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

Abstract

The application discloses a tracked vehicle design parameter and control parameter collaborative optimization method and related device, relates to the tracked vehicle design development technical field, and first establishes a dynamics model of a tracked vehicle, and then establishes a collaborative optimization model of the tracked 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 tracked vehicle. The application collaboratively optimizes the design parameters and control parameters of the tracked vehicle based on the dynamics model, and meets the requirements of short development cycle, high efficiency, fast updating and replacement and high performance.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tracked vehicle design and development, and in particular to a tracked vehicle design parameter and control parameter collaborative optimization method based on a dynamic model and related devices. BACKGROUND

[0002] Tracked vehicles have good passability and maneuverability on off-road roads, and are widely used in mining and excavation, transportation assistance, search and exploration, environmental monitoring and many other civilian and military fields. In recent years, with the development of vehicle technology, vehicle equipment upgrades rapidly, and performance requirements continue to improve, requiring rapid development iteration. There are the following needs for tracked vehicle design and development in the new era: stronger performance, shorter cycle, and higher requirements. However, traditional tracked vehicle design and development methods have a series of challenges, such as long development cycle, low efficiency, and slow updating, which have seriously affected the performance and competitiveness of tracked vehicles.

[0003] Traditional tracked vehicle design and development methods usually use static modeling technology, which cannot fully consider the dynamic response of the vehicle under different terrains and working conditions. Since the static modeling technology cannot accurately predict the actual performance of the vehicle during motion, a large number of tests and adjustments are often required, resulting in a long development cycle, low efficiency, and high cost. With the continuous progress of technology and the continuous change of user needs, due to the solidification of vehicle design under the traditional tracked vehicle design and development method, the adjustment and improvement process is tedious, making the updating speed unable to keep up with the changes in market demand, resulting in tracked vehicles often being unable to update in a timely manner, affecting the competitiveness and market share of the product. Modern tracked vehicles have higher and higher requirements for performance and stability, such as higher driving speed, stronger off-road capability, and more stable handling, etc. However, traditional tracked vehicle design and development methods have limitations in meeting these requirements and cannot fully tap the potential of the vehicle and optimize the design.

[0004] In summary, the traditional tracked vehicle design and development method cannot meet the current market demand for short development cycle, high efficiency, fast updating, and high performance requirements. SUMMARY

[0005] The purpose of the present application is to provide a tracked vehicle design parameter and control parameter collaborative optimization method and related devices, which can collaboratively optimize the design parameters and control parameters of tracked vehicles based on a dynamics model, meeting the demand for short development cycle, high efficiency, fast updating, and high performance requirements.

[0006] To achieve the above-mentioned purpose, the present application provides the following solutions:

[0007] In a first aspect, the present application provides a method for collaborative optimization of design parameters and control parameters of a tracked vehicle, comprising:

[0008] establishing a dynamics model of the tracked vehicle;

[0009] establishing a collaborative optimization model of the tracked vehicle based on the dynamics 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 constraint conditions, and the constraint conditions include dynamics constraints, variable value upper and lower bound constraints, path constraints and boundary constraints, wherein the dynamics constraints are established based on the dynamics model;

[0010] optimizing and solving the collaborative optimization model to obtain optimal design parameters and optimal control parameters of the tracked vehicle.

[0011] 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 method for collaborative optimization of design parameters and control parameters of a tracked vehicle.

[0012] In a third aspect, the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executable by a processor to implement the method for collaborative optimization of design parameters and control parameters of a tracked vehicle.

[0013] In a fourth aspect, the present application provides a computer program product comprising a computer program, wherein the computer program is executable by a processor to implement the method for collaborative optimization of design parameters and control parameters of a tracked vehicle.

[0014] According to the embodiments of the present application, the following technical effects are achieved:

[0015] The application provides a tracked vehicle design parameter and control parameter collaborative optimization method and related device, a dynamics model of the tracked vehicle is first established, a collaborative optimization model of the tracked vehicle is then established based on the dynamics model, optimization variables of the collaborative optimization model include state parameters, design parameters and control parameters, and finally the collaborative optimization model is optimized and solved to obtain optimal design parameters and optimal control parameters of the tracked vehicle. Since the collaborative optimization model is established based on the dynamics model, the actual performance of the vehicle in the movement process can be accurately predicted, so a large number of tests and adjustments are no longer needed, thereby solving the problems of long development cycle, low efficiency and high cost, and the vehicle design is more flexible, thereby solving the problems of vehicle design solidification, complicated adjustment and improvement process, and the problem that the updating speed cannot keep up with the changes in market demand. Since the design parameters and the control parameters are optimized simultaneously, the design and the control can be considered, thereby solving the problem of performance requirement limitation. The design parameters and the control parameters of the tracked vehicle are collaboratively optimized based on the dynamics model, thereby meeting the requirements of short development cycle, high efficiency, fast updating and high performance. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort on the basis of these drawings.

[0017] Figure 1 A flowchart of a tracked vehicle design parameter and control parameter collaborative optimization method provided for Embodiment 1 of the present application.

[0018] Figure 2 A principle diagram of a tracked vehicle design parameter and control parameter collaborative optimization method provided for Embodiment 1 of the present application.

[0019] Figure 3 A dynamics model framework diagram of a tracked vehicle provided for Embodiment 1 of the present application.

[0020] Figure 4 A solution diagram of a Jacobian matrix provided for Embodiment 1 of the present application.

[0021] Figure 5 A structure diagram of a computer device provided for Embodiment 2 of the present application. DETAILED DESCRIPTION

[0022] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application.

[0023] Embodiment 1

[0024] The embodiment provides a tracked vehicle design parameter and control parameter collaborative optimization method, as shown in Figure 1 and Figure 2 The tracked vehicle design parameter and control parameter collaborative optimization method comprises the following steps.

[0025] S1: establishing a dynamics model of a tracked vehicle.

