Double-ship cooperative operation coupling dynamic positioning cooperative control method

Abstract

The invention discloses a double-ship cooperative operation coupling dynamic positioning cooperative control method, which comprises the following steps of: establishing an adjacent ship hydrodynamic model through a numerical simulation and experiment combined method; based on the adjacent ship hydrodynamic model, designing a coupling dynamic positioning control algorithm, taking the motion state of the double ships and the adjacent ship hydrodynamic interference as input, and calculating and adjusting the thrusts and torques of the propellers of the double ships in real time through a control law combining a state feedback control law and a feedforward control law; a cooperative control strategy is formulated, relative position and attitude constraint conditions are introduced, control targets of the two ships are dynamically adjusted according to operation requirements, and mutual interference of movement of the two ships is avoided; a self-adaptive adjustment mechanism is adopted, and parameters of a control algorithm are adjusted in real time according to the actually measured ship motion state and environment parameters so as to adapt to different operation conditions and environment changes. According to the method, the dynamic positioning performance of the double ships is guaranteed, and the safety and efficiency of collaborative operation of the double ships are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ship dynamic positioning control, and particularly relates to a coupled dynamic positioning cooperative control method for double-ship cooperative operation. BACKGROUND

[0002] In the field of offshore engineering, double-ship cooperative operation is increasingly widely used, such as double-ship hoisting, double-ship pipe laying, and other operation scenarios. Ship dynamic positioning technology is a key technology for realizing precise positioning and maintaining a specific attitude of a ship at sea, and is crucial for ensuring the safety and efficiency of offshore engineering operations. However, in the process of double-ship cooperative operation, ship dynamic positioning faces many complex challenges, among which the influence of the water dynamics of neighboring ships is a problem that cannot be ignored.

[0003] Currently, traditional dynamic positioning technology is mainly designed and optimized for single-ship operation. In single-ship operation, the ship is mainly subjected to environmental loads such as wind, wave, and current, and the dynamic positioning system can well realize the positioning and attitude control of the ship. However, when two ships are cooperatively operating, the situation becomes more complex. The water dynamic interaction between neighboring ships changes the flow field distribution around the ships, thereby generating additional water dynamic disturbance forces and moments. The size and direction of such disturbance forces and moments change with factors such as the relative position, distance, and heading between the two ships, bringing great uncertainty to the dynamic positioning of the ship.

[0004] Some existing dynamic positioning control strategies have certain limitations in dealing with the influence of the water dynamics of neighboring ships. Some strategies only consider the influence of environmental loads, ignoring the influence of the water dynamics of neighboring ships, resulting in a decrease in the positioning accuracy of the ship during double-ship cooperative operation, making it difficult to meet the operation requirements. Some other strategies consider the existence of neighboring ships, but the modeling of the water dynamics of neighboring ships is not accurate enough, and the water dynamic disturbance of neighboring ships cannot be effectively compensated in real time, making the ship prone to large positional deviation and attitude fluctuation during operation, increasing the risk and cost of operation.

[0005] In addition, in double-ship cooperative operation, the cooperativeness between the two ships also needs to be considered. If the dynamic positioning control of the two ships is independent of each other and lacks effective coordination mechanisms, it may cause mutual interference between the movements of the two ships, further exacerbating the influence of the water dynamics of neighboring ships, and even may cause collisions and other safety accidents. Therefore, how to design a dynamic positioning cooperative control technology that can effectively cope with the influence of the water dynamics of neighboring ships and realize double-ship cooperative operation is a problem that needs to be solved in the field of ship dynamic positioning. SUMMARY

[0006] To address the aforementioned technical problems, this invention proposes a coupled dynamic positioning cooperative control method for dual-ship cooperative operations. In this method, to address the issue of vessel dynamic positioning being affected by the hydrodynamics of adjacent vessels during dual-ship cooperative operations, an optimized dynamic positioning cooperative control strategy is provided to ensure the dynamic positioning performance of both vessels and improve the safety and efficiency of dual-ship cooperative operations.

