Collaborative optimization method for design parameters and control parameters of tracked vehicle and related device

Abstract

The invention discloses a collaborative optimization method for design parameters and control parameters of a tracked vehicle and a related device, and relates to the technical field of design and development of tracked vehicles. A kinetic model of the tracked vehicle is firstly established, and then a collaborative optimization model of the tracked vehicle is established based on the kinetic 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, the constraint conditions comprise dynamic constraints, variable value upper and lower bound constraints, path constraints and boundary constraints, the dynamic constraints are constraints established based on the dynamic model, and the path constraints are constraints established based on the dynamic model. And finally, carrying out optimization solution on the collaborative optimization model to obtain optimal design parameters and optimal control parameters of the tracked vehicle. According to the application, collaborative optimization is carried out on the design parameters and the control parameters of the tracked vehicle based on the dynamical model, and the requirements of short development period, high efficiency, fast upgrading and high performance requirements are met.

Description

Technical Field

[0001] The present application relates to the technical field of tracked vehicle design and development, and in particular to a method for collaboratively optimizing design parameters and control parameters of a tracked vehicle based on a dynamic model and related devices. Background Art

[0002] Tracked vehicles have good passability and maneuverability on off-road roads, so they are widely used in many civil and military fields such as mining and excavation, transportation assistance, search and exploration, and environmental monitoring. In recent years, with the development of vehicle technology, vehicle equipment has been rapidly upgraded, and performance requirements have been continuously improved, and there is an urgent need for rapid development and iteration. The design and development of tracked vehicles in the new era have the following requirements: 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 updates and replacements. These problems 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 static modeling technology cannot accurately predict the actual performance of the vehicle during movement, it often requires a lot of experiments and adjustments, resulting in a long development cycle, low efficiency and high cost. With the continuous advancement of science and technology and the continuous changes in user needs, under the traditional tracked vehicle design and development methods, the vehicle design is solidified, and the adjustment and improvement process is cumbersome, making the update speed unable to keep up with the changes in market demand, resulting in tracked vehicles often unable to be updated in time, affecting the competitiveness and market share of the product. Modern tracked vehicles have increasingly higher requirements for performance and stability, such as higher driving speeds, stronger off-road capabilities and more stable handling. 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 design and development methods of tracked vehicles can no longer meet the current market demand for short development cycle, high efficiency, rapid updates and high performance requirements. Summary of the invention

[0005] The purpose of this application is to provide a method and related device for collaborative optimization of design parameters and control parameters of a tracked vehicle, which can collaboratively optimize the design parameters and control parameters of a tracked vehicle based on a dynamic model to meet the needs of short development cycle, high efficiency, fast updates and high performance requirements.

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

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

[0008] Establish a dynamic model of tracked vehicles;

[0009] A collaborative optimization model of a tracked 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 constraint conditions, the constraint conditions include dynamic constraints, upper and lower bound constraints of variable values, path constraints and boundary constraints, and the dynamic constraints are constraints established based on the dynamic model;

[0010] The collaborative optimization model is optimized and solved to obtain the 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 above-mentioned method for collaborative optimization of tracked vehicle design parameters and control parameters.

[0012] 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 tracked vehicle.

[0013] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned method for collaborative optimization of tracked vehicle design parameters and control parameters.

[0014] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0015] The present application provides a method and related device for collaborative optimization of design parameters and control parameters of a tracked vehicle. First, a dynamic model of a tracked vehicle is established, and then a collaborative optimization model of the tracked vehicle is established 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 parameters and optimal control parameters of the tracked vehicle. Since the present application establishes a collaborative optimization model based on a dynamic model, it can accurately predict the actual performance of the vehicle during movement, so a large number of tests and adjustments are no longer required, thereby solving the problems of long development cycle, low efficiency and high cost. At the same time, the vehicle design is more flexible, thereby solving the problem that the vehicle design is solidified, the adjustment and improvement process is cumbersome, and the speed of updating cannot keep up with the changes in market demand. Since the present application optimizes the design parameters and control parameters at the same time, it can take into account both design and control, thereby solving the problem of limitations in meeting performance requirements. The present application collaboratively optimizes the design parameters and control parameters of the tracked vehicle based on the dynamic model, meeting the needs for short development cycle, high efficiency, fast updating and high performance requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. 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 paying creative work.

[0017] Figure 1 A flow chart of a method for collaboratively optimizing design parameters and control parameters of a tracked vehicle provided in Example 1 of the present application.

