Underwater vehicle flow field and time-varying motion model predictive control co-simulation method

Abstract

The invention belongs to the technical field of underwater vehicle motion control, and particularly discloses an underwater vehicle flow field and time-varying motion model predictive control co-simulation method, which comprises the following steps of: determining initial parameters of an AUV (Autonomous Underwater Vehicle) kinetic model; on the basis of the initial parameters of the AUV kinetic model and the AUV navigation state information, continuously iterating the pose of the AUV until the pose deviation is within a preset range; the iteration comprises the following steps: carrying out online rolling identification on time-varying parameters of the AUV kinetic model, determining optimal parameters, and transmitting the optimal parameters to an MPC algorithm to update model parameters; through an ALOS guidance algorithm and an MPC algorithm, the rudder angle needed at the current moment is determined; determining a flow field force and a flow field moment through an N-S equation, and updating the current pose of the AUV through a CFD multi-degree-of-freedom motion equation; and judging whether the pose deviation is within a preset range or not. According to the invention, the accuracy and confidence of motion control simulation can be improved in a dynamically changing marine environment.

Description

Technical Field

[0001] The present application belongs to the technical field of underwater vehicle motion control, and more specifically, relates to a collaborative simulation method of underwater vehicle flow field and time-varying motion model predictive control. Background Art

[0002] With the deepening of ocean exploration and the intensification of resource development, the demand for unmanned underwater platforms in marine exploration is growing. Autonomous underwater vehicles (AUVs), as intelligent, unmanned ocean exploration equipment, have demonstrated significant advantages in performing complex tasks and improving exploration efficiency. However, the motion control and simulation accuracy of AUVs are directly dependent on the accuracy of their dynamic models and control algorithms.

[0003] In recent years, model predictive control (MPC) has been gradually applied to underwater robot path tracking. By predicting future trajectories and finding the optimal solution under multiple constraints, it balances real-time performance with multivariable control, demonstrating strong competitiveness. However, MPC has numerous parameters, and improper settings can lead to performance degradation or even instability. Parameters are typically tuned through simulation, but due to the complex ocean environment, its description of complex fluid dynamics phenomena (such as wake shedding, turbulence effects, friction, and boundary layer effects) is still insufficient, and the simulation results are often poor in practice. Furthermore, the traditional dynamic modeling process is complex and time-consuming. The lack of effective guidance during tuning in real ocean areas leads to high costs.

[0004] Furthermore, MPC relies heavily on the accuracy of the dynamics model. Due to the variability of the UUV's inherent characteristics and the complex ocean environment, the model parameters are subject to uncertainty. Even if the initial modeling is accurate, using fixed model parameters as a predictive model for the UUV's recovery process and even its entire lifecycle will still lead to reduced prediction accuracy and weakened control effectiveness. This uncertainty poses challenges to the stability and accuracy of the AUV, limiting its application in complex missions.

[0005] How to improve the accuracy of AUV dynamic models and simulation precision in a dynamically changing ocean environment is a technical problem that needs to be solved urgently in this field. Summary of the Invention

[0006] In view of the shortcomings of the existing technology, the purpose of this application is to improve the accuracy of AUV dynamics model and simulation accuracy in a dynamically changing ocean environment.

[0007] To achieve the above objectives, in a first aspect, the present application provides a method for collaborative simulation of flow field and time-varying motion model predictive control of an underwater vehicle, the method comprising: Determine the initial parameters of the autonomous underwater vehicle (AUV) dynamic model; Based on the initial parameters of the AUV dynamic model and the AUV navigation status information, the AUV's posture is continuously iterated until the posture deviation is within the preset range. The AUV navigation status information is obtained through CFD simulation; Iterate the AUV's pose, including: Based on the initial parameters of the AUV dynamic model and the AUV navigation status information, the time-varying parameters of the AUV dynamic model are identified online through the optimization algorithm to determine the optimal parameters of the AUV dynamic model; Based on the optimal parameters of the AUV dynamics model and the posture deviation between the AUV's current posture and the target posture, the required rudder angle at the current moment is determined through the adaptive line-of-sight ALOS guidance algorithm and the model predictive control MPC algorithm. The optimal parameters of the AUV dynamics model are passed to the MPC algorithm to update the model parameters of the MPC algorithm. Based on the rudder angle required at the current moment, the flow field force and flow field torque are determined through the NS (Navier-Stokes) equation, and the flow field force and flow field torque are transferred to the CFD multi-degree-of-freedom motion equation to update the current position of the AUV (obtaining the updated AUV position); Based on the target pose and the updated AUV pose, it is determined whether the pose deviation is within the preset range.

[0008] It is understandable that by integrating CFD simulation, OPRI algorithm and MPC control, the accuracy of AUV motion control simulation has been significantly improved, and its motion response can be accurately simulated in complex environments. This method achieves deep coupling of control algorithms and fluid dynamics, which can not only verify the effectiveness of different control strategies, but also support parameter debugging and control method optimization in complex environments, providing important technical support for practical applications. At the same time, this application can deeply analyze the complex flow field characteristics during the motion process, including wave loads, eddies and nonlinear flow phenomena, providing reliable guarantees for the efficient and stable navigation of AUVs in complex marine environments.

[0009] In one possible implementation, the online rolling identification of the time-varying parameters of the AUV dynamics model using the optimization algorithm includes: A function that solves an optimization problem using the particle swarm optimization algorithm (PSO).

[0010] Specifically, the OPRI algorithm collects AUV speed and force data, combines it with dynamic equations, and dynamically adjusts hydrodynamic coefficients and model parameters to adapt to changes in complex environments. Specifically, the algorithm uses particle swarm optimization (PSO) to perform online rolling identification of the time-varying parameters of the AUV dynamics model, enabling dynamic updating and real-time optimization of model parameters.

[0011] In actual AUV operation, due to the influence of complex nonlinear hydrodynamic forces, the parameters of its dynamic model often exhibit strong time-varying and nonlinear characteristics. Therefore, accurately identifying these parameters is crucial for improving control precision and motion performance. The PSO algorithm, by simulating the collaborative behavior of flocks of birds or fish during foraging, leverages its advantages such as strong global search capabilities, simple implementation, and fast convergence to address the nonlinear optimization problem in identifying AUV dynamic model parameters.

