A ship docking control method based on cooperation of ship double winches and thrusters

Abstract

The application provides a ship docking control method based on the cooperation of a ship double winch and a propeller, and relates to the technical field of marine unmanned equipment motion control. The application collects the relative smooth relative position data and the relative speed between the active ship and the passive ship in real time; constructs and utilizes a hydrodynamic disturbance observer to obtain the current time fluid mechanics disturbance force and the disturbance torque of the passive ship; generates the current time cooperative distribution matrix and constructs a nonlinear state space model to generate the future time control sequence according to the nonlinear state space model; constructs a rolling optimization objective function to solve the rolling optimization objective function within the preset safety boundary range by using an NMPC solver to obtain the future time control sequence corresponding to the optimal solution as the target control sequence, and to perform the docking process on the active ship and the passive ship according to the target control sequence, which alleviates the problems of docking yaw loss of control and low safety caused by hydrodynamic disturbance.

Description

Technical Field

[0001] This invention relates to the field of motion control technology for marine unmanned equipment, and in particular to a method for coordinated berthing control based on a ship's double winch and propeller. Background Technology

[0002] When vessels perform maritime material transfer or docking missions, the common non-contact operation procedure is as follows: the active vessel throws two towing cables to the passive vessel, which is in a "fire-off and floating" state. Then, the two winches on the active vessel simultaneously wind up the cables to physically pull the two vessels closer together to establish a rigid connection or to transfer materials.

[0003] However, when performing this operation in a real marine environment, when the parallel distance between the two vessels shrinks to an extremely close range (e.g., less than three times the beam of the vessel), a ship suction effect can easily occur. This leaves the passive vessel in a state of unpowered afloat, losing its own course correction ability. Under the coupling effect of the asymmetrical ship suction moment and wave disturbance, the passive vessel is prone to violent bow rolling motion, resulting in inconsistent tightening speeds of the bow and stern cables, which can lead to serious hull collision accidents and compromise safety.

[0004] Meanwhile, existing winch controls generally employ an independent constant tension winding mode, where the bow and stern winches each blindly tighten based solely on a set tension threshold. This control mode ignores the potential of winch tension to control the hull's yaw freedom, and cannot actively mitigate the yaw moment between the two vessels using the tension difference. This results in a lack of proactive adjustment capability for the hull's attitude during docking, making it difficult to guarantee the stability and success rate of the docking. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a coordinated berthing control method based on a double winch and a propeller for a vessel, so as to alleviate the problems of yaw loss and collision risk caused by near-field hydrodynamic interference during the flexible traction berthing phase of the prior art.

[0006] In a first aspect, embodiments of the present invention provide a coordinated berthing control method based on a ship's double winch and propeller, comprising: The relative pose data of the active and passive boats are collected in real time, and the relative pose data is filtered by a nonlinear filtering algorithm to obtain the denoised relative pose data and relative velocity at the current moment. A hydrodynamic disturbance observer is constructed and utilized to map the noise-filtered relative pose data and relative velocity at the current moment to obtain the hydrodynamic disturbance force and disturbance torque of the passive vessel at the current moment. Based on the current physical parameters of the propeller of the active vessel and the tension of the bow and stern winch cables, a current moment collaborative allocation matrix is ​​generated. Based on the current moment collaborative allocation matrix, the current moment hydrodynamic disturbance force and disturbance torque, and the current moment control sequence corresponding to the active vessel, a nonlinear state-space model is constructed to generate a future moment control sequence based on the nonlinear state-space model. Based on the preset optimal berthing information of the active vessel and the passive vessel, a rolling optimization objective function is constructed. The NMPC solver is used to solve the rolling optimization objective function within a preset safety boundary range to obtain the future time control sequence corresponding to the optimal solution as the target control sequence. According to the target control sequence, the active vessel and the passive vessel are berthed.

[0007] In conjunction with the first aspect, this embodiment of the invention provides a first possible implementation of the first aspect, wherein the real-time acquisition of relative pose data between the active and passive vessels, and the use of a nonlinear filtering algorithm to perform noise filtering on the relative pose data to obtain the noise-filtered relative pose data and relative velocity at the current moment, includes: The active vessel is triggered to acquire first relative pose data between the passive vessel and the active vessel via its visual sensor, and the active vessel is triggered to acquire second relative pose data between the passive vessel and the active vessel via its inertial measurement unit. Based on the timestamp information in the first relative pose data and the second relative pose data, the first relative pose data and the second relative pose data are fused to obtain the relative pose data of the active vessel and the passive vessel. Based on the nonlinear dynamic equations between the active and passive vessels, a nonlinear filtering algorithm is used to smooth and differentiate the relative pose data to obtain the noise-filtered relative pose data and relative velocity at the current moment.

[0008] In conjunction with the first aspect, this invention provides a second possible implementation of the first aspect, wherein constructing and utilizing a hydrodynamic disturbance observer to map the noise-filtered relative pose data and relative velocity at the current moment to obtain the hydrodynamic disturbance force and disturbance torque of the passive vessel at the current moment includes: Based on the length of the passive vessel and the preset dimensionless hydrodynamic coefficients, a hydrodynamic interference observer is constructed. The filtered relative pose data and relative velocity are substituted into the hydrodynamic interference observer and mapped to obtain the current hydrodynamic interference force and interference torque of the passive vessel.

