An Adaptive Particle Swarm Optimization PID Ship Heading Control Method

Abstract

This invention discloses an adaptive particle swarm optimization PID method for ship heading control, belonging to the field of ship heading control technology. It solves the problems of existing technologies, such as the difficulty in calculating the coverage width of survey lines in complex seabed topography, the large variation in width of the same straight line, and measurement difficulties. This invention employs a positional PID method for survey line layout. By determining the appropriate step size, the curve is transformed into a set of straight lines, and the ship's survey line is corrected with a fixed step size, ensuring that the final survey line is laid out as close as possible to the isobath. Secondly, an adaptive particle swarm optimization algorithm is introduced to obtain optimal PID parameters, optimizing the positional PID, thereby achieving more stable PID control and obtaining more accurate ship survey lines. Compared with traditional wiring schemes and manually adjusted PID parameters, this invention effectively solves the problem of high overlap between adjacent strips, significantly improving the feasibility and flexibility of the solution, and providing a new and effective solution for marine vessel survey line layout.

Description

Technical Field

[0001] This invention relates to the field of ship heading control technology, and in particular to an adaptive particle swarm optimization PID ship heading control method. Background Technology

[0002] The coverage rate of adjacent strips is one of the important issues in ship layout planning because it indirectly reflects the accuracy of the topography of the survey area. If the coverage rate is too low, the coverage areas of adjacent survey lines may not overlap, resulting in blind spots and missed targets.

[0003] Single-beam echo sounding is a technique that measures water depth by utilizing the propagation characteristics of sound waves in water. Sound waves propagate at a constant speed in a straight line in a homogeneous medium, and are reflected at different interfaces. Using this principle, a sound wave signal is emitted vertically from the transducer of a survey vessel towards the seabed, and the propagation time from emission to reception is recorded. The depth of the seawater is calculated from the speed and time of sound propagation in seawater. Single-beam echo sounding technology has advantages such as high measurement accuracy, high speed, and reliable operation, and can also handle very deep waters. Therefore, this technology is widely used in marine surveying, marine resource development, and hydrogeological investigations, playing an important role in marine ecological protection and resource utilization. This patent uses this method to process the seawater depth of the area to be measured.

[0004] Ship cabling planning primarily employs cabling design along isobaths, which is currently one of the most economical and practical methods. Based on relevant engineering and design experience, laying out survey lines along isobaths can effectively address the issue of low overlap between adjacent strips. However, if the seabed topography is complex, calculating the coverage width of the survey lines using this method becomes relatively difficult, and the width of the same straight survey line can vary significantly, making it difficult to control and potentially leading to measurement difficulties and omissions.

[0005] Therefore, an adaptive particle swarm optimization PID ship heading control method is needed. Summary of the Invention

[0006] To address the challenges of calculating the coverage width of survey lines using traditional methods in complex seabed topography, and the significant and uncontrollable variation in the width of the same survey line, which may still lead to measurement difficulties and omissions, this invention provides an adaptive particle swarm optimization PID ship heading control method. This method utilizes a position-based PID control approach, taking the error between the current and initial seawater depths as input, outputting the ship's heading angle, and then correcting the heading angle in real-time to achieve automatic control. The specific technical solution is as follows:

[0007] An adaptive particle swarm optimization PID ship heading control method includes the following steps:

[0008] S1: Collect seawater depth data and preprocess the seawater depth data using the Kalman filter algorithm;

[0009] S2: Data fitting is performed on seawater depth data to obtain a surface equation, and a tangent plane operation is performed on the surface equation to obtain a seawater isobath map;

[0010] S3: Establish a positional PID controller for controlling the layout of the survey line, and optimize the positional PID parameter values ​​through an adaptive particle swarm optimization algorithm;

[0011] S4: Determine the first survey line based on the seawater isobath map;

[0012] S5: The positional PID controller with optimized parameters is used to set up the survey line, adjust the heading angle in real time, output the next forward coordinate, and input the current seawater depth and the target seawater depth as error values ​​into the positional PID controller for control in real time.

[0013] S6: Determine whether the next forward coordinate is within the corresponding sea area. If it is, then control the layout of the survey line based on the coordinate and continue with step S5. If it is not, then end the layout.

[0014] Preferably, the Kalman filtering algorithm is specifically used as follows:

[0015]

[0016]

[0017] in, It is the system state matrix; It is the predictor of the state matrix; A It is the state transition matrix; B It is the control input matrix; H It is the state observation matrix; It is process noise; It measures noise.

