Rudder fin combined stabilization system cooperative control method

Abstract

The invention relates to the technical field of ship stabilization control, and discloses a rudder fin combined stabilization system cooperative control method comprising the following steps: 1, building a ship motion model; step 2, designing a fuzzy adaptive PID controller; 3, constructing a rudder fin interaction model; and 4, introducing a prediction control algorithm, and optimizing a control input sequence based on a model prediction principle. By establishing a ship motion model, designing a fuzzy self-adaptive PID controller, analyzing rudder fin interaction and introducing a predictive control algorithm, the problems that an existing rudder fin combined stabilization system is insufficient in cooperative control, in the ship navigation process, a ship is affected by external factors such as sea waves and sea wind, the ship is prone to swing motion, and the stability of the ship is poor are solved. Therefore, the stabilization effect and the course stability of the ship are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of ship roll stabilization control, and in particular to a coordinated control method for a rudder-fin combined roll stabilization system. Background Art

[0002] During a ship's voyage, external environmental factors such as waves and winds can cause the ship to experience rolling and pitching motions. These motions can severely impact the ship's navigational performance, safety, and the comfort of onboard equipment and personnel. Excessive rolling can cause the ship to lose stability, increasing the risk of capsizing; while pitching can affect propulsion efficiency and increase fuel consumption. Therefore, effectively reducing a ship's rolling motion has long been a key research topic in the field of marine engineering.

[0003] Early ship roll reduction technologies mainly relied on single roll reduction devices, such as fin stabilizers, roll reduction tanks, and rudder stabilizers. Fin stabilizers reduce the ship's roll by generating lift in the water through the fins. They have the advantages of good roll reduction and rapid response, but their effectiveness decreases significantly at low or zero speeds. Roll reduction tanks reduce roll by adjusting the ship's center of gravity through water flow. However, their effectiveness is greatly affected by ship speed and wave frequency, and their structural design is relatively complex. Rudder stabilizers use the roll torque generated by the rudder to reduce roll, but the main function of the rudder is to control heading. Roll reduction control may conflict with heading control, resulting in unstable heading.

[0004] To overcome the limitations of a single anti-roll device, a rudder-fin combined anti-roll system has emerged. Combining the advantages of rudder anti-roll and anti-roll fin anti-roll, it can achieve better anti-roll effects under different speeds and sea conditions. However, the existing rudder-fin combined anti-roll system still faces some problems in terms of coordinated control. Most of the existing control methods are based on traditional control theories, such as PID control, which often find it difficult to achieve ideal results when dealing with complex nonlinear and time-varying systems. Especially when there is large wave interference or the ship's operating conditions change, the frequent adjustment of the PID controller will affect the anti-roll effect. At the same time, the existing methods are not comprehensive enough in considering the interaction between the rudder and the fin, and fail to fully optimize the coordinated control between the rudder and the fin.

[0005] Therefore, those skilled in the art provide a coordinated control method for a rudder-fin combined anti-roll system to solve the problems raised in the above background technology. Summary of the Invention

[0006] The present invention aims to solve the above-mentioned problems.

[0007] To this end, the present invention adopts the following technical solution: a coordinated control method for a rudder-fin combined anti-roll system, the method comprising:

[0008] Step 1: Based on the ship data including ship mass, moment of inertia, hydrodynamic force, and wave interference force and moment, a ship motion model including roll, pitch and heading motion is established;

[0009] Step 2: Using the ship's roll angle, roll angular velocity, and heading deviation as input, a fuzzy adaptive PID controller is designed, and the parameters of the PID controller are adjusted in real time through the fuzzy inference mechanism;

[0010] Step 3: Establish an interaction model between the rudder and the fin based on the cooperative control algorithm, and modify the control instructions of the rudder and the fin;

[0011] Step 4: Introduce a predictive control algorithm to predict the ship's motion state based on the ship's current state and wave forecast information in the future, and adjust the control instructions of the rudder and fins in advance.