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

[0019] Figure 3 Schematic diagram of the tracked vehicle dynamics model framework provided in Example 1 of the present application.

[0020] Figure 4 A schematic diagram of solving the Jacobian matrix provided in Example 1 of the present application.

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

[0022] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0023] Example 1

[0024] This embodiment provides a method for collaboratively optimizing design parameters and control parameters of a tracked vehicle. Figure 1 and Figure 2 As shown, the method for collaboratively optimizing design parameters and control parameters of a tracked vehicle includes:

[0025] S1: Establish a dynamic model of tracked vehicles.

[0026] S2: A collaborative optimization model of a tracked 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 bounds of variable values, path constraints and boundary constraints, and the dynamic constraints are constraints established based on the dynamic model.

[0027] S3: Optimizing and solving 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, most of the design methods based on experience are adopted, 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 the movement, a large number of tests and adjustments are required, resulting in a long development cycle, low efficiency and high cost. Due to the rigidification of vehicle design, the adjustment and improvement process is cumbersome, resulting in a slow update speed. Second, the optimization space is limited. The independent design of each subsystem leads to limited overall performance, which leads to the limitations of the traditional tracked vehicle design and development methods in meeting performance requirements. The vehicle driving conditions are complex, and the design and control need to be combined to achieve the optimal performance, that is, the design parameters and control parameters are coupled to affect the overall performance, but the existing research only optimizes one of them, which reduces the global optimization space and limits the performance improvement. Therefore, this embodiment aims to provide a method for collaborative optimization of design parameters and control parameters of tracked vehicles based on a dynamic model, which can complete the collaborative optimization of design parameters and control parameters based on a dynamic model to solve the problems existing in the traditional design and development method, realize the rapid update and performance improvement of tracked vehicles, and meet the ever-changing market demand.

[0029] This embodiment uses the knowledge related to Newtonian dynamics to carry out refined modeling of an 8-DOF tracked armored vehicle, including 6 degrees of freedom, namely, longitudinal, lateral, vertical, pitch, roll, and yaw, as well as 2 degrees of freedom of the track winding on both sides, with a total of 8 degrees of freedom considering the contact with the ground. The dynamic modeling process of the 8-DOF tracked armored vehicle based on Newtonian dynamics is as follows:

[0030] The input of the multi-degree-of-freedom model is the torque of the driving wheels on both sides of the track, the angular velocity of the driving wheels, and the track driving force. The vehicle to be studied is regarded as a multi-rigid body system, including a sprung mass rigid body (i.e. the vehicle body) and two unsprung mass rigid bodies (i.e. the tracks on both sides). The movement of the vehicle body is caused by the combined effects of longitudinal, lateral, and vertical contact friction and shear force of the track, air resistance, suspension force, ground support force, etc. Figure 3 It is the input and output interface of each submodule of the vehicle dynamics model and the whole vehicle dynamics model architecture, including the interaction between the vehicle body, suspension, and track contact model. The reference system selects the geodetic coordinate system O1-X1Y1Z1 and the vehicle center of mass coordinate system O-XYZ. The external motion environment of the vehicle (i.e., X, Y, Z coordinates) is defined by the geodetic coordinate system, and the vehicle's own dynamic process (i.e., the relative position and relative speed of the track and the body) is analyzed by the vehicle center of mass 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 mass. The three rotational degrees of freedom are the roll angle φ, pitch angle θ, and yaw angle ψ of the vehicle's center of mass. The two track degrees of freedom are the angular velocity ω of the track winding on both sides. l and ω r The dynamic equations of each degree of freedom are established according to Newtonian dynamics as follows:

[0031] (1) Body translation

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

[0033] δma x =Fr x cosψ-Fr y sinψ(1)

[0034] In formula (1), δ is the rotation mass increase coefficient. When the tank is driving, the mass of the tank with the mass of rotating parts will increase by δ times; m is the vehicle mass of the tracked vehicle; a x is the acceleration of the tracked vehicle along the X direction; Fr x is the resultant force on the tracked vehicle when it moves in the X direction, specifically, the resultant force of the contact friction and shear force, air resistance, suspension force, ground support force, etc. acting on the center of mass of the vehicle when the tracked vehicle moves in the X direction; ψ is the yaw angle of the tracked vehicle; Fr yIt is the resultant force on the tracked vehicle when it moves in the Y direction. Specifically, it is the resultant force of the contact friction and shear force, air resistance, suspension force, ground support force, etc. acting on the center of mass of the vehicle when the tracked vehicle moves in the Y direction.

