Intelligent ship heading control method and system based on variable universe fuzzy logic

Abstract

The present invention provides an intelligent ship heading control method and system based on variable domain fuzzy logic, which belongs to the field of intelligent control of ship motion. In view of the control difficulties of intelligent ships in complex navigation environments caused by model uncertainty, the present invention proposes a data-driven adaptive heading controller, and innovatively introduces the concept of "variable domain" to optimize the traditional fuzzy self-tuning PID control method. By designing an adaptive scaling factor to dynamically adjust the fuzzy domain of input variables and output variables, it not only overcomes the high dependence of traditional fuzzy PID control methods on rules, but also significantly reduces the computational complexity and enhances adaptability to dynamic environments. This method greatly improves the adaptive ability of ship heading control and ensures excellent control effects under variable navigation conditions.

Description

Technical Field

[0001] The invention belongs to the field of intelligent control of ship motion, and in particular relates to an intelligent ship heading control method based on variable universe fuzzy logic. Background Art

[0002] With the rapid advancement of science and technology, the application of intelligent ships in military, scientific research, environmental protection, rescue and other fields has become more and more extensive. With high automation, efficiency and flexibility, intelligent ships have gradually replaced traditional manned ships and can perform various complex and dangerous tasks. For example, intelligent ships can be used for reconnaissance, patrol and anti-submarine warfare in the military field; in the field of scientific research, they can be used to collect marine data and environmental monitoring; in environmental protection, intelligent ships can conduct water quality testing and track pollution sources; in rescue missions, they can undertake tasks such as search and rescue and material transportation. Intelligent ship motion control technology is the key to completing these tasks, among which heading control technology is an indispensable and crucial link in the research and development process. During high-speed navigation, intelligent ships are often disturbed by environmental factors such as wind, waves and currents, and their heading control system has complex nonlinear characteristics, which puts high demands on the control algorithm. Therefore, the heading control technology of intelligent ships is particularly important. The core of this technology is the control of the steering gear of the intelligent ship. When the actual heading deviates from the set heading, the system feedbacks the rudder angle command through the control algorithm, controls the servo mechanism to adjust the steering gear, and then corrects the ship's heading to achieve the goal of intelligent navigation.

[0003] In the prior art, the most widely used controller for intelligent ship heading control is the PID controller proposed by Minorsky. However, due to its fixed initial value, it cannot change with the changes in the external environment. The single PID control effect is poor and cannot meet the application requirements of intelligent ships in complex and changeable water environments. Furthermore, with the development of intelligent control technology, some scholars have combined fuzzy logic ideas with PID controllers and proposed a fuzzy adaptive PID ship heading control algorithm, which provides fuzzy parameter adjustment rules and realizes online adjustment and tracking control of controller parameters. However, due to the fixedness of the fuzzy logic domain, it will lack sufficient fuzzy rules when the error is small, and cannot achieve a good control effect. Therefore, it will have to compromise between complexity and approximation accuracy to improve control accuracy. Summary of the invention

[0004] In order to solve the technical problem in the prior art that the stability and accuracy of automatic ship heading control are low under the influence of complex environmental interference such as wind, waves and currents, the present invention proposes an intelligent ship heading control method and system based on variable domain fuzzy logic.

[0005] In a first aspect of the present invention, the present invention provides an intelligent ship heading control method based on variable universe fuzzy logic, comprising:

[0006] Step S1, initializing the heading control system of the smart ship, including determining the basic ship parameters for heading control according to the boat type, loading and speed of different smart ships, and initializing the PID parameter values ​​according to the selected smart ship. ;

[0007] Step S2, designing a variable universe fuzzy PID heading controller, wherein the variable universe fuzzy PID heading controller comprises a fuzzy PID heading controller and a variable universe adjustment structure;

[0008] Step S3, obtaining the error between the actual heading and the expected heading of the ship and the rate of change of the error in real time, and inputting the heading deviation and the heading deviation change rate between the preset expected heading and the actual heading as input variables into the variable domain fuzzy PID controller to obtain the command rudder angle of the ship control ;

[0009] Step S4: obtain the command rudder angle for ship control The actual rudder angle of the ship is obtained through the processing of the steering gear servo system , and the actual rudder angle controlled by the ship Sent to the ship heading system for heading control.

[0010] Furthermore, the design steps of the variable domain adjustment mechanism specifically include:

[0011] Step S21, using a proportional function method to design the input heading deviation and the rate of change of deviation The expansion factor , , the calculation formula is as follows:

[0012]

[0013]

[0014] In the formula , The expansion factor is a design parameter, and the specific value of the parameter needs to be adjusted and set according to the actual situation of the smart ship;

[0015] Step S22, using fuzzy control method to adaptively design output The expansion factor .

[0016] Furthermore, initializing the heading control system of the intelligent ship in step S1 also includes constructing a ship heading control response model, which specifically includes:

[0017] The mathematical model of maneuvering motion of intelligent ships is described by two coordinate systems in a two-dimensional plane, where: is the north-east coordinate system, which is used to describe the position and heading angle of the smart ship. The vector is express; is the attached coordinate system, which is used to describe the longitudinal, transverse and bow speed states of the intelligent ship. The vector is express;

[0018] The motion control of intelligent ships is more concerned with the motion of intelligent ships in the plane with three degrees of freedom, so the heading angle of intelligent ships , navigation track intelligent ship , only depends on the u, v, and r of the smart ship, thus simplifying the smart ship into a planar three-degree-of-freedom motion, and its motion equation can be expressed as:

[0019]

[0020] In the formula is the added mass inertia matrix, is the damping matrix, is the Coriolis force matrix, To control force and torque, Represents the longitudinal control force, represents the yaw moment, is the environmental disturbance force and torque represents the longitudinal environmental interference force, Represents the lateral environmental disturbance force represents the environmental disturbance torque, is the coordinate system transformation matrix, the specific form of the matrix is ​​as follows:

[0021]

[0022] , , The matrix expression is as follows:

[0023]

[0024]

[0025]

[0026] According to the above formula, the three-degree-of-freedom kinematic equation of the intelligent ship can be derived:

[0027]

[0028] When there is no external interference, the ship motion is regarded as uniform linear motion. Therefore, the above equation is converted into the torque and momentum equation, and then linearized to obtain:

[0029]

[0030] Where X represents the resultant force acting in the longitudinal direction of the ship along the x-direction; Y represents the resultant force acting in the transverse direction of the ship along the y-direction; N represents the yaw moment acting on the ship around the vertical axis along the z-axis. is the hydrodynamic coefficient, m is the mass of the ship; only the first-order small quantities of X, Y, and N on the left side of the above formula are retained, because the movement of the ship in the forward direction is decoupled from the other two degrees of freedom, and the above formula is rewritten into the following spatial state form:

