jacobians matrix. The scaling of the Jacobians matrix of the residual constraint is defined as follows:

[0213]

[0214] Where, J ζ,i is the Jacobian matrix of the residual constraint; ξ i is the row vector of scaling factors in equation (59); is the scaling factor for the residual constraint.

[0215] Similarly, the scaling of the Jacobians matrix for path constraints is:

[0216]

[0217] Where, J g,i is the Jacobian matrix of the path constraint; is the scaling factor for the path constraint.

[0218]

[0219] Where, J b,i is the Jacobian matrix of the boundary constraints; is the scaling factor for the boundary constraints.

[0220] The gradient of the cost function is not scaled.

[0221] After the above problem transformation, the transformed problem is input into the underlying solver for solution, which can solve the optimal control parameters and the optimal design parameters at the same time.

[0222] In step S3, the collaborative optimization model is optimized and solved to obtain the optimal design parameters and optimal control parameters of the wheeled armored vehicle, specifically including:

[0223] (1) Linear interpolation is performed on the state parameters and control parameters respectively to obtain the state parameters and control parameters at each interpolation moment, so as to reconstruct the optimization variables and obtain the reconstructed optimization variables. The interpolation moments include the start moment, the end moment, and several intermediate moments between the start moment and the end moment. The reconstructed optimization variables include the design parameters and the state parameters and control parameters at each interpolation moment.

[0224] Linear interpolation is performed on the state parameters and the control parameters respectively, specifically including: linear interpolation is performed on the state parameters and the control parameters respectively using the Hermite-Simpson method.

[0225] The optimized variables after reconstruction are shown in formula (34).

[0226] (2) Calculate the gradient of the cost function with respect to the reconstructed optimized variables.

[0227] Calculating the gradient of the cost function with respect to the reconstructed optimized variable, specifically comprising: calculating the gradient of the cost function with respect to the reconstructed optimized variable using a complex step-size differentiation method.

[0228] The gradient is shown in formula (53).

[0229] (3) Based on the reconstructed optimization variables, the dynamic constraints, upper and lower bound constraints of variable values, path constraints and boundary constraints are reconstructed respectively to obtain the residual constraints, upper and lower bound constraints of reconstructed variable values, reconstructed path constraints and reconstructed boundary constraints.

[0230] The method for determining the residual constraint includes: reconstructing the dynamic constraint based on the reconstructed optimized variables using the Hermite-Simpson method to obtain the residual constraint.

[0231] The residual constraint, reconstructed path constraint, and reconstructed boundary constraint are shown in Equation (45), and the upper and lower bound constraints of the reconstructed variable values ​​are shown in Equation (29). It is only necessary to change the dimensions of the optimized variables.

[0232] (4) Calculate the first Jacobian matrix of the residual constraint, the second Jacobian matrix of the reconstructed path constraint, and the third Jacobian matrix of the reconstructed boundary constraint respectively.

[0233] The first Jacobian matrix of the residual constraint, the second Jacobian matrix of the reconstructed path constraint, and the third Jacobian matrix of the reconstructed boundary constraint are calculated respectively, specifically including: using a complex step differentiation method to calculate the first Jacobian matrix of the residual constraint, the second Jacobian matrix of the reconstructed path constraint, and the third Jacobian matrix of the reconstructed boundary constraint respectively.

[0234] The first Jacobian matrix is ​​shown in equation (48), the second Jacobian matrix is ​​shown in equation (50), and the third Jacobian matrix is ​​shown in equation (51).

[0235] (5) The reconstructed optimization variables, cost function, gradient, residual constraints, upper and lower bound constraints of the reconstructed variables, reconstructed path constraints, reconstructed boundary constraints, the first Jacobian matrix, the second Jacobian matrix and the third Jacobian matrix are used as inputs, and the nonlinear programming solver is used to solve the problem, and the optimal design parameters and optimal control parameters of the wheeled armored vehicle are obtained.