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

[0036] δma y =Fr x sinψ+Fr y cosψ(2)

[0037] In formula (2), a y is the acceleration of the tracked vehicle along the Y direction.

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

[0039] δma z =Fr z (3)

[0040] In formula (3), a z is the acceleration of the tracked vehicle along the Z direction; Fr z It is the resultant force on the tracked vehicle when it moves in the Z direction. Specifically, it is the resultant force of the contact friction and shear force, air resistance, suspension force, ground support force, etc. acting on the center of mass of the vehicle when the tracked vehicle moves in the Z direction.

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

[0042] F k =K k C k Δl i (4)

[0043] In formula (4), F k is the spring force; K k is the spring lever ratio; C k is the spring stiffness; Δl i It is the elastic deformation of the spring.

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

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

[0046] Spring force and damping force make up the suspension force.

[0047] (2) Body rotation

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

[0049]

[0050] In formula (6), I φ is the moment of inertia of the tracked vehicle when rotating around the X axis; is the second-order derivative of the roll angle of the tracked vehicle; M φ K is the resultant moment of the tracked vehicle when it moves in the X direction. Specifically, it is the resultant moment of the tracked vehicle when it moves in the X direction due to the combined action of the ground contact friction and shear force, air resistance, suspension force, ground support force and other forces on the center of mass of the vehicle. φ is the stiffness coefficient of the tracked vehicle suspension system when rotating around the X axis; φ is the roll angle of the tracked vehicle; C φ is the damping coefficient of the tracked vehicle suspension system when rotating around the X axis; is the first derivative of the roll angle of the tracked vehicle.

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

[0052]

[0053] In formula (7), I θ is the moment of inertia of the tracked vehicle when rotating around the Y axis; is the second-order derivative of the pitch angle of the tracked vehicle; M θ K is the resultant moment of the tracked vehicle when it moves in the Y direction. Specifically, it is the resultant moment of the tracked vehicle when it moves in the Y direction due to the contact friction and shear force, air resistance, suspension force, ground support force and other forces acting on the center of mass of the vehicle. θ is the stiffness coefficient of the tracked vehicle suspension system when rotating around the Y axis; θ is the pitch angle of the tracked vehicle; C θ is the damping coefficient of the tracked vehicle suspension system when rotating around the Y axis; is the first derivative of the pitch angle of the tracked vehicle.

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

[0055]

[0056] In formula (8), I ψ is the moment of inertia of the tracked vehicle when rotating around the Z axis; is the second-order derivative of the yaw angle of the tracked vehicle; Mψ K is the resultant moment of the tracked vehicle when it moves in the Z direction. Specifically, it is the resultant moment of the tracked vehicle when it moves in the Z direction due to the combined effects of the ground contact friction and shear force, air resistance, suspension force, ground support force, etc. acting on the center of mass of the vehicle. ψ is the stiffness coefficient of the tracked vehicle suspension system when rotating around the Z axis; ψ is the yaw angle of the tracked vehicle; C ψ is the damping coefficient of the tracked vehicle suspension system when rotating around the Z axis; is the first derivative of the yaw angle of the tracked vehicle.

[0057] (3) Track winding motion

[0058] The crawler track and driving wheel are analyzed as a whole, similar to the analysis of tire movement in vehicle dynamics. Specifically, the winding movement of the driving wheel and crawler track is considered as a whole, and the movement of the entire crawler track is equivalent to a tire. Since the crawler track grounding part is very long and the grounding pressure distribution is complex, the crawler track coordinate system is introduced. Let (x t ,y t ) is the coordinate of a point on the track contact section, x t is the vertical coordinate, y t is the horizontal coordinate.

[0059] For the two-dimensional dynamic analysis of the vehicle, the three degrees of freedom of motion, namely, lateral, longitudinal and yaw, are considered. Therefore, for the lateral and longitudinal forces of the track, the differential of the longitudinal force of the track is written as dF x =dFcosθ t , the differential of the lateral force of the track is written as dF y =dFsinθ t , heading angle v y is the speed of the vehicle along the Y direction; v x is the velocity of the vehicle along the X direction.