[0012] Furthermore, the ship motion model is established based on the following assumptions: the ship is initially sailing in a straight line at a uniform speed in still water, and other degrees of freedom motion other than sway, heave, and pitch are ignored; the waves are regular waves and there is a certain angle between the wave propagation direction and the ship heading; and the ship structure is rigid.

[0013] The ship motion model includes the ship's motion equations in three degrees of freedom: roll (φ), pitch (θ) and heading (ψ), which can be expressed as:

[0014] 1) Roll motion equation:

[0015] Among them, I x is the moment of inertia of the ship around the x-axis; is the roll angular acceleration; is the roll angular velocity; φ is the roll angle; is the roll damping coefficient; N φ is the roll restitution coefficient; M φw is the rolling disturbance moment of the wave on the ship; M φf is the rolling moment generated by the fin stabilizer; M φr The rolling moment produced by the rudder;

[0016] 2) Pitch motion equation:

[0017] Among them, I y is the moment of inertia of the ship around the y-axis; is the pitch angular acceleration; is the pitch angular velocity; θ is the pitch angle; is the pitch damping coefficient; N θ is the pitch restitution coefficient; M θw is the pitch disturbance moment of the wave on the ship; M θfis the pitch moment generated by the fin stabilizer; M θr pitching moment produced by the rudder;

[0018] 3) Heading motion equation:

[0019] Among them, I z is the moment of inertia of the ship around the z-axis; is the heading angular acceleration; is the heading angular velocity; ψ is the heading angle; is the heading damping coefficient; N ψ is the heading recovery coefficient; M ψw The disturbance torque of the wave on the ship's heading; M ψr The bow moment generated by the rudder;

[0020] The ship motion model further includes modeling the wave interference torque to obtain the wave interference torque on the ship, the pitch interference torque and the heading interference torque;

[0021] The ship motion model also includes modeling the moments generated by the fin stabilizers and rudders, and the equations are as follows:

[0022] The lift equation generated by the fin stabilizer is: Where, ρ is the density of water; V is the ship speed; S f is the area of ​​the fin stabilizer; C L (α f ) is the lift coefficient, and α is the fin attack angle f function;

[0023] The roll moment generated by the fin stabilizer is: M φf =F f h f , where h f is the vertical distance from the fin stabilizer to the center of gravity of the ship;

[0024] The lift equation generated by the rudder is: Among them, S r is the area of ​​the rudder; C L (α r ) is the lift coefficient of the rudder, which is the rudder angle α r function.

[0025] Furthermore, the input variables of the PID controller are: the ship's roll angle φ, the roll angular velocity and heading deviation Δψ;

[0026] The output variable of the PID controller is: the proportional coefficient K of the PID controller itself p , integral coefficient K i and differential coefficient K d ,

[0027] The specific steps of the fuzzy reasoning mechanism are:

[0028] 1) Determine the domain of each input variable;

[0029] 2) Define fuzzy subsets for each input variable, calculate the membership value of the input variable in each fuzzy subset, and thus match the fuzzy subsets for the input variable;

[0030] 3) Design a fuzzy rule base. The fuzzy rule form is: according to the fuzzy subset of the input variable combined with the preset fuzzy rules, the fuzzy subset of the output variable is obtained;

[0031] 4) Using the Mamdani fuzzy inference method, the fuzzy membership of the output variable is calculated based on the membership value of the input variable and the fuzzy rule base;

[0032] 5) Use the center of gravity method to defuzzify the fuzzy membership of the output variable obtained by fuzzy reasoning to obtain a specific value.