[0236] The nonlinear programming solver is used to solve the problem with the reconstructed optimization variables, cost function, gradient, residual constraint, upper and lower bound constraints of the reconstructed variable values, reconstructed path constraint, reconstructed boundary constraint, first Jacobian matrix, second Jacobian matrix and third Jacobian matrix as inputs, and the optimal design parameters and optimal control parameters of the wheeled armored vehicle are obtained, including:

[0237] 1) Using the reconstructed optimization variables, cost function, gradient, residual constraints, upper and lower bound constraints of the reconstructed variables, reconstructed path constraints, reconstructed boundary constraints, the first Jacobian matrix, the second Jacobian matrix, and the third Jacobian matrix as input, a nonlinear programming solver is used to solve the problem and obtain the solution of the current iteration. The solution includes the state parameters, design parameters, and control parameters of the current iteration.

[0238] 2) Determine whether the current number of iterations has reached the preset number of iterations.

[0239] 3) if no, then return to the step of "using the nonlinear programming solver to solve with the reconstructed optimization variables, the cost function, the gradient, the residual constraints, the reconstructed variable value upper and lower bound constraints, the reconstructed path constraints, the reconstructed boundary constraints, the first Jacobian matrix, the second Jacobian matrix and the third Jacobian matrix as inputs to obtain the solution result of the current iteration".

[0240] 4) if yes, then based on the solution result of each iteration before the current iteration, calculate the first scaling factor of the residual constraints, the second scaling factor of the reconstructed path constraints, the third scaling factor of the reconstructed boundary constraints, the fourth scaling factor of the first Jacobian matrix, the fifth scaling factor of the second Jacobian matrix and the sixth scaling factor of the third Jacobian matrix.

[0241] 5) scale the reconstructed optimization variables of the current iteration using the linear scaling method to obtain the scaled optimization variables, and based on the first scaling factor, the second scaling factor, the third scaling factor, the fourth scaling factor, the fifth scaling factor and the sixth scaling factor, scale the residual constraints, the reconstructed path constraints, the reconstructed boundary constraints, the first Jacobian matrix, the second Jacobian matrix and the third Jacobian matrix of the current iteration respectively to obtain the scaled residual constraints, the scaled path constraints, the scaled boundary constraints, the scaled first Jacobian matrix, the scaled second Jacobian matrix and the scaled third Jacobian matrix.

[0242] The scaled optimization variables are shown in equation (58), the scaled residual constraints are shown in equation (60), the scaled path constraints are shown in equation (61), the scaled boundary constraints are shown in equation (62), the scaled first Jacobian matrix is shown in equation (63), the scaled second Jacobian matrix is shown in equation (64), and the scaled third Jacobian matrix is shown in equation (65).

[0243] 6) using the scaled optimization variables, the cost function, the gradient, the scaled residual constraints, the reconstructed variable value upper and lower bound constraints, the scaled path constraints, the scaled boundary constraints, the scaled first Jacobian matrix, the scaled second Jacobian matrix and the scaled third Jacobian matrix as inputs, use the nonlinear programming solver to solve to obtain the solution result of the next iteration.

[0244] 7) determine whether the iteration is ended.

[0245] If the solution result makes the cost function value minimum, the iteration is ended.

[0246] 8) if the iteration is ended, determine the optimal design parameters and the optimal control parameters of the wheeled armored vehicle based on the solution result of the next iteration.

[0247] 9) if the iteration is not finished, then the iteration number is added by 1, and the step of "scaling the optimized variable after reconstruction of the current iteration by using the linear scaling method to obtain the scaled optimized variable" is returned.