[0060] Substituting the shear displacement into the shear force formula, the integral calculation formula for the lateral and longitudinal forces of the track contact can be obtained as follows:

[0061]

[0062]

[0063] In the above formula, F X is the longitudinal force of the track contact; b is the track shoe width; L is the track shoe length; c is the soil viscosity 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 is the deformation modulus of soil under shear stress; θ t F is the vehicle heading angle in contact with the ground; Yis the lateral force of the track contact. The track traction force is calculated by integration in the track coordinate system through the above formula, and the traction force on the entire track can be calculated according to the ground pressure distribution p(x,y) in the track coordinate system.

[0064] The dynamic equation for analyzing the winding motion of the left track is as follows:

[0065]

[0066] In formula (11), J is the track moment of inertia; is the first-order derivative of the angular velocity of the left track winding of the tracked vehicle; T L is the driving wheel torque of the left track of the tracked vehicle; F XL is the traction force of the left track when the tracked vehicle is traveling longitudinally; r is the radius of the driving wheel.

[0067] Similarly, the dynamic equation for analyzing the winding motion of the right track is as follows:

[0068]

[0069] In formula (12), is the first-order derivative of the angular velocity of the right track winding of the tracked vehicle; T R is the driving wheel torque of the right track of the tracked vehicle; F XR It is the traction force of the right track when the tracked vehicle is traveling longitudinally.

[0070] According to the above analysis, the dynamic model of the entire tracked vehicle is finally obtained as follows:

[0071]

[0072] In formula (13), δ is the rotation mass increase coefficient; m is the vehicle mass of the tracked vehicle; a x is the acceleration of the tracked vehicle along the X direction; Fr x is the resultant force on the tracked vehicle when it moves in the X direction; ψ is the yaw angle of the tracked vehicle; Fr y is the resultant force on the tracked vehicle when it moves in the Y direction; a y is the acceleration of the tracked vehicle along the Y direction; a z is the acceleration of the tracked vehicle along the Z direction; Fr z I is the resultant force on the tracked vehicle when it moves in the Z direction; φ is the moment of inertia of the tracked vehicle when rotating around the X axis; is the second-order derivative of the roll angle of the tracked vehicle; M φ K is the resultant moment of the tracked vehicle when it moves in the X direction; φ is the stiffness coefficient of the tracked vehicle suspension system when rotating around the X axis; φ is the roll angle of the tracked vehicle; Cφ is the damping coefficient of the tracked vehicle suspension system when rotating around the X axis; is the first-order derivative of the roll angle of the tracked vehicle; I θ is the moment of inertia of the tracked vehicle when rotating around the Y axis; is the second-order derivative of the pitch angle of the tracked vehicle; M θ K is the resultant moment of the tracked vehicle when it moves in the Y direction; θ is the stiffness coefficient of the tracked vehicle suspension system when rotating around the Y axis; θ is the pitch angle of the tracked vehicle; C θ is the damping coefficient of the tracked vehicle suspension system when rotating around the Y axis; is the first-order derivative of the pitch angle of the tracked vehicle; I ψ is the moment of inertia of the tracked vehicle when rotating around the Z axis; is the second-order derivative of the yaw angle of the tracked vehicle; M ψ K is the resultant moment of the tracked vehicle when it moves in the Z direction; ψ C is the stiffness coefficient of the tracked vehicle suspension system when rotating around the Z axis; ψ is the damping coefficient of the tracked vehicle suspension system when rotating around the Z axis; is the first-order derivative of the yaw angle of the tracked vehicle; J is the moment of inertia of the track; is the first-order derivative of the angular velocity of the left track winding of the tracked vehicle; T L is the driving wheel torque of the left track of the tracked vehicle; F XL is the traction force of the left track when the tracked vehicle is traveling longitudinally; r is the radius of the driving wheel; is the first-order derivative of the angular velocity of the right track winding of the tracked vehicle; T R is the driving wheel torque of the right track of the tracked vehicle; F XR It is the traction force of the right track when the tracked vehicle is traveling longitudinally.

[0073] Based on the dynamic model shown in formula (13), this embodiment further constructs the problem of collaborative optimal design and optimal control. According to the needs, multiple objectives such as maneuverability and vertical impact degree are considered to establish a cost function. According to the dynamic model, dynamic constraints are established. According to the vehicle driving conditions, the upper and lower bounds of the vehicle variable values ​​are extracted, and the upper and lower bounds of the variable values ​​are established. Path constraints and boundary constraints are designed according to the needs. Path constraints are restrictions on the vehicle position, speed, and obstacle avoidance of the vehicle on the path. Boundary constraints are restrictions on the state parameters at the start and end times. The collaborative optimal design and optimal control problem is established by combining the design parameters and the control parameters. After completing the cost function definition, constraint condition definition, and initial value guessing as needed, some optimization-related parameters are also set as needed, such as the conversion method m trans (used to calculate residual constraints), differential method m diff, allocation point number N n , zoom settings scal, etc.