[0012] The horizontal dynamics model of the original AUV (that is, the above-mentioned AUV dynamics model) is as follows: ; ; ; Where m represents the mass of the AUV, v is the lateral velocity, is the lateral acceleration, u is the forward velocity, r is the yaw angular velocity, is the yaw acceleration. , , , , is the hydrodynamic coefficient related to lateral velocity, yaw velocity, lateral acceleration, yaw acceleration and rudder angle, It is the rudder angle. represents the moment of inertia of the AUV around the z-axis, , , , , is the moment coefficient related to the lateral velocity, yaw velocity, lateral acceleration, yaw acceleration, and rudder angle. Finally, ψ is the yaw angle, is the rate of change of the yaw angle, that is, the yaw velocity.

[0013] Changes to the following form: ; ; ; ; Where, It represents the force on the hull in the y-axis direction; It represents the moment in the z-axis direction on the hull; Indicates the force on the rudder in the y-axis direction; Indicates the moment acting on the rudder in the z-axis direction.

[0014] In the model, Parameters such as the speed and speed of the vehicle may change with time and working conditions, so it is necessary to identify these time-varying parameters in real time to improve the accuracy of the model. The parameter identification problem of the AUV dynamic model can be reduced to a nonlinear optimization problem, whose goal is to continuously adjust the parameters. , so that the model predicts the value Compared with the actual measured value The error is the smallest. The mathematical expression of the optimization problem is as follows: The function of the optimization problem is as follows: ; in, is the AUV dynamics model parameter vector, represents fixed parameters, Represents the parameters to be optimized. The initial solution of the particle swarm optimization algorithm adopts the initial parameters of the AUV dynamics model. is the objective function, represents the measured value obtained through CFD simulation, represents the model prediction value obtained by the AUV dynamics model, , express The item, , express The item, The value is 4. Indicates the force on the hull in the direction of the y-axis (for the three axes in the coordinate system, the x-axis is the horizontal axis, the y-axis is the longitudinal axis, and the z-axis is the vertical axis), Indicates the moment in the z-axis (vertical axis) direction acting on the hull; Indicates the force on the rudder in the y-axis direction; It represents the moment in the z-axis direction on the rudder. is the input vector of the AUV dynamics model, represents the lateral velocity obtained by CFD simulation, represents the yaw rate obtained by CFD simulation, represents the lateral acceleration obtained by CFD simulation, represents the yaw acceleration obtained through CFD simulation, represents the rudder angle obtained by CFD simulation, Represents the error weighting matrix of forces and moments in each degree of freedom.

[0015] In one possible implementation, the adaptive line-of-sight ALOS guidance algorithm and the model predictive control (MPC) algorithm are used to determine the required rudder angle at the current moment, including: Based on the posture deviation between the AUV's current posture and the target posture, the desired heading angle is obtained through the ALOS guidance algorithm; The optimal parameters of the AUV dynamic model are transferred to the MPC algorithm to update the model parameters of the MPC algorithm. Based on the desired heading angle, AUV speed information and the current posture of the AUV, the rudder angle required at the current moment is obtained through the MPC algorithm after the model parameters are updated.

[0016] Specifically, the ALOS (Adaptive Line of Sight) guidance algorithm uses pose deviations combined with the AUV's dynamic model and constraints to predict motion states at multiple future moments and optimize the output rudder angle. Using an adaptive LOS guidance algorithm, the path tracking problem is transformed into a heading control problem. Convergence speed and tracking stability are improved by dynamically adjusting the line of sight distance Δ. When the lateral offset is large, Δ is minimized to improve convergence speed; when approaching the target path, Δ is maximized to prevent overshoot. Furthermore, the acceptance circle radius is adaptively adjusted based on the difference θ between the path angles of adjacent paths to ensure attitude adjustment during path switching.

[0017] Model Predictive Control (MPC) is widely used in underwater vehicle motion control due to its ability to effectively handle system constraints. MPC uses a mathematical model of the system to predict the system state over a period of time in the future at each sampling moment and uses this prediction to optimize control inputs. Its core components include a prediction model, a rolling optimization strategy, and a feedback correction scheme. For AUV path tracking, MPC constructs an optimization objective function to minimize tracking error and input rudder angle change, while considering rudder angle and rudder speed constraints. Using an active set algorithm to solve a quadratic programming problem, the optimal rudder angle increment is obtained, gradually achieving rolling optimization control for AUV path tracking.

[0018] In one possible implementation, the flow field force and flow field moment are determined by the following formula: ; ; Where, is the flow field force, is the surface of the AUV, is the local pressure on the surface of the AUV, is the normal vector of the AUV surface, is the shear stress on the surface of the AUV, is the flow field torque, is the distance vector from the center of mass of the AUV to the center of the surface.

[0019] Solving the NS equations includes simulating wave loads, eddy currents and nonlinear flow phenomena.

[0020] Based on the rudder angle required at the current moment, the NS equation can be solved to obtain and .

[0021] In one possible implementation, the local pressure on the surface of the AUV is , is determined by the following formula: ; in, is the infinitesimal element (small surface element) on the surface of AUV, is the sum of the static and dynamic pressures of the fluid, obtained based on the following formula; ; ; ; ; Where, 、 and Represent the horizontal axis, vertical axis and vertical axis of the Cartesian coordinate system respectively. is the dynamic viscosity, is the fluid density, is the velocity vector field of the fluid, 、 and They represent the velocity components of the fluid in the horizontal, longitudinal and vertical directions respectively. 、 and are the mass force components of the fluid in the horizontal, longitudinal and vertical directions respectively.

[0022] In one possible implementation, the above-mentioned transfer of flow field forces and flow field moments to the CFD multi-degree-of-freedom motion equations to update the current position of the AUV includes: Based on the flow field force and flow field torque, the angular velocity of the AUV and the velocity of the AUV center of mass are obtained through the CFD multi-degree-of-freedom motion equation; Update the current position of the AUV based on the angular velocity of the AUV and the velocity of the AUV center of mass; Among them, the CFD multi-degree-of-freedom motion equation is as follows: ; ; Where, is the sum of the mass of the AUV and the additional mass of the AUV, is the velocity of the AUV center of mass, is the flow field force, are external forces generated by multibody constraints or joints, For control, For the previous moment, For the current moment, is the moment of inertia tensor, is the angular velocity of the AUV, is the flow field torque, is the external torque generated by the multibody constraints or joints, To control the torque.

[0023] In one possible implementation, the current posture of the AUV is updated based on the angular velocity of the AUV and the velocity of the center of mass of the AUV, specifically including updating the current posture of the AUV using the following formula: ; ; ; ; ; ; in, 、 and are the position components of the AUV's position in the horizontal, longitudinal and vertical directions, For the next moment, For the current moment, 、 and are the velocity components of the AUV center of mass in the horizontal, longitudinal and vertical directions, 、 and are the rotation angle components of the AUV posture in the horizontal, longitudinal and vertical directions, 、 and are the angular velocity components of the AUV in the horizontal, longitudinal and vertical directions, is the interval between the next moment and the current moment; The updated AUV poses include 、 、 、 、 and .