[0007] To achieve the above objectives, the technical solution of the present invention is as follows:

[0008] A method for coupled dynamic positioning and cooperative control of two ships in cooperative operation includes the following steps:

[0009] Step 1: Establish a hydrodynamic model of the adjacent ship by combining numerical simulation and experiment.

[0010] Step 2: Based on the hydrodynamic model of the adjacent ship, a coupled dynamic positioning control algorithm is designed. The motion state of the two ships and the hydrodynamic disturbance of the adjacent ship are used as inputs. Through a control law that combines state feedback control law and feedforward control law, the thrust and torque of the two ships' propellers are calculated and adjusted in real time to compensate for the hydrodynamic disturbance of the adjacent ship and achieve accurate dynamic positioning of the two ships.

[0011] Step 3: Develop a collaborative control strategy, introduce relative position and attitude constraints, and dynamically adjust the control objectives of the two ships according to the operational requirements to avoid mutual interference between the two ships' motions.

[0012] Step 4: Adopt an adaptive adjustment mechanism to adjust the parameters of the control algorithm in real time based on the actual measured ship motion state and environmental parameters, so as to adapt to different operating conditions and environmental changes.

[0013] Preferably, step 1 includes the following steps:

[0014] Collect the geometric parameters and operating conditions of the two ships and use computational fluid dynamics software to establish a numerical model of the flow field of the two ships. Set boundary conditions and calculation parameters, simulate and analyze the flow field under different operating conditions, and obtain the numerical simulation results of the hydrodynamic interference force and torque of the adjacent ship.

[0015] A water tank experiment was conducted, and a scaled-down model of the two boats was made. The two boats worked together under different conditions in the experimental water tank, and the experimental measurement results of the hydrodynamic interference force and torque of the adjacent boat were measured.

[0016] The correction coefficients are determined by the least squares method to minimize the error between the corrected numerical simulation results and the experimental measurement results. Based on the correction coefficients, the numerical model of the flow field of the two ships is corrected to obtain the hydrodynamic model of the adjacent ship.

[0017] Preferably, step 2 includes the following steps:

[0018] Ship motion is described by a six-degree-of-freedom model, the first... The equations of motion for the ships are shown below:

[0019]

[0020] in, It is the first The inertia matrix of the ship, It is a velocity vector. It is the derivative of the ship's speed. It is the Coriolis-centripetal matrix. It is the damping matrix. It is the thruster control force vector. It is the environmental load vector. It is the hydrodynamic interference force vector of the adjacent ship; ;

[0021] Polynomial fitting or neural network is used to approximate the hydrodynamic disturbance force of the adjacent ship. Design state feedback control law With feedforward control law Associative control law And the stability is analyzed using Lyapunov stability theory, in which, It is a function obtained through a combination of numerical simulation and experimentation. It is a proportional gain matrix. It is the differential gain matrix. It is the error vector. yes Derivative.

[0022] Preferably, step 3 includes the following steps:

[0023] The two vessels share information in real time and determine the constraints on their relative positions and attitudes based on operational requirements, as shown below:

[0024]

[0025]

[0026] in, It is a relative position vector. Let be the desired relative position vector. The allowable relative position error threshold; This is the relative attitude vector. Let be the desired relative attitude vector. The allowable relative attitude error threshold;

[0027] Introducing a cooperative control objective function, and employing an optimization algorithm to optimize the control objective function. The minimization solution is performed as follows:

[0028]

[0029] in, and These are the weighting coefficients for relative position and relative attitude errors, respectively.

[0030] A collision warning mechanism is introduced to calculate the minimum distance between the two ships. ,when hour, A collision warning is triggered based on the collision warning distance threshold; collision avoidance constraints are introduced, and the safe zones for the two ships are defined as follows: and Then control input Must meet ,in These represent the distances between the safe zones of the two ships. This is the safe distance threshold.

[0031] Preferably, the optimization algorithm employs the gradient descent method.

[0032] Preferably, the real-time information sharing between the two ships is established through wireless communication technology, and error control coding and data compression technology are used to process the wirelessly transmitted information.

