Ship course control method and system

Abstract

The invention discloses a ship course control method and system, and belongs to the technical field of adaptive step length neurodynamics, computer vision target extraction and remote sensing image processing and control, and the method comprises the steps: constructing a nonlinear system model for ship course control; constructing an error function based on the nonlinear system model; the error function is adjusted through an error evolution formula; obtaining a control input expression based on the adjusted error function and the nonlinear system model; and constructing an integral enhanced zero gradient controller, solving the integral enhanced zero gradient controller to obtain control input, and obtaining optimized control input through iterative optimization. Compared with an existing similar controller, the integral enhanced zero gradient controller provided by the invention has the advantages that the precision and the response speed are remarkably improved, and the integral enhanced zero gradient controller has higher anti-noise capability and higher robustness.

Description

Technical Field

[0001] The present invention belongs to the field of adaptive step-length neural dynamics, computer vision target extraction, remote sensing image processing and control technology, and in particular relates to a ship heading control method and system. Background Art

[0002] Traditional ship heading control methods are mainly divided into three categories: classical control methods, intelligent control methods and robust control methods. The classical control method is represented by PID control, which realizes control through a simple feedback mechanism, but it has poor adaptability in nonlinear and complex environments and is not robust enough to parameter changes and environmental disturbances. Intelligent control methods include fuzzy control, neural network control and intelligent optimization algorithms. Fuzzy control uses fuzzy logic to process uncertain information, but the design and adjustment are complex and require a lot of expert knowledge; neural network control relies on the self-learning ability of artificial neural networks, but requires a lot of data for training and has high computing resource requirements; intelligent optimization algorithms (such as genetic algorithms and particle swarm algorithms) realize control through global optimization search, but the computational complexity is high and the real-time performance is poor. Robust control methods include sliding mode control and adaptive control. Sliding mode control responds to external interference by designing sliding mode surfaces, but it is easy to produce high-frequency jitter; adaptive control adapts to environmental changes by adjusting controller parameters online, but the implementation process is complex and has high requirements for system modeling.

[0003] With the development of the global shipping industry, ship heading control technology has become increasingly important in ensuring navigation safety and improving navigation efficiency. In actual navigation, ships need to maintain a stable heading in a complex and changing environment, including coping with external disturbances such as ocean currents and wind. Traditional ship heading control methods often show poor robustness and adaptability when facing these complex nonlinear environments. In recent years, with the development of control theory and neural network technology, control methods based on neurodynamics have shown significant advantages in solving nonlinear control problems. In particular, the ZeroingGradient (ZG) method, by combining Zeroing Dynamics (ZD) and Gradient Dynamics (GD), provides a control strategy that does not require inverse operation and has good convergence characteristics. However, the existing ZG controller often lacks sufficient anti-interference ability when facing noise interference, which limits its effect in practical applications. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a ship heading control method and system to solve the above problems existing in the prior art.

[0005] To achieve the above object, the present invention provides a ship heading control method, comprising:

[0006] Construct a nonlinear system model for ship heading control;

[0007] Constructing an error function based on the nonlinear system model; and adjusting the error function through an error evolution formula;

[0008] Obtaining a control input expression based on the adjusted error function and the nonlinear system model;

[0009] An integral enhanced zeroing gradient controller is constructed, the integral enhanced zeroing gradient controller is solved to obtain a control input, and an optimized control input is obtained through iterative optimization.

[0010] Optionally, the process of constructing a nonlinear system model for ship heading control includes: constructing a ship heading tracking model, converting the ship heading tracking model through nonlinear control theory, and introducing noise disturbance interference terms to obtain a nonlinear system model for ship heading control.

[0011] Optionally, the error function is an error function between the real-time output and the expected output of the nonlinear system model, which is expressed as follows:

[0012] z1(t)=y(t)-y d (t)

[0013] Among them, y d (t) represents the expected output, y(t) represents the system output, and z1(t) represents the output error.

[0014] Optionally, the control input expression is:

[0015]

[0016] Among them, x1(t) represents the yaw angle, x2(t) represents the yaw angular velocity; u(t) represents the control input, that is, the rudder angle; y(t) represents the system output, that is, the yaw angle; represents the second-order derivative of the system output, It represents the first-order derivative of the system output, σ0 represents the gain of the control input; σ1 represents the linear coefficient of the yaw angular velocity; σ2 represents the nonlinear coefficient of the yaw angular velocity, and θ represents the adjustment factor.