[0024] In a second aspect, the present application provides a collaborative simulation device for underwater vehicle flow field and time-varying motion model predictive control, comprising: An initial parameter determination module is used to determine the initial parameters of the autonomous underwater vehicle AUV dynamic model; The iteration module is used to continuously iterate the AUV's posture based on the initial parameters of the AUV dynamic model and the AUV's navigation status information until the posture deviation is within a preset range. The AUV's navigation status information is obtained through CFD simulation; The iteration module includes: a model optimal parameter determination unit, a rudder angle determination unit, an AUV posture update unit, and a posture deviation judgment unit, among which: The model optimal parameter determination unit is used to perform online rolling identification of the time-varying parameters of the AUV dynamic model through an optimization algorithm based on the initial parameters of the AUV dynamic model and the AUV navigation state information, and determine the optimal parameters of the AUV dynamic model; The rudder angle determination unit is used to determine the rudder angle required at the current moment based on the optimal parameters of the AUV dynamic model and the posture deviation between the AUV's current posture and the target posture, through the adaptive line of sight ALOS guidance algorithm and the model predictive control MPC algorithm. The optimal parameters of the AUV dynamic model are passed to the MPC algorithm to update the model parameters of the MPC algorithm; The AUV posture update unit is used to determine the flow field force and flow field torque based on the rudder angle required at the current moment through the NS equation, and transfer the flow field force and flow field torque to the CFD multi-degree-of-freedom motion equation to update the current posture of the AUV; The posture deviation judgment unit is used to judge whether the posture deviation is within a preset range based on the target posture and the updated AUV posture.

[0025] In a third aspect, the present application provides an electronic device comprising: at least one memory for storing programs; and at least one processor for executing the programs stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method described in the first aspect or any possible implementation of the first aspect.

[0026] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method described in the first aspect or any possible implementation of the first aspect.

[0027] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0028] In general, the above technical solutions conceived by this application have the following beneficial effects compared with the existing technologies: The collaborative simulation method for predictive control of underwater vehicle flow fields and time-varying motion models provided in this application integrates CFD, OPRI, and MPC. CFD simulation accurately simulates complex fluid dynamics phenomena and provides high-precision dynamic model support for MPC. Through OPRI technology, the system can dynamically identify and update model parameters, ensuring that the control strategy remains optimal in complex flow environments. Through the deep coupling of CFD simulation and the dynamic control algorithm (OPRI-MPC), real-time adjustment and optimization of AUV dynamic parameters are achieved, improving the accuracy of the AUV dynamic model and simulation precision in dynamically changing ocean environments.

[0029] This simulation method not only simulates a variety of complex underwater conditions in a laboratory environment but also enables parameter adjustment and tuning before underwater testing, significantly reducing experimental time. By exploring control boundary performance under extreme conditions, researchers can optimize control parameters in advance and improve the efficiency of actual underwater testing. This not only enhances the AUV's mission execution capabilities, but also significantly reduces experimental costs and improves its adaptability and reliability in complex marine environments.

[0030] In addition, the platform helps analyze and evaluate the performance limits of AUVs in extreme environmental conditions, ensuring that the vehicles can maintain optimal operational safety and reliability when facing challenging missions. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 1 is a flow chart of a collaborative simulation method for underwater vehicle flow field and time-varying motion model predictive control provided by an embodiment of the present application; Figure 2 Schematic diagram of the LOS guidance algorithm provided by an embodiment of the present application; Figure 3 This is a schematic diagram of the basic principle of MPC provided by the embodiment of the present application; Figure 4 This is a schematic diagram of a multi-waypoint test trajectory curve provided in an embodiment of the present application; Figure 5 This is a schematic diagram of a multi-waypoint test deviation curve provided in an embodiment of the present application; Figure 6 This is a schematic diagram of a heading curve for a multi-waypoint test provided in an embodiment of the present application; Figure 7 This is a schematic diagram of a rudder curve for a multi-waypoint test provided in an embodiment of the present application; Figure 8 This is a schematic diagram of the typical time position distribution of the AUV path tracking provided by the embodiment of the present application; Figure 9 is a spatial distribution diagram of the AUV surface pressure and the nearby flow velocity at four time points (a, b, c, d) provided in the embodiment of the present application; Figure 10 is a spatial distribution diagram of the AUV surface pressure and the nearby flow velocity at four time points (e, f, g, h) provided in the embodiment of the present application; Figure 11 is a schematic diagram of the geodetic coordinate system and the carrier coordinate system provided in an embodiment of the present application; Figure 12 Schematic diagram of the structure of the collaborative simulation device for underwater vehicle flow field and time-varying motion model predictive control provided by an embodiment of the present application; Figure 13 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0033] In the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0034] In the description of the embodiments of the present application, unless otherwise specified, "multiple" means two or more, for example, multiple processing units means two or more processing units, etc.; multiple elements means two or more elements, etc.

[0035] The embodiments of the present application are described below in conjunction with the drawings in the embodiments of the present application.

[0036] Figure 1 FIG. 1 is a flow chart of a collaborative simulation method for underwater vehicle flow field and time-varying motion model predictive control provided by an embodiment of the present application, such as Figure 1 As shown, the method includes the following steps S101 and S102.

[0037] Step S101, determining the initial parameters of the autonomous underwater vehicle AUV dynamic model; Step S102: Based on the initial parameters of the AUV dynamic model and the AUV navigation state information (the AUV navigation state information includes: AUV speed information, AUV force information, AUV position information, AUV attitude information), the AUV posture is continuously iterated until the posture deviation is within a preset range. The AUV navigation state information is obtained through CFD simulation. The iterative AUV pose in step S102 specifically includes: Based on the initial parameters of the AUV dynamic model and the AUV navigation status information, the time-varying parameters of the AUV dynamic model are identified online through the optimization algorithm to determine the optimal parameters of the AUV dynamic model; Based on the optimal parameters of the AUV dynamics model and the posture deviation between the AUV's current posture and the target posture, the required rudder angle at the current moment is determined through the adaptive line-of-sight ALOS guidance algorithm and the model predictive control MPC algorithm. The optimal parameters of the AUV dynamics model are passed to the MPC algorithm to update the model parameters of the MPC algorithm. Based on the rudder angle required at the current moment, the flow field force and flow field torque are determined through the NS equation, and the flow field force and flow field torque are transferred to the CFD multi-degree-of-freedom motion equation to update the current position of the AUV; Based on the target pose and the updated AUV pose, it is determined whether the pose deviation is within the preset range.