[0018] Preferably, the positional PID expression is as follows:

[0019]

[0020] in e ( k The value represents the wiring error, which is the target seawater depth minus the current seawater depth; the proportional term is... The integral term is The differential term is ; K p This is a proportional control parameter; K i These are integral control parameters;K d These are the differential control parameters.

[0021] Preferably, the particle swarm optimization algorithm is expressed as follows:

[0022]

[0023]

[0024] in, ω The inertial weight for velocity; c 1 For individual acceleration factors; c 2 As a social accelerator; For the first k During the nth iteration, the 1st i The velocity of each particle; For the first k During the nth iteration, the 1st i The location of each particle; To reach the first k Up to the nth iteration, the nth i The best position that each particle can reach; To reach the first k The best positions that all particles have reached up to the next iteration; k This represents the current iteration number; k max This represents the maximum number of iterations.

[0025] Preferably, the specific steps for optimizing positional PID parameter values ​​using the adaptive particle swarm optimization algorithm are as follows:

[0026] S01: Initialize the particle swarm parameters and define the formula for the inertia weight ω;

[0027] S02: Randomly initialize the velocity and position of each particle;

[0028] S03: Calculate the fitness value for each particle;

[0029] S04: For each particle, compare its fitness value with its individual extreme value. If the particle's fitness is greater, then update the individual extreme value.

[0030] S05: For each particle, compare its fitness value with the global extremum. If the particle's fitness is greater, then update the global extremum.

[0031] S06: Iteratively update the particle's velocity and position and update the inertia weight value;

[0032] S07: Perform boundary condition processing;

[0033] S08: Determine if the algorithm termination condition is met: if it is met, end the algorithm and output the optimal result; otherwise, return to S03 and repeat S04~S07.

[0034] Preferably, the inertial weight ω The formula is as follows:

[0035]

[0036] The inertial weight for velocity; This represents the maximum value of the inertia weight. This represents the minimum value of the inertia weight. k This represents the current iteration number; k max This represents the maximum number of iterations.

[0037] Preferably, step S02 specifically involves: in 3-dimensional space, i.e., three parameters to be optimized: K p , K i , K d The process involves randomly generating individuals that satisfy the constraints.

[0038]

[0039] in, express A population initialization matrix of dimension 1. for A random number matrix with dimensions ranging from [0, 1]. , They are respectively The matrix of upper and lower bounds for the values ​​of an individual. , , , The expressions are as follows:

[0040]

[0041] in, rand It represents a random number in the range [0,1].

[0042] Preferably, the seawater depth data is obtained using a single-beam survey method.

[0043] A computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the adaptive particle swarm optimization PID ship heading control method as described above.

[0044] A processor for running a program, wherein the program executes the adaptive particle swarm optimization PID ship heading control method as described above.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0046] 1. This invention, based on the traditional method of finding isobaths in marine surveying, introduces a PID negative feedback adjustment algorithm to design a method for autonomously finding isobath paths. Due to the introduction of PID control, more automatic heading control can be achieved, avoiding errors caused by manual settings. It has superior interference suppression and adjustment capabilities, significantly improving the system's robustness and flexibility, and providing a new and effective solution for marine surveying.

[0047] 2. Compared to traditional PID algorithms that rely on trial and error to manually adjust PID parameters, which can be time-consuming and prone to suboptimal results due to improper parameter settings, adaptive particle swarm optimization (PSO) optimizes PID parameters. This method is simple to use and possesses excellent global optimization capabilities. In its implementation, only the objective function calculation method needs to be designed, and the PID parameters to be optimized need to be input. The algorithm can then quickly find the parameters that minimize the sum of squared errors, significantly improving the accuracy of line finding. Attached Figure Description

[0048] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0049] Figure 1 This is a diagram of a traditional survey line layout.

[0050] Figure 2 It is a three-dimensional topographic map of the sea area to be measured;

[0051] Figure 3 It is a depth contour map of the sea area to be measured;

[0052] Figure 4 This is a block diagram of a positional PID controller.

[0053] Figure 5 This is a flowchart of the adaptive particle swarm optimization algorithm;

[0054] Figure 6 This is an iterative graph of the adaptive particle swarm optimization algorithm;

[0055] Figure 7 This is a flowchart of PID measurement line deployment;

[0056] Figure 8 This is a map showing the results of the automatic route layout survey. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0059] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0060] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0061] The following is combined with Figure 1-8 The embodiments of the present invention will be further described below.