[0248] In this embodiment, for the rapid development and design of an 8x8 wheeled armored vehicle, the multi-objective optimization framework capable of quickly solving large-scale complex mixed integer optimal design and optimal control problems is developed for the first time. Compared with the similar optimization framework in the international, the solving success rate is high and the speed is fast. In the optimization design of the whole vehicle, the optimization design and control of parts, and other aspects, the related performance of the wheeled armored vehicle is greatly improved from the design and control angles. The overall process of the embodiment is shown in Figure 1 The dynamics model of the wheeled vehicle is first built based on the Lagrange dynamics theory. The model is composed of the dynamics of the sprung mass and the unsprung mass. Then the model is transformed into residual constraints. The boundary constraints are established according to the driving conditions and the working boundaries of each subsystem. The multi-objective function is established according to the task requirements. The design parameters and the control parameters are taken as the optimization variables to complete the construction of the collaborative optimal design and optimal control problem. Finally, the problem is solved based on the improved collocation method, normalization method and scaling method. The optimal design parameters obtained by solving are applied to the vehicle development, and the optimal control parameters are applied to guide the development of the vehicle control algorithm.

[0249] The advantages of this embodiment are:

[0250] (1) A collaborative optimal design and optimal control framework for wheeled armored vehicles is constructed, which can optimize the design parameters and control parameters of the wheeled armored vehicle based on the dynamic model of the wheeled armored vehicle. The optimal design parameters can be used for vehicle design, and the optimal control parameters can be used as a reference for subsequent online control strategy development. The framework can apply gradient information, expand the optimization space, and have good optimization effect, which can improve the development efficiency of the wheeled armored vehicle. This is mainly related to the construction of the optimal design and optimal control problem, the rationality of the vehicle dynamics modeling and the efficient solving of the problem.

[0251] (2) For large-scale multi-degree-of-freedom optimal design and optimal control problems, the stability and efficiency of the problem solving are greatly improved. This is mainly because the vectorized dynamics modeling method of the vehicle, and in the solving part of the optimal problem, the direct collocation method is improved, the normalization method and scaling method are applied, and the stable and fast solving of the large-scale collaborative optimal problem is realized.

[0252] Embodiment 2

[0253] The embodiment provides a computer device, which comprises a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the collaborative optimization method of the design parameters and the control parameters of the wheeled vehicle in the above-mentioned embodiment 1 is realized.

[0254] Example 3

[0255] In this embodiment, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the method for collaboratively optimizing the design parameters and control parameters of a wheeled vehicle in the above-mentioned embodiment 1 is implemented.

[0256] Example 4

[0257] In this embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the method for collaboratively optimizing the design parameters and control parameters of a wheeled vehicle in the above-mentioned embodiment 1 is implemented.

[0258] 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.

[0259] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for collaborative optimization of design parameters and control parameters of a wheeled vehicle, characterized in that: include: Establish a dynamic model of wheeled vehicles; establishing a collaborative optimization model for wheeled vehicles based on the dynamic model; The optimization variables of the collaborative optimization model include state parameters, design parameters and control parameters; the collaborative optimization model includes a cost function and constraints, the constraints include dynamic constraints, upper and lower bound constraints on variable values, path constraints and boundary constraints, and the dynamic constraints are constraints established based on the dynamic model; Optimizing and solving the collaborative optimization model to obtain optimal design parameters and optimal control parameters of the wheeled vehicle; Among them, the collaborative optimization model of wheeled vehicles includes: Among them, J is the cost function; is the Mayer term; t0 is the starting time; t f is the end time; is the Lagrangian term; is the dynamic constraint; is the first-order derivative of the state parameter at time t; f[.] is the dynamic constraint function; x(t) is the state parameter at time t; u(t) is the control parameter at time t; p is the design parameter; x min ≤x(t)≤x max 、u min ≤u(t)≤u max 、p min ≤p≤p max The upper and lower bounds of the variable value are constrained; x min is the lower bound of the state parameter; x max is the upper bound of the state parameter; u min is the lower bound of the control parameter; u max is the upper bound of the control parameter; p min is the lower bound of the design parameter; p max is the upper bound of the design parameters; g min ≤g[x(t),u(t),t,p]≤g max is the path constraint; g min is the lower bound of the path constraint function; g[.] is the path constraint function; g max is the upper bound of the path constraint function; b min ≤b[x(t0),t0,x(t f ),t f ,p]≤b max is the boundary constraint; b min is the lower bound of the boundary constraint function; b[.] is the boundary constraint function; x(t0) is the state parameter at the starting time t0; x(t f ) is the end time t f State parameters of b max is the upper bound of the boundary constraint function; The collaborative optimization model is optimized and solved to obtain the optimal design parameters and optimal control parameters of the wheeled vehicle, specifically including: Linearly interpolating the state parameters and control parameters respectively to obtain the state parameters and control parameters at each interpolation moment, so as to reconstruct the optimization variables to obtain reconstructed optimization variables; the interpolation moments include a start moment, an end moment, and a plurality of intermediate moments between the start moment and the end moment; the reconstructed optimization variables include the design parameters and the state parameters and control parameters at each interpolation moment; Calculating the gradient of the cost function with respect to the reconstructed optimization variable; Based on the reconstructed optimization variables, the dynamic constraints, the upper and lower bound constraints of the variable values, the path constraints and the boundary constraints are reconstructed respectively to obtain residual constraints, upper and lower bound constraints of the reconstructed variable values, reconstructed path constraints and reconstructed boundary constraints; respectively calculating a first Jacobian matrix of the residual constraint, a second Jacobian matrix of the reconstructed path constraint, and a third Jacobian matrix of the reconstructed boundary constraint; The optimal design parameters and optimal control parameters of the wheeled vehicle are obtained by using a nonlinear programming solver with the reconstructed optimization variables, the cost function, the gradient, the residual constraint, the upper and lower bound constraints of the reconstructed variable values, the reconstructed path constraint, the reconstructed boundary constraint, the first Jacobian matrix, the second Jacobian matrix and the third Jacobian matrix as inputs.