[0033] Preferably, step 4 includes the following steps:

[0034] Real-time monitoring of ship motion status and environmental parameters;

[0035] Based on real-time monitoring of the ship's motion state and environmental parameters, the MRAC algorithm is used to design the reference model state equations. State equations of the actual system Define state error ,in, It is the state vector of the reference model. It is the system matrix of the reference model. It is the input matrix of the reference model. It is the input vector of the reference model; It is the state vector of the actual system. It is the system matrix of the actual system. It is the input matrix of the actual system. It is the input vector of the actual system;

[0036] To make state error The asymptotic convergence to zero is followed by an adaptive law designed using Lyapunov stability theory, as shown in the following formula:

[0037]

[0038] In the formula, and It is a positive definite symmetric adaptive gain matrix. It is a positive definite symmetric matrix, and K is the control gain matrix; The derivative of the reference gain matrix;

[0039] Integral processing is performed on the adaptive law; performance indicators are determined and periodically evaluated and optimized, and the adaptive gain matrix is ​​adjusted based on the evaluation and optimization results. and Adjustments will be made.

[0040] Based on the above technical solution, the beneficial effects of this invention are as follows: This invention addresses the problem of vessel dynamic positioning being affected by the hydrodynamics of adjacent vessels during collaborative operations, providing an optimized collaborative dynamic positioning control strategy. Through a combination of numerical simulation and experimentation, considering factors such as the relative position, distance, and heading of the two vessels, a precise hydrodynamic model of the adjacent vessel is established using computational fluid dynamics software simulation and corrected through tank experiments. Based on this model, the motion states of the two vessels and the hydrodynamic interference from the adjacent vessel are used as inputs. A coupled dynamic positioning control algorithm is designed through a combination of state feedback and feedforward control. A collaborative control strategy is formulated, introducing relative position and attitude constraints, achieving information sharing through a communication system, dynamically adjusting the control target, and avoiding mutual interference between the two vessels' motions. An adaptive adjustment mechanism is set up, using a model reference adaptive control algorithm to adjust the control algorithm parameters in real time based on the actual measured vessel motion states and environmental parameters. This invention improves the accuracy of dual-vessel dynamic positioning, enhances collaboration, improves the robustness and adaptability of the control algorithm, meets the requirements of marine engineering operations, and improves operational safety and efficiency. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of a dual-ship cooperative operation coupled dynamic positioning cooperative control method in one embodiment. Detailed Implementation

[0042] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0043] like Figure 1 The present embodiment provides a dual-ship cooperative operation coupled dynamic positioning cooperative control method, including the following steps:

[0044] Step 1: Establish a hydrodynamic model of the adjacent ship by combining numerical simulation and experiment.

[0045] In this embodiment, an accurate hydrodynamic model of the adjacent vessel is established by combining numerical simulation and experiments. The influence of factors such as the relative position, distance, and heading between the two vessels on the hydrodynamic disturbance force and torque is considered. Computational fluid dynamics (CFD) software is used to simulate and analyze the flow field under different operating conditions. Simultaneously, tank experiments are combined to verify and correct the model. Specific details are as follows:

[0046] 1.1 Basic Principles and Influencing Factors Analysis

[0047] In collaborative operations between two vessels, the hydrodynamic interference forces and moments of adjacent vessels are affected by factors such as the relative positions, distances, and headings of the two vessels. Let the positions of vessel 1 and vessel 2 be respectively... and ,spacing The two ships are respectively and Changes in relative position and heading alter the surrounding flow field distribution, resulting in different hydrodynamic disturbances.

[0048] 1.2 CFD-based numerical simulation modeling

[0049] A numerical model of the flow field between the two boats was established using computational fluid dynamics (CFD) software. The fluid motion was described based on the Navier-Stokes equations. in, It is the fluid velocity vector. It is time. It is the fluid density. It's pressure. It is kinematic viscosity. It is a volume force.

[0050] In numerical simulations, appropriate boundary conditions need to be set. For the two-ship model, the ship surfaces are set to no-slip boundary conditions, i.e. The far-field boundary is set as either the velocity inlet or the pressure outlet boundary condition.

[0051] The flow field is discretized by mesh generation, and the Navier-Stokes equations are solved using the finite volume method or the finite element method. For different relative positions... , ,spacing and heading , The combined operating conditions were simulated to obtain the hydrodynamic interference force from the adjacent ship. and torque .