[0074] According to the established dynamic model, the input of the coordinated optimal design and optimal control problem is provided, including the initial guess value x0 of the state parameter, the initial guess value u0 of the control parameter, and the termination time t f0 and the initial guess value p0 of the design parameter.

[0075]

[0076] In formula (14), Represents a two-dimensional matrix with i rows and j columns. N x,usr The number of nodes of the state parameters provided by the user. Each node can be considered as a moment, and the value of the state parameter of each node needs to be determined; n x is the number of state parameters; N u,usr The number of nodes for the control parameters provided by the user; n u is the number of control parameters; n p is the number of design parameters; if the termination time is also a parameter to be optimized, then n tf is 1, otherwise n tf 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 constraints g are given by the following formula, n g is the number of path constraints:

[0078]

[0079] In formula (15), 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.

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

[0081] Based on the dynamic model, a collaborative optimization model of tracked vehicles is established. 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. The dynamic constraints are constraints established based on the dynamic model. The collaborative optimization model is:

[0082]

[0083] In formula (16), 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 parameter; 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 termination time t f State parameters of b max 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 formula (17), X A , Y A and Z A are the absolute displacements of the center of mass of the vehicle along the geodetic coordinate system; φ, θ and ψ are the rotation angles of the vehicle body around the three axes of the geodetic coordinate system, namely the roll angle, pitch angle and yaw angle; θ ur ,θ ul They are the speeds of the driving wheels on both sides of the track.

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

[0091] Design parameters are customized according to requirements.

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

[0093] In order to improve the solution efficiency, this embodiment further performs problem transformation, converts the above problem into an NLP problem, and uses sparse matrix operations, improved direct point matching method, automatic normalization (i.e., scaling), and high-performance cluster computing as efficient solution algorithms. Sparse matrix operations and high-performance cluster computing are methods used in programming to improve the problem-solving efficiency, complete efficient problem solving, and simultaneously solve the optimal design parameters and optimal control parameters. The optimal design parameters are applied to vehicle development, and the optimal control parameters are applied to guide the development of vehicle control algorithms, ultimately achieving collaborative optimization control of tracked vehicles.

[0094] When solving the problem of collaborative optimal design and optimal control, this embodiment converts the problem into a large-scale NLP problem, and then calls the underlying solver, such as IPOPT, to solve the problem. In the problem conversion part, the format of variables and functions in the problem input is converted into the format required by the NLP solver. In this part, the local collocation method, differential method, automatic scaling method, etc. are developed, and 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 in the entire time interval can be discretized into N using the linear interpolation method. n The new variables are given by:

[0098]

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

[0100]

[0101] In formula (19), subscripts are used to represent different moments. is the control parameter 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 formula (20), is the state parameter between x1 and x2.

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

[0106] (1.2) Residual constraint

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

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

[0109]

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

[0111] Apply linear interpolation to calculate the control parameter u at time k k and the control parameter u at time k+1 k+1 The control parameters between as follows:

[0112]

[0113] Based on the Hermite-Simpson method, the residual constraint at x It can be expressed as:

[0114]

[0115] (1.3) Path constraints and boundary constraints

[0116] The path constraint is a function of state parameters, control parameters, design parameters and time. The boundary constraints are the starting state (i.e., the state parameters at the starting time) x0 and the ending state (i.e., the state parameters at the ending time) x f The path constraints are as follows:

[0117]

[0118] Finally, after all constraints are calculated, the NLP constraints are expressed as:

[0119]

[0120] All residual constraints are bounded by 0.

