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

Abstract

The invention discloses an amphibious vehicle design parameter and control parameter collaborative optimization method and a related device, and relates to the technical field of amphibious vehicle design and development. A dynamic model of an amphibious vehicle is firstly established, and then a collaborative optimization model of the amphibious vehicle is established based on the dynamic 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 amphibious vehicle. The design parameters and the control parameters of the amphibious vehicle are subjected to collaborative optimization based on the kinetic model, and the requirements for short development period, high efficiency, fast updating and high performance requirement are met.

Description

Technical Field

[0001] The present application relates to the technical field of amphibious vehicle design and development, and in particular to a method and related device for collaborative optimal design and optimal control of amphibious vehicles based on a dynamic model. Background Art

[0002] As a cross-domain platform, amphibious vehicles are of great significance in the fields of military, rescue and exploration. They have excellent environmental adaptability and can perform various tasks in complex terrain and waters. Amphibious vehicles are now widely used in the civilian field, participating in surveying and mapping, meteorological monitoring, transportation and other industries. However, for a long time, the research and development of amphibious vehicles has been facing many challenges, including long development cycle, high cost, and higher performance requirements. These problems have restricted the development and application of amphibious vehicles.

[0003] First, traditional amphibious vehicle development methods often use experience-based design methods, which are difficult to fully consider the actual operation of the vehicle in a dynamic environment. Due to the lack of support from dynamic models, traditional methods often cannot accurately predict the performance of vehicles in amphibious conditions, resulting in a large number of tests and adjustments during the development process, which prolongs the development cycle and increases costs.

[0004] Secondly, with the development of science and technology and the continuous changes in application requirements, existing amphibious vehicles often cannot keep up with the latest updates. Traditional development methods often make vehicle designs rigid, making it difficult to achieve rapid updates and flexible adjustments, thus limiting vehicle performance improvements and application development.

[0005] In summary, the traditional amphibious vehicle development method can no longer meet the current amphibious vehicle needs for rapid updates and high performance requirements. Summary of the invention

[0006] The purpose of this application is to provide a method and related device for collaborative optimization of design parameters and control parameters of an amphibious vehicle, which can collaboratively optimize the design parameters and control parameters of an amphibious vehicle based on a dynamic model to meet the needs of short development cycle, high efficiency, fast updates and high performance requirements.

[0007] To achieve the above objectives, this application provides the following solutions:

[0008] In a first aspect, the present application provides a method for collaboratively optimizing design parameters and control parameters of an amphibious vehicle, comprising:

[0009] Build dynamic models of amphibious vehicles;

[0010] A collaborative optimization model of an amphibious 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;

[0011] The collaborative optimization model is optimized and solved to obtain the optimal design parameters and optimal control parameters of the amphibious vehicle.

[0012] In a second aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned method for collaborative optimization of design parameters and control parameters of an amphibious vehicle.

[0013] 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 an amphibious vehicle.

[0014] 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 amphibious vehicle design parameters and control parameters.

[0015] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0016] The present application provides a method and related device for collaborative optimization of design parameters and control parameters of an amphibious 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 rigid, 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 amphibious vehicles based on a dynamic model to meet the needs of short development cycle, high efficiency, fast update and high performance requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] 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.

[0018] Figure 1 An application environment diagram of a method for collaboratively optimizing design parameters and control parameters of an amphibious vehicle provided in Example 1 of the present application;

[0019] Figure 2 A schematic flow chart of a method for collaboratively optimizing design parameters and control parameters of an amphibious vehicle provided in Example 1 of the present application;

[0020] Figure 3 A schematic diagram of solving the Jacobian matrix provided in Example 1 of the present application. DETAILED DESCRIPTION

[0021] 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, rather than all the embodiments. In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail in combination with the drawings and specific implementation methods.

[0022] Example 1

[0023] like Figure 1 and Figure 2 As shown, this embodiment provides a method for collaboratively optimizing design parameters and control parameters of an amphibious vehicle, including:

[0024] Step S1: Establish a dynamic model of the amphibious vehicle.

[0025] Step S2: Establishing a collaborative optimization model of an amphibious vehicle 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 bounds of variable values, path constraints and boundary constraints, and the dynamic constraints are constraints established based on the dynamic model.

[0026] Step S3: Optimizing and solving the collaborative optimization model to obtain optimal design parameters and optimal control parameters of the amphibious vehicle.