[0062] Traditional survey line layout diagrams, such as Figure 1 As shown in the figure, the survey lines are generally parallel to the contour lines, and arranging survey lines along the contour lines is one of the most widely used methods. However, when the contour lines are distributed in a curved manner, if the traditional route method is still used, there will be situations where the overlap rate of adjacent strips is too high or too low.

[0063] The error between the current seawater depth and the initial seawater depth is used as the input to the control system, which outputs the ship's heading angle in real time. The ship's heading angle is then adjusted and controlled in real time by the positional PID system to achieve the purpose of laying out the route along the isobath.

[0064] In the development of the field of automatic control, various PID automatic control methods have emerged. The traditional PID control method is as follows:

[0065] MERGEFORMAT (1)

[0066] This paper uses positional PID control to automatically control the ship's route, and the expression is as follows:

[0067] MERGEFORMAT (2)

[0068] Where u(k) is the ship's real-time heading angle. This represents the real-time difference between the heading angle and the target angle.

[0069] An adaptive particle swarm optimization algorithm is introduced to optimize and solve for the optimal values ​​of PID parameters, eliminating the need for manual PID parameter tuning and avoiding inaccuracies. This algorithm not only accelerates the PID parameter tuning process but also improves the stability of the positional PID system, achieving better control performance and robustness, and ultimately reaching the optimal wiring scheme.

[0070] Figure 2 and Figure 3 The process of analyzing seawater depth and contour line distribution is shown. Figure 2 In the diagram, the horizontal axis represents the independent variable x, the vertical axis represents the independent variable y, and the seawater depth represents the dependent variable z. The above fitting results were obtained using the MATLAB fitting toolbox. Figure 6 Analysis shows that a cubic equation was used for fitting, and the correlation coefficient R² was 1.000. This proves that using higher-order equations for regression fitting yields excellent results. Therefore, the correlation equation between seawater depth and sea area is as follows:

[0071]

[0072] The Kalman filter algorithm is introduced to process seawater depth data, and its expression is shown below:

[0073] MERGEFORMAT (3)

[0074] MERGEFORMAT (4)

[0075] in, It is the system state matrix; It is the system's input quantity; is the predictor of the state matrix; A is the state transition matrix; B It is the control input matrix; H It is the state observation matrix; It is process noise; It measures noise.

[0076] Based on the processing of seawater depth data, mathematical analysis is performed on the relevant data. Specifically, the MATLAB fitting toolbox is used to construct corresponding surface equations. These surface equations are used to identify different terrains, representing the terrain with surface equations to facilitate subsequent tangent plane operations. Then, tangent planes are drawn for different terrains, with very small inclination angles α to the horizontal plane, approximately dividing the seawater depth into multiple uniform parts, thereby drawing seawater isobath maps.

[0077] The block diagram of positional PID control is as follows: Figure 4 As shown. Positional PID can control the system by comparing its current position with the desired position, due to the error integral. Continuous accumulation, output u(k) This corresponds to the actual position of the actuator. If the control output malfunctions (the current state value of the controlled object is incorrect), then... u(k) Significant changes in the integral term will cause significant changes in the system. Furthermore, in positional PID controllers, even when the integral term reaches saturation, the error will continue to accumulate under the integral action. Once the error begins to reverse, the system needs time to exit the saturation region. Therefore, in... u(k) When the maximum and minimum values ​​are reached, the integration process must cease, and both integration and output limits must be applied to achieve the desired control results. Considering the influence of complex seabed topography, this invention designs a path layout method that autonomously adjusts the heading for a given initial depth. By inputting the error value between the current and initial seawater depths, the method calculates and outputs the heading angle. Under the premise of a 10% overlap rate, the starting position of the survey line is determined. A step size of 0.05 nautical miles is set, and the heading angle is continuously adjusted based on the deviation, thereby realizing a survey line layout scheme along the isobaths.

[0078] The overlap rate mentioned here is calculated as follows:

[0079] For curve surveying, an analogy method is used to traverse all points of the target surveying line and find the point closest to the current position. The formula for calculating the overlap rate is based on existing technology and will not be elaborated here.

[0080] To achieve both effectiveness and convenience in PID parameter optimization, this invention employs the Adaptive Particle Swarm Optimization (APSO) algorithm for parameter tuning.

[0081] The particle swarm optimization algorithm is expressed as follows:

[0082]

[0083]

[0084] in,

[0085]

[0086] ω The inertial weight for velocity; This represents the maximum value of the inertia weight. This represents the maximum value of the inertia weight. c 1 For individual acceleration factors; c 2 As a social accelerator; For the first d During the nth iteration, the 1st i The velocity of each particle; For the first d During the nth iteration, the 1st i The location of each particle; To reach the first d Up to the nth iteration, the nth i The best position that each particle can reach; To reach the first d The best positions that all particles have reached up to the next iteration; k This represents the current iteration number; k max This represents the maximum number of iterations.