[0031]

[0032]

[0033] in, is the geometric distance from the ship's center of gravity to the center, that is, the horizontal coordinate of the ship's center of mass, is the actual rudder angle; are the hydrodynamic coefficients, which refer to the hydrodynamic drag coefficient caused by the longitudinal velocity change and the longitudinal thrust component coefficient caused by the rudder angle deflection; is the longitudinal velocity increment of the ship; is the initial longitudinal velocity of the ship; is the moment of inertia of the ship rotating around the z-axis; the heading angle is introduced into the above equation After Laplace transformation, the transfer function of the heading and rudder angle of the intelligent ship is obtained as follows:

[0034]

[0035] in is the complex variable in the Laplace transform, which represents the frequency response of the system, is the ship turning index, is the ship stability index;

[0036] According to the characteristics of ship motion, the above formula is further simplified and reduced to a first-order model, namely the Nomoto model:

[0037]

[0038] In the formula , and further rewrite it into the form of differential equations, the differential equation of the intelligent ship steering response model is as follows:

[0039]

[0040] In the actual movement process, the intelligent ship is often in an unstable or critically stable state due to the interference of external high-frequency factors, and the movement of the intelligent ship is nonlinear. Considering the nonlinearity of the intelligent ship movement, the second term on the left side of the equation With a nonlinear term To replace it, and , then the relationship between the rudder angle and the heading angle controlled by the nonlinear intelligent ship heading control system is described as:

[0041]

[0042] Among them, the parameters represents the nonlinear coefficient and The indices are all related to the type, loading and speed of smart ships.

[0043] The scaling factor in step S22 The adaptive design steps include:

[0044] Design an independent fuzzy controller. In order to reduce the amount of calculation, the basic domain and fuzzy domain of the independent fuzzy controller input and output are set to the same value range. The input variable of the independent fuzzy controller is the heading deviation. and the rate of change of deviation , whose domains are , , Nine fuzzy languages ​​were selected, namely {NB, NM, NS, NZ, ZO, PZ, PS, PM, PB}, representing “negative large, negative medium, negative small, zero negative, zero, zero positive, positive small, positive medium, positive large” respectively; Seven fuzzy languages ​​are selected, namely {NB, NM, NS, ZO, PS, PM, PB}, representing "negative large, negative medium, negative small, zero, positive small, positive medium, positive large", and the membership functions are all selected as triangular; the output scaling factor of the independent fuzzy controller is designed Domain , 7 fuzzy languages ​​were selected, namely {CB, CM, CS, Z, AS, AM, AB}, which respectively represent “substantially compressed, moderately compressed, slightly compressed, unchanged, slightly expanded, moderately expanded, and substantially expanded”.

[0045] Furthermore, step S3 further includes:

[0046] Step S31, input the input variable into the fuzzy controller for fuzzification processing. The input process here is a process of changing from a precise quantity to a fuzzy quantity, so it is necessary to introduce a quantization factor to convert the input

[0047] The variables are quantified from the basic domain to the fuzzy domain to obtain the fuzzified input variables;

[0048] Step S32, based on the fuzzy rules and the fuzzified input variables, the fuzzy controller performs fuzzy reasoning to obtain a fuzzy control action;

[0049] Step S33, after fuzzy reasoning, the fuzzy control action is defuzzified using the center of gravity method, and finally a proportional factor is introduced to obtain an accurate action control amount;

[0050] Step S34, input the obtained precise motion control amount to the PID controller to obtain the command rudder angle for ship control .

[0051] Furthermore, the fuzzified input variables obtained in step S31 specifically include:

[0052] (1) Determine the input variable heading deviation and the rate of change of heading deviation Domain, before input and In order to accurately measure the quantity, the basic domain is determined according to the actual situation of the intelligent ship, that is, and The maximum and minimum values ​​of , ; After input and Transform into fuzzy quantity, determine its fuzzy domain, and form a set , ;

[0053] (2) Calculate the quantization factor, the input quantity includes heading deviation and the rate of change of heading deviation The quantization factor is , , the calculation formula is as follows:

[0054]

[0055]

[0056] (3) The input scaling factor obtained in step S2 is , Multiplying with the quantization factor, the input of the fuzzy controller of the variable universe fuzzy PID controller is and .

[0057] Furthermore, step S32 further includes:

[0058] Fuzzy reasoning is based on fuzzy rules to perform reasoning calculations. Fuzzy rules include:

[0059] Fuzzy conditional statements are multiple two-dimensional conditional statements, as shown below:

[0060]

[0061] in is a fuzzy language set, and the membership degree constitute;

[0062] Among them, the intelligent ship heading deviation and deviation change rate are divided into 7 fuzzy languages, with a total of 49 fuzzy rules. The fuzzy language set is {NB, NM, NS, ZO, PS, PM, PB}, "NB" means "Negative Big" and "PB" means "Positive Big", which respectively represent "negative big, negative middle, negative small, zero, positive small, positive middle, positive big"; the membership function adopts a triangular membership function.

[0063] Furthermore, the defuzzification process in step S33 further includes:

[0064] (1) Determine the output variable PID parameter adjustment amount The domain of Actual changes, proposed The scope of change, that is, to determine the basic domain, constitute the set , , ; Then confirm Fuzzy domain, forming a set , , ;

[0065] (2) According to the output variable PID parameter adjustment amount Determine the parameter adjustment amount based on the basic domain and fuzzy domain The scaling factors are , the calculation formula is as follows:

[0066]

[0067]

[0068]

[0069] (3) The input scaling factor obtained in step S2 is Multiply it by the proportional factor to achieve the purpose of dynamically adjusting the output domain; therefore, under the variable domain idea, the output of the fuzzy controller They are , , .

[0070] Furthermore, in step S34, the obtained precise motion control amount is input to the PID controller to obtain the command rudder angle for ship control. include:

[0071]

[0072] in, To set the deviation between the heading and the actual heading, They represent the proportional coefficient, integral coefficient and differential coefficient of the variable universe fuzzy PID controller output respectively, refer to the following formula:

[0073]

[0074] In the formula is the PID parameter value in PID heading control, is the output parameter adjustment amount, , , .