[0027] First, the traditional amphibious vehicle development method mostly adopts an experience-based design method, calculates the design parameters according to the requirements, and then optimizes the parameters based on the static model. However, this traditional method has problems such as small optimization space and poor dynamic operation effect, and cannot fully consider the dynamic response and performance of the vehicle in different water and land environments. Secondly, in the existing amphibious vehicle development process, each subsystem is often designed independently, lacking comprehensive consideration of the overall performance, resulting in limited overall vehicle performance. In addition, in view of the different characteristics of the two environments of water and land, traditional methods often need to be designed and optimized separately, which increases the development cost and cycle. Furthermore, the existing optimization algorithm has certain limitations in solving the development of amphibious vehicles. Although the evolutionary algorithm can consider the dynamic model, the parameter optimization efficiency is low; the dynamic programming or indirect method optimization algorithm is extremely sensitive to the problem scale and the initial solution, and it is difficult to achieve rapid optimization of multiple parameters. Therefore, this embodiment aims to provide a method for collaborative optimization of design parameters and control parameters of amphibious vehicles based on a dynamic model, which can complete the collaborative optimization of design parameters and control parameters based on a dynamic model, so as to solve the problems existing in the traditional design and development method, realize the rapid renewal and performance improvement of amphibious vehicles, and meet the ever-changing market demand. The use of advanced dynamic modeling technology can more accurately predict the actual performance of vehicles in amphibious conditions, thereby reducing test costs and development cycles. At the same time, the dynamic model method can achieve rapid iteration and flexible adjustment, which is conducive to vehicle upgrading and performance optimization.

[0028] In this embodiment, the present invention first solves each unknown quantity in the control equation by the finite volume method, performs a detailed calculation on the pump-propelling system of the amphibious vehicle, forms a thrust database as a support for the whole vehicle simulation, and establishes a high-precision 6-DOF water dynamics model of the amphibious vehicle based on rigid body dynamics and fluid dynamics and other related theories. According to different sea conditions, the influence of wind, waves, and currents on the movement of the amphibious vehicle is refined, and an environmental disturbance model is established. The above models are combined to form a complete dynamic simulation model. The modeling process of the dynamic model of the amphibious vehicle is as follows.

[0029] Step 1.1: Establishment of support database based on computational fluid dynamics and finite volume method

[0030] First, determine the configuration of the propulsion system of the amphibious vehicle, taking the common dual-thruster amphibious vehicle as an example. In order to accurately describe the performance of the propulsion system under different control parameters, a database supporting the simulation of amphibious vehicles is established. Due to the complex shape of the pump system and the changes in the flow field caused by the movement, the hydrodynamic calculation and analysis is relatively complex. Therefore, the motion equation of viscous incompressible fluid is used to solve the problem of the external flow field of the propulsion pump, as shown in the following formula:

[0031]

[0032] Where ρ is the fluid density; v is the velocity vector of the fluid at time t; is the Laplace operator; p is the pressure; F is the external force vector; the constant μ is the dynamic viscosity coefficient (dynamic viscosity μ).

[0033] Considering that the flow in the pipeline of the power system of the amphibious vehicle is a circular pipe flow, and the water jet problem in the rotating flow domain such as the pump blade guide vane, the multi-coordinate sliding grid model (MRF) is combined with the RNG k-ε model for calculation. The RNG k-ε model improves the Standard k-ε model based on the renormalization group theory and considers the influence of the average strain rate in the rotating coordinate system.

[0034] The eddy viscosity of this model can be expressed as a function of the turbulent dissipation rate ε and the turbulent kinetic energy k:

[0035]

[0036] The transport equation of turbulent kinetic energy k is expressed as follows:

[0037]

[0038] In the formula, x i represents the partial derivatives along the three axes, u i represents the three axial velocities. The transport equation of the dissipation rate ε is expressed as follows:

[0039]

[0040] in, u j is the velocity component, Indicates u i and u j The expanded form of the partial derivative of a vector is: c1=1.42,c μ =0.085, c2=1.68, σ k =0.7179,σ ε =1.3.

[0041] The pump propulsion calculation domain is divided into four parts: the outlet section, the impeller area, the guide vane area, and the water inlet channel section. The dimensionless wall distance is used to divide the near-wall area of ​​each part. (where τ ωThe grid structure near the wall is arranged after the calculation of the values ​​of ( y is the wall shear stress, y is the distance from the wall to the measuring point, and μ is the dynamic viscosity of the fluid). The height of the first layer of grid on the nozzle wall is about 0.35 mm. The sliding grid model (MRF) based on the multi-motion reference system is used to process the rotating area. The water inlet flow channel section and the impeller area, the impeller area and the guide vane area are connected through the frozen rotor interface, and the guide vane area and the vector nozzle section are connected through the static-static interface.

[0042] The rotating flow domain is set as a rotating grid, and the conventional flow area is set as a stationary grid. The inlet flow channel forms an unstructured grid by appropriately adding nodes, local grid encryption is performed in the inlet and outlet areas, and boundary layer grids are used in the flow channel and the near-wall area of ​​the pump shaft.