[0038] It is understood that the collaborative simulation method for predictive control of underwater vehicle flow fields and time-varying motion models provided in this application can integrate CFD, OPRI, and MPC. CFD simulation can accurately simulate complex fluid dynamics phenomena and provide high-precision dynamic model support for MPC. Through OPRI technology, the system can dynamically identify and update model parameters, ensuring that the control strategy remains optimal in complex flow field environments. Through the deep coupling of CFD simulation and dynamic control algorithms (OPRI-MPC), real-time adjustment and optimization of AUV dynamic parameters can be achieved, improving the accuracy of the AUV dynamic model and simulation precision in dynamically changing ocean environments.

[0039] This method achieves a deep coupling of guidance, model predictive control, and CFD algorithms through a multidisciplinary collaborative simulation framework. It accurately simulates the motion response of an AUV in a real environment, verifies the effectiveness of different model predictive control parameters and strategies, and deeply analyzes the complex flow field characteristics during motion, including wave loads, eddies, and nonlinear flow phenomena. This application significantly improves the accuracy and confidence of motion control simulations, providing important technical support for navigation control optimization, parameter debugging, control algorithm verification, and exploration of control boundary performance for AUVs in complex environments.

[0040] The following is an illustrative description of the collaborative simulation method of underwater vehicle flow field and time-varying motion model predictive control provided by this application through several examples.

[0041] The research object of this application is a small autonomous underwater vehicle developed in the laboratory. Similar to conventional propeller-driven AUVs, the main propeller at the stern is the primary power source, while the stern rudder and stern elevator form a cross-rudder as the AUV's steering mechanism. All three are the system's primary control inputs, enabling the AUV's three-dimensional motion control. Therefore, the research object is underactuated and cannot achieve horizontal lateral movement. The strong coupling of the AUV's motion increases the difficulty of motion control.

[0042] The AUV's exterior is symmetrical, with a basic balance of top, bottom, left, and right. To ensure hydrodynamic performance, reduce power consumption during navigation, and improve endurance, the AUV's main body adopts the most commonly used Myring design. The AUV's hull is divided into three sections: the bow, midship, and stern sections. The bow section is 0.11 meters long, the midship section is cylindrical and 0.74 meters long, and the stern section, including the thrusters, is 0.22 meters long. The total hull length is 1.07 meters, and the hull diameter is 0.14 meters. The AUV has a dry weight of 8.25 kg. The main propulsion engine uses a waterproof thruster with a maximum thrust of 70 Newtons. The AUV can reach a maximum speed of 2 meters per second and a designed cruising speed of 1 meter per second. At cruising speed, the AUV has an endurance of up to four hours.

[0043] Both the rudder and elevator utilize standard NACA0021 airfoils. A single rudder blade has a span of 0.08 m, a chord of 0.05 m, an aspect ratio of 1.6, and a maximum thickness of 0.01 m. The distance between the rudder blade center and the AUV's central axis is 0.1 m, and the distance between the rudder blade center and the AUV's front end is 0.89 m. The AUV's relevant technical parameters are shown in Figure 1.

[0044] Table 1 Basic parameters of AUV

[0045] The motion of underwater robots can be regarded as the motion of rigid bodies in six degrees of freedom. In order to facilitate the study of the relevant laws of AUV motion and better describe the motion state and force state of AUV, the definition of the coordinate system of AUV, related terms and symbol rules must take into account the conventions of fluid mechanics and rigid body kinematics as well as the simplicity of description. This paper adopts the system recommended by the International Water Tank Conference (ITTC) and the terminology bulletin of the Society of Naval Architecture and Marine Engineering (SNAME) to establish two right-handed Cartesian rectangular coordinate systems. Figure 11 As shown, one is the geodetic coordinate system , used to describe the motion trajectory and posture of AUV, the other is the carrier coordinate system , which is used to describe the external force on the AUV during its motion.

[0046] When an AUV navigates underwater, it will involve six degrees of freedom. The motion state of each degree of freedom, including displacement, velocity, and force, needs to be represented by mathematical symbols. The physical meaning and symbol definitions of the motion state of each degree of freedom of the AUV are shown in Table 2.

[0047] Table 2 AUV coordinate system symbol definition table

[0048] When using an MPC algorithm to solve a path-following problem, the model is typically converted to state-space form for computational convenience. To simplify the model, it is assumed that the AUV's horizontal motion can ignore the effects of heave, roll, and pitch. Since the path-following problem does not impose time constraints, the propeller speed is assumed to be constant, and the forward velocity remains essentially constant, resulting in a speed of approximately 0.9 m / s.

[0049] The AUV horizontal motion model is shown in formula (1) to formula (6).

[0050] Formula 1 is as follows: ; ; ; Where, is the Z-axis moment of inertia of the AUV, are the AUV rudder angle dynamic model parameters: ; ; ; ; -7.840; ; ; ; ; .

[0051] Take the lateral velocity , heading angular velocity and heading angle is the state variable , then the system matrix motion equation is as follows (this reflects the relationship between the parameters of the MPC and AUV dynamics model). Formula (2) is as follows: ; The AUV horizontal motion model is written in standard state space form, formula (3) is as follows: ; ; Formula (4) is as follows: ; Formula (5) is as follows: ; Formula (6) is as follows: .

[0052] It is difficult to calculate the optimal solution for online optimization problems with multiple constraints using logic circuits, and digital computers are generally required to solve them. To facilitate computer processing, the Euler method is used to discretize the AUV continuous state space equation. The discretized system matrix is ​​as follows: ; ; ; in, is the discrete time step, is the identity matrix.

[0053] The state space equation of the discretized system is: ; ; Considering the errors in the modeling process and the noise interference in the control process, in order to eliminate the errors in the steady state, the state space equation based on the augmented form is adopted in the model predictive control based on the state space equation. The rate of change of the control input is used as the control input to enable the system to achieve zero static error control. After performing the differential operation on both ends of the state space equation, we can obtain: ; ; The following symbols are introduced: ; ; The augmented state space equation can be obtained as follows: ; ; Right now: ; ; For computational fluid dynamics (CFD) simulations, a background domain was constructed with a total length, width, and height of 80 meters, including an 8-meter underwater portion. The downstream boundary was designated as the pressure outlet boundary, while all other boundaries were designated as velocity inlet boundaries. The densification area within the background domain was determined by the AUV's target path and was covered with a cylindrical area with a diameter of 1.2 meters.