[0087] like Figure 5 As shown, the implementation process of the APSO algorithm to optimize PID parameters is as follows:

[0088] First, the definition of inertia weight in the adaptive particle swarm optimization algorithm is defined as follows: Based on the PID parameter tuning principle, this invention uses the ASPO algorithm to solve for the optimal solution of PID parameters. The specific expression of the function is shown in (5), and the optimal parameters of the PID can be selected to minimize the optimization function.

[0089] MERGEFORMAT (5)

[0090] in It is expressed as the sum of the differences between the real-time heading angle and the target angle.

[0091] Next, we begin optimizing the PID parameters:

[0092] STEP 1: Initialize the number of iterations Initialize the particle swarm parameters (particle position and velocity, individual optimal position, global optimal position), and define the formula for the inertia weight ω;

[0093] STEP2: Randomly initialize the velocity and position of each particle;

[0094] In 3D space (i.e., 3 parameters to be optimized): K p 、K i、K d Randomly generate those that satisfy the constraints. (i.e., population size) individuals, the generation operation is:

[0095]

[0096] in, express A population initialization matrix of dimension 1. for A random number matrix with dimensions ranging from [0, 1]. , They are respectively A matrix containing the upper and lower bounds of the values ​​that an individual can take. Note: This represents the dot product of matrices, i.e., element-wise multiplication. , , , The expressions are as follows:

[0097]

[0098] in, express A random number within a given range.

[0099] To prevent Overflow, settings To control Scope:

[0100] MERGEFORMAT (6)

[0101] STEP3: Calculate the fitness value for each particle;

[0102] Assign the initial population individual information to the objective function, and calculate the fitness of the initial population according to equation (5) to find the initial optimal individual.

[0103] STEP4: For each particle, compare its fitness value with its individual extreme value. If the particle's fitness is good, then update the individual extreme value.

[0104] MERGEFORMAT (7)

[0105] STEP5: For each particle, compare its fitness value with the global extremum. If the particle's fitness is good, update the individual extremum.

[0106] MERGEFORMAT (8)

[0107] STEP 6: Iteratively update the particle's velocity and position and update the inertia weight values;

[0108] make Update the iteration count and calculate the next particle.

[0109] STEP 7: Perform boundary condition processing;

[0110] STEP8: Determine if the algorithm termination condition is met: if it is met, end the algorithm and output the optimal result; otherwise, return to STEP3 and repeat STEP4~STEP7.

[0111] To verify that the solved PID parameter values ​​can more effectively control the survey line layout, this invention simulates the ship survey line layout process in the MATLAB platform to test the stability and feasibility of the scheme.

[0112] The results of the partial optimal parameter solutions of the PID obtained after optimization in this example are shown in Table 1.

[0113] Table 1 Optimal Solution of PID Parameters

[0114]

[0115] Figure 6 This is the iterative graph of the adaptive particle swarm optimization algorithm. The optimal solution is obtained after 48 iterations, indicating that the algorithm has good convergence.

[0116] Figure 7 This is a flowchart illustrating the survey line layout process of this invention. The flowchart clearly demonstrates the specific method for survey line layout. The first stage uses Kalman filtering to preprocess historical single-beam survey line data, followed by data fitting. The second stage uses an adaptive particle swarm optimization algorithm to optimize the PID parameters. The third stage utilizes the PID parameters obtained from the adaptive particle swarm optimization algorithm, determines the starting point and target depth through overlap rate, and then uses the ship's heading angle as the PID control object to achieve complete survey line layout. Furthermore, regarding... Figure 7 The first survey line obtained is as follows: According to Figure 3 The isobath analysis and visualization of the sea area to be measured is based on the equal distribution of isobath maps to determine the first boundary isobath between shallow and deep water areas.

[0117] Figure 8 A diagram of the ship's survey line layout scheme is presented. Simulation results show that this scheme can effectively lay out survey lines along the isobaths, with stable and sensitive layout, demonstrating the effectiveness and superiority of the method. The survey line layout scheme obtained in this example is shown in Table 2.