[0043] Finally, the reliability of the simulation results is analyzed based on the safety factor to ensure the reliability of the database. According to the commonly used control quantities of dual-thruster amphibious vehicles, namely the speed of the dual-side propulsion pumps and the opening of the dual-side water gates, the propulsion force and torque generated by the amphibious vehicle propulsion system in the three degrees of freedom of longitudinal, transverse and bow pitch under different working conditions are calculated, and a propulsion system database is formed to facilitate subsequent development. Depending on the type of amphibious vehicle propulsion system to be calculated, it can be extended to different forms of propulsion systems such as propellers, pod propulsion systems, and azimuth propulsion systems.

[0044] Step 1.2: Construction of the water dynamics model of the amphibious vehicle based on rigid body dynamics and fluid dynamics

[0045] In the derivation of the kinematic equations of an amphibious vehicle, the following two assumptions are usually made: (1) the amphibious vehicle is a rigid body; and (2) the NED coordinate system {n} is regarded as an inertial system (ignoring the rotation speed of the earth).

[0046] According to assumption (2), we have:

[0047]

[0048] in and are the velocity vectors of the center of mass of the rigid body relative to the inertial system {i} and the NED coordinate system {n}, respectively. and is the angular velocity vector of the rigid body relative to the inertial frame {i} and the NED coordinate frame {n}.

[0049] According to Euler's first law, we can deduce:

[0050]

[0051] in is the external force acting on the center of gravity, represents the time differential in the inertial frame {i}, and is defined as the derivative of the variable with respect to time. The S function is a vector cross product. The above vector expression describes the translation about CG (center of gravity) in the body coordinate system {b}:

[0052]

[0053] The derivation of rotational dynamics is similar to the above derivation process:

[0054]

[0055] in, and represents the angular velocity of the rigid body in the coordinate systems {i} and {n}. is the external moment acting on the center of gravity, I g is the rotational inertia matrix about the center of gravity. The above vector expression in {b} describes the rotational motion about the CG:

[0056]

[0057] According to the Newton-Euler equations for translational and rotational motion of the center of gravity CG, the equation of motion for CG can be obtained:

[0058]

[0059] in, The mass matrix representing the center of gravity and the parameter M RB Same meaning. The Coriolis matrix representing the center of gravity, and the parameter C RB Same meaning.

[0060] Transform equation (10) into the equation of motion about point CO (the origin of the body coordinate system):

[0061]

[0062] Where H represents the transformation matrix, Represents the center of gravity vector.

[0063] Expanding and simplifying the first line of equation (11) yields the translation about CO:

[0064]

[0065] Where m represents the rigid body mass.

[0066] Expanding the second line of equation (11) yields the rotational motion about CO:

[0067]

[0068] Rigid body dynamics are expressed in vector form:

[0069]

[0070] Consider the rigid body dynamics of an ocean vehicle:

[0071]

[0072] Among them, M RB is the rigid body mass matrix, C RB is the rigid body Coriolis force and centripetal force matrix, τ RB is the generalized external force, including the fluid dynamics τ hyd , hydrostatic τ hs 、Wind and wave force τ wind and τ wave and the control force τ control u, v, w, p, q, r are the velocities and angular velocities of the six degrees of freedom (surge, sway, heave, roll, pitch, and yaw).

[0073] In fluid dynamics, it is usually assumed that the fluid dynamic forces and fluid dynamic moments acting on a rigid body can be linearly superimposed. The fluid dynamic forces consist of the following components: the additional mass M caused by the inertia of the fluid surrounding the vehicle A ; Due to the rotation of the body coordinate system {b} relative to the north-east ground {n}, the additional mass produces the corresponding Coriolis and centripetal matrices C A (v r ). Radiation-induced potential damping D due to energy carried by surface waves P Viscous damping τ due to surface friction, wave drift damping, vortex shedding, and lift / drag visc Potential damping and viscous damping constitute nonlinear damping. The resulting nonlinear fluid dynamic mass-spring-damper force can be expressed as:

[0074]

[0075] τ hs = -g(η)-g0 (17);

[0076] Among them, D(v r ) represents nonlinear damping; g0 represents controllable restoring force.

[0077] The nonlinear control equation can be obtained:

[0078]

[0079] Here, τ represents the generalized external force.

[0080] In general, there are coupling terms for all degrees of freedom. However, in practical applications, some coupling terms may be very small, so these coupling terms with small effects are ignored when deriving the model. In the process of deriving the model, the following assumptions are usually applied:

[0081] An amphibious vehicle is a rigid body, meaning its shape and structure remain unchanged during motion.