[0121] (1.4) Jacobians matrix

[0122] 1) Jacobians matrix of residual constraints

[0123] Without considering the state parameters and control parameters at the midpoint of each discrete time interval, the residual constraint is x a ,u a ,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:

[0124]

[0125] To avoid repeated calculations, when performing matrix calculations, first calculate the derivatives of ζ with respect to x, u, and p. The Jacobians matrix can be simplified to:

[0126]

[0127] In formula (27), is a matrix with the i-th column being 1 and the rest of the elements being 0; h is the time interval between two adjacent nodes; T s2 is the transformation matrix; T s2 ,T a ,T b Given by:

[0128]

[0129] 2) Jacobians matrix of path constraint Calculate the derivative of path constraint g with respect to x, u, p, t as follows:

[0130]

[0131] 3) Jacobians matrix of boundary constraints

[0132] Compute the bounding constraints b for x0,x f ,p,t’s derivative is as follows:

[0133] (1.5) Cost function

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

[0135]

[0136] (1.6) Gradient of the cost function

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

[0138]

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

[0140]

[0141] (2) Differentiation method

[0142] Most NLP solvers require the first-order derivatives of the constraint c(y), and accurate calculation of these derivatives helps to achieve faster convergence. The differential method used in this embodiment is complex step differential. The complex step differential is approximated based on Taylor expansion:

[0143]

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

[0145] Ignore h 2 and higher order terms, the approximation error is O(h 2 ) can be approximated as:

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

[0147] (3) Scaling

[0148] (3.1) Variable scaling

[0149] The linear scaling method is used to scale the state parameters, control parameters, termination time and design parameters as follows:

[0150]

[0151]

[0152]

[0153] (3.2) Constrained scaling

[0154] A gradient-based average norm method is applied to scale the constraints for faster convergence.

[0155] The residual constraint is scaled as follows:

[0156]

[0157] In formula (38), N scal is the number of loops used to solve the scaling factor; ζ j,i is the i-th residual constraint in the j-th cycle.

[0158] The path constraint is scaled as follows:

[0159]

[0160] In formula (39), g j,i is the i-th path constraint in the j-th cycle.

[0161] The bounds constraints are scaled as follows:

[0162]

[0163] In formula (40), b j,i is the i-th boundary constraint in the j-th cycle.

[0164] (3.3) Scaling of Jacobians

[0165] 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:

[0166]

[0167]

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

[0169] The scaling of the Jacobians matrix for the path constraint is:

[0170]

[0171]

[0172] In formula (42), J g,i is the Jacobian matrix of the path constraint; is the scaling factor for the path constraint.

[0173]

[0174]

[0175] In formula (43), J b,i is the Jacobian matrix of the boundary constraints; is the scaling factor for the boundary constraints.

[0176] After the above problem transformation, the transformed problem is input into the underlying solver for solving, which can solve the optimal control parameters and the optimal design parameters at the same time.

[0177] Then, in S3, the collaborative optimization model is optimized and solved to obtain the optimal design parameters and optimal control parameters of the tracked vehicle, which specifically include:

[0178] (1) 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.

[0179] The state parameters and the control parameters are linearly interpolated respectively, specifically including: using the Hermite-Simpson method to linearly interpolate the state parameters and the control parameters respectively.

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

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

[0182] Calculating the gradient of the cost function with respect to the reconstructed optimization variable specifically includes: calculating the gradient of the cost function with respect to the reconstructed optimization variable using a complex step-size differentiation method.

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

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

[0185] The method for determining the residual constraint includes: based on the reconstructed optimization variables, the dynamic constraint is reconstructed using the Hermite-Simpson method to obtain the residual constraint.

[0186] The residual constraint, reconstructed path constraint and reconstructed boundary constraint are shown in formula (25), and the upper and lower bound constraints of the reconstructed variable values ​​are shown in formula (16). It only requires changing the dimension of the optimized variable.

[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 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 the 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.

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

[0190] (5) The reconstructed optimization variables, cost function, gradient, residual constraints, upper and lower bounds 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 tracked vehicle are obtained.

[0191] The optimal design parameters and optimal control parameters of the tracked vehicle are obtained by using the nonlinear programming solver with the reconstructed optimization variables, cost function, gradient, residual constraint, upper and lower bound constraints of the reconstructed variable value, reconstructed path constraint, reconstructed boundary constraint, first Jacobian matrix, second Jacobian matrix and third Jacobian matrix as inputs, and solving them using the nonlinear programming solver, including:

[0192] 1) Using the reconstructed optimization variables, cost function, gradient, residual constraints, upper and lower bound constraints of the reconstructed variable values, 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, which includes the state parameters, design parameters and control parameters of the current iteration.

[0193] 2) Determine whether the current number of iterations has reached the preset number of iterations.

[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, reconstructed path constraints, reconstructed boundary constraints, the first Jacobian matrix, the second Jacobian matrix and the third Jacobian matrix as input, solving using the nonlinear programming solver to obtain the solution result of the current iteration".