[0009] In conjunction with the first aspect, this embodiment of the invention provides a third possible implementation of the first aspect, wherein the current physical parameters of the propulsion system of the active vessel include the thrust of the two main propulsion systems and the rudder angle of the active vessel; Based on the current physical parameters of the propeller of the active vessel and the tension of the bow and stern winches, a cooperative allocation matrix for the current moment is generated. Then, based on the cooperative allocation matrix, the current hydrodynamic disturbance force and disturbance torque, and the corresponding current control sequence of the active vessel, a nonlinear state-space model is constructed. Finally, a future control sequence is generated based on the nonlinear state-space model, including: The thrust of the two main thrusters, the rudder angle, and the tension of the bow and stern winches are combined to form the current moment control input vector of the active vessel; The cooperative allocation matrix is ​​constructed based on the angle between the physical installation position of each actuator of the active vessel and the cable of the bow and stern winches; Calculate the product between the cooperative allocation matrix and the current control input vector to obtain the resultant force and resultant moment of the active vessel at the center of the hull. Using the current hydrodynamic disturbance force and disturbance torque as feedforward compensation for the nonlinear state space, and combining the hull inertia transformation matrix between the active vessel and the passive vessel, the unpowered state evolution term, the resultant force, and the resultant torque, the nonlinear state space model is constructed. The nonlinear state-space model is discretized using the NMPC solver, and iteratively solved in the prediction time domain to generate the future control sequence.

[0010] In conjunction with the first aspect, this embodiment of the invention provides a fourth possible implementation of the first aspect, wherein the step of constructing a rolling optimization objective function based on the preset optimal berthing information of the active vessel and the passive vessel includes: Obtain the future berthing information corresponding to the future time control sequence; Calculate the state deviation between the future berthing information and the optimal control sequence corresponding to the preset optimal berthing information; The rate of change of the future time control sequence is obtained, and the state deviation, the future time control sequence, and the rate of change are respectively weighted and summed with their respective weight matrices to construct the rolling optimization objective function.

[0011] In conjunction with the first aspect, this embodiment of the invention provides a fifth possible implementation of the first aspect, wherein the preset safety boundary range includes collision avoidance safety boundary constraints, the double winch pulling force constraints of the active boat, and the propeller thrust constraints of the active boat; then, the step of using the NMPC solver to solve the rolling optimization objective function within the preset safety boundary range to obtain the future time control sequence corresponding to the optimal solution as the target control sequence includes: Determine candidate future time control sequences that satisfy the collision avoidance safety boundary constraints, the winch tension constraints, and the thruster thrust constraints; Based on the candidate future time-time control sequence, the NMPC solver is triggered to perform a solution process to obtain the target control sequence that makes the value of the rolling optimization objective function optimal.

[0012] Secondly, embodiments of the present invention provide a coordinated berthing control device based on a ship's double winch and propeller, comprising: The acquisition and processing module is used to acquire the relative pose data of the active and passive boats in real time, and to use a nonlinear filtering algorithm to filter the relative pose data to obtain the denoised relative pose data and relative velocity at the current moment. The construction module is used to construct and utilize the hydrodynamic disturbance observer to map the noise-filtered relative pose data and relative velocity at the current moment to obtain the hydrodynamic disturbance force and disturbance torque of the passive vessel at the current moment. The predictive processing module is used to generate a current-moment collaborative allocation matrix based on the current physical parameters of the propeller of the active vessel and the tension of the bow and stern winch cables, and to construct a nonlinear state-space model based on the current-moment collaborative allocation matrix, the current-moment hydrodynamic disturbance force and disturbance torque, and the current-moment control sequence corresponding to the active vessel, so as to generate a future-moment control sequence based on the nonlinear state-space model. The solution module is used to construct a rolling optimization objective function based on the preset optimal berthing information of the active vessel and the passive vessel, and to use the NMPC solver to solve the rolling optimization objective function for the optimal solution within a preset safety boundary range, so as to obtain the future time control sequence corresponding to the optimal solution as the target control sequence. The control module is used to perform berthing procedures on the active vessel and the passive vessel according to the target control sequence.

[0013] Thirdly, embodiments of the present invention also provide an electronic device, comprising: At least one processor; and at least one memory communicatively connected to the processor, wherein the memory stores program instructions executable by the processor, which, when invoked, can perform the cooperative docking control method as described in the first aspect of the invention.

[0014] Fourthly, embodiments of the present invention also provide a computer-readable storage medium, characterized in that the computer-readable storage medium stores computer instructions that cause the computer to execute the cooperative docking control method as described in the first aspect of the present invention.

[0015] This invention acquires the relative pose data of the active and passive boats in real time and uses a nonlinear filtering algorithm to filter the relative pose data to obtain the filtered relative pose data and relative velocity at the current moment. A hydrodynamic disturbance observer is constructed and used to map the filtered relative pose data and relative velocity at the current moment to obtain the hydrodynamic disturbance force and disturbance torque of the passive boat at the current moment. Based on the current physical parameters of the active boat's propellers and the tension of the bow and stern winches, a cooperative allocation matrix is ​​generated for the current moment. A nonlinear state-space model is constructed based on the cooperative allocation matrix, the current hydrodynamic disturbance force and disturbance torque, and the current moment control sequence corresponding to the active boat. A future moment control sequence is generated based on the nonlinear state-space model. A rolling optimization objective function is constructed based on the preset optimal berthing information of the active and passive boats. An NMPC solver is used to solve the rolling optimization objective function within a preset safety boundary range to obtain the future moment control sequence corresponding to the optimal solution as the target control sequence. Berthing is performed on the active and passive boats according to the target control sequence.

[0016] The embodiments of this invention bring the following beneficial effects: They abandon the traditional independent control framework of bow and stern winches and propose the concept of differential tension for bow and stern winches. This involves estimating the ship's suction interference torque (hydrodynamic interference) in real time and applying unequal pulling forces (differential tension) through the bow and stern winches to provide an active correction torque for the unpowered floating vessel (passive vessel); simultaneously, the active vessel's own propeller compensates for lateral suction. Combined with nonlinear model predictive control (NMPC), this achieves high-precision, collision-free parallel berthing of the two vessels at extremely close range.