[0033] Furthermore, the interaction model includes the following equations:

[0034] The lift equation generated by the fin stabilizer is:

[0035] The rudder's torque equation is:

[0036] in, is the lift coefficient of the fin stabilizer under the interaction model, is the fin angle δ of the fin stabilizer f and rudder angle δ r Function of l r is the characteristic length of the rudder, is the rudder's torque coefficient;

[0037] The correction is based on the cooperative control algorithm, and the rudder angle command δ obtained by the traditional control algorithm is r0 and fin angle command δ f0 Make corrections;

[0038] Corrected fin angle command δ f =δ f0 +Δδ f , the corrected rudder angle command δ r =δ r0 +Δδ r

[0039] Among them, Δδ f is the fin angle that needs to be corrected due to the influence of the rudder, calculated based on the change in the lift of the fin stabilizer; Δδ rThe rudder angle that needs to be corrected due to the influence of the fin stabilizer can be calculated based on the change in the rudder's turning moment;

[0040] The lift of the fin stabilizer and the turning moment of the rudder under different combinations of rudder angles and fin angles were measured and compared with the results calculated by the interaction model. The parameters of the interaction model were adjusted using the least squares optimization algorithm.

[0041] Furthermore, the prediction of the predictive control algorithm is as follows:

[0042] First, the ship motion model is converted into a ship motion differential equation, which can be expressed as: Where M is the mass matrix of the ship, is the acceleration vector of the ship, are the Coriolis and centripetal force matrices, is the velocity vector of the ship, D(η) is the damping coefficient matrix, η is the displacement vector of the ship, F w is the wave disturbance force and moment vector, F c are the control force and torque vectors of the rudder and fin;

[0043] Secondly, the differential equation is discretized to obtain the discrete time prediction model of the ship motion state: η(k+1)=Aη(k)+B1F w (k)+B2F c (k)

[0044] Where k is the discrete time step, A is the system state transfer matrix, B1 is the wave disturbance input matrix, and B2 is the control input matrix;

[0045] Thirdly, the spectral analysis method is used to process the wave forecast information and transform the wave spectrum S(ω) into the wave disturbance force and torque vector F in the ship motion model. w , Where ω is the frequency of the wave; H i (ω i ) is the wave transfer function, Δω is the frequency interval, and n is the number of frequency discrete points;

[0046] Then, the predictive control problem is transformed into an optimization problem,

[0047] Defining performance indicator functions

[0048] Satisfy the rudder and fin control input constraints F c,min ≤F c (k)≤F c,max ,

[0049] Solve to get the optimal control input sequence Pick As the current control input, where η r (k) is the desired motion state vector of the ship, Q1 and Q2 are weighting matrices; N is the total discrete time step;

[0050] Finally, the actual motion state information of the ship is obtained in real time and compared with the current control input, and the prediction model is corrected to predict the control input of the next time step, which is the control instruction of the rudder and fin.

[0051] The present invention provides a coordinated control method for a rudder-fin combined anti-roll system. It has the following beneficial effects:

[0052] 1. The present invention adopts fuzzy adaptive PID controller to control the ship's roll angle θ and roll angular velocity. The heading deviation Δψ is used as input to adjust the proportional coefficient K of the PID controller in real time. p , integral coefficient K i and differential coefficient K d , combined with the predictive control algorithm, the control instructions of the rudder and fins are adjusted in advance according to the current motion state of the ship and future wave forecast information, so as to achieve better anti-rolling effect under different speeds and sea conditions.

[0053] 2. The present invention fully considers the interaction between the rudder and the fin in the cooperative control algorithm, establishes an interaction model between the rudder and the fin, reduces the influence of the rudder anti-roll on the ship's heading, and ensures the ship's heading stability.

[0054] 3. By establishing a fuzzy adaptive PID controller, this invention can automatically adjust control parameters based on the vessel's motion state, adapting to varying operating conditions and disturbances. Furthermore, the predictive control algorithm can predict the vessel's motion trends in advance. Through real-time updates and feedback corrections, this improves the system's real-time performance and anti-interference capabilities, enhancing its adaptability and robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0056] Figure 1 The following is a flow chart of the method of the present invention, wherein the interaction relationship and data flow of each module in the figure are explained as follows:

[0057] The system is based on the ship motion model (A), calculates the ship motion state based on the ship mass matrix, hydrodynamic coefficients and wave interference torque, and provides input parameters such as roll angle and heading deviation for the fuzzy adaptive PID controller (B).