[0195] 4) If so, 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, and scale the residual constraints, reconstructed path constraints, reconstructed boundary constraints, first Jacobian matrix, second Jacobian matrix and 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 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.

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

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

[0199] 7) Determine whether the iteration is completed.

[0200] If the solution minimizes the cost function value, the iteration ends.

[0201] 8) If the iteration is completed, the optimal design parameters and optimal control parameters of the tracked vehicle are determined based on the solution results of the next iteration.

[0202] 9) If the iteration is not finished, 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 a linear scaling method to obtain scaled optimized variables".

[0203] This embodiment provides a collaborative optimization method for the optimal design parameters and optimal control parameters of an 8-degree-of-freedom tracked vehicle based on a dynamic model. For the rapid development and design of tracked vehicles, a multi-objective optimization framework that can quickly solve large-scale complex mixed integer optimal design and optimal control problems is developed for the first time. Compared with similar optimization frameworks in the world, it has a high success rate and fast speed. In terms of vehicle optimization design, component optimization design and control, it also greatly improves the relevant performance of tracked vehicles from the perspective of design and control. First, a dynamic model of a tracked armored vehicle is built based on Newton's dynamics theory. The 8-degree-of-freedom model considered includes 6 degrees of freedom, namely, longitudinal, lateral, vertical, roll, pitch, and yaw, and 2 degrees of freedom for winding tracks on both sides. Then, the above dynamic model is converted into residual constraints, and upper and lower bounds, path constraints, and boundary constraints of variable values ​​are established according to driving conditions and the working boundaries of each subsystem. A cost function is established according to task requirements, and design parameters and control parameters are used as optimization variables at the same time to complete the construction of collaborative optimal design and optimal control problems. 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 process of dynamic modeling of tracked armored vehicles, in addition to the conventional six degrees of freedom of the vehicle body, emphasis is placed on the influence of the lateral and longitudinal movement of the track winding when the track contacts the ground on the dynamic characteristics of the vehicle body, and the model has a 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 of tracked vehicles based on the dynamic model of the tracked vehicle. The optimal design parameters can be used for vehicle design, and the optimal control parameters can be used as a reference for the subsequent online control strategy formulation. The framework can apply gradient information to expand the optimization space and has good optimization effects, 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 solution stability and efficiency are greatly improved. This is mainly due to the vehicle vector dynamics modeling method. In the solution part of the optimal problem, the direct point matching method is improved and the automatic normalization algorithm is applied, which realizes the stable and fast solution of large-scale collaborative optimal problems.

[0208] Example 2

[0209] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for collaborative optimization of design parameters and control parameters of a tracked vehicle is implemented.

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

[0211] In an 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 collaborative optimization of design parameters and control parameters of a tracked vehicle as described in Example 1.

[0212] Example 3

[0213] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for collaboratively optimizing design parameters and control parameters of a tracked vehicle described in Embodiment 1 is implemented.

[0214] Example 4

[0215] An embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the method for collaborative optimization of design parameters and control parameters of a tracked vehicle described in Example 1.