[0017] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0019] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 A flowchart illustrating a coordinated berthing control method based on a ship's double winch and propeller, provided in an embodiment of the present invention; Figure 2 Overall architecture diagram of the ship's double winch and propeller coordinated berthing control system provided for simulation experiments; Figure 3 A schematic diagram of the near-field hydrodynamic interference force analysis model provided for simulation experiments; Figure 4 A schematic diagram of the control logic flow based on nonlinear model predictive control (NMPC) provided for simulation experiments; Figure 5 A schematic diagram of the relative trajectory and differential tension curves of the two boats during the berthing and approach process, provided for simulation experiments; Figure 6 This is a schematic diagram of a coordinated berthing control device based on a boat winch and a propeller, provided in an embodiment of the present invention. Figure 7 A block diagram of an exemplary electronic device provided in an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] When vessels perform maritime material transfer or docking missions, the common non-contact operation procedure is as follows: the active vessel throws two towing cables to the passive vessel, which is in a "fire-off and floating" state. Then, the two winches on the active vessel simultaneously wind up the cables to physically pull the two vessels closer together to establish a rigid connection or to transfer materials.

[0023] However, existing technologies have significant drawbacks when performing this operation in a real marine environment. When the parallel distance between the two vessels narrows to an extremely close range (e.g., less than three times the beam of the vessel), the water flow channel between the two hulls narrows sharply, resulting in a significant increase in the relative velocity of the water flow. According to Bernoulli's principle, the water pressure inside the two hulls will be significantly lower than the water pressure outside, thus generating strong near-field hydrodynamic interference, i.e., the "ship suction effect".

[0024] Existing technologies have the following core shortcomings in addressing the above phenomena: First, passive boats pose a high risk of yaw and loss of control. Because passive boats are in a state of unpowered afloat, they lose their ability to correct their course. Under the coupling effect of asymmetrical ship suction torque and wave disturbance, passive boats are prone to violent bow rolling, resulting in inconsistent tightening speeds of the bow and stern cables, which can lead to serious collision accidents and compromise safety.

[0025] Secondly, the winch control logic is rigid. Existing winch control generally adopts an independent constant tension winding mode, where the bow and stern winches each blindly tighten based solely on a set tension threshold. This control mode ignores the potential of winch tension to control the hull's yaw freedom, and cannot actively resolve the yaw moment between the two vessels using the tension difference. This results in a lack of active adjustment capability for the hull's attitude during docking, making it difficult to guarantee the stability and success rate of the docking.

[0026] Based on this, embodiments of the present invention provide a coordinated berthing control method based on a ship's double winch and propeller, which can solve the problems of berthing yaw loss and low safety caused by hydrodynamic interference in the prior art.

[0027] To facilitate understanding of this embodiment, a detailed description of the cooperative berthing control method based on a boat's double winch and propeller, as disclosed in this embodiment, will be provided first. (Refer to...) Figure 1 , Figure 1 This is a flowchart illustrating a coordinated berthing control method based on a ship's double winch and propeller, provided as an embodiment of the present invention. Figure 1 As shown, the method includes: Step 101: Collect the relative pose data of the active and passive boats in real time, and use a nonlinear filtering algorithm to filter the noise of the relative pose data to obtain the noise-filtered relative pose data and relative velocity at the current moment.

[0028] It should be noted that the execution subject in this embodiment of the invention is a collaborative berthing control device, or a device or component that is installed or carries a collaborative berthing control device, such as a server or a ship control component. This embodiment uses a collaborative berthing control device as the execution subject for illustration.

[0029] Optionally, the visual sensor on the active boat is triggered to collect the first relative pose data between the passive boat and the active boat, and the inertial measurement unit on the active boat is triggered to collect the second relative pose data between the passive boat and the active boat. Using the vision sensors mounted on the active vessel, the first relative pose data of the active and passive vessels in a local coordinate system, such as the relative longitudinal distance, is measured in real time. Relative lateral distance and relative yaw angle Simultaneously, the inertial measurement unit (IMU) on the active vessel is triggered to collect second relative pose data between the passive vessel and the active vessel, such as using accelerometers and gyroscopes to collect the angular velocity and angular velocity of the active vessel in the local coordinate system. Then, based on the timestamp information in the first relative pose data and the second relative pose data, the first relative pose data and the second relative pose data are fused to obtain the relative pose data of the active vessel and the passive vessel.

[0030] The timestamp information in the aforementioned first and second relative pose data is time-aligned, using the IMU sampling period as a reference (e.g., every 0.005 seconds). When the visual data has not yet arrived, the system only runs the IMU; at the instant the visual data arrives (0.05 seconds), the system timestamps the IMU data and the visual data at that moment and performs a "fusion calculation." It is conceivable that when there is a gap between two sets of first relative pose data, state recursion can be performed using the IMU, i.e., the optimal state (position / velocity / heading) fused from the previous moment plus the angular velocity / acceleration measured by the current IMU; thus achieving the fusion processing of the first and second relative pose data.

[0031] Based on the nonlinear dynamic equations between the active and passive vessels, a nonlinear filtering algorithm is used to smooth and differentiate the relative pose data to obtain the noise-filtered relative pose data and relative velocity at the current moment.

[0032] It should be noted that the cooperative berthing control device pre-stores the dynamic equations between the active and passive vessels, i.e., the ship's kinematic equations, which are used to calculate the predicted state at the current moment. The dynamic equations here are similar to the subsequent nonlinear state-space model.

[0033] Specifically, the nonlinear filtering algorithm adopted in this embodiment of the invention can be an algorithm based on the Kalman filter (EKF), which constructs a state vector as follows: ,in The relative longitudinal velocity, Relative lateral velocity, The relative angular velocity is used. High-frequency noisy measurement data from visual and IMU systems are used as observation inputs, and the nonlinear kinematic equations of the two vessels are combined to continuously update the time (state prediction) and measurement (gain correction). In this filtering closed loop, the state transition matrix is ​​used to smoothly differentiate the position variable, filtering out high-frequency noise caused by waves, thus directly obtaining a smooth and continuous relative velocity from the optimal estimated state. With relative angular velocity .

[0034] Compared to existing technologies that typically employ the position difference method The acquisition speed is greatly amplified by noise from ocean waves. This embodiment does not use a conventional differential filter, but instead constructs an extended Kalman filter (EKF), setting the state vector as... This special treatment is a prerequisite tailored for subsequent steps, because the subsequent hydrodynamic calculations are highly dependent on the square of the velocity term, and the velocity with noise in conventional methods will cause the system to diverge instantaneously.