[0075] In a second aspect of the present invention, the present invention provides a control system for implementing any one of the methods described in the first aspect, comprising:

[0076] A calculation module, used for calculating a heading deviation and a heading deviation rate according to an actual heading and an expected heading of the ship;

[0077] The variable universe fuzzy PID controller is used to calculate the command rudder angle for ship control based on the heading deviation and heading deviation rate. ;

[0078] Steering gear servo system, used to control the rudder angle according to the command of the ship control Get the actual rudder angle of the ship control , and the actual rudder angle controlled by the ship Send to the ship heading system;

[0079] Ship heading system, used to control the actual rudder angle of the ship Control of the ship.

[0080] Furthermore, the variable universe fuzzy PID controller also includes:

[0081] The variable domain regulating mechanism adopts a proportional function to design a variable domain regulating mechanism based on the heading deviation. and the rate of change of deviation The expansion factor of the output is adjusted by using fuzzy control method to adaptively design the output parameter adjustment amount. The expansion factor .

[0082] Aiming at the model uncertainty problem of intelligent ships under complex navigation conditions, the present invention designs a ship heading adaptive controller based on data driving. Taking into account the problems of high rule dependence, poor environmental adaptability, low dynamic adjustability and the like of fuzzy self-tuning PID in intelligent ship heading control, the "variable domain" idea is introduced on the basis of fuzzy self-tuning PID, and an adaptive scaling factor is designed to dynamically adjust the fuzzy domain, so as to overcome the strong dependence of fuzzy PID on rules, reduce the calculation complexity, improve the dynamic adaptability, and thus improve the ship heading adaptive control effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 It is a flow chart of the intelligent ship heading control method based on variable universe fuzzy logic;

[0084] Figure 2 It is the motion coordinate system of the intelligent ship;

[0085] Figure 3 Principle diagram of variable universe fuzzy PID heading control algorithm;

[0086] Figure 4 Fuzzy rule table;

[0087] Figure 5 Schematic diagram of domain compression;

[0088] Figure 6 Stretch Factor Fuzzy rule table;

[0089] Figure 7 Membership function graph;

[0090] Figure 8 Comparison chart of the control effect of keeping the heading at 30°;

[0091] Fig. 9 Comparison chart of the heading maintaining 30° rudder angle output;

[0092] Fig.10 Comparison chart of the control effect of keeping the heading at 60°;

[0093] Fig.11 Comparison chart of rudder output when heading is maintained at 60°;

[0094] Fig.12 Comparison chart of the control effect of keeping the heading at 90°;

[0095] Fig.13 Comparison chart of rudder angle output when heading is maintained at 90°;

[0096] Fig.14Comparison of tracking effects when heading changes from 60° to 30°;

[0097] Fig.15 Adjustment amount Change comparison chart;

[0098] Fig.16 Adjustment amount Change comparison chart. DETAILED DESCRIPTION

[0099] The purpose of the present invention is to introduce the variable domain idea to optimize and improve the fuzzy PID in the limitation and deficiency of the fuzzy PID in the intelligent ship heading control, and propose an intelligent ship heading control method based on variable domain fuzzy logic. The flow chart of the method of the present invention is as follows: Figure 1 shown.

[0100] (1) Ship heading maneuvering response model

[0101] like Figure 2 , two coordinate systems in a two-dimensional plane are used to describe the mathematical model of the maneuvering motion of intelligent ships. is the north-east coordinate system, which is used to describe the position and heading angle of the smart ship. The vector is express; is the attached coordinate system, which is used to describe the longitudinal, transverse and bow speed states of the intelligent ship. The vector is express.

[0102] According to some literature, the motion control of intelligent ships is more concerned with the motion of intelligent ships in the plane with three degrees of freedom, while the effects of heave, roll and pitch on the motion of most ships are small and can often be ignored. , navigation track intelligent ship It can be considered that it only depends on the u, v, and r of the smart ship, so the smart ship is simplified to a planar three-degree-of-freedom motion, and its motion equation can be expressed as:

[0103] (1)

[0104] In the formula is the added mass inertia matrix, is the damping matrix, is the Coriolis force matrix, To control force and torque, Represents the longitudinal control force, represents the yaw moment, is the environmental disturbance force and torque represents the longitudinal environmental interference force, Represents the lateral environmental disturbance force represents the environmental disturbance torque, is the coordinate system transformation matrix, the specific form of the matrix is ​​as follows:

[0105] (2)

[0106] , , The matrix expression is as follows:

[0107]

[0108]

[0109] (3)

[0110] According to the above formula, the three-degree-of-freedom kinematic equation of the intelligent ship can be derived:

[0111] (4)

[0112] When the external disturbance is small, the ship's motion will maintain the initial equilibrium state, that is, it can be regarded as uniform linear motion. Therefore, converting equation (4) into the torque and momentum equation and linearizing it can be obtained:

[0113]

[0114] Where X represents the resultant force acting in the longitudinal direction of the ship along the x-direction; Y represents the resultant force acting in the transverse direction of the ship along the y-direction; N represents the yaw moment acting on the ship around the vertical axis along the z-axis. is the hydrodynamic coefficient, m is the mass of the ship; only the first-order small quantities of X, Y, and N on the left side of equation (5) are retained, because the movement of the ship in the forward direction is decoupled from the other two degrees of freedom, equation (5) can be rewritten as the following spatial state form:

[0115]

[0116]

[0117] in, is the geometric distance from the ship's center of gravity to the center, that is, the horizontal coordinate of the ship's center of mass, is the actual rudder angle; are the hydrodynamic coefficients, which refer to the hydrodynamic drag coefficient caused by the longitudinal velocity change and the longitudinal thrust component coefficient caused by the rudder angle deflection; is the longitudinal velocity increment of the ship; is the initial longitudinal velocity of the ship; is the moment of inertia of the ship rotating around the z-axis; the heading angle is introduced into equation (6) After Laplace transformation, the transfer function of the heading and rudder angle of the intelligent ship is obtained as follows:

[0118]

[0119] Since the characteristics of ship motion are large time lag, large inertia, less rudder angle behavior, and low frequency characteristics, Japanese scholar Nomoto further simplified equation (7) and reduced it to a first-order model, namely the Nomoto model:

[0120]

[0121] In the formula , by changing equation (8) into the form of a differential equation, the differential equation of the intelligent ship steering response model can be obtained as follows:

[0122]

[0123] In the actual movement of smart ships, due to the interference of high-frequency factors such as wind and waves, smart ships are often in an unstable or critically stable state, and the movement of smart ships is nonlinear. Considering the nonlinearity of the movement of smart ships, the second term on the left side of equation (9) is With a nonlinear term To replace it, and , then the relationship between the rudder angle and the heading angle controlled by the nonlinear intelligent ship heading control system can be described as:

[0124]

[0125] parameter as well as The indices are all related to the type, loading and speed of smart ships.