[0026] S2: establishing a collaborative optimization model of the tracked vehicle based on the dynamics model; optimization variables of the collaborative optimization model comprise state parameters, design parameters and control parameters; the collaborative optimization model comprises a cost function and constraint conditions, and the constraint conditions comprise dynamics constraints, variable value upper and lower bound constraints, path constraints and boundary constraints, and the dynamics constraints are constraints established based on the dynamics model.

[0027] S3: performing optimization solution on the collaborative optimization model to obtain optimal design parameters and optimal control parameters of the tracked vehicle.

[0028] At present, there are many problems in the design and development of tracked vehicles. First, the design method based on experience is mostly used, and the dynamic response of the vehicle under different terrains and working conditions cannot be fully considered. Since the static modeling technology cannot accurately predict the actual performance of the vehicle during the movement, a large number of tests and adjustments are needed, resulting in a long development cycle, low efficiency and high cost. Since the vehicle design is fixed, the adjustment and improvement process is complicated, resulting in slow updating and replacement. Second, the optimization space is limited, and the overall performance is limited due to independent design of each subsystem, resulting in limitations of the traditional tracked vehicle design and development method in meeting performance requirements. The vehicle driving conditions are complex, and the combination of design and control is needed to achieve optimal performance, that is, the design parameters and control parameters coupled affect the overall performance. However, the existing research only optimizes one of them, which reduces the global optimization space and limits the performance improvement. Therefore, the embodiment aims to provide a tracked vehicle design parameter and control parameter collaborative optimization method based on a dynamic model, which can complete the collaborative optimization of design parameters and control parameters based on the dynamics model, so as to solve the problems existing in the traditional design and development method, realize the rapid updating and replacement of the tracked vehicle and performance improvement, and meet the changing market demand.

[0029] This embodiment utilizes Newtonian dynamics to perform refined modeling of an 8-DOF tracked armored vehicle, including six degrees of freedom (longitudinal, lateral, vertical, pitch, roll, and yaw) and two degrees of freedom (track winding on both sides), totaling eight degrees of freedom considering ground contact. The dynamic modeling process of the 8-DOF tracked armored vehicle based on Newtonian dynamics is as follows:

[0030] The inputs to the multi-degree-of-freedom model are the torques of the driving wheels on both sides of the track, the angular velocities of the driving wheels, and the driving force of the track. The vehicle under study is regarded as a multi-rigid-body system, including one sprung mass rigid body (i.e., the vehicle body) and two unsprung mass rigid bodies (i.e., the two side tracks). The motion of the vehicle body is generated by the combined action of longitudinal, lateral, and vertical contact friction and shear forces, air resistance, suspension forces, and ground support forces. Figure 3 This section provides the input / output interfaces for each submodule of the vehicle dynamics model and the overall vehicle dynamics model architecture, including the interactions between the body, suspension, and track contact models. The reference frames selected are the geodetic coordinate system O1-X1Y1Z1 and the vehicle's center-of-gravity coordinate system O-XYZ. The geodetic coordinate system defines the vehicle's external motion environment (i.e., X, Y, Z coordinates), while the vehicle's own dynamic processes (i.e., the relative position and relative velocity between the tracks and the body) are analyzed using the vehicle's center-of-gravity coordinate system. The three translational degrees of freedom are the longitudinal displacement x, lateral displacement y, and vertical displacement z of the vehicle's center of gravity; the three rotational degrees of freedom are the roll angle φ, pitch angle θ, and yaw angle ψ of the vehicle's center of gravity; and the two track degrees of freedom are the angular velocities ω of the tracks on both sides. l and ω r Based on Newtonian dynamics, the dynamic equations for each degree of freedom are established as follows:

[0031] (1) Vehicle translation

[0032] The equation of motion of the vehicle's center of mass along the X direction is:

[0033] δma x =Fr x cosψ-Fr y sinψ(1)

[0034] In equation (1), δ is the rotational mass increase coefficient; when a tank is moving, the mass of a tank with rotating parts will increase by δ times; m is the total mass of the tracked vehicle; a x Fr represents the acceleration of the tracked vehicle along the X direction. x ψ is the net force acting on the tracked vehicle when it translates along the X direction. Specifically, it is the resultant force acting on the vehicle's center of mass by the combined forces of ground contact friction, shear force, air resistance, suspension force, and ground support force; ψ is the yaw angle of the tracked vehicle; Fr yThis refers to the resultant force acting on a tracked vehicle when it translates along the Y direction. Specifically, it is the resultant force acting on the vehicle's center of mass by the combined action of the ground contact friction, shear force, air resistance, suspension force, and ground support force.

[0035] The equation of motion of the vehicle's center of mass along the Y direction is:

[0036] δma y =Fr x sinψ+Fr y cosψ(2)

[0037] In equation (2), a y Let be the acceleration of the tracked vehicle along the Y direction.

[0038] The translational equation of the vehicle's center of mass along the Z direction is:

[0039] δma z =Fr z (3)

[0040] In equation (3), a z Fr represents the acceleration of the tracked vehicle along the Z direction. z This refers to the resultant force acting on a tracked vehicle when it translates along the Z direction. Specifically, it is the resultant force acting on the vehicle's center of mass by the combined action of the ground contact friction, shear force, air resistance, suspension force, and ground support force.

[0041] Among them, the spring force and damping force of the suspension system considered in vertical motion are as follows:

[0042] F k =K k C k Δl i (4)

[0043] In equation (4), F k For spring force; K k C is the spring-lever ratio; k For spring stiffness; Δl i This refers to the elastic deformation of the spring.

[0044] F c =K c C c D i (5)

[0045] In equation (5), F c For damping force; K c C is the damper lever ratio; c D is the damping coefficient of the damper. i The velocity is the dynamic deflection.

[0046] The spring force and the damping force together constitute the suspension force.