[0052] 1.3 Experiment-based model validation and correction

[0053] A water tank experiment was conducted, and a scaled-down model of the two boats was constructed. During the experiment, the actual values ​​of the hydrodynamic interference forces and moments of the adjacent boats under different operating conditions were measured. and .

[0054] To correct the CFD model, correction coefficients are introduced. and : in, and It is the corrected hydrodynamic disturbance force and torque.

[0055] The correction coefficient is determined using the least squares method. and This minimizes the error between the corrected simulation results and the experimental measurements. in, It refers to the number of experimental conditions.

[0056] 1.4 Final Hydrodynamic Model of the Adjacent Ship

[0057] After correction and optimization, the final hydrodynamic model of the adjacent vessel was obtained. This model can be represented as the hydrodynamic disturbance forces and moments of the adjacent vessel being relative to their positions. , ,spacing and heading , Functions: This model can be used for the design of subsequent coupled dynamic positioning control algorithms to accurately compensate for hydrodynamic interference from neighboring vessels and improve the accuracy of dual-ship dynamic positioning.

[0058] Step 2: Based on the hydrodynamic model of the adjacent ship, a coupled dynamic positioning control algorithm is designed. The motion state of the two ships and the hydrodynamic interference of the adjacent ship are used as inputs. Through a control law that combines state feedback control law and feedforward control law, the thrust and torque of the two ships' propellers are calculated and adjusted in real time to compensate for the hydrodynamic interference of the adjacent ship and achieve precise dynamic positioning of the two ships.

[0059] In this embodiment, a six-degree-of-freedom motion model of the ship is constructed. Polynomial fitting or neural networks are used to approximate the hydrodynamic interference force of adjacent ships. Design status feedback With feedforward Associative control law The stability is analyzed using Lyapunov stability theory. Specific details are as follows:

[0060] 2.1 Establishment of Ship Motion Model

[0061] In a two-ship cooperative operation, the motion of each ship can be described using a six-degree-of-freedom model. For the... ships ( Its equation of motion in the inertial coordinate system can be expressed as: in, It is the first The inertia matrix of a ship includes the ship's mass and moment of inertia; It is the first The ship's velocity vector includes linear velocity and angular velocity; It is the derivative of the ship's speed; It is the Coriolis-centripetal matrix, which represents velocity. The function; It's the damping matrix, and also the velocity. The function; It is the first The control force vector generated by the ship's propulsion system; It is the first The environmental load vector acting on a ship mainly includes the forces of wind, waves, and current; It is the first The vector of hydrodynamic interference force experienced by the ship from the adjacent ship.

[0062] 2.2 Calculation of hydrodynamic interference force from adjacent vessels

[0063] Using a hydrodynamic model of adjacent ships, factors such as relative position, distance, and heading between the two ships are considered. Let the relative position vector between the two ships be... The relative heading angle is Then the first Hydrodynamic interference force on a ship from a neighboring ship It can be represented as: in, This function is obtained through a combination of numerical simulation and experimentation. In practical calculations, methods such as polynomial fitting or neural networks can be used to approximate this function. For example, when using polynomial fitting: in, These are the fitting coefficients, determined through experimental data and numerical simulation results; n, m, and p are the number of nodes in the neural network or polynomial, and j, k, and l are the current index. This item is the index.

[0064] 2.3 State Feedback and Feedforward Control Design

[0065] To achieve precise dynamic positioning of the two ships, a combination of state feedback and feedforward control is employed. First, the error vector is defined. ,in It is the first The expected position vector of the ships, It is the first The actual position vector of the ship.

[0066] The state feedback control law is designed as follows: in, It is a proportional gain matrix. These are differential gain matrices, and their values ​​can be optimized using methods such as pole placement or linear quadratic regulators (LQRs). yes Derivative.