[0126] (2) Intelligent ship heading control method based on variable universe fuzzy logic

[0127] PID control has many applications in the field of ship heading control due to its simple algorithm, strong reliability and clear physical meaning. However, due to the fixed parameters, when the navigation of intelligent ships is disturbed by complex environments, the stability of the PID heading control system will deteriorate. Therefore, the present invention introduces a variable universe fuzzy controller on the basis of the traditional PID heading controller to construct a variable universe fuzzy PID controller to adaptively adjust the intelligent ship heading controller. Three parameters are adjusted to improve the effectiveness and stability of the heading control of intelligent ships.

[0128] The principle diagram of variable universe fuzzy PID heading control algorithm is as follows Figure 4As shown in FIG. 1 , the variable domain fuzzy PID controller proposed in the present invention is mainly composed of a fuzzy controller and a variable domain adjustment mechanism, wherein the fuzzy controller mainly includes four parts: knowledge base, fuzzification, fuzzy reasoning and defuzzification. The main process of the method of the present invention is to calculate the deviation between the preset desired heading and the actual heading. Proportional, integral and differential processing is performed, wherein the proportional, integral and differential coefficients are adjusted according to the input heading deviation through the variable domain fuzzy controller. The change in heading deviation Obtain and then output the corresponding command rudder angle Finally, the steering angle is limited within a given range through the steering gear servo link, and the actual steering angle is output. To the ship heading system, the heading of the smart ship is adaptively controlled. The control law is:

[0129]

[0130] in, To set the deviation between the heading and the actual heading, They represent the proportional coefficient, integral coefficient and differential coefficient of the variable universe fuzzy PID controller output respectively, refer to the following formula:

[0131]

[0132] In the formula is the PID parameter value in PID heading control, It is the parameter adjustment output by the variable universe fuzzy controller.

[0133] The detailed technical scheme of the intelligent ship heading control method based on variable universe fuzzy logic proposed in the present invention is as follows:

[0134] 1. Design the domain of input and output variables of the heading adaptive control system

[0135] One feature of the variable domain fuzzy controller that is different from other controllers is that it is designed based on the idea of ​​fuzzy mathematics. Fuzzy mathematics is different from the precise "yes or no" logic in traditional mathematics. Fuzzy mathematics allows things to belong to a certain set to a certain extent by introducing the concept of "partially belonging". This fuzzy mathematics idea is very suitable for describing the uncertainty of heading deviation changes in ship adaptive heading control systems. The set here can be understood as the domain of variables. The variable domain fuzzy controller proposed in this invention is based on heading deviation. and the rate of change of heading deviation is the input, PID parameter adjustment amount is the output.

[0136] First determine the input variables and Domain, before input and In order to accurately measure the quantity, the basic domain is determined according to the actual situation of the intelligent ship, that is, and The maximum and minimum values ​​of , After input and Transform into fuzzy quantity, determine its fuzzy domain, and form a set , Similarly, the output Basic domain and fuzzy domain , ; , ; , .

[0137] 2. Design the quantization factor and proportional factor of the heading adaptive control system

[0138] In the method of the present invention, the process of fuzzifying the input quantity needs to be multiplied by a coefficient , through the coefficient Discretize the basic domain of the input precise quantity to the fuzzy domain of the fuzzy quantity. This coefficient is called the quantization factor. Considering the variable domain fuzzy PID heading adaptive controller designed by the present invention, determine the heading deviation quantization factor. and the heading deviation change rate quantification factor Similarly, the process of defuzzifying the output also requires multiplication by a coefficient , through the coefficient The fuzzy domain of the output fuzzy quantity is continuous to the basic domain of the precise quantity. This coefficient is called the proportional factor, which determines the parameter adjustment amount. The scaling factors are . With input and output For example, the quantization factor With the scale factor The calculation formula is as follows:

[0139]

[0140]

[0141] 3. Design fuzzy rules and membership functions for heading adaptive control system

[0142] Fuzzy rules are the core of fuzzy mathematics and an important part of the variable domain fuzzy logic proposed in the present invention. They are used to describe the relationship between input and output and are suitable for processing systems with uncertainty, ambiguity or complexity, such as ship heading control systems. Fuzzy rules are usually expressed in the form of fuzzy conditional statements, using fuzzy sets and membership functions to define conditions and results. Fuzzy conditional statements include simple conditional statements, multiple simple conditional statements, multidimensional conditional statements and multiple multidimensional conditional statements. The variable domain fuzzy PID controller proposed by the method of the present invention is composed of a two-dimensional fuzzy controller. Therefore, the fuzzy conditional statements adopted by the method of the present invention are multiple two-dimensional conditional statements, as shown below:

[0143]

[0144] in is a fuzzy language set, and the membership degree constitute.

[0145] In the method of the present invention, the setting of the fuzzy language set determines the accuracy of the entire intelligent ship heading control system. The selection of fuzzy language affects the control accuracy of fuzzy control. Too few fuzzy languages ​​will result in low accuracy and poor control effect. Too many fuzzy languages, although they can improve the control accuracy to a certain extent, will greatly increase the computational complexity. Generally, 3 to 9 fuzzy languages ​​are appropriate. Therefore, it is necessary to select an appropriate number of fuzzy languages. The present invention comprehensively considers various factors and divides the intelligent ship heading deviation and deviation change rate into 7 fuzzy languages. The fuzzy language set is {NB, NM, NS, ZO, PS, PM, PB}. "NB" stands for "Negative Big" and "PB" stands for "Positive Big", which respectively represent "negative big, negative medium, negative small, zero, positive small, positive medium, positive large", and are used to represent the output parameter adjustment amount. With the heading deviation and the rate of change of heading deviation The changing relationship between them.

[0146] In the method of the present invention, the membership function is used to describe the fuzzy relationship between the variable and the fuzzy language set. Common membership function types include triangular, ladder, generalized bell, Z, etc. The input and output variables of the method of the present invention are designed with triangular membership functions. The triangular membership function has a simple structure and low computational complexity. In addition, the triangular membership function can flexibly approximate the fuzzy set in the intelligent ship navigation control, and can adapt to different data modes and requirements by adjusting the position of the vertex.