[0047] (2) Vehicle body rotation

[0048] The vehicle body rotates about the X direction, and the roll dynamics equation of the vehicle's center of mass is:

[0049]

[0050] In equation (6), I φ Let X be the moment of inertia of the tracked vehicle when it rotates about the X-axis. M is the second derivative of the roll angle of a tracked vehicle; φ K represents the net torque experienced by the tracked vehicle during translation along the X direction. Specifically, it is the net torque generated by the combined forces acting on the vehicle's center of mass, including ground contact friction, shear force, air resistance, suspension force, and ground support force. φ φ is the stiffness coefficient of the tracked vehicle's suspension system when rotating about the X-axis; φ is the roll angle of the tracked vehicle; C φ The damping coefficient of the tracked vehicle suspension system when rotating about the X-axis; Let be the first derivative of the roll angle of a tracked vehicle.

[0051] The vehicle body rotates about the Y direction, and the pitch dynamics equation of the vehicle's center of mass is:

[0052]

[0053] In equation (7), I θ Let be the moment of inertia of the tracked vehicle when it rotates about the Y-axis; M is the second derivative of the pitch angle of a tracked vehicle. θ K represents the resultant torque experienced by the tracked vehicle during translation along the Y direction. Specifically, it is the resultant torque generated by the combined forces acting on the vehicle's center of mass, including ground contact friction, shear force, air resistance, suspension force, and ground support force. θ θ is the stiffness coefficient of the tracked vehicle's suspension system when rotating about the Y-axis; θ is the pitch angle of the tracked vehicle; C θ The damping coefficient of the tracked vehicle suspension system when rotating about the Y-axis; Let be the first derivative of the pitch angle of the tracked vehicle.

[0054] The vehicle body rotates about the Z direction, and the yaw dynamic equation of the vehicle's center of mass is:

[0055]

[0056] In equation (8), I ψ The moment of inertia of the tracked vehicle when rotating about the Z-axis; M is the second derivative of the yaw angle of a tracked vehicle.ψ K represents the resultant torque experienced by the tracked vehicle during its translational motion along the Z-direction. Specifically, it is the resultant torque generated by the combined forces acting on the vehicle's center of mass, including ground contact friction, shear force, air resistance, suspension force, and ground support force. ψ ψ is the stiffness coefficient of the tracked vehicle's suspension system when rotating about the Z-axis; ψ is the yaw angle of the tracked vehicle; C ψ The damping coefficient of the tracked vehicle suspension system when rotating around the Z-axis; Let be the first derivative of the yaw angle of a tracked vehicle.

[0057] (3) Track winding motion

[0058] We analyze the track and drive sprocket as a whole, analogous to the analysis of tire motion in vehicle dynamics. Specifically, we consider the winding motion of the drive sprocket and the track as a single unit, with the entire track's motion equivalent to that of a tire. Because the track's contact patch is very long and the ground pressure distribution is complex, we introduce a track coordinate system, denoted as (x...). t ,y t Let x be the coordinates of a point on the track contact section. t The vertical axis is y t The horizontal axis is denoted as .

[0059] For the two-dimensional planar dynamics analysis of the vehicle, considering the motion in three degrees of freedom—lateral, longitudinal, and yaw—the differential of the longitudinal force of the track can be written as dF. x =dFcosθ t The differential of the lateral force of the track can be written as dF y =dFsinθ t heading angle v y v is the velocity of the vehicle body along the Y direction; x Let X be the velocity of the vehicle body along the X direction.

[0060] Substituting the shear displacement into the shear force formula, we can obtain the integral formulas for calculating the transverse and longitudinal forces in the contact with the ground:

[0061]

[0062]

[0063] In the above formula, F X ρ is the longitudinal force in contact with the ground; b is the track plate width; L is the track plate length; c is the soil adhesion coefficient; p(x,y) is the ground pressure distribution; μ is the soil shear resistance angle; j is the track shear displacement; K w θ represents the deformation modulus of the soil under shear stress. t For the heading angle of the ground contact vehicle; F YThe lateral force at ground contact is given by the above formula. The track traction force can be calculated by integration in the track coordinate system. At the same time, the traction force on the entire track can be calculated based on the ground pressure distribution p(x,y) in the track coordinate system.

[0064] The dynamic equations for the left track winding motion are as follows:

[0065]

[0066] In equation (11), J is the moment of inertia of the track. T is the first derivative of the angular velocity of the left track winding of a tracked vehicle; L F represents the torque of the drive wheel of the left track of a tracked vehicle. XL τ is the traction force of the left track when the tracked vehicle is moving longitudinally; r is the radius of the drive wheel.

[0067] Similarly, the dynamic equations for the right track winding motion are analyzed as follows:

[0068]

[0069] In equation (12), T is the first derivative of the angular velocity of the right track winding on a tracked vehicle. R F represents the torque of the drive wheel of the right track of a tracked vehicle. XR This refers to the traction force of the right track when the tracked vehicle is traveling longitudinally.

[0070] Based on the above analysis, the final dynamic model of the entire tracked vehicle is as follows:

[0071]