[0058] The controller (B) dynamically adjusts the PID parameters through a three-layer fuzzy inference structure and coordinates with the rudder-fin interaction model (C) to correct the control instructions. The interaction model realizes the nonlinear coupling compensation of the rudder angle and the fin angle through three-dimensional surface fitting.

[0059] The predictive control algorithm (D) utilizes a rolling-horizon optimizer, combined with wave frequency and amplitude information provided by the wave spectrum parameter analysis module (E), to predict the ship's motion trends and generate an optimal control sequence. User commands (F) are input into the system through the roll reduction level setting interface. The combined control output drives the steering gear servo system and the fin angle hydraulic unit (G). Simultaneously, the MEMS sensor array and the Kalman filter data fusion unit (H) form a closed-loop feedback loop to update the ship's attitude data in real time. The double-line arrows in the figure represent the real-time closed-loop control process, while the single-line arrows indicate the parameter transfer and data interaction between modules. DETAILED DESCRIPTION

[0060] To achieve the above objectives, the present invention is implemented through the following technical solutions. The present invention provides a coordinated control method for a rudder-fin combined anti-roll system, the method comprising:

[0061] Step 1: Establish ship motion model:

[0062] (1) Basic assumptions about ship motion:

[0063] Assume that the ship is initially sailing in a straight line at a uniform speed in still water, and ignore other degrees of freedom except sway, heave and pitch;

[0064] Assume that the waves are regular waves and there is a certain angle between the wave propagation direction and the ship's heading;

[0065] The ship structure is considered to be rigid and the elastic deformation of the hull is not considered.

[0066] (2) Dynamic equation of ship motion:

[0067] The motion equations of a ship in the three degrees of freedom of roll (φ), pitch (θ) and heading (ψ) are expressed as:

[0068] Roll motion equation: Among them I x is the ship's moment of inertia around the x-axis, is the roll angular acceleration, is the roll angular velocity, φ is the roll angle, is the roll damping coefficient, Nφ is the roll restitution coefficient, M φw is the wave disturbance torque, M φf is the moment generated by the fin stabilizer, M φr is the rolling moment produced by the rudder.

[0069] Pitch motion equation: Among them I y is the ship's moment of inertia around the y-axis, is the pitch angular acceleration, is the pitch angular velocity, θ is the pitch angle, is the pitch damping coefficient, N θ is the pitch restitution coefficient, M θw is the wave disturbance torque, M θf is the pitch moment generated by the fin stabilizer, M θr The pitching moment produced by the rudder.

[0070] Equation of heading motion: Among them I z is the ship's moment of inertia around the z-axis, is the heading angular acceleration, is the heading angular velocity, ψ is the heading angle, is the heading damping coefficient, N ψ is the heading recovery coefficient, M ψw is the wave disturbance torque, M ψr The bow moment generated by the rudder.

[0071] (3) Modeling of wave interference torque:

[0072] The wave is modeled using the spectrum analysis method. Assuming that the wave is a linear micro-amplitude wave, its wave surface equation is: where a i is the amplitude of the i-th frequency component, k i is the wave number, β i is the angle between the wave propagation direction and the ship’s heading, ω i is the wave frequency, ∈ i is the random phase, x is the horizontal abscissa in the wave propagation direction, which is consistent with the ship's heading, and y is the horizontal ordinate perpendicular to the x direction.

[0073] Taking the rolling disturbance torque as an example, according to the slice theory, in is the rolling disturbance moment of the ship on the slice at position x. The calculation method of pitch disturbance moment and heading disturbance moment is the same as that of rolling disturbance moment.

[0074] (4) Modeling of the moments generated by the fin stabilizers and rudders:

[0075] The moment generated by the fin stabilizer: According to the airfoil theory, the lift generated by the fin stabilizer is calculated as:

[0076]

[0077] Where, ρ is the density of water, V is the ship speed, S f is the area of ​​the fin stabilizer, C L (α f ) is the lift coefficient, which is the fin attack angle α f function.