[0147] In summary, the present invention designs the output adjustment parameters according to the influence of the changes of various PID parameters on the heading control effect in the actual intelligent ship heading control, and combines the expert experience. Fuzzy rules such as Figure 4 As shown. There are 7*7=49 fuzzy rules in total. Taking the first fuzzy rule as an example, its specific representation is as follows:

[0148]

[0149] 4. Design of fuzzy reasoning method for heading adaptive control system

[0150] The fuzzy reasoning process is based on fuzzy language sets and fuzzy rules to infer the relationship between input variables and output variables. Specifically, it refers to the reasoning process of drawing possible imprecise conclusions from imprecise premise sets, such as "if the kiwi fruit is soft, then the kiwi fruit can be eaten". The fuzzy reasoning methods include Zadeh method and Mamdani direct reasoning method. The basic principle can be understood as follows: assuming that the fuzzy implication relationship "if A then B" (simple conditional statement) is represented by A→B, the fuzzy output can be obtained according to the fuzzy relationship matrix R. The difference between the two fuzzy reasoning methods lies in the different calculation logic of the fuzzy relationship matrix R. The specific formula is as follows:

[0151]

[0152]

[0153] Formula 17 is the specific formula of Zadeh fuzzy reasoning method, and Formula 18 is the specific formula of Mamdani fuzzy reasoning method. Represents fuzzy sets The difference between the two is that Zadeh fuzzy reasoning method takes into account the situation of "if A is not, then B is not", but the actual situation is that the assumption of such conditional statement is not necessarily valid, so the present invention adopts Mamdani direct reasoning method for fuzzy reasoning.

[0154] 5. Design a defuzzification method for heading adaptive control system

[0155] In the present invention, the result of fuzzy reasoning is a fuzzy language set, which is usually a combination of the conclusions obtained from fuzzy rules composed of multiple fuzzy conditional statements. Its membership function is segmented, and the intelligent ship heading control system can only be controlled by a certain numerical value. Therefore, it is necessary to convert the fuzzy language set into an accurate quantity first, and then send it to the actuator, that is, the heading control system for control. This is the defuzzification process, also called the clarification process, which can be regarded as a mapping from fuzzy space to clear space. The defuzzification methods include the maximum membership method, the area average method and the centroid method. In the present invention, the defuzzification method adopts the centroid method, which has the advantages of its smoothness, stability and comprehensive utilization of fuzzy information. It is particularly suitable for processing complex fuzzy system control problems. The specific formula is as follows:

[0156]

[0157] In the formula is the final control signal value, is the membership function of the fuzzy output set. In the present invention, the membership function selects a triangular shape such as Figure 7 As shown, compared with other membership function types, it can intuitively represent the membership relationship of the input variables.

[0158] 6. Design a variable domain adjustment mechanism for heading adaptive control system

[0159] The main innovation of the intelligent ship heading control method based on variable universe fuzzy logic proposed in the present invention is to introduce a variable universe adjustment mechanism, which designs an adaptive expansion factor and , given to the fuzzy controller, it mainly affects the fuzzification and defuzzification processes. Multiply the input heading deviation by the quantization factor and the rate of change of heading deviation The domain of the discourse can be adaptively expanded and expanded. Similarly, the expansion factor Multiplying by the scaling factor makes the output The domain of discourse can be adaptively expanded and scalable. By realizing the dynamic adjustment of the domain of discourse, the shortcomings of the fuzzy PID controller in realizing the adaptive control of the heading of intelligent ships, such as poor system control performance and unsatisfactory heading control effect caused by the fixedness of its fuzzy domain of discourse, are well solved. Because, once the type of membership function and the fuzzy rule are determined, with the continuous changes of the heading deviation and the heading deviation change rate, its domain of discourse will appear too large or too small, which will easily lead to various situations such as excessive or insufficient adjustment of the three PID parameters.

[0160] like Figure 5 The diagram shows the expansion and contraction of the domain. The essence of the variable domain fuzzy logic idea is to introduce a deviation and The purpose of dynamically adjusting the domain is achieved by multiplying the domain with the scaling factor. When the error becomes smaller, the domain will be compressed under the action of the scaling factor. For example, the fuzzy amount NS before compression in the figure becomes NB after the introduction of the scaling factor to compress the domain. This shows that the system considers the error to be too small before the domain is compressed, and the system considers it to be too large after the domain is compressed. This shows that the system's sensitivity to the error increases, thereby improving the control accuracy of the system.

[0161] The key part of the variable domain regulation mechanism is the design method of the adaptive scaling factor. Currently, the design methods of the scaling factor are mainly divided into two categories: functional type and fuzzy type. The fuzzy type method mainly designs the scaling factor based on experience. The fuzzy rules are then used to output the corresponding scaling factor for the design of the variable domain; the functional method mainly sets the scaling factor based on the error and error rate of change The formula is divided into two types: exponential type and proportional type, as shown below:

[0162]

[0163]

[0164] In the formula,

[0165] In the prior art, the design of the telescopic factor is usually based on a single functional type or fuzzy type, that is, whether it is the telescopic factor of the input or output, the same design form is adopted. If the functional telescopic factor design method is adopted, the factor parameter settings will be different considering the different characteristics of the input and output, which will increase the complexity of the design parameters. Similarly, if the fuzzy telescopic factor design method is adopted, the design requirements for the fuzzy rules will become higher, making the entire control system complicated, the simulation difficult to implement, and the amount of calculation is large. In the method of the present invention, an innovative adaptive telescopic factor design method combining the functional type and the fuzzy type is proposed, which is as follows:

[0166] For the input scaling factor and The present invention adopts a proportional function to design a heading deviation-based and the rate of change of deviation The adaptive formula of the scaling factor is as follows:

[0167]

[0168]

[0169] In the formula , The parameters for designing the telescopic factor need to be adjusted and set according to the actual situation of the smart ship.

[0170] For the design of the output scaling factor, the present invention adopts a fuzzy control method to adaptively design the output parameter adjustment amount. The expansion factor (In order to reduce the complexity of calculation, the quantization factor and the proportional factor are both set to 1, that is, the basic domain and the fuzzy domain are equal). The specific design method is as follows: Design the input variable heading deviation of the fuzzy controller and the rate of change of deviation The domain of , Nine fuzzy languages ​​were selected, namely {NB, NM, NS, NZ, ZO, PZ, PS, PM, PB}, representing “negative large, negative medium, negative small, zero negative, zero, zero positive, positive small, positive medium, positive large” respectively. Seven fuzzy languages ​​are selected, namely {NB, NM, NS, ZO, PS, PM, PB}, which represent "negative large, negative medium, negative small, zero, positive small, positive medium, positive large", and the membership functions are all selected as triangles. Design the output scaling factor of the fuzzy controller Domain , 7 fuzzy languages ​​are selected, namely {CB, CM, CS, Z, AS, AM, AB}, which respectively represent "large compression, moderate compression, slight compression, unchanged, slight expansion, moderate expansion, large expansion". According to the relationship between output and input, the law of domain compression change, and the specific situation of intelligent ship heading control, the present invention designs a fuzzy rule table of expansion factor, such as Figure 6 shown.