[0072] In equation (13), δ is the rotational mass increase coefficient; m is the total mass of the tracked vehicle; a x Fr represents the acceleration of the tracked vehicle along the X direction. x ψ is the net force acting on the tracked vehicle when it translates along the X direction; ψ is the yaw angle of the tracked vehicle; Fr y a is the net force acting on the tracked vehicle when it translates along the Y direction; y Let a be the acceleration of the tracked vehicle along the Y direction; z Fr represents the acceleration of the tracked vehicle along the Z direction. z I is the net force acting on the tracked vehicle when it translates along the Z direction; φ Let X be the moment of inertia of the tracked vehicle when it rotates about the X-axis. M is the second derivative of the roll angle of a tracked vehicle; φ K is the net torque experienced by the tracked vehicle when it translates along the X direction; φ φ is the stiffness coefficient of the tracked vehicle's suspension system when rotating about the X-axis; φ is the roll angle of the tracked vehicle; Cφ The damping coefficient of the tracked vehicle suspension system when rotating about the X-axis; I is the first derivative of the roll angle of a tracked vehicle; θ Let be the moment of inertia of the tracked vehicle when it rotates about the Y-axis; M is the second derivative of the pitch angle of a tracked vehicle. θ K is the net torque experienced by the tracked vehicle when it translates along the Y direction; θ θ is the stiffness coefficient of the tracked vehicle's suspension system when rotating about the Y-axis; θ is the pitch angle of the tracked vehicle; C θ The damping coefficient of the tracked vehicle suspension system when rotating about the Y-axis; I is the first derivative of the pitch angle of a tracked vehicle; ψ The moment of inertia of the tracked vehicle when rotating about the Z-axis; M is the second derivative of the yaw angle of a tracked vehicle. ψ K is the resultant torque experienced by the tracked vehicle when it translates along the Z direction; ψ C is the stiffness coefficient of the tracked vehicle suspension system when rotating about the Z-axis; ψ The damping coefficient of the tracked vehicle suspension system when rotating around the Z-axis; Let J be the first derivative of the yaw angle of the tracked vehicle; J is the moment of inertia of the track. T is the first derivative of the angular velocity of the left track winding of a tracked vehicle; L F represents the torque of the drive wheel of the left track of a tracked vehicle. XL The traction force of the left track when the tracked vehicle is moving longitudinally is r; r is the radius of the drive sprocket. T is the first derivative of the angular velocity of the right track winding on a tracked vehicle. R F represents the torque of the drive wheel of the right track of a tracked vehicle. XR This refers to the traction force of the right track when the tracked vehicle is traveling longitudinally.

[0073] Based on the dynamic model shown in equation (13), this embodiment further constructs a collaborative optimal design and optimal control problem. Considering multiple objectives such as maneuverability and vertical impact, a cost function is established. Based on the dynamic model, dynamic constraints are established. According to the vehicle's driving conditions, upper and lower bounds of vehicle variable values ​​are extracted, and upper and lower bound constraints are established. Path constraints and boundary constraints are designed according to requirements. Path constraints limit the vehicle's position, speed, and obstacle avoidance on the path, while boundary constraints limit the state parameters at the start and end times. The collaborative optimal design and optimal control problem is established by combining design parameters and control parameters. After defining the cost function, constraints, and initial value guesses as needed, some optimization-related parameters are also set as required, such as the transformation method m. trans (used for calculating residual constraints), differential method m diff, allocation points N n Scaling settings, etc.

[0074] Based on the established dynamic model, the inputs for the cooperative optimal design and optimal control problem are provided, including the initial guesses of the state parameters x0, the initial guesses of the control parameters u0, and the termination time t. f0 And the initial guessed value p0 of the design parameters.

[0075]

[0076] In equation (14), Let N represent a two-dimensional matrix with i rows and j columns. x,usr The number of nodes providing state parameters to the user, where each node can be considered a moment in time, and the value of the state parameters for each node needs to be determined; n x N represents the number of state parameters. u,usr The number of nodes providing control parameters to the user; n u The number of control parameters; n p Let n be the number of design parameters; if the termination time is also a parameter to be optimized, then n is... tf It is 1, otherwise n tf It is 0. For the global transformation method, the initial values ​​of the start and end times of each stage will be generated by the optimal control framework.

[0077] The above state parameters, control parameters, termination time, upper and lower bounds of design parameters, and inequality constraint g are given by the following formula, n g Number of path constraints:

[0078]

[0079] In equation (15), x min This is the lower bound of the state parameters; x max This is the upper bound of the state parameters; u min This is the lower bound of the control parameter; u max p is the upper bound of the control parameter. min p is the lower bound of the design parameters. max This is the upper bound of the design parameters; t fmin g is the lower bound of the termination time; min This is the lower bound of the path constraint function; g max This is the upper bound of the path constraint function.

[0080] In addition to the initial values ​​and upper and lower bound constraints mentioned above, the problem input also includes the Lagrange term of the cost function. Meyer First-order dynamic constraint function f, path constraint function g, boundary constraint function b.

[0081] A collaborative optimization model for the tracked vehicle is then 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 include dynamic constraints, upper and lower bound constraints on variable values, path constraints, and boundary constraints. The dynamic constraints are established based on the dynamic model. The collaborative optimization model is as follows:

[0082]

[0083] In equation (16), J is the cost function; For the Meyer term; t0 is the initial time; t f The termination time; For Lagrange terms; For dynamic constraints; Let f(t) be the first derivative of the state parameters 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 Constraints on the upper and lower bounds of variable values; x min This is the lower bound of the state parameters; x max This is the upper bound of the state parameters; u min This is the lower bound of the control parameter; u max p is the upper bound of the control parameter. min p is the lower bound of the design parameters. max This represents the upper bound of the design parameters; g min ≤g[x(t),u(t),t,p]≤g max For path constraints; g min g[.] represents the lower bound of the path constraint function; g[.] represents the path constraint function; g max b is the upper bound of the path constraint function; min ≤b[x(t0),t0,x(t f ),t f ,p]≤b max For boundary constraints; b min b[.] represents the lower bound of the boundary constraint function; b[.] represents the boundary constraint function; x(t0) represents the state parameters at the initial time t0; x(t f (t) represents the termination time. f State parameters; b max This is the upper bound of the boundary constraint function.

[0084] The state parameters are:

[0085] x = [qbv T q bω T q uθ T ] T

[0086] q bv =[X A Y A Z A ] T (17)

[0087] q bω =[φ θ ψ] T

[0088] q uθ =[θ ur θ ul ] T

[0089] In equation (17), X A Y A and Z A φ, θ, and ψ represent the absolute displacement of the vehicle's center of gravity along the geodetic coordinate system; φ, θ, and ψ represent the rotation angles of the vehicle body about the three axes of the geodetic coordinate system, namely, roll angle, pitch angle, and yaw angle; θ ur ,θ ul These represent the rotational speeds of the drive wheels on both sides of the track.

[0090] The control parameter is the torque of the drive wheels on both sides of the track.

[0091] Design parameters can be customized according to requirements.

[0092] After making the above inputs, Equation (16) is solved to obtain the optimal control parameters and the optimal design parameters.