[0078] The rolling moment generated by the fin stabilizer is M φf =F f h f , where h f It is the vertical distance from the fin stabilizer to the center of gravity of the ship.

[0079] The torque generated by the rudder: the lift generated by the rudder Among them S r is the area of ​​the rudder, C L (α r ) is the lift coefficient of the rudder, which is the rudder angle α r The rolling moment and bow moment generated by the rudder can be calculated based on the installation position and geometric relationship of the rudder.

[0080] Step 2: Design a fuzzy adaptive PID controller:

[0081] (1) Controller structure and variable definition:

[0082] The input variables of the PID controller are: the ship's roll angle φ, the roll angular velocity and heading deviation Δψ;

[0083] The output variable of the PID controller is: the proportional coefficient K of the PID controller itself p , integral coefficient K i and differential coefficient K d ;

[0084] The control law of the PID controller is: Where u(t) is the control output and e(t) is the error signal.

[0085] (2) Fuzzy processing: Determine the domain of the input variable, such as the domain of the roll angle φ is [φ min ,φ max ], roll angular velocity The domain is The domain of heading deviation Δψ is [Δψ min ,Δψ max ].

[0086] Fuzzy subsets are defined for each input variable, such as negative large (NB), negative medium (NM), negative small (NS), zero (ZO), positive small (PS), positive medium (PM), and positive large (PB).

[0087] Taking the roll angle φ as an example, the membership function can adopt the triangle membership function:

[0088] like Among them, a1 and a2 are membership function parameters, which are determined according to actual conditions;

[0089] (3) Fuzzy rule base design: The fuzzy rule base establishes the fuzzy relationship between input variables and output variables based on expert experience and experimental data;

[0090] Typical fuzzy rules are: If φ is NB and is NB and Δψ is NB, then K p PB, K i NB, K d is PS; if φ is ZO and is ZO and Δψ is ZO, then K p For PM, K i For PS, K d For ZO;

[0091] The general form of fuzzy rules is R j : If φ is and yes And Δψ is Then K p yes K i yes K d yes Where j = 1, 2, ..., N, N is the number of fuzzy rules, is the fuzzy subset of input variables, is the fuzzy subset of the output variable;

[0092] (4) Fuzzy reasoning: Using Mamdani fuzzy reasoning method, for the jth fuzzy rule, the prerequisite membership degree Where ∧ represents the smaller operation;

[0093] Output variable K p , K i and K d The fuzzy membership degrees are Where ∨ means taking the larger operation;

[0094] (5) Defuzzification: Defuzzification is performed using the center of gravity method, with K p For example,

[0095] The exact value is

[0096] Step 3. Consider the interaction between the rudder and the fin:

[0097] (1) The physical nature of the interaction between the rudder and the fin: In a rudder-fin combined roll stabilization system, the rudder and the fin change the flow field around the ship when they are in operation. The rotation of the rudder will cause the flow field at the stern of the ship to change, and this change will propagate to the location of the fin stabilizer, thereby affecting the lift characteristics of the fin stabilizer. Similarly, the movement of the fin stabilizer will also cause disturbances in the flow field around the ship, thereby affecting the hydrodynamic performance of the rudder.

[0098] (2) Establish the interaction model between rudder and fin:

[0099] Let the rudder angle be δ r , the fin angle of the fin stabilizer is δ f Fin lift By fitting the experimental data, where a i (i=0,1,…) is the fitting coefficient;

[0100] Rudder torque Using polynomial fitting, where b i (i=0,1,…) is the fitting coefficient.