[0171] (1) Experimental design

[0172] According to the intelligent ship heading control method based on variable universe fuzzy logic proposed above, SRUSV is taken as the simulation research object, and the steering response model parameters K and T are taken as 1.082 and 0.902 respectively. , , a heading control simulation experiment is carried out to verify the effectiveness and stability of the method of the present invention.

[0173] First is the specific design of the main fuzzy controller:

[0174] In the simulation experiment, according to the actual situation of SRUSV, the input and output domain of the heading adaptive control system is designed, as follows: Set the heading deviation The basic domain is , the discretized fuzzy domain is , heading deviation change rate The basic domain is , the discretized fuzzy domain is , the output parameter adjustment amount The basic domain is , the fuzzy domain is , The basic domain is , the fuzzy domain is , The basic domain is , the fuzzy domain is According to formula 13, the heading deviation can be calculated The quantitative factor =0.2, heading deviation change rate The quantitative factor =1. According to formula 14, the adjustment parameter can be calculated The scaling factor =0.3, adjustment parameters The scaling factor =1, adjust the parameters The scaling factor =0.3.

[0175] In the simulation experiment, the fuzzy rules proposed in the present invention are used to establish a reference, and the heading deviation and the deviation change rate are divided into 7 fuzzy languages. The fuzzy language set is determined as {NB, NM, NS, ZO, PS, PM, PB}, which respectively represent "negative large, negative medium, negative small, zero, positive small, positive medium, positive large" to represent the adjustment parameters. With the error and error rate of change The membership functions are all triangular, such as Figure 7 As shown, the fuzzy reasoning method uses the Mamdani direct reasoning method, and the defuzzification method uses the centroid method. There are 49 fuzzy rules in total. Taking the first fuzzy rule as an example, its specific representation is as follows:

[0176]

[0177] Then comes the design of the variable domain regulation mechanism:

[0178] The design of the variable domain regulation mechanism mainly consists of two parts: determining the functional expansion factor and Design parameters and The specific value of; Determine the fuzzy scaling factor The domain, fuzzy rules and related design parameters.

[0179] In the simulation experiment, the scaling factor Design parameters , , Stretch Factor Design parameters , . Stretch Factor The domain of , heading deviation and the rate of change of deviation The domain of (For ease of calculation, the quantization factor and the scale factor are both set to 1), Nine fuzzy languages ​​were selected, namely {NB, NM, NS, NZ, ZO, PZ, PS, PM, PB}, representing “negative large, negative medium, negative small, zero negative, zero, zero positive, positive small, positive medium, positive large” respectively. Seven fuzzy languages ​​are selected, namely {NB, NM, NS, ZO, PS, PM, PB}, representing "negative large, negative medium, negative small, zero, positive small, positive medium, positive large", and the membership functions are all selected as triangular. The fuzzy language chooses 7, namely {CB, CM, CS, Z, AS, AM, AB} (here because Non-negative constraints, the choice of fuzzy language is different), representing "large compression, moderate compression, slight compression, unchanged, slight expansion, moderate expansion, large expansion". The fuzzy rule table is as follows: Figure 6 shown.

[0180] In the simulation experiment, in order to demonstrate the effectiveness of the variable universe fuzzy PID heading controller in dealing with uncertain disturbances, the influence of sea breeze and waves on the motion of intelligent ships was simulated, and Gaussian white noise interference was set as time-varying interference. At the same time, a second-order wave transfer function was used to simulate the wave interference, refer to the following formula:

[0181]

[0182] In the formula, is the dominant wave frequency, which is taken as 0.606 in the present invention; is the damping coefficient, which is 0.3 in the present invention; is the gain constant. The present invention simulates level 6 sea conditions and takes 0.4198.

[0183] In the simulation experiment, an important link is the design of the steering gear servo system. The present invention performs a limiting design on the steering angle. Generally, the steering angle and rudder angle changes Should meet the following form:

[0184]

[0185] The present invention takes into account the actual situation of SRUSV and limits the rudder angle , rudder angle changes .

[0186] The present invention uses three control methods, PID, fuzzy PID, and variable domain fuzzy PID, to conduct a heading control simulation experiment comparison, mainly conducting heading keeping and heading change experiments, and analyzing and verifying the effectiveness and stability of the method proposed in the present invention in the heading control of intelligent ships. The simulation experiment parameters are set as follows:

[0187] Table 1 Simulation experiment parameter settings

[0188]

[0189] Table Represents the initial value of PID parameters, represents the quantization factor of the variable universe fuzzy PID controller, represents the proportional factor of the variable universe fuzzy PID controller, Represents the design parameters of the telescoping factor of the variable universe fuzzy PID controller.

[0190] (2) Results analysis

[0191] In terms of heading keeping, the simulation experiment time is 50s, and the planned changes of 30°, 60°, and 90° are simulated to simulate the heading keeping effect of intelligent ships under six levels of sea conditions, and analyze and verify the effectiveness and stability of the variable domain fuzzy PID heading adaptive controller.

[0192] Figure 8 This is a comparison chart of the control effects of the method of the present invention when the heading is maintained at 30°. It can be seen from the figure that the three control methods can all achieve heading control, but the control effects are different. PID control (thin line in the figure) reaches the target heading angle in about 4 seconds, but there is an overshoot of 7.71%; fuzzy PID control (thick line in the figure), the adjustment time is about 10 seconds, there is an overshoot of 1.46%; variable domain fuzzy PID control (dashed line in the figure), the adjustment time is about 5 seconds, there is almost no overshoot, and the overshoot is only 0.49%. Fig. 9 This is a comparison diagram of the rudder angle output when the heading is maintained at 30° under the method of the present invention. It can be seen from the figure that the steering stability of the variable domain PID control is the best.

[0193] Fig.10 This is a comparison chart of the control effects of the method of the present invention when the heading is kept at 60°. It can be seen from the figure that the three control methods can all achieve heading keeping control, but the control effects are different. PID control (thin line in the figure) reaches the target heading angle in about 5 seconds, but there is an overshoot of 16.46%; fuzzy PID control (thick line in the figure), the adjustment time is about 13 seconds, there is an overshoot of 1.71%; variable domain fuzzy PID control (dashed line in the figure), the adjustment time is about 7 seconds, there is an overshoot of 2.01%. Fig.11This is a comparison diagram of the rudder angle output when the heading is maintained at 60° under the method of the present invention. It can be seen from the figure that the steering stability of the variable domain PID control is the best.