[0035] Step 102: Construct and utilize a hydrodynamic disturbance observer to map the noise-filtered relative pose data and relative velocity at the current moment to obtain the hydrodynamic disturbance force and disturbance torque of the passive vessel at the current moment.

[0036] Optionally, a hydrodynamic disturbance observer is constructed based on the length of the passive vessel and the preset dimensionless hydrodynamic coefficients. The filtered relative pose data and relative velocity are then substituted into the hydrodynamic disturbance observer and mapped to obtain the hydrodynamic disturbance force and disturbance torque of the passive vessel at the current moment.

[0037] Specifically, the captain of the passive vessel is obtained by reading the vessel's information. L And retrieve dimensionless hydrodynamic coefficients from the coefficient database. The dimensionless hydrodynamic coefficients are expressed as... 、 and , The dimensionless swayforce coefficient represents the magnitude of the lateral attraction force generated by the ship suction effect. The dimensionless yaw moment coefficient represents the magnitude of the yaw moment caused by the ship suction effect. The fitting index (or empirical parameter) representing the dimensionless distance is used to characterize the nonlinear decay law of hydrodynamic force as the relative distance between the two vessels changes.

[0038] Optionally, dimensionless hydrodynamic coefficients can be synthesized through offline fitting. The optional offline fitting process involves: establishing a computational fluid dynamics (CFD) simulation model for the current vessel, and then performing the simulation at different relative distances within a gridded configuration. and relative velocity The simulation was run multiple times to extract multiple sets of discrete data points on the ship's hull forces. Then, a least-squares nonlinear regression algorithm was used to fit curves to these discrete data (for example, to obtain the distance decay index). ≈1.8), extracting fixed constant coefficients. 、 and index This information is then pre-stored in the collaborative docking control device.

[0039] Furthermore, the hydrodynamic disruptor can be represented by the following formula:

[0040] in, F suction This refers to the hydrodynamic sway suction force experienced by the passive vessel, which is the hydrodynamic disturbance force experienced by the passive vessel. M interaction This represents the bow rolling torque experienced by the passive vessel, which is the disturbance torque experienced by the passive vessel. p This is a known constant, representing the density of seawater (approximately 1025 kg / m³). 3 ); L The length of the passive vessel is given; the other parameters are the same as described above and will not be repeated here.

[0041] In summary, the filtered relative pose and relative velocity data obtained in step 101 are substituted into the hydrodynamic jammer in step 102 to obtain the current hydrodynamic interference force and torque of the passive vessel. Then, processing according to step 103 yields the future control sequence for controlling the berthing of the active and passive vessels.

[0042] Step 103: Based on the current physical parameters of the propeller of the active boat and the tension of the bow and stern winches, generate the current moment coordination allocation matrix, and construct a nonlinear state-space model based on the current moment coordination allocation matrix, the current moment hydrodynamic disturbance force and disturbance torque and the current moment control sequence corresponding to the active boat, and generate the future moment control sequence based on the nonlinear state-space model.

[0043] It should be noted that the current physical parameters of the active propulsion system include the thrust of the two main propulsion units and the rudder angle. Optionally, this can be achieved by utilizing (…). T p1 , T p2 () represents the thrust of the two main propulsion units of the active vessel;delta 1, delta 2) Indicates the rudder angle of the active vessel.

[0044] Then, the thrust of the two main propellers, the rudder angle, and the tension of the bow and stern winches are combined into the current moment control input vector of the active vessel.

[0045] Assume the current bow and stern winch cable tension combination of the active vessel is expressed as the cable tension of the bow and stern winches ( T w _ bow , T w _ stern This allows us to obtain the current control input vector of the active vessel. .

[0046] A collaborative allocation matrix is ​​constructed based on the physical installation positions of each actuator of the active vessel and the angle between the cables of the bow and stern winches.

[0047] Subsequently, based on the physical installation positions (longitudinal and transverse lever arms) of each actuator on the active vessel, and the angles between the physical installation positions and the cables of the bow and stern winches, a transformation matrix reflecting the control force mapping relationship can be constructed. .

[0048] Specifically, the allocation matrix The structural example is as follows: Assuming the center of gravity of the active vessel is the origin of the coordinate system, the longitudinal lever arms of the guide cable holes of the bow and stern winches are respectively... The lateral lever arm of the two main thrusters is d p The longitudinal force Fx, lateral force Fy, and yaw moment M are mapped to the ship's center of gravity. z The distribution relationship can be expanded using a matrix. For example, the yaw moment components. M Z The calculation example is as follows:

[0049] in, The lever arm from the cable attachment point to the center of gravity. The real-time angle between the cable and the longitudinal axis of the hull is the angle between the installation position and the cables of the bow and stern winches; in addition, the yaw moment component is the physical quantity of the hydrodynamic disturbance moment to be corrected.

[0050] Furthermore, through the above expression, the control quantities of heterogeneous actuators can be uniformly transformed into generalized forces in the center-of-gravity coordinate system.

[0051] Correspondingly, since passive boats have no self-powered propulsion, the control torque they experience comes solely from the differential tension of the bow and stern cables, and the corresponding yaw moment component... It can be represented as:

[0052] The meanings of the variables in this calculation formula are the same as those described above.

[0053] Understandably, by adjusting T w_bow ≠ T w_stern (i.e., differential tension), artificially generated control torque to counteract the aforementioned hydrodynamic disturbance torque. M interaction For multi-vessel scenarios, the differential tension allocation logic operates independently on each pair of "active-passive" nodes performing berthing. The preferred allocation architecture is a distributed decoupled control mechanism. When multiple vessels berth in formation, the entire network is split into multiple independent topology pairs of "one active vessel + one passive vessel". The controller of each active vessel only receives the relative state data of its corresponding bound passive vessel and independently solves the control sequence corresponding to the differential tension of its bow and stern winches. There is no cross-coupling of tension control sequences between active vessel nodes, thus effectively avoiding the curse of dimensionality and control sequence delays in multi-body coordination.