[0082] The ocean currents in a basin environment are steady and irrotational, that is, the flow speed and direction remain unchanged in space and time.

[0083] An amphibious vehicle is considered a box-shaped vehicle, meaning that its waterline area remains constant.

[0084] An amphibious vehicle is bilaterally symmetrical, that is, it has XZ symmetry.

[0085] The longitudinal motion of an amphibious vehicle is decoupled, that is, its longitudinal motion is independent of the motion of other degrees of freedom.

[0086] These assumptions can help simplify calculations when simplifying the model and making derivations, and make the model easier to apply to the analysis and design of real-world situations.

[0087] The rigid body inertia matrix M RB Expands to:

[0088]

[0089] Among them, x g ,y g , z g Represents the position vector of the center of gravity along the xyz axis. represents the moment of inertia matrix.

[0090] Expand the Coriolis force and centripetal force matrix C in parametric form RB (v):

[0091]

[0092] The inertia matrix M of the decoupled added mass is given by A :

[0093]

[0094] Among them, X, Y, Z, K, M, and N represent six degrees of freedom, including surge, sway, heave, roll, pitch, and pitch. The superscript in formula (21) indicates the meaning related to acceleration. The superscript indicates that the term is a coefficient acting on the superscript parameter.

[0095] For a rigid body moving in an ideal fluid, the hydrodynamic Coriolis and centripetal force matrices C are A (v) can be MA Parameterization:

[0096]

[0097] Linear damping D consists of potential damping and viscous damping:

[0098]

[0099] D n (ν r ) is the nonlinear damping matrix caused by quadratic damping and higher-order terms. D(v r )=D+D n (v r ).

[0100]

[0101] Hydrostatic force is caused by the tilt of the amphibious vehicle due to disturbance, which breaks the balance between gravity and buoyancy. It can be expressed as follows:

[0102]

[0103] Where g(η) represents the restoring force, A wp It is an amphibious vehicle, the waterline area, is the displacement volume of the amphibious vehicle when floating, GM T To ensure horizontal stability, GM L For vertical center height, L fo is the x-axis distance from the floating center CF to the {b} origin CO. θ, φ, z represent the three Euler angles of the rigid body system relative to the inertial system. So far, the additional mass matrix M A , the additional mass Coriolis force and centripetal force matrix C A (v r ), the fluid dynamic damping matrix D(v r ) give the expressions of fluid dynamics derivatives. According to the inertia matrix calculated by the amphibious vehicle itself and the amphibious vehicle thrust database, the water dynamics model of the amphibious vehicle can be obtained.

[0104] Step 1.3: Establish environmental disturbance models under different sea conditions

[0105] In the dynamic system of an amphibious vehicle, environmental forces usually include the forces and torques exerted on the vehicle by wind, waves and ocean currents. In the control system, it is usually assumed that wind and wave disturbances can be superimposed. In general, environmental forces have highly nonlinear characteristics and are additive and multiplicative to the dynamic equations of motion.

[0106] Wind is defined as air moving relative to the Earth's surface. Let V w and γ wrepresent wind speed and angle of attack respectively.

[0107] Angle of attack γ w With wind speed V w Wind direction β w , and the heading ψ are:

[0108] γ w =ψ-β w -π(26);

[0109] Definition of relative wind speed V rw and relative wind direction γ rw :

[0110]

[0111] The relative wind speed component is

[0112]

[0113] Define the wind force formula:

[0114]

[0115] In the formula, ρ a is the air density, H Fw and H Lw They are the orthographic projection area A Fw and side projection area A Lw The height of the centroid above the water surface, L oa It is the total length of the amphibious vehicle. X (γ rw ), C Y (γ rw ), C Z (γ rw ), C K (γ rw ), C M (γ rw ), C N (γ rw ) is the wind coefficient related to the angle of attack.

[0116] The wave force can be considered as an average slowly varying part and an oscillating part. In order to study the influence of wave force on the control system, the first-order wave force and the second-order wave force of the oscillating part are usually considered separately, as shown in the following formula.

[0117] τ wave =τ wave1 +τ wave2 (30);

[0118] The wave force RAO is converted into the form of transfer function, and the wave spectrum is approximated by a linear filter as follows:

[0119] τ wave1 =H s (s)n(s) (31);

[0120] Among them, H s (s) = Kdiag{h1(s), h2(s), h3(s), h4(s), h5(s), h6(s)} is the approximate diagonal matrix of the 6DOF wave spectrum S(ω), K = diag{k1, k2, k3, k4, k5, k6} is the adjustable gain, and n(s) is the zero-mean Gaussian white noise. This approximate model has good performance and robustness in closed-loop feedback control systems. Taking a single degree of freedom as an example, the wave spectrum can be approximated by a second-order system:

[0121]

[0122] Where λ is the damping coefficient selected according to sea conditions and wave spectrum, ω e is the encounter frequency. Usually the value of k is defined as follows:

[0123] k=2λω e σ (33);

[0124] Where σ is a constant describing the wave intensity. The value of λ is obtained by fitting the wave spectrum curve.