[0054] Based on overlapping mesh theory, the flow region is divided into a background region, overlapping region 1 (hull), overlapping region 2 (upper rudder), overlapping region 3 (lower rudder), overlapping region 4 (left rudder), and overlapping region 5 (right rudder). The overlapping region grid is 1.5 meters long, 0.5 meters wide, and 0.5 meters high. The simulation uses a hexagonal cut volume grid as the primary mesh type, and a prismatic layer grid is used to model the hull surface boundary layer. The grid near the wave surface is refined along the wave height to ensure accurate wave generation and propagation. Furthermore, the interface between the background grid and each overlapping region is refined to ensure the stability and accuracy of the dynamic overlapping mesh algorithm. The grid is also refined in the stern rudder region of the AUV to ensure the accuracy of the local flow field and facilitate the calculation of the disturbance effects caused by the rudder rotation.

[0055] Ignoring the compressibility and temperature of seawater, the Reynolds-averaged Navier-Stokes (RANS) method and the VOF (Volume of Fluid) wave model are used. The governing equations include the mass conservation equation and the NS equations. The SST k-ω model is used as the turbulence model to accurately predict the fluid motion state near the wall and in the far field. The NS governing equations are: ; ; ; ; Where x, y, and z represent Cartesian coordinates, p represents pressure, is the dynamic viscosity, is the fluid density, 、 、 are the velocity components in the x, y, and z directions respectively, and X, Y, and Z are the mass force components in the x, y, and z directions respectively.

[0056] The Online Parameter Rolling Identification (OPRI) algorithm is constructed. The AUV's dynamic model is an important foundation for describing its motion characteristics and force behavior. Because AUVs are subject to complex nonlinear hydrodynamic forces during actual operation, their dynamic model parameters often have strong time-varying and nonlinear characteristics. Therefore, accurately identifying the parameters of the AUV's dynamic model is crucial for improving the AUV's control accuracy and motion performance. This section uses the Particle Swarm Optimization (PSO) algorithm to perform online rolling identification of the time-varying parameters of the AUV's dynamic model, enabling dynamic updating and real-time optimization of the model parameters. By simulating swarm intelligence behavior, the PSO algorithm offers advantages such as strong global search capabilities, simple implementation, and fast convergence, making it ideally suited for solving nonlinear optimization problems in AUV dynamic model parameter identification.

[0057] From the following formula: ; ; ; ; Where, It represents the force on the hull in the y-axis direction; It represents the moment in the z-axis direction on the hull; Indicates the force on the rudder in the y-axis direction; Indicates the moment acting on the rudder in the z-axis direction.

[0058] In the model, Parameters such as the speed and speed of the vehicle may change with time and working conditions, so it is necessary to identify these time-varying parameters in real time to improve the accuracy of the model. The parameter identification problem of the AUV dynamic model can be reduced to a nonlinear optimization problem, whose goal is to continuously adjust the parameters. , so that the model predicts the value Compared with the actual measured value The error is the smallest. The mathematical expression of the optimization problem is as follows: ; in, is the model parameter vector to be identified, Indicates a fixed parameter, which does not participate in optimization and has a value of 0.5 times the initial parameter. Represents the parameter to be optimized, and its initial value is randomly generated within the range of plus or minus 0.3; is the objective function, which represents the error between the model prediction value and the actual measurement value; are the external forces and moments calculated by CFD; are the forces and moments calculated by the dynamic model; is the input vector of the AUV, where the acceleration term is calculated by the dynamic model, and the velocity term and rudder angle term are provided by CFD; is the error weighting matrix of the force and moment for each degree of freedom.

[0059] The particle swarm optimization algorithm (PSO) is a global optimization algorithm based on swarm intelligence. Its basic concept is to find the global optimal solution in the solution space by simulating the collaborative behavior of flocks of birds or schools of fish during foraging. The core of the algorithm lies in approximating the optimal solution through the collaborative search of a swarm of particles. Each particle represents a feasible solution, and its position is determined by the parameters of the problem to be optimized. Particles continuously adjust their speed and position to find the optimal solution. The speed and position update formula is: ; in, and They represent the speed and position of particle i at the tth iteration, w is the inertia weight, which is used to balance the global search and local search capabilities, c1 and c2 are learning factors, which control the particle to move to the individual optimal position and the global optimal position The movement degree of r1 and r2 is a random number in the range of [0,1], which is used to increase the randomness of the search. In this way, the particle can continuously update its position in the solution space and gradually approach the global optimal solution.

[0060] In the identification of AUV dynamic model parameters, the specific implementation steps of the particle swarm optimization algorithm include: first, initializing the position and velocity of the particle swarm, and the position of each particle represents a set of model parameters to be identified. Then calculate the fitness value of each particle, that is, the objective function , represents the sum of the absolute values ​​of the errors between the model prediction value and the actual measurement value. Then the speed and position of the particle are adjusted according to the update formula, and the individual optimal position of the particle is updated. and the global optimal position Repeat the above process until the objective function value meets the accuracy requirement or reaches the maximum number of iterations, and finally output the optimal parameters .

[0061] The ALOS-MPC control algorithm was constructed. The AUV studied in this paper has a rudder-propeller controlled structure, and its motion control system is underactuated, requiring more degrees of freedom than the number of actuators. Using LOS guidance to transform the path tracking problem into a heading control problem or a pitch control problem can overcome the AUV's underactuated nature. As a classic guidance method, the LOS guidance algorithm offers advantages such as computational simplicity and precise guidance. It has been widely used in fields such as autonomous driving tracking, missile guidance and control, and AUV motion control. It can not only track straight lines, but also transform curved path problems into straight path tracking problems.