[0101] (3) Considering interactions in cooperative control algorithms:

[0102] Assume that the rudder angle command obtained based on the traditional control algorithm is δ r0 , the fin angle command is δ f0 ;

[0103] Based on the cooperative control algorithm, the rudder angle command δ obtained by the traditional control algorithm is r0 and fin angle command δ f0 Make corrections:

[0104] For the fin stabilizer, the corrected fin angle command δ f =δ f0 +Δδ f ,

[0105] Among them, Δδ f The fin angle that needs to be corrected due to the influence of the rudder can be calculated based on the change in the lift of the fin stabilizer. Assuming that the expected lift of the fin stabilizer is F f0 , due to the rudder angle δ r0 The actual lift is F f (δ f0 ,δ r0), in order to make the lift of the fin stabilizer reach the desired lift, the fin angle needs to be corrected. Get the corrected fin angle Δδ f , so that the lift of the fin stabilizer reaches the desired lift F f0 ;

[0106] For the rudder, the corrected rudder angle command δ r =δ r0 +Δδ r , where Δδ r is the rudder angle that needs to be corrected due to the influence of the fin stabilizer, which can be calculated based on the change in the rudder's turning moment. Assume that the desired turning moment of the rudder is M r0 , due to the fin angle δ f0 The actual torque is M r (δ r0 ,δ f0 ), by solving The corrected rudder angle δ is obtained r , so that the rudder's turning moment reaches the desired turning moment M r0 .

[0107] (4) Experimental verification and model optimization: The lift and rudder torque of the fin stabilizer under different rudder angle and fin angle combinations are measured experimentally and compared with the results calculated by the interaction model. If there is a large error, the polynomial fitting coefficient a is adjusted using an optimization algorithm such as the least squares method. i and b i , improve model accuracy and achieve optimal coordinated control of rudder and fin.

[0108] Step 4: Introduce predictive control algorithm:

[0109] (1) The basic principle of the predictive control algorithm: Use the system model to predict the system output within a certain period of time in the future, optimize the current control input based on the prediction results, and achieve optimal control of the system. In the rudder-fin combined anti-roll system, the ship's motion trend is predicted based on the current motion state of the ship and future wave forecast information, and the rudder and fin control instructions are adjusted in advance to improve the system's real-time performance and control effect.

[0110] (2) Ship motion state prediction model: Based on the ship motion model, the roll, pitch and heading motion of the ship can be expressed by the following differential equations:

[0111]

[0112] After discretization, we get η(k+1)=Aη(k)+B1F w (k)+B2F c (k), where k is the discrete time step, A is the system state transfer matrix, B1 is the wave disturbance input matrix, and B2 is the control input matrix.

[0113] (3) Wave forecast information processing: The wave forecast information is given in the form of wave spectrum S(ω). The spectrum analysis method is used to convert the wave spectrum information into the wave interference force and torque vector F in the ship motion model. w According to linear wave theory, the wave interference force and moment vector Among them, H i (ω i ) is the wave transfer function, Δω is the frequency interval, and n is the number of frequency discrete points.

[0114] (4) Predictive control optimization problem: defining the performance index function where η r (k) is the desired motion state vector of the ship, Q1 and Q2 are weighted matrices. At the same time, the rudder and fin control inputs satisfy F c,min ≤F c (k)≤F c,max Constraints. Solve the optimization problem to obtain the optimal control input sequence in the next N time steps Pick As the control input at the current moment.

[0115] (5) Real-time update and feedback correction: In each time step, the actual motion state information of the ship is obtained in real time and compared with the prediction results. The prediction model is corrected according to the comparison results to reduce the prediction error. The corrected prediction model is used for prediction and control of the next time step.

[0116] The innovation of the present invention is:

[0117] (1) Establish a comprehensive ship motion model: Based on the principles of ship dynamics, make reasonable assumptions and establish a ship motion model that includes roll, pitch, and heading motion.

[0118] Consideration of the ship's mass, moment of inertia, hydrodynamic coefficient, and the influence of wave interference forces and moments provides a basis for the design of collaborative control algorithms.

[0119] (2) Design of fuzzy adaptive PID controller: Based on the ship's roll angle φ and roll angular velocity and heading deviation Δψ as input, and the proportional coefficient K of the PID controller is adjusted in real time through fuzzy processing, design of fuzzy rule base, fuzzy reasoning and defuzzification. p , integral coefficient K i and differential coefficient K d , improving the adaptability and robustness of the controller.