[0125] The second-order wave drift force is approximated by a random walk process: Where d is zero-mean Gaussian white noise.

[0126] Ocean currents are horizontal and vertical circulation systems of seawater caused by gravity, wind friction and changes in water density in different parts of the ocean. The generalized velocity of an irrotational steady ocean current is defined as: where u c ,v c ,w c is expressed in the body coordinate system {b}, that is,

[0127] The relative speed is defined as:

[0128]

[0129] according to We can get:

[0130]

[0131] Substituting the whole vehicle dynamics model into the dynamics model, we can get the dynamics model under the influence of ocean currents:

[0132]

[0133] Among them, M and C (vr ) represents the inertia matrix and Coriolis force matrix under the influence of ocean currents.

[0134] After establishing the dynamic model of the amphibious vehicle, we further construct the collaborative optimal design and optimal control problem, consider multiple objectives such as the mobility and lightweight of the amphibious vehicle, establish the cost function, use the dynamic model as the residual constraint, extract the upper and lower bounds of the vehicle state according to the vehicle driving conditions, establish the boundary constraints, and design the path constraints and boundary constraints according to the requirements. The path constraint is to restrict the dynamic state of the vehicle such as displacement and speed when driving on the path to ensure that the vehicle follows a specific trajectory. The boundary constraint is to restrict the state parameters (such as position, speed, acceleration, etc.) of the vehicle at the starting and ending times to ensure that the initial and final states of the vehicle meet specific requirements. The collaborative optimal design and optimal control problem is established by combining the design parameters and the control parameters. Complete the objective function, upper and lower bounds of the constraints, and guess the initial value as needed. It is also necessary to set some optimization-related parameters, such as the transformation method m trans 、Differential method diff , allocation point number N n , scaling setting scal, the parameters plot that need to be drawn in the iteration, and the weight w of the control parameter to be smoothed u wait.

[0135] According to the established vehicle dynamics model, the input of the problem is provided, including the initial guess value x0 of the state quantity, the initial guess value u0 of the control quantity, and the terminal time t f0 and design variable p0.

[0136]

[0137] Where N x,usr is the number of nodes of the state parameter provided by the user, N u,usr is the number of nodes of the control 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 is the number of control parameters. If the terminal time is also a parameter to be optimized, then n tf is 1, otherwise it is 0, n p is the number of design parameters, represents a two-dimensional matrix with i rows and j columns. For the global transformation method, the initial values ​​of the start time and end time of each stage will be generated by the optimal control framework.

[0138] The above state parameters, control parameters, terminal time, upper and lower bounds of design parameters and inequality constraints g are given by the following formula:

[0139]

[0140] Among them, n g is the number of path constraints. 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.

[0141] In addition to the above initial values ​​and upper and lower bound constraints, the problem input also includes the Lagrangian term L and Mayer term M of the cost function, the first-order dynamic constraint function f, the path constraint function g, and the boundary constraint function b.

[0142] Based on the dynamic model, a collaborative optimization model of the amphibious vehicle 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:

[0143]

[0144] Among them, J is the cost function; for Meyer Item; is the Lagrangian term; is the dynamic constraint; is the first-order derivative of the state parameter at time t; f[.] is the dynamic constraint function; x(t) is the state parameter at time t; u(t) is the control parameter at time t; p is the design parameter; x min ≤x(t)≤x max 、u min ≤u(t)≤u max 、p min ≤p≤p max The upper and lower bounds of the variable value are constrained; x min is the lower bound of the state parameter; x max is the upper bound of the state parameter; u min is the lower bound of the control parameter; u max is the upper bound of the control parameter; p min is the lower bound of the design parameter; p max is the upper bound of the design parameters; g min ≤g[x(t),u(t),t,p]≤g maxis 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.