[0054] Calculate the product between the cooperative allocation matrix and the control input vector at the current moment to obtain the resultant force and resultant moment of the active vessel at the center of the hull.

[0055] Optionally, the resulting resultant force and resultant moment can be expressed as: Thus, the resulting resultant force and resultant torque counteract the hydrodynamic disturbance force and disturbance torque.

[0056] Using the current hydrodynamic disturbance force and disturbance torque as feedforward compensation for the nonlinear state space, and combining the hull inertia transformation matrix between the active and passive vessels, the unpowered state evolution term, resultant force and resultant torque, a nonlinear state space model is constructed.

[0057] The calculation will yield F suction of M interaction and as a known external feedforward compensation term D ext This achieves feedforward compensation for interference. Assume... This represents the unpowered state evolution term between active and passive vessels (such as the natural deceleration caused by the hydrodynamic damping coefficient). The inertial transformation matrix of the hull (mainly containing the reciprocals of the hull mass and moment of inertia) is used to construct the nonlinear state-space model: ,in, This refers to the collaborative allocation result constructed in the aforementioned steps, representing the resultant force and resultant torque formed by all heterogeneous actuators at the ship's center of gravity.

[0058] The core function of this linear state-space model is to directly convert the resultant force and resultant torque generated by the coordinated distribution into the motion acceleration and angular acceleration between the active and passive boats, based on Newton's second law.

[0059] The nonlinear state-space model is discretized using the NMPC solver and iteratively solved in the prediction time domain to generate control sequences for future time periods.

[0060] It should be noted that the cooperative berthing control device is equipped with an NMPC solver, which is used to predict and optimize the motion state between vessels.

[0061] Then, the process can be carried out according to steps 104 to 105 to obtain the target control sequence.

[0062] Step 104: Based on the preset optimal berthing information of the active and passive vessels, construct a rolling optimization objective function, and use the NMPC solver to solve the rolling optimization objective function within the preset safety boundary range to obtain the future time control sequence corresponding to the optimal solution as the target control sequence.

[0063] Step 105: Perform berthing procedures for the active and passive boats according to the target control sequence.

[0064] Optionally, obtain the berthing information of the future time corresponding to the control sequence of the future time; calculate the state deviation between the berthing information of the future time and the optimal control sequence corresponding to the preset optimal berthing information; obtain the rate of change of the control sequence of the future time, and calculate the weighted sum of the state deviation, the control sequence of the future time, and the rate of change with their respective weight matrices to construct the rolling optimization objective function.

[0065] Then, candidate future time control sequences that satisfy the collision avoidance safety boundary constraints, winch tension constraints, and thruster thrust constraints are determined. Based on the candidate future time control sequences, the NMPC solver is triggered to perform the solution process to obtain the target control sequence that makes the value of the rolling optimization objective function optimal.

[0066] Specifically, the berthing information corresponding to the future time control sequence can be obtained based on NMPC. The future time control sequence can be obtained according to the aforementioned steps, and the corresponding berthing information can be queried in a preset control sequence to berthing information correspondence table. This preset control sequence to berthing information correspondence table is set by those skilled in the art based on practical experience.

[0067] Furthermore, based on the berthing information at future times, a rolling optimization objective function can be constructed for NMPC optimization processing, which can be expressed by the following formula. J express:

[0068] in, For berthing information in future moments, The optimal berthing information is preset (the two boats are parallel and the distance decreases smoothly); To control the input vector, To control the rate of change of the quantity; , , These are the diagonal weight matrices for each item. These diagonal weight matrices can be set by those skilled in the art based on practical experience, or they can be automatically generated based on the current state.

[0069] The objective function of this rolling optimization aims to find a control sequence that minimizes tracking error, reduces energy consumption, and ensures the smoothest actuator movement.

[0070] Compared to existing technologies (such as traditional single-variable PID control or independent winch constant tension start-stop logic), the beneficial effect of this rolling optimization objective function design is as follows: Existing technologies typically control the thruster and the head and tail winches as two independent systems, which easily leads to command conflicts (i.e., system internal friction) between "blindly tightening the winch" and "reverse collision avoidance by the thruster" at extremely close distances. This solution, however, unifies the two heterogeneous actuators within a single framework through this function, completely eliminating system internal friction at the mathematical level and achieving perfect decoupling and coordination of multiple mechanisms; simultaneously, it introduces a penalty term for the rate of change of control variables. This significantly reduces mechanical wear and overall energy consumption, ensuring a smooth trajectory.

[0071] Optionally, collision avoidance safety boundary constraints (such as...) can be introduced during the optimization process. The maximum power constraint of the winch is also included. Hard constraints are set when solving the rolling optimization objective function. Collision avoidance safety boundary constraints are set as nonlinear inequalities. ( (For safety margin); the winch tension constraint is set to... The thrust constraint of the propulsion unit is set to... .

[0072] The aforementioned constraints can be pre-set in the cooperative berthing control device. During the processing, the NMPC solver is triggered to first determine whether the future time control sequence satisfies these constraints. If it does, it is used as a candidate future time control sequence.

[0073] Solving this constrained nonlinear optimization problem involves finding the target control sequence that maximizes the value of the rolling optimization objective function. The optimal state can be either minimizing the value or achieving convergence.

[0074] Finally, the active and passive boats were berthed according to the determined target control sequence.

[0075] It is understood that the method provided in this embodiment of the invention can be used as a pre-optimization process for the berthing of vessels. That is, this invention uses the instructions output by the NMPC as the desired cable tension. T w The underlying head and tail winch motor drivers operate in Torque Control Mode, controlling the motor output torque. Strictly ensure the required tension difference ( (Drum radius). Since both boats are suspended on the water, the tension of the cable acting on the hull, combined with the forces and torques of hydrodynamics, will produce relative acceleration. The relative speed and relative distance between the active and passive boats. y rel This is the result of the natural evolution of the system dynamics equations under the combined effects of these dynamic tensions and other hydrodynamic forces. Therefore, by adjusting the tension (i.e., torque) at high frequency, the convergence of the berthing distance is indirectly and smoothly controlled, rather than forcibly controlling the winch speed to specify the distance, thus achieving stable berthing of the two vessels under the suppression of hydrodynamic interference.