[0017] Optionally, the process of constructing an integral-enhanced zeroing gradient controller includes:

[0018] The error integral term is introduced to improve the zeroing gradient controller and obtain the integral enhanced zeroing gradient controller. The expression of the integral enhanced zeroing gradient controller is as follows:

[0019]

[0020] Among them, α>0, represents the convergence parameter, β represents the integral coefficient, is the first-order derivative of the control input.

[0021] Optionally, the optimization control process includes: iteratively updating the state variables of the system, wherein the state variables include the yaw angle and the acceleration of the yaw angle, and calculating the error between the yaw angle output by the system and the expected yaw angle based on the updated state variables; if the error does not converge, returning to the setting step of the error function until the error converges or the current number of iterations exceeds the preset maximum number of iterations, to obtain the optimized control input.

[0022] The present invention also provides a ship heading tracking system, comprising:

[0023] Model building module, used to build a ship heading tracking model;

[0024] A model conversion module, used for converting the ship heading tracking model into a nonlinear system model by using nonlinear control theory;

[0025] An adaptive step size establishment module, used for setting an error function based on the nonlinear system model, and establishing an integral enhanced zeroing gradient controller based on the error function;

[0026] The measurement error introduction module is used to introduce the measurement error term that is resistant to noise disturbance, construct the error function of the integral enhanced zeroing gradient controller, and output the information at the future time;

[0027] The target extraction module is used to solve the problem by using the integral enhanced zeroing gradient controller, determine the optimal threshold by the optimization method, and complete the extraction of the ship heading information.

[0028] The present invention also provides a computing device for executing the ship heading control method.

[0029] The present invention also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the ship heading control method.

[0030] Compared with the prior art, the present invention has the following advantages and technical effects:

[0031] The present invention constructs a nonlinear system model for ship heading control; constructs an error function based on the nonlinear system model; adjusts the error function through an error evolution formula; obtains a control input expression based on the adjusted error function and the nonlinear system model; constructs an integral enhanced zeroing gradient controller, solves the integral enhanced zeroing gradient controller to obtain a control input, and obtains an optimized control input through iterative optimization.

[0032] The present invention uses an optimized adaptive step size to calculate future time information at each moment, avoiding speed saturation of the solution and reducing the error of the model under a fixed step size; in the adaptive step size calculation process, the error information of the estimated future moment is fully utilized to improve the accuracy of the IEZG controller, thereby improving the accuracy of the ship's heading tracking; solving the future time information based on the IEZG controller avoids the pseudo-inverse of the nonlinear matrix and improves the calculation efficiency. Compared with the existing similar controllers, the IEZG controller proposed by the present invention not only has a significant improvement in accuracy and response speed, but also has stronger noise resistance and higher robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0034] Figure 1 This is a structural diagram of an IEZG controller of a ship heading control system according to an embodiment of the present invention;

[0035] Figure 2 The ship trajectory tracking performance of different controllers under constant noise v(t)=100 interference in the embodiment of the present invention;

[0036] Figure 3 The ship trajectory tracking performance of different controllers under constant noise under random noise v(t)=0.01×[-1,1] interference according to an embodiment of the present invention. DETAILED DESCRIPTION

[0037] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0038] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0039] Embodiment 1

[0040] This embodiment provides a ship heading tracking method based on an integral enhanced zeroing gradient controller method, comprising:

[0041] S1. Establish system model:

[0042] By modeling the external environmental factors (such as ocean currents and wind speed) to which the ship is subjected during actual navigation, an integrated enhanced zeroing gradient (IEZG) controller is proposed. Figure 1 shown.

[0043] Since the ship is affected by external factors such as wind and waves during navigation, the ship may deviate from the predetermined course. In order to ensure safe and accurate navigation, it is necessary to effectively control the course of the ship. This embodiment provides an equation describing the relationship between the rudder angle and the yaw angular velocity, and the formula is as follows:

[0044]

[0045] Among them, x2(s) represents the representation of yaw angular velocity in the frequency domain, u(s) represents the representation of rudder angle in the frequency domain, K represents the gain constant, and T represents the time constant. This formula describes the relationship between rudder angle and yaw angular velocity in the case of small rudder angle.

[0046] Preferably, the previous function does not take into account many factors that affect the relationship between rudder angle and yaw rate, such as ship size, water depth, water salinity, etc. Therefore, a new function is proposed, especially for large rudder angles, demonstrated by a reverse spiral maneuver, introducing the Norrbinn coefficient α:

[0047] In order to describe the nonlinear effect at large rudder angles, a new equation is introduced as follows:

[0048]

[0049] Among them, x2 represents the representation of yaw angular velocity in the frequency domain, represents the second-order derivative of x2, represents the first-order derivative of x2, and u represents the control input, i.e. the rudder angle. This formula takes nonlinear factors into consideration, making it more accurate when operating at large rudder angles.