2. The method for collaborative optimization of wheeled vehicle design parameters and control parameters according to claim 1, characterized in that: The dynamic model of a wheeled vehicle includes the generalized motion equations of the sprung mass of the wheeled vehicle and the generalized motion equations of the unsprung mass of the wheeled vehicle; The generalized equation of motion for the sprung mass of a wheeled vehicle is: Among them, M gb = [h A,b T [M b [h A,b + ∑[h A,b T [h b,u,i T [M ui [h b,u,i [h A,b ;​​​ The generalized equation of motion for the unsprung mass of a wheeled vehicle is: Among them, [M gu ]=diag{m u,fr ,…,m u,rl ,j u,fr ,…j u,rl }; Where Q b represents the generalized force acting on the sprung mass of a wheeled vehicle; Q u represents the generalized force acting on the unsprung mass of a wheeled vehicle; q b represents the generalized coordinates of the vehicle body; q u represents the generalized coordinates of the tire; and Indicates q b The first and second derivatives of and Indicates q u The first and second derivatives of Indicates M gb The first derivative of Indicates M gu The first derivative of M b 、M ui are the mass matrices of the vehicle body and the unsprung mass when calculating the lateral and longitudinal kinetic energy, respectively; h b,u,i Indicates the relative positions of the eight tires of an 8×8 wheeled vehicle in the vehicle body coordinate system. The subscript i indicates the order of the eight tires, which are the right wheel of the first axle, the left wheel of the first axle, the right wheel of the second axle, the left wheel of the second axle, the right wheel of the third axle, the left wheel of the third axle, the right wheel of the fourth axle, and the left wheel of the fourth axle; ψ is the yaw angle of the vehicle body; m u,fr ,…,m u,rl represents the mass of the eight tires of an 8×8 wheeled vehicle; j u,fr ,…j u,rl Represents the moment of inertia of the eight tires of an 8×8 wheeled vehicle.

3. The method for collaborative optimization of design parameters and control parameters of a wheeled vehicle according to claim 1, characterized in that: Performing linear interpolation on the state parameters and the control parameters respectively, specifically comprising: performing linear interpolation on the state parameters and the control parameters respectively using the Hermite-Simpson method; The method for determining the residual constraint includes: reconstructing the dynamic constraint based on the reconstructed optimization variable using the Hermite-Simpson method to obtain the residual constraint.