[0076] This invention breaks through the traditional single control paradigm of constant tension synchronous cable winding by using differential correction control technology, endowing the cable with correction control function. Through the differential pulling mechanism of the bow and stern winches, it effectively restores the course-maintaining ability of the fire-extinguished floating vessel in severe sea conditions, solving the problem of near-range yaw loss of control for unpowered (passive) vessels. The ship suction effect feedforward compensation mechanism incorporates near-field hydrodynamic interference (lateral suction and repulsion torque) into the controller feedforward model for the first time. Before the hull experiences suction instability, the propeller and winch work together to apply a compensating torque to counteract the interference, ensuring that the two vessels always maintain a safe parallel alignment. The distance is significantly improved, enhancing the anti-ship suction capability and eliminating the risk of collision. Based on the nonlinear model predictive control (NMPC) algorithm, the multi-actuator collaborative optimization framework integrates the propeller system and the deck double winch system into the same optimization objective, eliminating the control logic conflict between winch cable retrieval and propeller collision avoidance. It achieves berthing convergence with the goal of minimum energy consumption and optimal trajectory smoothness, which is superior to the traditional independent control strategy. The overall scheme can still work stably in complex sea conditions through dynamic modeling and multi-mechanism collaboration, with outstanding environmental adaptability, greatly improving berthing efficiency and safety.

[0077] To verify the effectiveness of the method provided in this invention, the simulation experiment was conducted using a full physics engine simulation in MATLAB 2023b / Simulink combined with ROS (Robot Operating System). The simulation environment and parameter settings were as follows: The system ran on a high-performance computing platform equipped with an Intel Core i9 processor and 32GB of memory. Model and parameter settings: The lengths of the active and passive boats were set. L =6.0m, ship width W =1.8m, drainage volume M =1500kg. The hydrodynamic interference coefficient based on CFD offline fitting is set to... , , .

[0078] NMPC controller parameter settings: System control step size set to... (Corresponding to a 20Hz control frequency); Prediction time domain set to Step (predicting trajectory for the next 2 seconds); control time domain set to Step. The state error weight matrix Q assigns extremely high weights (e.g., 10) to the yaw angle error. 4 The control weights R and S are set to conventional diagonal matrices. The maximum safe output pulling force of the winch is set to... T max =3000N, the maximum safe distance between the two boats to avoid collision is set as follows: .

[0079] Specifically, the simulation process can be performed as follows.

[0080] 1. State initialization and environment awareness Figure 2 The overall architecture diagram of the ship's winch and propeller coordinated berthing control system provided for simulation experiments is as follows: Figure 2 As shown, the system architecture can be divided into three layers: a perception layer, a solution layer, and an execution layer. The perception layer includes the visual sensor and inertial measurement unit mentioned in the previous embodiments, as well as a multi-source fusion state module (used to acquire the relative pose data and relative velocity at the current moment), corresponding to the data processing part of the method provided in the embodiments of the invention. The ship suction effect hydrodynamic observer (hydrodynamic disturbance observer) under the solution layer is used to acquire the hydrodynamic disturbance force (such as...) at the current moment. Figure 2 Disturbance forces and disturbance torques (such as) Figure 2The system uses torque calculations and a nonlinear model predictive controller (NMPC solver) to calculate the corresponding target control sequence. The execution layer controls the propulsion system and differential winch system of the active vessel, enabling berthing of both the active and passive vessels. In the experiment, data from the visual camera and IMU was read at a frequency of 20Hz. At the current moment, the system calculates the relative distance between the two vessels. Relative yaw angle .

[0081] 2. Catastrophic prediction and calculation of ship suction effect Combination Figure 3 , Figure 3 A schematic diagram of the near-field hydrodynamic interference force analysis model provided for simulation experiments. (See diagram below.) Figure 3 The near-field hydrodynamic interference force analysis model shown indicates that as the distance between the hulls decreases to 4.5m, the hydrodynamic interference observer calculates that the current acceleration of the water flow between the hulls causes the passive vessel to experience an extremely strong inward suction force. F suction =1500N, and due to a slight deviation of 3° in the yaw angle, the suction force in the bow area is much greater than that in the stern, generating a disturbance torque that causes the bow to deflect violently inward. M interaction =4500 N·m.

[0082] Correspondingly, the architecture of the method in the aforementioned embodiments is similar to the system architecture diagram of the simulation experiment.

[0083] 3. Failure of traditional methods The traditional method uses constant tension synchronous tightening, with both winches outputting a pulling force of 1000N, which cannot provide anti-yawing torque. The bow of the passive boat will quickly collide with the active boat under the action of hydrodynamic force.

[0084] 4. NMPC Differential Cooperative Solving Figure 4 A schematic diagram of the control logic flow based on nonlinear model predictive control (NMPC) is provided for the simulation experiment, such as... Figure 4 As shown, the system enters a closed-loop optimization cycle. The NMPC controller will... M interaction =4500 N·m is substituted into the dynamic equation as a feedforward disturbance term. In the prediction time domain (e.g., the next 5 seconds), to counteract the bow's inward deflection trend, the optimizer calculates that the tension of the stern cable needs to be increased, and the tension of the bow cable needs to be decreased. The optimal control sequence output by the solution is: the target value for the stern winch tension is set as follows: T w_stern =2200N, the target tension value of the first winch is set to... T w_bow =800N. Meanwhile, to offset... F suctionThe overall lateral suction force of 1500N is calculated to require the main thruster and side thrusters of the active vessel to output a total of 1200N of reverse thrust outward.

[0085] 5. Servo closed-loop execution The control sequence is sent down to the underlying servo mechanism. The stern winch motor accelerates the winding, while the bow winch motor slows down appropriately. This 1400N "differential tension" instantly generates a huge external rotation torque through the bow and stern lever arms, counteracting the internal rotation suction force of the hydrodynamics.