[0067] The feedforward control law is used to compensate for hydrodynamic disturbances from adjacent vessels and environmental loads, and is designed as follows:

[0068] Then the first The overall control law of the ship is:

[0069] 2.4 Stability Analysis of Control Algorithm

[0070] To ensure the stability of the control algorithm, Lyapunov stability theory is used for analysis. The Lyapunov function is defined as follows: in, It is a positive definite symmetric matrix. For Find the time derivative: control law Substituting the equations of motion into the ship's equations, and after a series of derivations and simplifications, we obtain... The expression. By choosing the appropriate , and , making This ensures the error vector It is asymptotically stable, meaning that the ship can converge to the desired position.

[0071] 2.5 Real-time Calculation and Implementation of Control Algorithm

[0072] In practical applications, it is necessary to calculate the control law in real time. The ship's motion state and environmental parameters measured by sensors are input into the control algorithm, and the error vector is first calculated. and Then, calculations are performed based on the hydrodynamic model of the adjacent ship. Then calculate the state feedback control law. and feedforward control law Finally, the overall control law is obtained. .Will The signal is sent to the propulsion control system to drive the propulsion mechanism and achieve dynamic positioning control of the ship.

[0073] Step 3: Develop a collaborative control strategy, introduce relative position and attitude constraints, and dynamically adjust the control objectives of the two ships according to the operational requirements to avoid mutual interference between the two ships' movements.

[0074] In this embodiment, relative position and attitude constraints are constructed. , Information sharing and communication mechanisms are established through wireless communication technology, and error control coding and data compression techniques are adopted, based on a cooperative control objective function. Using gradient descent Dynamically adjust control objectives, introduce collision warning mechanisms and collision avoidance constraints. To avoid interference between movements. Specific instructions are as follows:

[0075] 3.1 Construction of Relative Position and Attitude Constraints

[0076] In collaborative operations involving two vessels, strict constraints must be placed on the relative positions and attitudes of the two vessels to ensure safety and efficiency. Let the vessels... and ships The positions are respectively and The attitudes are represented by Euler angles as follows: and .

[0077] relative position vector Relative attitude vector .

[0078] For different operational scenarios, such as dual-ship crane operations, the relative positional error between the two ships must be within a certain range, which can be expressed as a constraint condition: in Let be the desired relative position vector. This is the allowable relative position error threshold.

[0079] The relative posture also needs to meet certain constraints, such as: in Let be the desired relative attitude vector. The threshold for the allowable relative attitude error.

[0080] 3.2 Information Sharing and Communication Mechanisms

[0081] To achieve coordinated control between the two ships, an efficient information sharing and communication mechanism needs to be established. Wireless communication technologies, such as Wi-Fi and 4G / 5G, should be employed, and communication equipment should be installed on both ships to ensure real-time and stable data transmission.

[0082] Ship To ships The transmitted information vector is ,in For ships velocity vector; ship To ships The transmitted information vector is .

[0083] To ensure the reliability of information transmission, error control coding techniques, such as Cyclic Redundancy Check (CRC) codes, are employed. Simultaneously, to reduce communication latency, data compression techniques are used to process the transmitted information.

[0084] 3.3 Dynamic Adjustment of Cooperative Control Objectives

[0085] Based on information sharing, the control objectives of the two ships are dynamically adjusted according to operational requirements. A cooperative control objective function is introduced. Its definition is: in and These are the weighting coefficients for relative position and relative attitude errors, which can be adjusted according to the specific requirements of the operation.

[0086] In dynamic positioning control systems, optimization algorithms (such as gradient descent) are used to optimize the control objective function. A minimization solution is performed to adjust the control objectives of the two ships in real time. Let the control input vector be... ,in and Ships and ships The thruster control input.

[0087] According to the gradient descent method, the update formula for the control input is: in For the first Time-based control input, For learning rate, To control the objective function exist The gradient at that point.

[0088] 3.4 Mechanisms to avoid mutual interference between movements

[0089] To avoid interference between the motions of the two ships, a collision warning mechanism is introduced. The minimum distance between the two ships is calculated. ,when hour( (The collision warning distance threshold) triggers a collision warning.

[0090] Simultaneously, collision avoidance constraints are incorporated into the control algorithm. Let the safe zones for the two ships be... and Then control input Must meet: in Indicates the distance between the safe zones of the two ships. This is a safe distance threshold. This method ensures that the two vessels will not collide during collaborative operations, improving operational safety.