[0062] The principle of LOS horizontal plane guidance algorithm is as follows Figure 2 As shown, waypoints 、 and The connecting straight line is the target path. The current target path is a straight line. , then the current course angle is The offset from the center of mass of the AUV to the current target path is The control goal of path tracking is to converge the offset to 0, that is, The guidance strategy adopted by the LOS algorithm is to determine a sight guidance point on the current target path. , simplifying line tracking to point tracking. There are two ways to select the waypoint. The first is to draw a circle with a certain radius with the AUV center of mass as the center, and the point closer to the next waypoint among the intersections with the current target path is selected as the waypoint. The second type is that the AUV particle makes a perpendicular line to the current target path, and the distance from the perpendicular foot to the current target path is a fixed length. The point is The first method needs to calculate the intersection point and there may be no intersection point. The second method is prone to oscillation and cannot converge under large offset and high speed conditions. Since the AUV has a low speed, the second method is selected. for: ; To ensure that the AUV is guided to the sight point To get closer, you need to point the heading , then the expected heading angle is: ; Due to the drift angle The heading angle is not equal to the course angle. The course angle is: ; From the above formula, we can know that the expected heading angle is: ; In the traditional LOS guidance algorithm, the line of sight distance The lateral offset is fixed at the beginning of the path tracking. Too large, the AUV is required to quickly approach the current target path, but due to the fixed line of sight distance Due to the limitation of the hull, the hull cannot turn quickly, resulting in slow convergence. After the AUV approaches the current target path, the AUV is required to track smoothly and have a short line of sight distance. It is easy to cause overshoot and oscillation problems, so this section adopts the method based on the lateral offset. Size, real-time adjustment of sight distance Length, variable sight distance The length is determined by the following formula.

[0063] ; From the above formula we can see that the sight distance The length of the horizontal offset Decrease and increase, and are the maximum and minimum values ​​of the sight distance, is the line of sight convergence coefficient. According to experience, the maximum and minimum values ​​of the line of sight should be 2 to 5 times the length of the boat. The value of needs to be adjusted according to the AUV's maneuverability. When the lateral offset is large, Taking a smaller value can increase the convergence speed. When the AUV approaches the target path, Taking a larger value ensures convergence stability and effectively prevents overshoot.

[0064] When switching the target path, it is usually judged whether the current AUV enters the acceptance circle of the current target path end point, such as Figure 2 If the difference between the flight path angles of adjacent paths is large, that is, When the radius is large, it is necessary to turn in advance so that the AUV has enough time to adjust its attitude. If the radius of the acceptance circle is too small, the turning will lag and cause a large overshoot. When it is small, the early turn will lead to incomplete tracking of the current path, so a method based on The method of adaptively adjusting the acceptance circle radius. The formula for the acceptance circle radius is as follows: ; in and is a parameter to be determined, It is related to the maneuverability of the AUV and is generally 1 to 2 times the full rudder turning radius. Indicates the angle at which the target path turns.

[0065] MPC has good ability to handle constraints and has strong engineering practice application capabilities, so it is very meaningful to apply MPC theory to the field of underwater vehicle motion control. The basic principles of MPC are as follows: Figure 3 At each sampling moment, through the established system mathematical model, the current moment The measurement output predicts the system state for a period of time in the future, which is called the prediction horizon. , to predict the time domain The error between the internal prediction output and the target trajectory is used as the optimization objective function to solve the prediction time domain The predictive control input series that minimizes the objective function is used as the first element of the predictive control input series at the current moment. At the next moment, the above process is repeated to obtain the control input of the next moment. In this way, the local optimal control input of each moment can be obtained by rolling calculation.

[0066] From the above process, we can see that the basic framework of MPC includes three aspects: the prediction model for predicting the future state of the system, the rolling optimization strategy for solving the optimization objective function, and the feedback correction scheme.

[0067] The model prediction algorithm is based on the AUV dynamic model, so a prediction model can be established. It only needs to know the control input and initial state to predict the AUV motion state at any time in the future. Assuming that the control input sequence in the system prediction time domain is known, the prediction time domain can be predicted by the current state. System output within. Future The state prediction equation of the step system is as follows: ; The output matrix Substituting into the system state prediction equation, the system output prediction equation can be obtained as follows: ; use and The coefficient matrix representing the above equation is: ; in Indicates the current Predicted at all times AUV system status at all times, Indicates the current The system output sequence in the prediction time domain of the moment prediction, Indicates the current System input sequence at time.

[0068] As a type of optimal control, the most important step in model predictive control is to construct an optimization objective function. For the AUV path tracking problem, we must first ensure that the tracking error in the prediction time domain is minimized, and we must also consider the energy consumption issue, that is, the minimum change in the input rudder angle. Therefore, the optimization objective function is constructed as follows: ; in is the output tracking error weighting matrix, Enter the weighting matrix for the rudder angle change.

[0069] Substituting the prediction equation into the optimization objective function, we can obtain: ; The first term in the formula is a constant and can be ignored. The remaining part can be written in the standard quadratic programming form as follows: ; in , , is the desired heading angle sequence.

[0070] MPC is widely concerned because it can handle system constraints well. In actual situations, the AUV actuator will be constrained by a limited rate of change and saturation state. In order to obtain better control effects, these system constraints need to be taken into account. , the rudder angle in the rudder prediction time domain can be obtained as shown in the following formula.

[0071] ; The coefficient matrix in the above formula is and If , the saturation constraint of the rudder is: ; ; in, 、 are the upper and lower limit sequences of the rudder amplitude respectively.

[0072] The rudder speed constraint expression is as follows: ; in, and They are the upper and lower limit sequences of the rudder speed respectively.

[0073] The optimization objective function is combined with the control input constraints to form a quadratic program. The active set algorithm is a classic algorithm for solving quadratic programming problems, especially suitable for problems with linear equality and inequality constraints. This paper adopts it for optimization.

[0074] Get the optimal rudder angle increment sequence Afterwards, take The first rudder angle increment plus the rudder angle at a moment can be used to obtain the rudder angle command at the current moment. Repeat the above steps at the next moment, and the rolling optimization is used to achieve predictive control of the AUV path tracking.

[0075] The CFD multi-degree-of-freedom motion equations of the underwater vehicle are constructed, and the flow field calculation output items are embedded in them. The specific form is as follows: ; ; Where, is body mass, is the velocity of the center of mass, represents the external force generated by the rudder, Represents flow field forces (including flow, wave force, viscous force, etc.). is the moment of inertia tensor, is the angular velocity of the underwater vehicle, represents the external torque generated by the rudder, Represents the flow field torque (including flow, wave force, viscous force, etc.).

[0076] The high-precision motion control joint simulation framework is implemented through collaborative simulation of CFD and MATLAB, ignoring the effects of water compressibility and water temperature, and adopting the Reynolds-averaged Navier-Stokes method (RANS) and VOF (Volume of Fluid) wave model; MATLAB is mainly responsible for the AUV's adaptive model predictive controller based on online rolling identification.

[0077] The operation of the framework mainly includes the following steps: (1) The rudder angle output by the controller at time t is input into the Navier-Stokes equations to calculate the rudder force and rudder torque. Combined with the current flow field force and torque, the motion response of the AUV (including position, velocity, and attitude) is calculated. The motion state at time t+1 is then fed back to the CFD module to update the flow field boundary conditions.