4. The method for collaborative optimization of design parameters and control parameters of a wheeled vehicle according to claim 1, characterized in that: Calculating the gradient of the cost function with respect to the reconstructed optimization variable, specifically comprising: calculating the gradient of the cost function with respect to the reconstructed optimization variable using a complex step-size differentiation method; Calculating the first Jacobian matrix of the residual constraint, the second Jacobian matrix of the reconstructed path constraint, and the third Jacobian matrix of the reconstructed boundary constraint respectively, specifically including: calculating the first Jacobian matrix of the residual constraint, the second Jacobian matrix of the reconstructed path constraint, and the third Jacobian matrix of the reconstructed boundary constraint respectively using a complex step differentiation method.

5. The method for collaborative optimization of design parameters and control parameters of a wheeled vehicle according to claim 1, characterized in that: The optimal design parameters and optimal control parameters of the wheeled vehicle are obtained by using a nonlinear programming solver, taking the reconstructed optimization variables, the cost function, the gradient, the residual constraint, the upper and lower bound constraints of the reconstructed variable values, the reconstructed path constraint, the reconstructed boundary constraint, the first Jacobian matrix, the second Jacobian matrix, and the third Jacobian matrix as inputs, and performing a solution. Specifically, the solution includes: Using the reconstructed optimization variables, the cost function, the gradient, the residual constraint, the upper and lower bound constraints of the reconstructed variable values, the reconstructed path constraint, the reconstructed boundary constraint, the first Jacobian matrix, the second Jacobian matrix, and the third Jacobian matrix as input, and solving the problem using a nonlinear programming solver to obtain a solution result for the current iteration; Determine whether the current number of iterations reaches the preset number of iterations; If not, return to the step of "using the reconstructed optimization variables, the cost function, the gradient, the residual constraint, the upper and lower bound constraints of the reconstructed variable values, the reconstructed path constraint, the reconstructed boundary constraint, the first Jacobian matrix, the second Jacobian matrix and the third Jacobian matrix as input, solving using a nonlinear programming solver to obtain a solution result for the current iteration"; If so, then calculating, based on the solution results of each iteration before the current iteration, a first scaling factor of the residual constraint, a second scaling factor of the reconstructed path constraint, a third scaling factor of the reconstructed boundary constraint, a fourth scaling factor of the first Jacobian matrix, a fifth scaling factor of the second Jacobian matrix, and a sixth scaling factor of the third Jacobian matrix; Scaling the reconstructed optimization variables of the current iteration using a linear scaling method to obtain scaled optimization variables, and scaling the residual constraint, the reconstructed path constraint, the reconstructed boundary constraint, the first Jacobian matrix, the second Jacobian matrix, and the third Jacobian matrix of the current iteration based on the first scaling factor, the second scaling factor, the third scaling factor, the fourth scaling factor, the fifth scaling factor, and the sixth scaling factor, respectively, to obtain scaled residual constraints, scaled path constraints, scaled boundary constraints, scaled first Jacobian matrix, scaled second Jacobian matrix, and scaled third Jacobian matrix; Using the scaled optimization variable, the cost function, the gradient, the scaled residual constraint, the upper and lower bound constraints of the reconstructed variable value, the scaled path constraint, the scaled boundary constraint, the scaled first Jacobian matrix, the scaled second Jacobian matrix, and the scaled third Jacobian matrix as input, solving the problem using a nonlinear programming solver to obtain a solution result for the next iteration; Determine whether the iteration is completed; If the iteration is completed, the optimal design parameters and optimal control parameters of the wheeled vehicle are determined based on the solution results of the next iteration; If the iteration is not completed, the number of iterations is increased by 1, and the process returns to the step of "scaling the reconstructed optimized variables of the current iteration using the linear scaling method to obtain scaled optimized variables".

6. 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 collaborative optimization of design parameters and control parameters of a wheeled vehicle according to any one of claims 1 to 5.

7. 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 collaboratively optimizing design parameters and control parameters of a wheeled vehicle according to any one of claims 1 to 5 is implemented.

8. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for collaboratively optimizing design parameters and control parameters of a wheeled vehicle according to any one of claims 1 to 5 is implemented.

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