[0086] Figure 5 The schematic diagram of the relative trajectory and differential tension curve of the two boats during the berthing and approach process is provided for the simulation experiment, combined with... Figure 5 The above graph (relative distance convergence curve) shows that the passive vessel, under the asymmetrical traction of the two cables, is leveled out and smoothly converges to a limit distance of 1 meter at a safe relative speed of 0.2 m / s and a strictly maintained relative yaw angle of 0° ± 0.5°. Combined with... Figure 5 As shown in the figure below (dynamic curve of differential tension of the two winches), when the relative distance approaches the near-field danger zone (e.g., within 4m), the solid tension line of the tail winch rises rapidly, forming a clear differential tension zone between it and the dashed tension line of the head winch. This dynamic tension difference suppresses hydrodynamic yaw disturbances and achieves collision-free convergence throughout the entire process.

[0087] in, Figure 5 The calculation of the control increment in the future prediction time domain can be understood as the process of solving the target control sequence. Multiple target control sequences in the prediction time domain can be calculated. At this time, the first term of the control sequence can be extracted as the control sequence for controlling the berthing of the active and passive boats.

[0088] also, Figure 5 Whether the relative distance in the data is aligned with the compaction condition is a verification condition for further verifying the accuracy of the target control sequence, which can ensure the accuracy of control and improve the safety of vessel berthing.

[0089] Figure 6 This is a schematic diagram of a coordinated berthing control device based on a ship's double winch and propeller, provided as an embodiment of the present invention. Figure 6 As shown, the device 20 includes: The acquisition and processing module 201 is used to acquire the relative pose data of the active vessel and the passive vessel in real time, and to use a nonlinear filtering algorithm to filter the relative pose data to obtain the denoised relative pose data and relative velocity at the current moment. Module 202 is used to build and utilize a hydrodynamic disturbance observer to map the noise-filtered relative pose data and relative velocity at the current moment in order to obtain the hydrodynamic disturbance force and disturbance torque of the passive boat at the current moment. The predictive processing module 203 is used to generate a current moment cooperative allocation matrix based on the current physical parameters of the propeller of the active boat and the tension of the bow and stern winch cables, and to construct a nonlinear state space model based on the current moment cooperative allocation matrix, the current moment hydrodynamic disturbance force and disturbance torque and the current moment control sequence of the active boat, so as to generate the future moment control sequence based on the nonlinear state space model. The solver module 204 is used to construct a rolling optimization objective function based on the preset optimal berthing information of the active and passive boats, and to use the NMPC solver to solve the rolling optimization objective function within the preset safety boundary range to obtain the future time control sequence corresponding to the optimal solution as the target control sequence. The control module 205 is used to perform berthing procedures for the active and passive boats according to the target control sequence.

[0090] The device provided in this application embodiment has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0091] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0092] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0093] Figure 7 A block diagram of an exemplary electronic device provided in an embodiment of the present invention. Figure 7 The electronic device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0094] like Figure 7As shown, the electronic device is represented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: one or more processors 410, memory 430, and communication bus 440 connecting different system components (including memory 430 and processing unit 410).

[0095] Communication bus 440 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, Industry Standard Architecture (ISA) buses, Micro Channel Architecture (MAC) buses, Enhanced ISA buses, Video Electronics Standards Association (VESA) local buses, and Peripheral Component Interconnect (PCI) buses.

[0096] Electronic devices typically include a variety of computer-readable media. These media can be any available media that can be accessed by the electronic device, including volatile and non-volatile media, and removable and non-removable media.

[0097] Memory 430 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory. The electronic device may further include other removable / non-removable, volatile / non-volatile computer system storage media. Memory 430 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application.

[0098] A program / utility having a set (at least one) of program modules can be stored in memory 430. Such program modules include—but are not limited to—an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. The program modules typically perform the functions and / or methods described in the embodiments of this application.

[0099] Processor 410 executes various functional applications and data processing by running programs stored in memory 430, such as implementing embodiments of this application.Figure 1 The method provided in the illustrated embodiment.

[0100] This application provides a non-transitory computer-readable storage medium that stores computer instructions, which cause the computer to execute embodiments of this application. Figure 1 The method provided in the illustrated embodiment.

[0101] The aforementioned computer-readable storage medium may be any combination of one or more computer-readable media. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or flash memory, optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium that contains or stores a program that may be used by or in connection with an instruction execution system, apparatus, or device.

[0102] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0103] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0104] Computer program code for performing the operations of the embodiments of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0105] The foregoing has described specific embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0106] In the description of the embodiments of this application, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the embodiments of this application. In the embodiments of this application, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in the embodiments of this application, as well as the features of different embodiments or examples.

[0107] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of embodiments of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0108] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order according to the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0109] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0110] It should be noted that the terminals involved in the embodiments of this application may include, but are not limited to, personal computers (PCs), personal digital assistants (PDAs), wireless handheld devices, tablet computers, mobile phones, MP3 players, MP4 players, etc.

[0111] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0112] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0113] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0114] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present application should be included within the scope of protection of the present application.

Claims

1. A method for coordinated berthing control of a boat based on a double winch and a propeller, characterized in that, include: The relative pose data of the active and passive boats are collected in real time, and the relative pose data is filtered by a nonlinear filtering algorithm to obtain the denoised relative pose data and relative velocity at the current moment. A hydrodynamic disturbance observer is constructed and utilized to map the noise-filtered relative pose data and relative velocity at the current moment to obtain the hydrodynamic disturbance force and disturbance torque of the passive vessel at the current moment. Based on the current physical parameters of the propeller of the active vessel and the tension of the bow and stern winch cables, a current moment collaborative allocation matrix is ​​generated. Based on the current moment collaborative allocation matrix, the current moment hydrodynamic disturbance force and disturbance torque, and the current moment control sequence corresponding to the active vessel, a nonlinear state-space model is constructed to generate a future moment control sequence based on the nonlinear state-space model. Based on the preset optimal berthing information of the active vessel and the passive vessel, a rolling optimization objective function is constructed. The NMPC solver is used to solve the rolling optimization objective function within a preset safety boundary range to obtain the future time control sequence corresponding to the optimal solution as the target control sequence. According to the target control sequence, the active vessel and the passive vessel are berthed.