[0145] The state parameters are:

[0146]

[0147] Among them, q bv represents the position vector of the center of mass of the vehicle body, X A ,Y A ,Z A is the absolute displacement of the center of mass along the global coordinate system; q bω represents the rotation angle of the vehicle body’s center of mass, φ, ψ are the rotation angles of the vehicle body around the three axes of the global coordinate system;

[0148] Design parameters are defined based on requirements;

[0149] The control parameters are the two rotation angles of the vehicle's propellers;

[0150] Input the initial guess values ​​of each state quantity and control quantity of the discrete point, and input the initial values ​​of the design variables according to the design parameters to be optimized. After the above input, solve the problem, that is, solve equation (39). First, transform the problem into an NLP problem. Through sparse matrix operations, improved direct collocation method, automatic normalization, high-performance cluster computing and external relaxation method, the efficient problem solving efficiency is improved, the problem is solved efficiently, and the optimal design parameters and vehicle optimal control parameters are solved simultaneously. The optimal design parameters are applied to vehicle development, and the optimal control parameters guide the development of vehicle control algorithms, ultimately realizing the coordinated optimization control of amphibious vehicles.

[0151] When solving collaborative optimal design and optimal control problems, it is necessary to convert the problem into a large-scale NLP problem, and then call 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, different collocation methods, differential methods, automatic scaling methods, etc. are developed. The last part uses the IPOPT solver to solve the problem.

[0152] (1)Local point allocation method

[0153] (1.1) NLP solver variables

[0154] In the local collocation method, the continuous state and control quantities in the entire time interval can be discretized into N n nodes, the new variable is given by the following formula:

[0155]

[0156] Then, the discrete state parameters, control parameters and design parameters are reconstructed into NLP column vectors:

[0157]

[0158] For the Hermite-Simpson method (local collocation method), when the control variable in each sampling interval is also selected as the parameter to be optimized, the reconstructed NLP variable is:

[0159]

[0160] When the state quantity in each sampling interval is also selected as the parameter to be optimized, the reconstructed NLP variable is as follows:

[0161]

[0162] In the formula, subscripts are used to represent different moments, and the underlined variables are obtained by calculating the corresponding two variables before and after them, that is, for and Calculated control parameters. for and Calculated state parameters.

[0163] The upper and lower bounds of the variables in the above formula (40) must also be reconstructed based on the reconstructed NLP variables.

[0164] (1.2) Constraints (this step is to process the constraints)

[0165] 1) Residual constraints of the Hermite-Simpson method

[0166] Based on the third-order Hermite interpolation, at x k With x k+1 The state variables between and its derivatives It can be deduced as follows:

[0167]

[0168] Where h is the time interval between discrete points, that is, the time interval between two adjacent moments (that is, two adjacent nodes).

[0169] Based on the Hermite-Simpson method, The residual constraint at It can be expressed as:

[0170]

[0171] 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 variables between

[0172]

[0173] In order to reduce repeated calculations in numerical calculations, matrix calculation method is applied, so the state variable x and the control variable It can be expressed as:

[0174]

[0175] is the dynamic equation of the system:

[0176]

[0177] Transformation matrix T s1 and T s2 for:

[0178]

[0179] The residual constraint ζ can be expressed by applying matrix calculation as:

[0180]

[0181] 2) Path constraints and boundary constraints

[0182] Path constraints are functions of state variables, control variables, design variables, and time, while boundary constraints are functions of initial and terminal states:

[0183]

[0184] Finally, after all constraints are calculated, the NLP constraints are expressed as:

[0185]

[0186] All residual constraints are bounded by 0.

[0187] (1.3) Jacobians matrix - Jacobian matrix (this step is to find the derivatives of the above constraints with respect to the variables. This matrix is ​​derived because it is needed in the solver solution process)

[0188] 1) Jacobians matrix of residual constraints

[0189] Without considering the state and control quantities 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 3 As shown, x a ,u a ,x b ,u b The specific values ​​are as follows:

[0190]

[0191] To avoid repeated calculations, perform matrix calculations and first calculate the derivatives of ζ with respect to x, u, and p:

[0192]

[0193] Therefore, the Jacobians matrix can be simplified to:

[0194]

[0195] In the formula is a matrix whose i-th column is 1 and the rest of the elements are all 0, T a ,T b Given by:

[0196]

[0197] in,

[0198] 2) Jacobians matrix of path constraints and boundary constraints

[0199] First, calculate the derivative of the path constraint g with respect to x, u, p, t as follows:

[0200]

[0201] Then calculate the boundary constraints b for x0,x f ,p,t’s derivative is as follows:

[0202]

[0203] (1.4) Cost function

[0204] The input cost function consists of Mayer terms and Lagrange terms, which can be expressed as:

[0205]

[0206] The cost function J here is a general expression. The cost function J in formula (39) takes the final time and energy consumption as the target and is a special case.