[0194] Fig.12 This is a comparison chart of the control effects of the method of the present invention when the heading is kept at 90°. It can be seen from the figure that the three control methods can all achieve heading keeping control, but the control effects are different. PID control (thin line in the figure) reaches the target heading angle in about 7s, but there is an overshoot of 14.74%; fuzzy PID control (thick line in the figure), the adjustment time is about 15s, there is an overshoot of 1.50%; variable domain fuzzy PID control (dashed line in the figure), the adjustment time is about 7s, there is an overshoot of 0.85%. Fig.13 This is a comparison diagram of the rudder angle output when the heading is kept at 90° under the method of the present invention. It can be seen from the figure that the steering stability of the variable domain PID control is the best.

[0195] In terms of heading change, the simulation experiment time is 40s, and the heading is set to change between 60-30°, simulating the tracking effect of smart ship heading change under level 6 sea conditions. Fig.14 This is a comparison diagram of the tracking effect of a heading change of 60-30° under the method of the present invention. It can be seen from the figure that although the PID control (thin line in the figure) has a faster tracking response speed, there is a large overshoot, and the fuzzy PID control (thick line in the figure) has a reduced overshoot, but its tracking response speed is the slowest among the three methods. The variable domain fuzzy PID control (dashed line in the figure) is significantly better than the other two control methods in terms of both tracking response speed and overshoot.

[0196] Table 2 shows the main control performance parameters of the three methods in the ship heading adaptive control. According to the results in the table, it can be concluded that the method of the present invention has good control effect and strong adaptability in realizing the intelligent ship heading adaptive control.

[0197] Table 2 Simulation experiment parameter settings

[0198]

[0199] Fig.15 and 16 is the output regulation value of fuzzy PID and variable domain fuzzy PID and In this simulation experiment, due to the proposed Small, close to 0.001, the adjustment amount output by the two methods The difference is not big, so it is not considered. From the figure, we can see that the adjustment amount output by the variable domain fuzzy PID is The final adjustment value has been stabilized in 6 seconds, and the adjustment value output by the fuzzy PID , it is still changing at 6s and has not stabilized to the appropriate adjustment value. It can be seen that after introducing the variable domain idea, the dynamic adjustment domain can make the system reach the optimal state more quickly. The optimal adjustment value determined by the variable universe fuzzy PID is about -0.01, while the optimal adjustment value determined by the fuzzy PID is -0.2. It can be seen that the variable universe fuzzy PID has higher adjustment accuracy, so the control effect is better.

Claims

1. An intelligent ship heading control method based on variable universe fuzzy logic, characterized in that: include: Step S1, initializing the heading control system of the smart ship, including determining the basic ship parameters for heading control according to the boat type, loading and speed of different smart ships, and initializing the PID parameter values ​​according to the selected smart ship. ; Step S2, designing a variable universe fuzzy PID heading controller, wherein the variable universe fuzzy PID heading controller comprises a fuzzy PID heading controller and a variable universe regulating mechanism; The design steps of the variable domain adjustment mechanism specifically include: Step S21, using a proportional function method to design the input heading deviation and the rate of change of deviation The expansion factor , , the calculation formula is as follows: ; ; In the formula , all , is the design parameter of the expansion factor, and the specific value of the parameter needs to be adjusted and set according to the actual situation of the smart ship; Step S22, using fuzzy control method to adaptively design output The expansion factor ; Step S3, obtaining the error between the actual heading and the expected heading of the ship and the rate of change of the error in real time, and inputting the heading deviation and the heading deviation change rate between the preset expected heading and the actual heading as input variables into the variable domain fuzzy PID controller to obtain the command rudder angle of the ship control ; The step S3 further comprises: Step S31, input the input variable into the fuzzy controller for fuzzification processing. The input process here is a process of changing from a precise quantity to a fuzzy quantity, so it is necessary to introduce a quantization factor to convert the input The variables are quantified from the basic domain to the fuzzy domain to obtain the fuzzified input variables; Step S32, based on the fuzzy rules and the fuzzified input variables, the fuzzy controller performs fuzzy reasoning to obtain a fuzzy control action; Step S33, after fuzzy reasoning, the fuzzy control action is defuzzified using the center of gravity method, and finally a proportional factor is introduced to obtain an accurate action control amount; Step S34, input the obtained precise motion control amount to the PID controller to obtain the command rudder angle for ship control ; Step S4: obtain the command rudder angle for ship control The actual rudder angle of the ship is obtained through the processing of the steering gear servo system , and the actual rudder angle controlled by the ship Sent to the ship heading system for heading control.