[0078] (2) The motion state information of the AUV at time t+1 (including speed, posture and force) is imported into the online rolling parameter identification module to update the dynamic model parameters of the AUV's current motion state.

[0079] (3) The dynamic model at time t+1 and the motion response of the AUV are then imported into the ALOS-MPC controller to calculate the required rudder angle at time t+1.

[0080] This forms a closed-loop feedback control, which is performed once in each control cycle. To improve simulation efficiency, parallel computing technology is used to accelerate the collaborative calculation of CFD and MPC.

[0081] Due to sensor limitations, the AUV's underwater positioning accuracy was insufficient during the lake trials. To facilitate position acquisition, the AUV was set to a slightly positive buoyancy state, maintaining it close to the water surface. GPS positioning enabled the AUV's position to be determined. On the day of the test, the lake experienced a northwest wind of approximately 2 m / s. Horizontal path tracking involves sailing close to the water surface, which is affected by wind and wave disturbances. This allowed for a better verification of the controller's tracking performance. The CFD simulation conditions, including wind speed and direction, were consistent with the actual experimental setup.

[0082] The multi-waypoint path tracking test method: The AUV propeller speed is kept constant (the thrust is kept constant during simulation) and the speed is approximately 0.9m / s. Initially, the AUV is remotely controlled to the vicinity of starting point A. It is then switched to autonomous tracking mode. The AUV passes through points B, C, and D in sequence and reaches the vicinity of point E to end the test. The control cycle is 0.2s, and the simulation ends when the AUV enters the acceptance circle of the last waypoint. The control parameters are consistent with the simulation and are as follows: The ALOS+MPC control effect based on the time-varying parameter model proposed in this paper is compared with the ALOS+PID control effect based on the fixed parameter model of the control group. Figures 4 to 7 shown. Figure 4 This is a trajectory curve diagram for a multi-waypoint test; Figure 5 This is the deviation curve diagram for multi-waypoint test; Figure 6 Heading curve diagram for multi-waypoint test Figure 7 This is the rudder curve diagram for multi-waypoint test.

[0083] Four performance indicators are selected to quantitatively compare and analyze the multi-waypoint path tracking effects of the three control algorithms, namely the average value of tracking offset, the average overshoot of the heading during three turns, the average time required for three turns, and the total change of the rudder angle. The four performance indicators obtained are shown in Table 3.

[0084] Table 3 Multi-waypoint test performance index table

[0085] Through detailed data analysis and simulation results, this application proves that the proposed high-fidelity simulation framework is highly close to the actual experiment, and the ALOS+ORPI+MPC control algorithm proposed in this paper has smaller tracking offset error and less tracking overshoot.

[0086] The advantage of the joint simulation framework is that it can clearly analyze the distribution of flow fields, pressure, etc. at each time and in each area during the AUV movement. In order to further analyze the response of the UUV to trajectory deviation in Case 3 (ALOS+ORPI+MPC, CFD) in Table 3, the flow fields at 8 typical time moments are analyzed. Figure 8 This reflects the position distribution of the AUV during the path tracking process at these 8 moments. Figure 9 、 Figure 10 .

[0087] This study addresses the dynamic modeling, control algorithms, and simulation issues in AUV motion control. It proposes a high-precision motion control co-simulation framework that integrates CFD and MPC. This framework aims to address the issue of "simulation without authenticity" in AUV autonomous navigation in complex flow environments. Furthermore, this framework leverages real-time AUV navigation data provided by CFD to perform online rolling parameter identification on the MPC dynamic model, improving MPC control accuracy. This provides theoretical support for high-precision control and simulation of AUV autonomous navigation, and has significant engineering application value and academic significance.

[0088] Figure 12 Schematic diagram of the structure of the underwater vehicle flow field and time-varying motion model predictive control collaborative simulation device provided by the embodiment of the present application, such as Figure 12 As shown, the device includes: an initial parameter determination module 10 and an iteration module 20. An initial parameter determination module 10 is used to determine the initial parameters of the autonomous underwater vehicle AUV dynamic model; The iteration module 20 is used to continuously iterate the AUV's posture based on the initial parameters of the AUV dynamic model and the AUV's navigation status information until the posture deviation is within a preset range. The AUV's navigation status information is obtained through CFD simulation.

[0089] It is understandable that the detailed functional implementation of each of the above units / modules can be found in the introduction of the aforementioned method embodiment, and will not be repeated here.

[0090] It should be understood that the above-mentioned device is used to execute the method in the above-mentioned embodiment. The implementation principle and technical effect of the corresponding program module in the device are similar to those described in the above-mentioned method. The working process of the device can refer to the corresponding process in the above-mentioned method and will not be repeated here.

[0091] Based on the method in the above embodiment, an embodiment of the present application provides an electronic device, Figure 13 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application, such as Figure 13As shown, the electronic device may include: a processor (Processor) 810, a communication interface (Communications Interface) 820, a memory (Memory) 830, and a communication bus 840. The processor 810, the communication interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute the method in the above embodiment.

[0092] In addition, the logic instructions in the aforementioned memory 830 can be implemented in the form of a software functional unit and, when sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the portion that contributes to the prior art, or the portion of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application.

[0093] Based on the method in the above embodiment, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method in the above embodiment.

[0094] Based on the method in the above embodiment, an embodiment of the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method in the above embodiment.

[0095] It is understood that the processor in the embodiments of the present application may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0096] The method steps in the embodiments of the present application can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and storage medium can be located in an ASIC.

[0097] It will be understood that the various numerical numbers involved in the embodiments of the present application are merely distinctions for the convenience of description and are not intended to limit the scope of the embodiments of the present application.

[0098] It is easy for those skilled in the art to understand that the above is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A collaborative simulation method for underwater vehicle flow field and time-varying motion model predictive control, characterized in that: include: Determine the initial parameters of the autonomous underwater vehicle (AUV) dynamic model; Based on the initial parameters of the AUV dynamic model and the AUV navigation status information, the AUV's posture is continuously iterated until the posture deviation is within the preset range. The AUV navigation status information is obtained through CFD simulation; Iterate the AUV's pose, including: Based on the initial parameters of the AUV dynamic model and the AUV navigation status information, the time-varying parameters of the AUV dynamic model are identified online through the optimization algorithm to determine the optimal parameters of the AUV dynamic model; Based on the optimal parameters of the AUV dynamics model and the posture deviation between the AUV's current posture and the target posture, the required rudder angle at the current moment is determined through the adaptive line-of-sight ALOS guidance algorithm and the model predictive control MPC algorithm. The optimal parameters of the AUV dynamics model are passed to the MPC algorithm to update the model parameters of the MPC algorithm. Based on the rudder angle required at the current moment, the flow field force and flow field torque are determined through the NS equation, and the flow field force and flow field torque are transferred to the CFD multi-degree-of-freedom motion equation to update the current position of the AUV; Based on the target pose and the updated AUV pose, it is determined whether the pose deviation is within the preset range.