[0091] Step 4: Adopt an adaptive adjustment mechanism to adjust the parameters of the control algorithm in real time based on the actual measured ship motion state and environmental parameters, so as to adapt to different operating conditions and environmental changes.

[0092] In this embodiment, the ship position error is selected. Speed ​​error Environmental interference Parameter monitoring is performed based on the Model Reference Adaptive Control (MRAC) algorithm, with a reference model set. With actual system Design Adaptive Law , Adjust the control algorithm parameters and optimize them through integral processing and evaluation, utilizing the mean square error. Adjust the adaptive gain matrix. Specific instructions are as follows:

[0093] 4.1 Selection of monitoring parameters and data acquisition

[0094] In a dynamic positioning control system, an adaptive adjustment module needs to be installed to monitor the ship's motion status and environmental parameters in real time. The selected monitoring parameters include the ship's position error. Speed ​​error and environmental interference These parameters are collected by sensors installed on both ships, such as the Global Positioning System (GPS) for measuring position, the Inertial Measurement Unit (IMU) for measuring speed and attitude, and wind and wave meters for measuring environmental parameters.

[0095] 4.2 Adaptive Algorithm Design

[0096] Based on the monitored parameters, a Model Reference Adaptive Control (MRAC) algorithm is used to adjust the parameters of the control algorithm in real time. Let the state equation of the reference model be: in, It is the state vector of the reference model. It is the system matrix of the reference model. It is the input matrix of the reference model. It is the input vector of the reference model.

[0097] The state equation of the actual system is: in, It is the state vector of the actual system. It is the system matrix of the actual system. It is the input matrix of the actual system. It is the input vector of the actual system.

[0098] Define state error Then the error dynamic equation is:

[0099] To make state error Asymptotically convergent to zero, design an adaptive law. Assume the control input... for: in, and It is the gain matrix to be adjusted.

[0100] The adaptive law is designed using Lyapunov stability theory, and the Lyapunov function is selected. ,in It is a positive definite symmetric matrix. , , and It is a positive definite symmetric adaptive gain matrix, and K is the control gain matrix. .

[0101] right Taking the derivative and setting it to less than zero, we obtain the adaptive law:

[0102] 4.3 Parameter Adjustment and Optimization

[0103] Based on the adaptive law, the parameters of the control algorithm, such as the gain coefficient and filter parameters, are adjusted in real time. In practical applications, to avoid drastic changes in parameters, the adaptive law is integrated, resulting in:

[0104] The adaptive adjustment module is periodically evaluated and optimized. Performance metrics, such as mean squared error (MSE), are calculated by comparing the output of the actual system with the output of the reference model. in, It is the output vector of the actual system. It is the output vector of the reference model. It represents the number of sampling points.

[0105] According to performance indicators For the adaptive gain matrix and Adjustments are made to ensure that the adaptive adjustment module can accurately and effectively adjust the control parameters, thereby improving the robustness and adaptability of the control algorithm.

[0106] Through the above adaptive adjustment mechanism, the coupled dynamic positioning and collaborative control of the two ships can operate stably under different operating conditions and environmental changes, thereby improving the dynamic positioning accuracy of the two ships and the safety and efficiency of collaborative operations.

[0107] It should be understood that although the steps in the flowchart above are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart above may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0108] The above are merely preferred embodiments of the present application and are not intended to limit the embodiments of the present application. For those skilled in the art, the embodiments of the present application can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of the present application should be included within the protection scope of the embodiments of the present application.

Claims

1. A method for coupled dynamic positioning cooperative control of two vessels operating in cooperation, characterized by, The method comprises the following steps: Step 1, a numerical simulation combined with experiments is used to establish a water dynamic model of the adjacent ship; Step 2, based on the water dynamic model of the adjacent ship, a coupled dynamic positioning control algorithm is designed, and the motion state of the two ships and the water dynamic disturbance of the adjacent ship are taken as inputs, a control law combining a state feedback control law and a feedforward control law is used to calculate and adjust the propeller thrust and torque of the two ships in real time to compensate for the water dynamic disturbance of the adjacent ship, so as to realize accurate dynamic positioning of the two ships; Step 3, a cooperative control strategy is formulated, relative position and attitude constraints are introduced, and the control targets of the two ships are dynamically adjusted according to the operation requirements to avoid mutual interference of the two ships; Step 4, an adaptive adjustment mechanism is used to adjust the parameters of the control algorithm in real time according to the actually measured motion state of the ship and environmental parameters to adapt to different operation conditions and environmental changes.