[0093] To improve solution efficiency, this embodiment further transforms the problem into an NLP problem. It employs efficient solution algorithms, including sparse matrix operations, an improved direct collocation method, automatic normalization (i.e., scaling), and high-performance cluster computing. Sparse matrix operations and high-performance cluster computing are methods used during programming to improve problem-solving efficiency, achieving a highly efficient solution. Simultaneously, it solves for optimal design parameters and optimal control parameters. The optimal design parameters are applied to vehicle development, while the optimal control parameters guide the development of vehicle control algorithms, ultimately achieving collaborative optimization control of tracked vehicles.

[0094] In solving the problem of collaborative optimal design and optimal control, this embodiment transforms the problem into a large-scale NLP problem, and then calls a low-level solver, such as IPOPT, to solve the problem. In the problem transformation part, the format of variables and functions in the problem input is transformed into the format required by the NLP solver. In this part, local collocation method, differential method, automatic scaling method, etc. are developed. Finally, the IPOPT solver is applied to solve the problem.

[0095] (1) Local point allocation method

[0096] (1.1) NLP solver variables

[0097] In the local collocation method, the continuous state parameters and control parameters over the entire time interval can be discretized into N using linear interpolation. n The new variables obtained from the given nodes are given by the following formula:

[0098]

[0099] For the Hermite-Simpson method, when the control parameters in each sampling interval are also selected as parameters to be optimized, the reconstructed NLP variables are:

[0100]

[0101] In equation (19), subscripts are used to represent different times. These are the control parameters between u1 and u2.

[0102] When the state parameters in each sampling interval are also selected as parameters to be optimized, the reconstructed NLP variables are:

[0103]

[0104] In equation (20), These are the state parameters between x1 and x2.

[0105] The upper and lower bounds of the variables in equation (15) should also be reconstructed based on the reconstructed NLP variables.

[0106] (1.2) Residual Constraints

[0107] 1) Residual constraints of the Hermite-Simpson method

[0108] Based on third-order Hermite interpolation, the state parameter x at time k k With the state parameter x at time k+1 k+1 The state parameter x and its derivative between The following can be derived:

[0109]

[0110] In equation (21), h is the time interval between two adjacent moments (i.e., two adjacent nodes).

[0111] The control parameter u at time k is calculated using linear interpolation. k With the control parameter u at time k+1 k+1 Control parameters between as follows:

[0112]

[0113] Residual constraints at x based on the Hermite-Simpson method It can be represented as:

[0114]

[0115] (1.3) Path constraints and boundary constraints

[0116] Path constraints are functions of state parameters, control parameters, design parameters, and time. Boundary constraints are the initial state (i.e., the state parameters at the start time) x0 and the final state (i.e., the state parameters at the end time) x. f The function has the following path constraints:

[0117]

[0118] Finally, after all constraint calculations are completed, the NLP constraints are represented as:

[0119]

[0120] All residual constraints have upper and lower bounds of 0.

[0121] (1.4) Jacobian matrix

[0122] 1) Jacobian matrix of residual constraints

[0123] Residual constraints when the state parameters and control parameters at the midpoint of each discrete time interval are not considered It is x a ,u a ,x b ,u b ,t f Functions of p, such as Figure 4 As shown, x a ,u a ,x b ,u b The specific values ​​are as follows:

[0124]

[0125] To avoid redundant calculations, when performing matrix calculations, we first calculate the derivative of ζ with respect to x, u, and p. The Jacobian matrix can then be simplified to:

[0126]

[0127] In equation (27), Let T be a matrix where the i-th column is 1 and all other elements are 0; h is the time interval between two adjacent nodes; s2 T is the transformation matrix; s2 ,T a ,T b It is given by the following formula:

[0128]

[0129] 2) The Jacobian matrix of the path constraint is used to calculate the derivative of the path constraint g with respect to x, u, p, t, as follows:

[0130]

[0131] 3) Jacobian matrix of boundary constraints

[0132] Calculate boundary constraint b for x0, x f The derivatives of p and t are as follows:

[0133] (1.5) Cost Function

[0134] The input cost function consists of Meyer and Lagrange terms, and can be expressed as:

[0135]

[0136] (1.6) Gradient of the cost function

[0137] The cost function is [x,u,t] f The gradient of the function [,p] with respect to the state parameters and control parameters is as follows:

[0138]

[0139] Then, the gradient of the cost function with respect to the termination time and design parameters is calculated, and the NLP gradient is finally obtained, as follows:

[0140]

[0141] (2) Differential method

[0142] Most NLP solvers require constraints on the first derivative of c(y), and accurate calculation of these derivatives helps achieve faster convergence. In this embodiment, the differential method used is complex step-size differentiation. Complex step-size differentiation is approximately based on Taylor expansion:

[0143]

[0144] In equation (34), x is the state variable used to calculate the derivative; i is the imaginary symbol; and h is the time interval.

[0145] Ignore h 2 For terms of order 1 and higher, the approximation error is O(h). 2 The first derivative of () can be approximated as:

[0146] f'(x)=Im[f(x+ih)] / h(35)

[0147] (3) Scaling

[0148] (3.1) Variable scaling

[0149] The state parameters, control parameters, termination time, and design parameters are scaled using a linear scaling method, as follows:

[0150]

[0151]

[0152]

[0153] (3.2) Constrained Scaling

[0154] Applying a gradient-based mean norm method to scale constraints enables faster convergence.

[0155] The scaling of the residual constraints is as follows:

[0156]

[0157] In equation (38), N scal ζ is the number of iterations used to solve for the scaling factor. j,i This is the i-th residual constraint in the j-th loop.

[0158] The scaling of path constraints is as follows:

[0159]

[0160] In equation (39), g j,i This is the i-th path constraint in the j-th loop.

[0161] The scaling of boundary constraints is as follows:

[0162]

[0163] In equation (40), b j,i This is the i-th boundary constraint in the j-th loop.