[0207] (1.5) Gradient of the cost function

[0208] The cost function is [x,u,t f ,p], so its gradient to state variables and control variables is as follows:

[0209]

[0210] Then calculate the gradient of the cost function with respect to the terminal time and the design variables, and finally obtain the NLP gradient as follows:

[0211]

[0212] (2) Difference method (when calculating the Jacobians matrix, it is necessary to differentiate the variables, and this difference method is used to differentiate)

[0213] 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 difference method implemented in this patent is the central finite difference method. The central finite difference approximation method is:

[0214]

[0215] In the formula, x is the state quantity used to calculate the derivative; h i For the time interval.

[0216] (3) Scaling (3.1) Variable scaling

[0217] Apply linear scaling methods to scale state variables, control variables, terminal times, and design variables:

[0218]

[0219] (3.2) Constrained scaling

[0220] Apply a gradient-based average norm method to scale the constraints for faster convergence:

[0221] The residual constraint is scaled as follows:

[0222]

[0223] Where 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.

[0224] The path constraint is scaled as follows:

[0225]

[0226] In formula (67), g j,i is the i-th path constraint in the j-th cycle.

[0227] The bounds constraints are scaled as follows:

[0228]

[0229] In formula (68), b j,i is the i-th boundary constraint in the j-th cycle.

[0230] The cost function is not scaled.

[0231] (3.3) Scaling of Jacobians

[0232] 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:

[0233]

[0234] In the formula, J ζ,i is the Jacobian matrix of the residual constraint; ξ i is the row vector composed of the scaling factors in equation (65); is the scaling factor for the residual constraint.

[0235] Similarly, the scaling of the Jacobians matrix for path constraints is:

[0236]

[0237] In the formula, J g,iis the Jacobian matrix of the path constraint; is the scaling factor for the path constraint.

[0238]

[0239] In the formula, J b,i is the Jacobian matrix of the boundary constraints; is the scaling factor for the boundary constraints.

[0240] The gradient of the cost function is not scaled.

[0241] 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.

[0242] Then in step S3, the collaborative optimization model is optimized and solved to obtain the optimal design parameters and optimal control parameters of the amphibious vehicle, which specifically include:

[0243] (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.

[0244] 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.

[0245] The optimized variables after reconstruction are shown in formula (44).

[0246] (2) Calculate the gradient of the cost function with respect to the reconstructed optimized variable.

[0247] 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.

[0248] The gradient is shown in formula (62).

[0249] (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.

[0250] 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.

[0251] The residual constraint, reconstructed path constraint and reconstructed boundary constraint are shown in formula (53), and the upper and lower bound constraints of the reconstructed variable values ​​are shown in formula (39). It only requires changing the dimension of the optimized variable.

[0252] (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.

[0253] 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.

[0254] The first Jacobian matrix is ​​shown in formula (56), the second Jacobian matrix is ​​shown in formula (58), and the third Jacobian matrix is ​​shown in formula (59).

[0255] (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 amphibious vehicle are obtained.

[0256] The optimal design parameters and optimal control parameters of the amphibious 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 input, and solving by nonlinear programming solver, including:

[0257] 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.

[0258] 2) Determine whether the current number of iterations has reached the preset number of iterations.

[0259] 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".

[0260] 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.

[0261] 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.

[0262] The scaled optimization variables are shown in formula (64), the scaled residual constraints are shown in formula (66), the scaled path constraints are shown in formula (67), the scaled boundary constraints are shown in formula (68), the scaled first Jacobian matrix is ​​shown in formula (69), the scaled second Jacobian matrix is ​​shown in formula (70), and the scaled third Jacobian matrix is ​​shown in formula (71).

[0263] 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.

[0264] 7) Determine whether the iteration is completed.

[0265] If the solution minimizes the cost function value, the iteration ends.

[0266] 8) If the iteration is completed, the optimal design parameters and optimal control parameters of the amphibious vehicle are determined based on the solution results of the next iteration.

[0267] 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".

[0268] In this embodiment, for the rapid development and design of amphibious vehicles, the present invention has developed for the first time a multi-objective optimization framework that can quickly solve large-scale complex mixed integer optimal design and optimal control problems. Compared with similar optimization frameworks in the world, it has a high success rate and fast speed, and greatly improves the relevant performance of amphibious vehicles from the perspective of design and control in terms of vehicle optimization design, component optimization design and control. First, the control equation is solved by the finite volume method to determine each unknown quantity, accurately calculate the pump-propelling system of the amphibious vehicle, and establish a thrust database to support vehicle simulation. Based on the theory of rigid body dynamics and fluid dynamics, a high-precision 6-degree-of-freedom dynamic model of the amphibious vehicle is constructed, and the influence of wind, waves, and currents on the vehicle motion under different sea conditions is carefully considered, and a corresponding environmental disturbance model is established. Secondly, the above model is converted into residual constraints, boundary constraints are set according to the vehicle driving conditions and the working boundaries of each subsystem, and multi-objective functions are established according to task requirements. Design parameters and control parameters are simultaneously used as optimization variables to construct collaborative optimal design and optimal control problems. Finally, the improved collocation method, normalization method and scaling method are used to solve the problem and obtain the optimal design parameters, which are applied to vehicle development and guide the development of vehicle control algorithms.