[0120] (3) Considering the interaction between rudder and fin: Analyze the physical nature of the interaction between rudder and fin from the perspective of fluid dynamics and establish an interaction model, such as the lift of the fin stabilizer. Rudder torque In the collaborative control algorithm, the rudder and fin control instructions are corrected according to the model, and the accuracy is improved through experimental verification and model optimization to achieve optimal collaborative control.

[0121] (4) Introducing the predictive control algorithm: Based on the model prediction principle, a discrete-time prediction model of the ship's motion state is established: η(k+1) = Aη(k) + B1F w (k)+B2F c (k), process wave forecast information, transform the predictive control problem into an optimization problem, and define the performance index function The optimal control input sequence is solved while satisfying the control input constraints, and real-time updates and feedback corrections are performed to improve the anti-roll effect, real-time performance and anti-interference ability.

[0122] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A coordinated control method for a rudder-fin combined anti-roll system, characterized in that: The following steps are involved: Step 1: Based on the ship data including ship mass, moment of inertia, hydrodynamic force, and wave interference force and moment, a ship motion model including roll, pitch and heading motion is established; Step 2: Using the ship's roll angle, roll angular velocity, and heading deviation as input, a fuzzy adaptive PID controller is designed, and the parameters of the PID controller are adjusted in real time through the fuzzy inference mechanism; Step 3: Establish an interaction model between the rudder and the fin based on the cooperative control algorithm, and modify the control instructions of the rudder and the fin; Step 4: Introduce a predictive control algorithm to predict the ship's motion state based on the ship's current state and wave forecast information in the future, and adjust the control instructions of the rudder and fins in advance.

2. The method according to claim 1, characterized in that In the step 1, The ship motion model is established based on the following assumptions: the ship is initially sailing in a straight line at a uniform speed in still water, and other degrees of freedom motion other than sway, heave, and pitch are ignored; the waves are assumed to be regular waves and the wave propagation direction has a certain angle with the ship's heading; and the ship structure is assumed to be rigid. The ship motion model includes the ship's motion equations in three degrees of freedom: roll (φ), pitch (θ) and heading (ψ), which can be expressed as: 1) Roll motion equation: Among them, I x is the moment of inertia of the ship around the x-axis; is the roll angular acceleration; is the roll angular velocity; φ is the roll angle; is the roll damping coefficient; N φ is the roll restitution coefficient; M φw is the rolling disturbance moment of the wave on the ship; M φf is the rolling moment generated by the fin stabilizer; M φr The rolling moment produced by the rudder; 2) Pitch motion equation: Among them, I y is the moment of inertia of the ship around the y-axis; is the pitch angular acceleration; is the pitch angular velocity; θ is the pitch angle; is the pitch damping coefficient; N θ is the pitch restitution coefficient; M θw is the pitch disturbance moment of the wave on the ship; M θf is the pitch moment generated by the fin stabilizer; M θr pitching moment produced by the rudder; 3) Heading motion equation: Among them, I z is the moment of inertia of the ship around the z-axis; is the heading angular acceleration; is the heading angular velocity; ψ is the heading angle; is the heading damping coefficient; N ψ is the heading recovery coefficient; M ψw The disturbance torque of the wave on the ship's heading; M ψr The bow moment generated by the rudder; The ship motion model further includes modeling the wave interference torque to obtain the wave interference torque on the ship, the pitch interference torque and the heading interference torque; The ship motion model also includes modeling the moments generated by the fin stabilizers and rudders, and the equations are as follows: The lift equation generated by the fin stabilizer is: Where, ρ is the density of water; V is the ship speed; S f is the area of ​​the fin stabilizer; C L (α f ) is the lift coefficient, and α is the fin attack angle f function; The roll moment generated by the fin stabilizer is: M φf =F f h f , where h f is the vertical distance from the fin stabilizer to the center of gravity of the ship; The lift equation generated by the rudder is: Among them, S r is the area of ​​the rudder; C L (α r ) is the lift coefficient of the rudder, which is the rudder angle α r function.