[0118] Table 2 Route Layout Plan

[0119]

[0120] In summary, this invention improves upon the traditional PID control structure by employing a positional PID method. By controlling the error between the current system position and the desired target position, an ideal control result can be obtained. This scheme offers more stable control and stronger robustness. Furthermore, an adaptive particle swarm optimization algorithm is used to optimize the PID parameters. This algorithm is simple to use, robust, and possesses excellent global optimization capabilities. In practical implementation, only the objective function needs to be designed; the algorithm can then be used to find suitable parameters for positional PID control, greatly improving the effectiveness and convenience of the tuning process and effectively solving the problems raised in the background art.

[0121] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.

[0122] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical functional division. In actual implementation, there may be other division methods, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored.

[0123] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0124] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. An adaptive particle swarm optimization PID ship heading control method, characterized in that, Includes the following steps: S1: Collect seawater depth data and preprocess the seawater depth data using the Kalman filter algorithm; S2: Data fitting is performed on seawater depth data to obtain a surface equation, and a tangent plane operation is performed on the surface equation to obtain a seawater isobath map; S3: Establish a positional PID controller for controlling the layout of the survey line, and optimize the positional PID parameter values ​​through an adaptive particle swarm optimization algorithm; S4: Determine the first survey line based on the seawater isobath map; S5: The positional PID controller with optimized parameters is used to set up the survey line, adjust the heading angle in real time, output the next forward coordinate, and input the current seawater depth and the target seawater depth as error values ​​into the positional PID controller for control in real time. S6: Determine whether the next forward coordinate is within the corresponding sea area. If it is, then control the layout of the survey line based on the coordinate and continue with step S5. If it is not, then end the layout. The positional PID expression is as follows: in e ( k The value represents the wiring error, which is the target seawater depth minus the current seawater depth; the proportional term is... The integral term is The differential term is ; K p This is a proportional control parameter; K i These are integral control parameters; K d These are differential control parameters; The specific steps for optimizing positional PID parameters using the adaptive particle swarm optimization algorithm are as follows: S01: Initialize the particle swarm parameters and define the formula for the inertia weight ω; S02: Randomly initialize the velocity and position of each particle; S03: Calculate the fitness value for each particle; S04: For each particle, compare its fitness value with its individual extreme value. If the particle's fitness is greater, then update the individual extreme value. S05: For each particle, compare its fitness value with the global extremum. If the particle's fitness is greater, update the global extremum. S06: Iteratively update the particle's velocity and position and update the inertia weight value; S07: Perform boundary condition processing; S08: Determine if the algorithm termination condition is met: if it is met, end the algorithm and output the optimal result; otherwise, return to S03 and repeat S04~S07. The inertial weight ω The formula is as follows: The inertial weight for velocity; This represents the maximum value of the inertia weight. This represents the minimum value of the inertia weight. k This represents the current iteration number; k max This represents the maximum number of iterations.

2. The adaptive particle swarm optimization PID ship heading control method according to claim 1, characterized in that, The Kalman filter algorithm is as follows: in, It is the system state matrix; It is the predictor of the state matrix; A It is the state transition matrix; B It is the control input matrix; H It is the state observation matrix; It is process noise; It measures noise.

3. The adaptive particle swarm optimization PID ship heading control method according to claim 1, characterized in that, The particle swarm optimization algorithm is expressed as follows: in, ω The inertial weight for velocity; c 1 For individual acceleration factors; c 2 As a social accelerator; For the first k During the nth iteration, the 1st i The velocity of each particle; For the first k During the nth iteration, the 1st i The location of each particle; To reach the first k Up to the nth iteration, the nth i The best position that each particle can reach; To reach the first k The best positions that all particles have reached up to the next iteration; k This represents the current iteration number; k max This represents the maximum number of iterations.

4. The adaptive particle swarm optimization PID ship heading control method according to claim 1, characterized in that, Step S02 specifically involves: in 3-dimensional space, i.e., 3 parameters to be optimized: K p , K i , K d The process involves randomly generating individuals that satisfy the constraints. in, express A population initialization matrix of dimension 1. for A random number matrix with dimensions ranging from [0, 1]. , They are respectively The matrix of upper and lower bounds for the values ​​of an individual. , , , The expressions are as follows: in, rand It represents a random number in the range [0,1].

5. The adaptive particle swarm optimization PID ship heading control method according to claim 1, characterized in that, The seawater depth data is obtained through single-beam surveying.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the adaptive particle swarm optimization PID ship heading control method according to any one of claims 1 to 5.

7. A processor, characterized in that, The processor is used to run a program, wherein the program executes the adaptive particle swarm optimization PID ship heading control method according to any one of claims 1 to 5.

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