2. The cooperative docking control method according to claim 1, characterized in that, The real-time acquisition of relative pose data between the active and passive vessels, and the use of a nonlinear filtering algorithm to filter the relative pose data to obtain the filtered relative pose data and relative velocity at the current moment, includes: The active vessel is triggered to acquire first relative pose data between the passive vessel and the active vessel via its visual sensor, and the active vessel is triggered to acquire second relative pose data between the passive vessel and the active vessel via its inertial measurement unit. Based on the timestamp information in the first relative pose data and the second relative pose data, the first relative pose data and the second relative pose data are fused to obtain the relative pose data of the active vessel and the passive vessel. Based on the nonlinear dynamic equations between the active and passive vessels, a nonlinear filtering algorithm is used to smooth and differentiate the relative pose data to obtain the noise-filtered relative pose data and relative velocity at the current moment.

3. The cooperative docking control method according to claim 1, characterized in that, The construction and utilization of the hydrodynamic disturbance observer to map the noise-filtered relative pose data and relative velocity at the current moment to obtain the hydrodynamic disturbance force and disturbance torque of the passive vessel at the current moment includes: Based on the length of the passive vessel and the preset dimensionless hydrodynamic coefficients, a hydrodynamic interference observer is constructed. The filtered relative pose data and relative velocity are substituted into the hydrodynamic interference observer and mapped to obtain the current hydrodynamic interference force and interference torque of the passive vessel.

4. The cooperative docking control method according to claim 1, characterized in that, The current physical parameters of the propulsion system of the active vessel include the thrust of the two main propulsion systems and the rudder angle. Based on the current physical parameters of the propeller of the active vessel and the tension of the bow and stern winches, a cooperative allocation matrix for the current moment is generated. Then, based on the cooperative allocation matrix, the current hydrodynamic disturbance force and disturbance torque, and the corresponding current control sequence of the active vessel, a nonlinear state-space model is constructed. Finally, a future control sequence is generated based on the nonlinear state-space model, including: The thrust of the two main thrusters, the rudder angle, and the tension of the bow and stern winches are combined to form the current moment control input vector of the active vessel; The cooperative allocation matrix is ​​constructed based on the angle between the physical installation position of each actuator of the active vessel and the cable of the bow and stern winches; Calculate the product between the cooperative allocation matrix and the current control input vector to obtain the resultant force and resultant moment of the active vessel at the center of the hull. Using the current hydrodynamic disturbance force and disturbance torque as feedforward compensation for the nonlinear state space, and combining the hull inertia transformation matrix between the active vessel and the passive vessel, the unpowered state evolution term, the resultant force, and the resultant torque, the nonlinear state space model is constructed. The nonlinear state-space model is discretized using the NMPC solver, and iteratively solved in the prediction time domain to generate the future control sequence.

5. The cooperative docking control method according to claim 1, characterized in that, The step of constructing a rolling optimization objective function based on the preset optimal berthing information of the active vessel and the passive vessel includes: Obtain the future berthing information corresponding to the future time control sequence; Calculate the state deviation between the future berthing information and the optimal control sequence corresponding to the preset optimal berthing information; The rate of change of the future time control sequence is obtained, and the state deviation, the future time control sequence, and the rate of change are respectively weighted and summed with their respective weight matrices to construct the rolling optimization objective function.

6. The cooperative docking control method according to claim 1, characterized in that, The preset safety boundary range includes collision avoidance safety boundary constraints, the double winch pulling force constraints of the active boat, and the propeller thrust constraints of the active boat; then, the process of using the NMPC solver to solve the rolling optimization objective function within the preset safety boundary range to obtain the future time control sequence corresponding to the optimal solution as the target control sequence includes: Determine candidate future time control sequences that satisfy the collision avoidance safety boundary constraints, the winch tension constraints, and the thruster thrust constraints; Based on the candidate future time-time control sequence, the NMPC solver is triggered to perform a solution process to obtain the target control sequence that makes the value of the rolling optimization objective function optimal.

7. A cooperative berthing control device based on a boat's double winch and propeller, characterized in that, include: The acquisition and processing module is used to acquire the relative pose data of the active and passive boats in real time, and to use a nonlinear filtering algorithm to filter the relative pose data to obtain the denoised relative pose data and relative velocity at the current moment. The construction module is used to construct and utilize the hydrodynamic disturbance observer to map the noise-filtered relative pose data and relative velocity at the current moment to obtain the hydrodynamic disturbance force and disturbance torque of the passive vessel at the current moment. The predictive processing module is used to generate a current-moment collaborative allocation matrix based on the current physical parameters of the propeller of the active vessel and the tension of the bow and stern winch cables, and to construct a nonlinear state-space model based on the current-moment collaborative allocation matrix, the current-moment hydrodynamic disturbance force and disturbance torque, and the current-moment control sequence corresponding to the active vessel, so as to generate a future-moment control sequence based on the nonlinear state-space model. The solution module is used to construct a rolling optimization objective function based on the preset optimal berthing information of the active vessel and the passive vessel, and to use the NMPC solver to solve the rolling optimization objective function for the optimal solution within a preset safety boundary range, so as to obtain the future time control sequence corresponding to the optimal solution as the target control sequence. The control module is used to perform berthing procedures on the active vessel and the passive vessel according to the target control sequence.

8. An electronic device, characterized in that, include: At least one processor; as well as At least one memory communicatively connected to the processor, wherein: The memory stores program instructions that can be executed by the processor, and the processor can execute the cooperative docking control method as described in any one of claims 1 to 6 by calling the program instructions.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause the computer to perform the cooperative docking control method as described in any one of claims 1 to 6.

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