[0216] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0217] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will 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 tracked vehicle, characterized in that: The method for collaboratively optimizing design parameters and control parameters of a tracked vehicle comprises: Establish a dynamic model of tracked vehicles; A collaborative optimization model of a tracked 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 constraint conditions, the constraint conditions include dynamic constraints, upper and lower bound constraints of variable values, path constraints and boundary constraints, and the dynamic constraints are constraints established based on the dynamic model; The collaborative optimization model is optimized and solved to 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 a tracked vehicle according to claim 1, characterized in that: The dynamic model of the tracked vehicle is: Where δ is the rotational mass increase coefficient; m is the vehicle mass of the tracked vehicle; a x is the acceleration of the tracked vehicle along the X direction; Fr x is the resultant force on the tracked vehicle when it moves in the X direction; ψ is the yaw angle of the tracked vehicle; Fr y is the resultant force on the tracked vehicle when it moves in the Y direction; a y is the acceleration of the tracked vehicle along the Y direction; a z is the acceleration of the tracked vehicle along the Z direction; Fr z I is the resultant force on the tracked vehicle when it moves in the Z direction; φ is the moment of inertia of the tracked vehicle when rotating around the X axis; is the second-order derivative of the roll angle of the tracked vehicle; M φ K is the resultant moment of the tracked vehicle when it moves in the X direction; φ is the stiffness coefficient of the tracked vehicle suspension system when rotating around the X axis; φ is the roll angle of the tracked vehicle; C φ is the damping coefficient of the tracked vehicle suspension system when rotating around the X axis; is the first-order derivative of the roll angle of the tracked vehicle; I θ is the moment of inertia of the tracked vehicle when rotating around the Y axis; is the second-order derivative of the pitch angle of the tracked vehicle; M θ K is the resultant moment of the tracked vehicle when it moves in the Y direction; θ is the stiffness coefficient of the tracked vehicle suspension system when rotating around the Y axis; θ is the pitch angle of the tracked vehicle; C θ is the damping coefficient of the tracked vehicle suspension system when rotating around the Y axis; is the first-order derivative of the pitch angle of the tracked vehicle; I ψ is the moment of inertia of the tracked vehicle when rotating around the Z axis; is the second-order derivative of the yaw angle of the tracked vehicle; M ψ K is the resultant moment of the tracked vehicle when it moves in the Z direction; ψ C is the stiffness coefficient of the tracked vehicle suspension system when rotating around the Z axis; ψ is the damping coefficient of the tracked vehicle suspension system when rotating around the Z axis; is the first-order derivative of the yaw angle of the tracked vehicle; J is the moment of inertia of the track; is the first-order derivative of the angular velocity of the left track winding of the tracked vehicle; T L is the driving wheel torque of the left track of the tracked vehicle; F XL is the traction force of the left track when the tracked vehicle is traveling longitudinally; r is the radius of the driving wheel; is the first-order derivative of the angular velocity of the right track winding of the tracked vehicle; T R is the driving wheel torque of the right track of the tracked vehicle; F XR It is the traction force of the right track when the tracked vehicle is traveling longitudinally.

3. The method for collaborative optimization of design parameters and control parameters of a tracked vehicle according to claim 1, characterized in that: The collaborative optimization model of tracked vehicles is: 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 parameter; 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 termination time t f State parameters of b max is the upper bound of the boundary constraint function.

4. The method for collaborative optimization of design parameters and control parameters of a tracked vehicle according to claim 1, characterized in that: The collaborative optimization model is optimized and solved to obtain the optimal design parameters and optimal control parameters of the tracked vehicle, specifically including: Linearly interpolating the state parameters and the control parameters respectively to obtain the state parameters and the control parameters at each interpolation time, so as to reconstruct the optimization variables to obtain the reconstructed optimization variables; the interpolation time includes the starting time, the ending time and a plurality of intermediate times between the starting time and the ending time; the reconstructed optimization variables include the design parameters and the state parameters and the control parameters at each interpolation time; 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; 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 input, a nonlinear programming solver is used to solve the problem, so as to obtain the optimal design parameters and optimal control parameters of the tracked vehicle.

5. The method for collaborative optimization of design parameters and control parameters of a tracked vehicle according to claim 4, characterized in that: 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; The method for determining the residual constraint includes: based on the reconstructed optimization variables, reconstructing the dynamic constraint using the Hermite-Simpson method to obtain the residual constraint.

6. The method for collaborative optimization of design parameters and control parameters of a tracked vehicle according to claim 4, 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; 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.

7. The method for collaborative optimization of design parameters and control parameters of a tracked vehicle according to claim 4, characterized in that: The optimal design parameters and optimal control parameters of the tracked vehicle are obtained by 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 inputs and solving using a nonlinear programming solver, specifically including: 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 input, using a nonlinear programming solver to solve and obtain a solution result of the current iteration; Determine whether the current number of iterations reaches the preset number of iterations; If not, return to the step of "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 input, solving using a nonlinear programming solver to obtain a 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; Scale the reconstructed optimization variables of the current iteration by a linear scaling method to obtain scaled optimization variables, and scale 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 the scaled residual constraint, the scaled path constraint, the scaled boundary constraint, the scaled first Jacobian matrix, the scaled second Jacobian matrix, and the scaled third Jacobian matrix; Taking 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, using a nonlinear programming solver to solve and obtain a solution result for the next iteration; Determine whether the iteration is completed; If the iteration is finished, 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, 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 by using the linear scaling method to obtain the scaled optimized variables".

8. 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 according to any one of claims 1 to 7.

9. 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 tracked vehicle according to any one of claims 1 to 7 is implemented.

10. 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 tracked vehicle according to any one of claims 1 to 7 is implemented.

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