2. According to claim 1, a method for intelligent ship heading control based on variable universe fuzzy logic is characterized in that: Initializing the heading control system of the intelligent ship in step S1 also includes constructing a ship heading control response model, which specifically includes: The mathematical model of maneuvering motion of intelligent ships is described by two coordinate systems in a two-dimensional plane, where: is the north-east coordinate system, which is used to describe the position and heading angle of the smart ship. The vector is express; is the attached coordinate system, which is used to describe the longitudinal, transverse and bow speed states of the intelligent ship. The vector is express; The motion control of intelligent ships is more concerned with the motion of intelligent ships in the plane with three degrees of freedom, so the heading angle of intelligent ships , navigation track intelligent ship , only depends on the u, v, and r of the smart ship, thus simplifying the smart ship into a planar three-degree-of-freedom motion, and its motion equation is expressed as: ； In the formula is the added mass inertia matrix, is the damping matrix, is the Coriolis force matrix, To control force and torque, Represents the longitudinal control force, represents the yaw moment, is the environmental disturbance force and torque represents the longitudinal environmental interference force, Represents the lateral environmental disturbance force represents the environmental disturbance torque, is the coordinate system transformation matrix, the specific form of the matrix is ​​as follows: ； , , The matrix expression is as follows: ； ； ； According to the above formula, the three-degree-of-freedom kinematic equation of the intelligent ship is derived: ； When external interference is not considered, the ship motion is regarded as uniform linear motion. Therefore, the above equation is converted into the torque and momentum equation, and then linearized to obtain: ； Where X represents the resultant force acting in the longitudinal direction of the ship along the x-direction; Y represents the resultant force acting in the transverse direction of the ship along the y-direction; N represents the yaw moment acting on the ship around the vertical axis along the z-axis. is the hydrodynamic coefficient, m is the mass of the ship; keep only the first-order small quantities of X, Y, and N on the left side of the above formula, because the movement of the ship in the forward direction is decoupled from the other two degrees of freedom, the above formula can be rewritten as the following spatial state form: ； ； in, is the geometric distance from the ship's center of gravity to the center, i.e., the abscissa of the ship's center of mass; is the actual rudder angle; are the hydrodynamic coefficients, which refer to the hydrodynamic drag coefficient caused by the longitudinal velocity change and the longitudinal thrust component coefficient caused by the rudder angle deflection; is the longitudinal velocity increment of the ship; is the initial longitudinal velocity of the ship; is the moment of inertia of the ship rotating around the z-axis; the heading angle is introduced into the above equation After Laplace transformation, the transfer function of the heading and rudder angle of the intelligent ship is obtained as follows: ； in is the complex variable in the Laplace transform, which represents the frequency response of the system, is the ship turning index, is the ship stability index; According to the characteristics of ship motion, the above formula is simplified and reduced to a first-order model, namely the Nomoto model: ； In the formula , rewrite it into the form of differential equation, and get the differential equation of the intelligent ship steering response model as follows: ； In the actual movement process, the intelligent ship is often in an unstable or critically stable state due to the interference of external high-frequency factors, and the movement of the intelligent ship is nonlinear. Considering the nonlinearity of the intelligent ship movement, the second term on the left side of the equation With a nonlinear term To replace it, and , then the relationship between the rudder angle and the heading angle controlled by the nonlinear intelligent ship heading control system is described as: ； Among them, the parameters represents the nonlinear coefficient and The indices are all related to the type, loading and speed of smart ships.

3. The intelligent ship heading control method based on variable universe fuzzy logic according to claim 1 is characterized in that: The scaling factor in step S22 The adaptive design steps include: Design an independent fuzzy controller. In order to reduce the amount of calculation, the basic domain and fuzzy domain of the independent fuzzy controller input and output are set to the same value range. The input variable of the independent fuzzy controller is the heading deviation. and the rate of change of deviation , whose domains are , , Nine fuzzy languages ​​were selected, namely {NB, NM, NS, NZ, ZO, PZ, PS, PM, PB}, representing "negative large, negative medium, negative small, zero negative, zero, zero positive, positive small, positive medium, positive large" respectively; Seven fuzzy languages ​​are selected, namely {NB, NM, NS, ZO, PS, PM, PB}, representing "negative large, negative medium, negative small, zero, positive small, positive medium, positive large", and the membership functions are all selected as triangular; the output scaling factor of the independent fuzzy controller is designed Domain , 7 fuzzy languages ​​were selected, namely {CB, CM, CS, Z, AS, AM, AB}, which respectively represent "substantially compressed, moderately compressed, slightly compressed, unchanged, slightly expanded, moderately expanded, and substantially expanded".

4. According to claim 1, a method for intelligent ship heading control based on variable universe fuzzy logic is characterized in that: The fuzzified input variables obtained in step S31 specifically include: (1) Determine the input variable heading deviation and the rate of change of heading deviation Domain, before input and In order to accurately measure the quantity, the basic domain is determined according to the actual situation of the intelligent ship, that is, and The maximum and minimum values ​​of , ; After input and Transform into fuzzy quantity, determine its fuzzy domain, and form a set , ; (2) Calculate the quantization factor, the input quantity includes heading deviation and the rate of change of heading deviation The quantization factor is , , the calculation formula is as follows: ； ； (3) The input scaling factor obtained in step S2 is , Multiplying with the quantization factor, the input of the fuzzy controller of the variable universe fuzzy PID controller is and .

5. The intelligent ship heading control method based on variable universe fuzzy logic according to claim 1 is characterized in that: The step S32 further comprises: Fuzzy reasoning performs reasoning calculations based on fuzzy rules, which include: Fuzzy conditional statements are multiple two-dimensional conditional statements, as shown below: ； in is a fuzzy language set, and the membership degree constitute; Among them, the intelligent ship heading deviation and deviation change rate are divided into 7 fuzzy languages, with a total of 49 fuzzy rules. The fuzzy language set is {NB, NM, NS, ZO, PS, PM, PB}, "NB" means "Negative Big", "PB" means "Positive Big", respectively representing "negative big, negative middle, negative small, zero, positive small, positive middle, positive big"; the membership function adopts a triangular membership function.

6. The intelligent ship heading control method based on variable universe fuzzy logic according to claim 1 is characterized in that: The defuzzification process in step S33 further includes: (1) Determine the output variable PID parameter adjustment amount The domain of Actual changes, proposed The scope of change, that is, to determine the basic domain, constitute the set , , ; Then confirm Fuzzy domain, forming a set , , ; (2) According to the output variable PID parameter adjustment amount Determine the parameter adjustment amount based on the basic domain and fuzzy domain The scaling factors are , the calculation formula is as follows: ； ； ； (3) The input scaling factor obtained in step S2 is Multiply it by the proportional factor to achieve the purpose of dynamically adjusting the output domain; therefore, under the variable domain idea, the output of the fuzzy controller They are , , .

7. The intelligent ship heading control method based on variable universe fuzzy logic according to claim 1 is characterized in that: In step S34, the obtained precise motion control amount is input to the PID controller to obtain the command rudder angle for ship control. include: ； in, To set the deviation between the heading and the actual heading, They represent the proportional coefficient, integral coefficient and differential coefficient of the variable universe fuzzy PID controller output respectively, refer to the following formula: ； In the formula is the PID parameter value in PID heading control, is the output parameter adjustment amount, , , .

8. A control system for implementing the method according to any one of claims 1 to 7, characterized in that: include: A calculation module, used for calculating a heading deviation and a heading deviation rate according to an actual heading and an expected heading of the ship; The variable universe fuzzy PID controller is used to calculate the command rudder angle for ship control based on the heading deviation and heading deviation rate. ; Steering gear servo system, used to control the rudder angle according to the command of the ship control Get the actual rudder angle of the ship control , and the actual rudder angle controlled by the ship Send to the ship heading system; Ship heading system, used to control the actual rudder angle of the ship Control of the ship.

9. The control system according to claim 8, characterized in that: The variable universe fuzzy PID controller also includes: Variable domain adjustment mechanism, used to design a heading deviation based on a proportional function and the rate of change of deviation The expansion factor , , using fuzzy control method to adaptively design the output parameter adjustment amount The expansion factor .

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