[0164] (3.3) Scaling of the Jacobian matrix

[0165] The scaling of the Jacobian matrix takes into account the relationship between the scaling variable and the scaling constraint. The scaling of the residual-constrained Jacobian matrix is ​​defined as follows:

[0166]

[0167]

[0168] In equation (41), J ζ,i ξ is the Jacobian matrix of the residual constraints; i Let be the row vector composed of the scaling factors in equation (37); This is the scaling factor for the residual constraint.

[0169] The Jacobian matrix for path constraints is scaled as follows:

[0170]

[0171]

[0172] In equation (42), J g,i Let be the Jacobian matrix for path constraints; This is the scaling factor for the path constraint.

[0173]

[0174]

[0175] In equation (43), J b,i The Jacobian matrix is ​​the boundary constraint matrix; This is the scaling factor for the boundary constraints.

[0176] After the problem is transformed as described above, the transformed problem is input into the underlying solver for solving, which can simultaneously solve for the optimal control parameters and the optimal design parameters.

[0177] In S3, the collaborative optimization model is solved to obtain the optimal design parameters and optimal control parameters of the tracked vehicle, specifically including:

[0178] (1) Perform linear interpolation on the state parameters and control parameters respectively to obtain the state parameters and control parameters at each interpolation time, so as to reconstruct the optimization variables and obtain the reconstructed optimization variables. The interpolation time includes the start time, the end time and several intermediate times between the start time and the end time. The reconstructed optimization variables include the design parameters and the state parameters and control parameters at each interpolation time.

[0179] Linear interpolation is performed on the state parameters and control parameters respectively, specifically by using the Hermite-Simpson method to perform linear interpolation on the state parameters and control parameters respectively.

[0180] The optimized variables after reconstruction are shown in equation (20).

[0181] (2) Calculate the gradient of the cost function with respect to the reconstructed optimization variables.

[0182] The gradient of the cost function with respect to the reconstructed optimization variables is calculated, specifically by using the complex step-size differentiation method.

[0183] The gradient is shown in equation (33).

[0184] (3) Based on the reconstructed optimization variables, the dynamic constraints, variable value upper and lower bound constraints, path constraints and boundary constraints are reconstructed respectively to obtain residual constraints, reconstructed variable value upper and lower bound constraints, reconstructed path constraints and reconstructed boundary constraints.

[0185] The methods for determining residual constraints include: based on the reconstructed optimization variables, the Hermite-Simpson method is used to reconstruct the dynamic constraints to obtain residual constraints.

[0186] The residual constraints, reconstructed path constraints, and reconstructed boundary constraints are shown in Equation (25), and the upper and lower bound constraints of the reconstructed variable values ​​are shown in Equation (16). Only the dimension of the optimization variable needs to be changed.

[0187] (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.

[0188] The first Jacobian matrix of the residual constraints, the second Jacobian matrix of the reconstructed path constraints, and the third Jacobian matrix of the reconstructed boundary constraints are calculated respectively. Specifically, the first Jacobian matrix of the residual constraints, the second Jacobian matrix of the reconstructed path constraints, and the third Jacobian matrix of the reconstructed boundary constraints are calculated respectively using the complex step-size differential method.

[0189] The first Jacobian matrix is ​​shown in Equation (27), the second Jacobian matrix is ​​shown in Equation (29), and the third Jacobian matrix is ​​shown in Equation (30).

[0190] (5) Using the reconstructed optimization variables, cost function, gradient, residual constraints, upper and lower bound constraints of the reconstructed variable values, path constraints, boundary constraints, first Jacobian matrix, second Jacobian matrix and third Jacobian matrix as input, the nonlinear programming solver is used to solve the problem and obtain the optimal design parameters and optimal control parameters of the tracked vehicle.

[0191] Using the reconstructed optimization variables, cost function, gradient, residual constraints, upper and lower bound constraints on the reconstructed variable values, path constraints, boundary constraints, first Jacobian matrix, second Jacobian matrix, and third Jacobian matrix as input, a nonlinear programming solver is used to solve for the optimal design parameters and optimal control parameters of the tracked vehicle, specifically including:

[0192] 1) Using the reconstructed optimization variables, cost function, gradient, residual constraints, upper and lower bound constraints of the reconstructed variable values, path constraints, boundary constraints, first Jacobian matrix, second Jacobian matrix, and third Jacobian matrix as input, the nonlinear programming solver is used to solve the problem and obtain the solution result of the current iteration. The solution result includes the state parameters, design parameters, and control parameters of the current iteration.

[0193] 2) Determine whether the current iteration count has reached the preset iteration count.

[0194] 3) If not, return to the step of “using the reconstructed optimization variables, cost function, gradient, residual constraints, upper and lower bound constraints of the reconstructed variable values, path constraints, boundary constraints, first Jacobian matrix, second Jacobian matrix and third Jacobian matrix as input, and using the nonlinear programming solver to obtain the solution result of the current iteration”.

[0195] 4) If so, then based on the solution results of each iteration before the current iteration, calculate the first scaling factor of the residual constraint, the second scaling factor of the reconstructed path constraint, the third scaling factor of the reconstructed boundary constraint, 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.

[0196] 5) Scale the reconstructed optimization variables of the current iteration using a linear scaling method to obtain scaled optimization variables. 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, reconstructed path constraints, reconstructed boundary constraints, the first Jacobian matrix, the second Jacobian matrix, and the third Jacobian matrix of the current iteration, 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.

[0197] The scaled optimization variables are shown in Equation (36), the scaled residual constraints are shown in Equation (38), the scaled path constraints are shown in Equation (39), the scaled boundary constraints are shown in Equation (40), the scaled first Jacobian matrix is ​​shown in Equation (41), the scaled second Jacobian matrix is ​​shown in Equation (42), and the scaled third Jacobian matrix is ​​shown in Equation (43).