2. The collaborative simulation method of underwater vehicle flow field and time-varying motion model predictive control according to claim 1, characterized in that: The online rolling identification of the time-varying parameters of the AUV dynamics model by the optimization algorithm includes: A function that solves the optimization problem using the particle swarm optimization algorithm; The function of the optimization problem is as follows: ； in, is the AUV dynamics model parameter vector, represents fixed parameters, Represents the parameters to be optimized. The initial solution of the particle swarm optimization algorithm adopts the initial parameters of the AUV dynamics model. is the objective function, represents the measured value obtained through CFD simulation, represents the model prediction value obtained by the AUV dynamics model, , express The item, , express The item, The value is 4. represents the force on the hull in the y-axis direction, It represents the moment in the z-axis direction on the hull; Indicates the force on the rudder in the y-axis direction; It represents the moment in the z-axis direction on the rudder. is the input vector of the AUV dynamics model, represents the lateral velocity obtained by CFD simulation, represents the yaw rate obtained by CFD simulation, represents the lateral acceleration obtained by CFD simulation, represents the yaw acceleration obtained through CFD simulation, represents the rudder angle obtained by CFD simulation, Represents the error weighting matrix of forces and moments in each degree of freedom.

3. The collaborative simulation method of underwater vehicle flow field and time-varying motion model predictive control according to claim 1, characterized in that: The method of determining the required rudder angle at the current moment by using the adaptive line-of-sight ALOS guidance algorithm and the model predictive control MPC algorithm includes: Based on the posture deviation between the AUV's current posture and the target posture, the desired heading angle is obtained through the ALOS guidance algorithm; The optimal parameters of the AUV dynamic model are transferred to the MPC algorithm to update the model parameters of the MPC algorithm. Based on the desired heading angle, AUV speed information and the current posture of the AUV, the rudder angle required at the current moment is obtained through the MPC algorithm after the model parameters are updated.

4. The collaborative simulation method of underwater vehicle flow field and time-varying motion model predictive control according to claim 1, characterized in that: The flow field force and the flow field moment are determined by the following formula: ； ； Where, is the flow field force, is the surface of the AUV, is the local pressure on the surface of the AUV, is the normal vector of the AUV surface, is the shear stress on the surface of the AUV, is the flow field torque, is the distance vector from the center of mass of the AUV to the center of the surface.

5. The collaborative simulation method of underwater vehicle flow field and time-varying motion model predictive control according to claim 4 is characterized in that: Local pressure on the surface of the AUV , is determined by the following formula: ； in, is the infinitesimal element on the surface of AUV, is the sum of the static and dynamic pressures of the fluid.

6. The collaborative simulation method of underwater vehicle flow field and time-varying motion model predictive control according to claim 1, characterized in that: The transfer of flow field forces and flow field moments to the CFD multi-degree-of-freedom motion equations to update the current position of the AUV includes: Based on the flow field force and flow field torque, the angular velocity of the AUV and the velocity of the AUV center of mass are obtained through the CFD multi-degree-of-freedom motion equation; Update the current position of the AUV based on the angular velocity of the AUV and the velocity of the AUV center of mass; Among them, the CFD multi-degree-of-freedom motion equation is as follows: ； ； Where, is the sum of the mass of the AUV and the additional mass of the AUV, is the velocity of the AUV center of mass, is the flow field force, are external forces generated by multibody constraints or joints, For control, For the previous moment, For the current moment, is the moment of inertia tensor, is the angular velocity of the AUV, is the flow field torque, is the external torque generated by the multibody constraints or joints, To control the torque.

7. The method for collaborative simulation of underwater vehicle flow field and time-varying motion model predictive control according to claim 6, characterized in that: The updating of the current posture of the AUV based on the angular velocity of the AUV and the velocity of the center of mass of the AUV specifically includes updating the current posture of the AUV by the following formula: ； ； ； ； ； ； in, 、 and are the position components of the AUV's position in the horizontal, longitudinal and vertical directions, For the next moment, For the current moment, 、 and are the velocity components of the AUV center of mass in the horizontal, longitudinal and vertical directions, 、 and are the rotation angle components of the AUV posture in the horizontal, longitudinal and vertical directions, 、 and are the angular velocity components of the AUV in the horizontal, longitudinal and vertical directions, is the interval between the next moment and the current moment; The updated AUV poses include 、 、 、 、 and .

8. A collaborative simulation device for underwater vehicle flow field and time-varying motion model predictive control, characterized in that: include: An initial parameter determination module is used to determine the initial parameters of the autonomous underwater vehicle AUV dynamic model; The iteration module is used to continuously iterate the AUV's posture based on the initial parameters of the AUV dynamic model and the AUV's navigation status information until the posture deviation is within a preset range. The AUV's navigation status information is obtained through CFD simulation; The iteration module includes: a model optimal parameter determination unit, a rudder angle determination unit, an AUV posture update unit, and a posture deviation judgment unit, among which: The model optimal parameter determination unit is used to perform online rolling identification of the time-varying parameters of the AUV dynamic model through an optimization algorithm based on the initial parameters of the AUV dynamic model and the AUV navigation state information, and determine the optimal parameters of the AUV dynamic model; The rudder angle determination unit is used to determine the rudder angle required at the current moment based on the optimal parameters of the AUV dynamic model and the posture deviation between the AUV's current posture and the target posture, through the adaptive line of sight ALOS guidance algorithm and the model predictive control MPC algorithm. The optimal parameters of the AUV dynamic model are passed to the MPC algorithm to update the model parameters of the MPC algorithm; The AUV posture update unit is used to determine the flow field force and flow field torque based on the rudder angle required at the current moment through the NS equation, and transfer the flow field force and flow field torque to the CFD multi-degree-of-freedom motion equation to update the current posture of the AUV; The posture deviation judgment unit is used to judge whether the posture deviation is within a preset range based on the target posture and the updated AUV posture.

9. An electronic device, characterized in that: include: at least one memory for storing a computer program; At least one processor is used to execute the program stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed on a processor, the processor is caused to execute the method according to any one of claims 1 to 7.

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