[0269] The advantages of this embodiment are:

[0270] (1) The thrust database formed by computational fluid dynamics is used to improve the credibility and accuracy of the model while avoiding the need for large amounts of calculations during platform operation.

[0271] (2) The amphibious vehicle dynamics model is established based on rigid body dynamics and fluid dynamics, and is derived based on the Newton-Euler formula, so that the model can accurately analyze the complex three-dimensional motion process of amphibious vehicles in water space, and the model has high applicability.

[0272] (3) The present invention constructs a collaborative optimal design and optimal control framework for amphibious vehicles, which can optimize the design parameters and control parameters of amphibious vehicles based on the dynamic model of amphibious vehicles. 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, expand the optimization space, and has good optimization effects, which can improve the efficiency of amphibious vehicle development. 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 problems.

[0273] (4) The present invention greatly improves the stability and efficiency of solving large-scale multi-degree-of-freedom optimal design and optimal control problems. This is mainly because in the solution part of the optimal problem, the direct point matching method is improved, and the normalization method and scaling method are applied to achieve stable and fast solution to large-scale collaborative optimal problems.

[0274] Example 2

[0275] This embodiment provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the method for collaborative optimization of design parameters and control parameters of an amphibious vehicle of the above-mentioned embodiment 1 is implemented.

[0276] Example 3

[0277] This embodiment provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for collaboratively optimizing the design parameters and control parameters of an amphibious vehicle of the above-mentioned embodiment 1.

[0278] Example 4

[0279] This embodiment provides a computer program product, including a computer program, which, when executed by a processor, implements the method for collaborative optimization of amphibious vehicle design parameters and control parameters of the above-mentioned embodiment 1.

[0280] 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.

[0281] 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 an amphibious vehicle, characterized in that: include: Build dynamic models of amphibious vehicles; Establishing a collaborative optimization model of an amphibious vehicle 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 bounds 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 amphibious vehicle.

2. The method for collaborative optimization of design parameters and control parameters of an amphibious vehicle according to claim 1, characterized in that: The dynamic model of the amphibious vehicle is: In the formula, v r represents the relative speed; M represents the inertia matrix under the influence of ocean currents; C(v r ) represents the Coriolis force matrix under the influence of ocean currents; D(v r ) represents nonlinear damping; g0 represents controllable restoring force; τ represents generalized external force; g(η) represents restoring force; τ wind Indicates wind force; τ wave Represents wave force.

3. The method for collaborative optimization of design parameters and control parameters of an amphibious vehicle according to claim 1, characterized in that: The collaborative optimization model for amphibious vehicles includes: Among them, J is the cost function; for Meyer Item; is the Lagrangian term; is the dynamic constraint; is the first-order derivative of the state parameter at time t; f[.] is the dynamic constraint function; x(t) is the state parameter at time t; u(t) is the control parameter at time t; p is the design parameter; x min ≤x(t)≤x max 、u min ≤u(t)≤u max 、p min ≤p≤p max The upper and lower bounds of the variable value are constrained; x min is the lower bound of the state parameter; x max is the upper bound of the state parameter; u min is the lower bound of the control parameter; u max is the upper bound of the control parameter; p min is the lower bound of the design parameter; p max is the upper bound of the design parameters; g min ≤g[x(t),u(t),t,p]≤g max is the path constraint; g min is the lower bound of the path constraint function; g[.] is the path constraint function; g max is the upper bound of the path constraint function; b min ≤b[x(t0),t0,x(t f ),t f ,p]≤b max is the boundary constraint; b min is the lower bound of the boundary constraint function; b[.] is the boundary constraint function; x(t0) is the state parameter at the starting time t0; x(t f ) is the 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 an amphibious 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 amphibious 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; 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 are used as inputs and solved using a nonlinear programming solver to obtain the optimal design parameters and optimal control parameters of the amphibious vehicle.

5. The method for collaborative optimization of design parameters and control parameters of an amphibious 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 an amphibious 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 an amphibious vehicle according to claim 4, characterized in that: The optimal design parameters and optimal control parameters of the amphibious 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 by 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 amphibious 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 optimization variables of the current iteration using the linear scaling method to obtain the scaled optimization 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 an amphibious 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 an amphibious 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 an amphibious vehicle according to any one of claims 1 to 7 is implemented.

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