[0198] 6) Using the scaled optimization variables, cost function, gradient, scaled residual constraints, reconstructed variable value upper and lower bound constraints, scaled path constraints, scaled boundary constraints, scaled first Jacobian matrix, scaled second Jacobian matrix, and scaled third Jacobian matrix as input, the nonlinear programming solver is used to solve the problem and obtain the solution result for the next iteration.

[0199] 7) Determine if the iteration has ended.

[0200] The iteration ends if the solution minimizes the cost function value.

[0201] 8) If the iteration ends, determine the optimal design parameters and optimal control parameters of the tracked vehicle based on the solution results of the next iteration.

[0202] 9) If the iteration has not ended, increment the iteration count by 1 and return to the step of "scaling the reconstructed optimized variable of the current iteration using the linear scaling method to obtain the scaled optimized variable".

[0203] This embodiment provides a collaborative optimization method for optimal design and control parameters of an 8-DOF tracked vehicle based on a dynamic model. For the rapid development and design of tracked vehicles, it is the first to develop a multi-objective optimization framework capable of rapidly solving large-scale complex mixed-integer optimal design and optimal control problems. Compared with similar international optimization frameworks, it boasts a higher success rate and faster speed, significantly improving the performance of tracked vehicles from both design and control perspectives in areas such as overall vehicle optimization design, component optimization design, and control. First, a dynamic model of the tracked armored vehicle is constructed based on Newtonian dynamics theory. The considered 8-DOF model includes six degrees of freedom (longitudinal, lateral, vertical, roll, pitch, and yaw) and two degrees of freedom (track winding on both sides). Then, the dynamic model is transformed into residual constraints. Based on the driving conditions and the working boundaries of each subsystem, upper and lower bound constraints, path constraints, and boundary constraints are established for variable values. A cost function is established according to mission requirements, and design parameters and control parameters are simultaneously used as 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 and automatic normalization algorithm. The optimal design parameters obtained are applied to vehicle development, and the optimal control parameters are used to guide the development of vehicle control algorithms.

[0204] The advantages of this embodiment are as follows:

[0205] (1) In the dynamic modeling of tracked armored vehicles, in addition to the conventional 6 degrees of freedom of the vehicle body, the influence of the lateral and longitudinal motion of the track winding on the dynamic characteristics of the vehicle body when the track contacts the ground is emphasized, and the model has high accuracy.

[0206] (2) A collaborative optimal design and optimal control framework for tracked vehicles was constructed, which can simultaneously optimize the design parameters and control parameters based on the dynamic model of tracked vehicles. 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 formulation. The framework can apply gradient information to expand the optimization space and has good optimization effect, which can improve the development efficiency of tracked vehicles. This is mainly related to the construction of optimal design and optimal control problems, the rationality of vehicle dynamics modeling, and the efficient solution of the problem.

[0207] (3) For large-scale multi-degree-of-freedom optimal design and optimal control problems, the stability and efficiency of the solution are greatly improved. This is mainly due to the vectorized dynamics modeling method of the vehicle. In the solution part of the optimal problem, the direct collocation method is improved and the automatic normalization algorithm is applied, which realizes the stable and fast solution of large-scale cooperative optimal problems.

[0208] Example 2

[0209] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows.Figure 5 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network. When executed by the processor, the computer program implements a method for the collaborative optimization of design and control parameters of a tracked vehicle.

[0210] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0211] In one exemplary embodiment, a computer device is also provided, including: 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 co-optimizing tracked vehicle design parameters and control parameters as described in Embodiment 1.

[0212] Example 3

[0213] This application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for co-optimizing the design parameters and control parameters of tracked vehicles as described in Embodiment 1.

[0214] Example 4

[0215] This application provides a computer program product, including a computer program that, when executed by a processor, implements the method for collaborative optimization of tracked vehicle design parameters and control parameters as described in Embodiment 1.

[0216] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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.

[0217] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for collaborative optimization of design parameters and control parameters of a tracked vehicle, characterized in that, The method for co-optimizing the design and control parameters of the tracked vehicle includes: Establish a dynamic model for the tracked vehicle; A collaborative optimization model for tracked vehicles is established based on the aforementioned 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, which include dynamic constraints, upper and lower bound constraints on variable values, path constraints, and boundary constraints. The dynamic constraints are established based on the aforementioned dynamic model. The optimal design parameters and optimal control parameters of the tracked vehicle are obtained by optimizing and solving the aforementioned collaborative optimization model. The dynamic model of the tracked vehicle is as follows: ； in, Add a coefficient to the rotating mass; For the overall weight of tracked vehicles; Let X be the acceleration of the tracked vehicle along the X direction; This is the net force acting on the tracked vehicle when it translates along the X direction; For tracked vehicles, this refers to the yaw angle. This is the net force acting on the tracked vehicle when it translates along the Y direction; Let Y be the acceleration of the tracked vehicle along the Y direction; Let Z be the acceleration of the tracked vehicle along the Z direction; This is the net force acting on the tracked vehicle when it translates along the Z direction; The moment of inertia of the tracked vehicle when rotating about the X-axis; Let be the second derivative of the roll angle of a tracked vehicle; This is the resultant torque experienced by the tracked vehicle when it translates along the X direction; The stiffness coefficient of the tracked vehicle suspension system when rotating about the X-axis; The side tilt angle of a tracked vehicle; The damping coefficient of the tracked vehicle suspension system when rotating about the X-axis; The first derivative of the roll angle of a tracked vehicle; Let be the moment of inertia of the tracked vehicle when it rotates about the Y-axis; Let be the second derivative of the pitch angle of the tracked vehicle; This is the resultant torque experienced by the tracked vehicle when it translates along the Y direction; The stiffness coefficient of the tracked vehicle suspension system when rotating about the Y-axis; The pitch angle of a tracked vehicle; The damping coefficient of the tracked vehicle suspension system when rotating about the Y-axis; Let be the first derivative of the pitch angle of the tracked vehicle; The moment of inertia of the tracked vehicle when rotating about the Z-axis; Let be the second derivative of the yaw angle of a tracked vehicle; This is the resultant torque experienced by the tracked vehicle when it translates along the Z direction; The stiffness coefficient of the tracked vehicle suspension system when rotating about the Z-axis; The damping coefficient of the tracked vehicle suspension system when rotating around the Z-axis; Let be the first derivative of the yaw angle of a tracked vehicle; The track's moment of inertia; The first derivative of the angular velocity of the left track winding of a tracked vehicle; This refers to the torque of the drive wheel of the left track of a tracked vehicle. The traction force of the left track when the tracked vehicle is moving longitudinally; The radius of the driving wheel; The first derivative of the angular velocity of the right track winding of the tracked vehicle; This refers to the torque of the drive wheel of the right track of a tracked vehicle. The traction force of the right track when the tracked vehicle is moving longitudinally; The collaborative optimization model is optimized and solved to obtain the optimal design parameters and optimal control parameters of the tracked vehicle, specifically including: Linear interpolation is performed on the state parameters and control parameters respectively to obtain the state parameters and control parameters at each interpolation time, so as to reconstruct the optimization variables and obtain the reconstructed optimization variables; the interpolation time includes the start time, the end time, and several intermediate times between the start time and the end time; the reconstructed optimization variables include the design parameters and the state parameters and control parameters at each interpolation time. Calculate the gradient of the cost function with respect to the reconstructed optimization variables; Based on the reconstructed optimization variables, the dynamic constraints, the upper and lower bound constraints of variable values, the path constraints, and the boundary constraints are reconstructed respectively to obtain residual constraints, reconstructed upper and lower bound constraints of variable values, reconstructed path constraints, and reconstructed boundary constraints. 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. Using the reconstructed optimization variables, the cost function, the gradient, the residual constraints, the upper and lower bound constraints of the reconstructed variable values, the reconstructed path constraints, the reconstructed boundary constraints, the first Jacobian matrix, the second Jacobian matrix, and the third Jacobian matrix as inputs, a nonlinear programming solver is used to solve the problem and obtain the optimal design parameters and optimal control parameters of the tracked vehicle.