2. The method according to claim 1, characterized in that, The step 1 comprises the following steps: Geometric parameters and operation condition information of the two ships are collected, a numerical model of the flow field of the two ships is established by using computational fluid dynamics software, boundary conditions and calculation parameters are set, and the flow field under different conditions is simulated and analyzed to obtain numerical simulation results of the water dynamic disturbance force and torque of the adjacent ship; A water tank experiment is performed, a scale model of the two ships is made, and the cooperative operation of the two ships under different conditions is simulated in the experimental tank to measure experimental measurement results of the water dynamic disturbance force and torque of the adjacent ship; A correction coefficient is determined by using the least square method to minimize the error between the corrected numerical simulation results and the experimental measurement results, and the numerical model of the flow field of the two ships is corrected based on the correction coefficient to obtain the water dynamic model of the adjacent ship.

3. The method according to claim 1, characterized in that, The step 2 comprises the following steps: Six degree of freedom model description of ship motion, part The equations of motion of the ship are given by wherein is the inertia matrix of the th ship, is the velocity vector, is the derivative of the ship velocity, is the Coriolis-centripetal force matrix, is the damping matrix, is the thruster control force vector, is the environmental load vector, is the neighboring ship hydrodynamic disturbance force vector; ; Polynomial fitting or neural network is used to approximate the hydrodynamic disturbance force of the adjacent ship. Design state feedback control law With feedforward control law Associative control law And the stability is analyzed using Lyapunov stability theory, in which, It is a function obtained through a combination of numerical simulation and experimentation. It is a proportional gain matrix. It is the differential gain matrix. It is the error vector. yes Derivative.

4. The method according to claim 1, characterized in that, The step 3 comprises the following steps: Real-time information is shared between the two ships, and constraint conditions of the relative position and relative attitude of the two ships are determined according to the operation requirements as follows: wherein, is a relative position vector, is a desired relative position vector, is an allowed relative position error threshold; is a relative attitude vector, is a desired relative attitude vector, is an allowed relative attitude error threshold; The cooperative control objective function is introduced, and an optimization algorithm is used to minimize the control objective function as shown below: wherein, and are weight coefficients for the relative position and relative attitude errors, respectively. Introduce collision warning mechanism, calculate the minimum distance between two ships When , is the collision warning distance threshold, triggering collision warning; Introduce collision avoidance constraints, set the safety area of two ships as and , then the control input needs to meet , where represents the distance between the safety areas of two ships, is the safety distance threshold.

5. The method according to claim 4, wherein, The optimization algorithm uses the gradient descent method.

6. The method of claim 4, wherein, The real-time information shared between the two ships is established by using wireless communication technology to share information, and error control coding and data compression technology are used to process the wireless transmission information.

7. The method according to claim 1, wherein, The step 4 comprises the following steps: The motion state of the ship and the environmental parameters are monitored in real time; According to real-time monitoring of the motion state and environmental parameters of the ship, a reference model state equation is set by using an MRAC algorithm , an actual system state equation , a state error is defined , wherein is a state vector of the reference model, is a system matrix of the reference model, is an input matrix of the reference model, is an input vector of the reference model; is a state vector of the actual system, is a system matrix of the actual system, is an input matrix of the actual system, is an input vector of the actual system; In order to make the state error Asymptotically converge to zero, the Lyapunov stability theory is designed adaptive law, as follows: wherein and is a positive definite symmetric adaptive gain matrix, is a positive definite symmetric matrix, K is a control gain matrix; is a derivative of the reference gain matrix; integrating the adaptive law; determining a performance index periodically evaluating the optimization and adjusting the adaptive gain matrix according to the result of the evaluation optimization and adjusting.

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