3. The method according to claim 1, characterized in that In the step 2, The input variables of the PID controller are: the ship's roll angle φ, roll angular velocity φ and heading deviation Δψ; The output variable of the PID controller is: the proportional coefficient K of the PID controller itself p , integral coefficient K i and differential coefficient K d , The specific steps of the fuzzy reasoning mechanism are: 1) Determine the domain of each input variable; 2) Define fuzzy subsets for each input variable, calculate the membership value of the input variable in each fuzzy subset, and thus match the fuzzy subsets for the input variable; 3) Design a fuzzy rule base. The fuzzy rule form is: according to the fuzzy subset of the input variable combined with the preset fuzzy rules, the fuzzy subset of the output variable is obtained; 4) Using the Mamdani fuzzy inference method, the fuzzy membership of the output variable is calculated based on the membership value of the input variable and the fuzzy rule base; 5) Use the center of gravity method to defuzzify the fuzzy membership of the output variable obtained by fuzzy reasoning to obtain a specific value.

4. The method according to claim 1, wherein In step 3, The interaction model includes the following equations, including: The lift equation generated by the fin stabilizer is: The rudder's torque equation is: in, is the lift coefficient of the fin stabilizer under the interaction model, is the fin angle δ of the fin stabilizer f and rudder angle δ r Function of l r is the characteristic length of the rudder, is the rudder's torque coefficient; The correction is based on the cooperative control algorithm, and the rudder angle command δ obtained by the traditional control algorithm is r0 and fin angle command δ f0 Make corrections; Corrected fin angle command δ f =δ f0 +Δδ f , the corrected rudder angle command δ r =δ r0 +Δδ r Among them, Δδ f is the fin angle that needs to be corrected due to the influence of the rudder, calculated based on the change in the lift of the fin stabilizer; Δδ r The rudder angle that needs to be corrected due to the influence of the fin stabilizer can be calculated based on the change in the rudder's turning moment; The lift of the fin stabilizer and the turning moment of the rudder under different combinations of rudder angles and fin angles were measured and compared with the results calculated by the interaction model. The parameters of the interaction model were adjusted using the least squares optimization algorithm.

5. The method according to claim 1, wherein In the step 4, The prediction of the predictive control algorithm is as follows: First, the ship motion model is converted into a ship motion differential equation, which can be expressed as: Where M is the mass matrix of the ship, is the acceleration vector of the ship, are the Coriolis and centripetal force matrices, is the velocity vector of the ship, D(η) is the damping coefficient matrix of the ship, η is the displacement vector of the ship, F w is the wave disturbance force and moment vector, F c are the control force and torque vectors of the rudder and fin; Secondly, the differential equation is discretized to obtain the discrete time prediction model of the ship motion state: η(k+1)=Aη(k)+B1F w (k)+B2F c (k) Where k is the discrete time step, A is the system state transfer matrix, B1 is the wave disturbance input matrix, and B2 is the control input matrix; Thirdly, the spectral analysis method is used to process the wave forecast information and transform the wave spectrum S(ω) into the wave disturbance force and torque vector F in the ship motion model. w , Where ω is the frequency of the wave; H i (ω i ) is the wave transfer function, Δω is the frequency interval, and n is the number of frequency discrete points; Then, the predictive control problem is transformed into an optimization problem, Defining performance indicator functions Satisfy the rudder and fin control input constraints F c,min ≤F c (k)≤F c,max , Solve to get the optimal control input sequence Pick As the current control input, where η r (k) is the desired motion state vector of the ship, Q1 and Q2 are weighting matrices; N is the total discrete time step; Finally, the actual motion state information of the ship is obtained in real time and compared with the current control input, and the prediction model is corrected to predict the control input of the next time step, which is the control instruction of the rudder and fin.

Images