2. The method for collaborative optimization of design parameters and control parameters of tracked vehicles according to claim 1, characterized in that, The cooperative optimization model for tracked vehicles is as follows: ； in, J The cost function; For Meyer's term; t 0 represents the starting time; t f The termination time; For Lagrange terms; For dynamic constraints; for t The first derivative of the state parameter at time t; These are the dynamic constraint functions; for t State parameters at any given time; for t Timing control parameters; For design parameters; Constraints on the upper and lower bounds of variable values; This is the lower bound of the state parameters; This is the upper bound of the state parameters; This is the lower bound of the control parameter; This is the upper bound of the control parameter; This is the lower bound of the design parameters; This is the upper bound of the design parameters; For path constraints; This is the lower bound of the path constraint function; This is a path constraint function; This is the upper bound of the path constraint function; For boundary constraints; This is the lower bound of the boundary constraint function; For boundary constraint functions; The starting time State parameters; Termination time State parameters; This is the upper bound of the boundary constraint function.

3. The method for collaborative optimization of design parameters and control parameters of tracked vehicles according to claim 1, characterized in that, Linear interpolation is performed on the state parameters and control parameters respectively, specifically by using the Hermite-Simpson method to perform linear interpolation on the state parameters and control parameters respectively; The method for determining the residual constraints includes: based on the reconstructed optimization variables, reconstructing the dynamic constraints using the Hermite-Simpson method to obtain the residual constraints.

4. The method for collaborative optimization of design parameters and control parameters of tracked vehicles according to claim 1, characterized in that, Calculating the gradient of the cost function with respect to the reconstructed optimization variables specifically includes: calculating the gradient of the cost function with respect to the reconstructed optimization variables using the complex step-size differentiation method; The calculation of 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 is specifically included by using the complex step-size differential 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.

5. The method for collaborative optimization of design parameters and control parameters of tracked vehicles according to claim 1, characterized in that, Using the reconstructed optimization variables, the cost function, the gradient, the residual constraints, the upper and lower bound constraints of the reconstructed variable values, the reconstructed path constraints, the 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, obtaining the optimal design parameters and optimal control parameters of the tracked vehicle, specifically including: Using the reconstructed optimization variables, the cost function, the gradient, the residual constraints, the upper and lower bound constraints of the reconstructed variable values, the reconstructed path constraints, the reconstructed boundary constraints, the first Jacobian matrix, the second Jacobian matrix, and the third Jacobian matrix as inputs, a nonlinear programming solver is used to solve the problem and obtain the solution result of the current iteration. Determine if the current iteration count has reached the preset iteration count; 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, and using a nonlinear programming solver to solve the problem to obtain the solution result of the current iteration". If so, then based on the solution results of each iteration before the current iteration, calculate the first scaling factor of the residual constraint, the second scaling factor of the reconstructed path constraint, the third scaling factor of the reconstructed boundary constraint, 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. The reconstructed optimization variables of the current iteration are scaled using a linear scaling method to obtain scaled optimization variables. 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, the residual constraints, reconstructed path constraints, reconstructed boundary constraints, the first Jacobian matrix, the second Jacobian matrix, and the third Jacobian matrix of the current iteration are scaled 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 variables, the cost function, the gradient, the scaled residual constraints, the upper and lower bound constraints of the reconstructed variable values, 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, a nonlinear programming solver is used to solve the problem and obtain the solution result for the next iteration. Determine if the iteration has ended; If the iteration ends, the optimal design parameters and optimal control parameters of the tracked vehicle are determined based on the solution results of the next iteration. If the iteration is not finished, increment the iteration count by 1 and return to the step of "scaling the reconstructed optimized variable of the current iteration using the linear scaling method to obtain the scaled optimized variable".

6. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method for co-optimizing the design parameters and control parameters of the tracked vehicle as described in any one of claims 1-5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for co-optimizing the design parameters and control parameters of the tracked vehicle as described in any one of claims 1-5.

8. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method for co-optimizing the design parameters and control parameters of the tracked vehicle as described in any one of claims 1-5.

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