[0050] First, a nonlinear system model of ship heading control is established to describe the relationship between the rudder angle and the yaw angular velocity. The formula is as follows:

[0051]

[0052] Among them, x1(t) represents the yaw angle, x2(t) represents the yaw angular velocity, represents the yaw acceleration, u(t) represents the control input, i.e. the rudder angle, y(t) represents the system output, i.e. the yaw angle, σ0 represents the gain of the control input, σ1 represents the linear coefficient of the yaw velocity, and σ2 represents the nonlinear coefficient of the yaw velocity.

[0053] S2. Construct error function:

[0054] Based on the zeroing dynamic error method, the error is detected and controlled by constructing an error function, the formula is as follows:

[0055] z1(t)=y(t)-y d (t),

[0056] Among them, y d (t) represents the expected output, that is, the expected yaw angle, and z1(t) is the output error.

[0057] S3, adjustment error:

[0058] The error is adjusted by controlling the evolution formula, which is as follows:

[0059]

[0060] Get the yaw angular velocity x2(t):

[0061]

[0062] It represents the expected output, that is, the first-order derivative of the expected yaw angle, that is, the expected yaw rate.

[0063] In order to obtain the control input, i.e. the rudder angle u(t), the control strategy needs to be further adjusted. Considering that the control input variable u(t) has not yet been introduced into the controller, the auxiliary error variable z2(t) is defined as follows:

[0064]

[0065] A control strategy similar to that of z1(t) is adopted for z2(t), and the formula is as follows:

[0066]

[0067] Here, θ represents the adjustment factor.

[0068] S4. Calculate control input:

[0069] Rearranging the control evolution formula of S3 above yields:

[0070]

[0071] Thus, the specific expression of the control input u(t) is obtained, and the formula is as follows:

[0072]

[0073] S5. Construct the zeroing gradient (ZG) controller:

[0074] In order to avoid the control variable u(t) falling into singularity, the specific expression of the control input u(t) can be transformed into a minimization root-finding problem. Therefore, a square-based energy function ∈(t) is constructed:

[0075]

[0076] where ρ(t) is defined as:

[0077]

[0078] Considering that the gradient descent method seeks a solution through the negative gradient direction, a zeroed gradient (ZG) controller of the following form is designed for the ship tracking problem:

[0079]

[0080] Where α>0 represents the convergence parameter.

[0081] S6. On the basis of , it is particularly important to consider that when the controller is subject to a small disturbance, the tracking performance cannot achieve the desired accuracy. Therefore, an error integral term is added to construct an integral enhanced zeroing gradient controller, and the controller formula is as follows:

[0082]

[0083] Where β represents the integral coefficient.

[0084] S7. Numerical simulation: To verify the effectiveness of the IEZG controller, the following experimental settings were performed: MATLAB R2022a was run on a computer with an Intel(R) Core(TM) i5-9400F CPU (2.90GHz) and 16.00GB RAM to compare the performance of the IEZG controller, the zeroed gradient (ZG) controller, and the adaptive zeroed gradient (AZG) controller under constant noise and random noise. The noise was set to constant noise v(t) = 100 and random noise v(t) = 0.01×[-1,1]. The experimental parameters include initial conditions x1(0) = x2(0) = 0, u(0) = 100sin(2πt), controller parameters σ0 = 1, σ1 = 0.5, σ2 = 0, and the desired trajectory y d (t) = sin(0.2πt), and the total simulation time is T = 15 seconds. The evaluation indicators include trajectory tracking error, control input, response time, and steady-state error. Figure 2The output tracking performance of different controllers for ship trajectory control (1) under the disturbance of constant noise v(t) = 100, (a), (b), (c), (d) represent the output trajectory and expected path, output tracking error, logarithmic scale of output tracking error, and control input generated by IEZG controller respectively; (e), (f), (g), (h) represent the output trajectory and expected path, output tracking error, logarithmic scale of output tracking error, and control input generated by adaptive zero gradient (AZG) controller for comparison respectively; (i), (j), (k), (l) represent the output trajectory and expected path, output tracking error, logarithmic scale of output tracking error, and control input generated by zero gradient (ZG) controller for comparison respectively. Figure 3 The output tracking performance of different controllers for ship trajectory control under random noise v(t) = 0.01 × [-1, 1] interference, (a), (b), (c), (d) respectively represent the output trajectory and expected path, output tracking error, output tracking error logarithmic scale, and control input generated by the IEZG controller; (e), (f), (g), (h) respectively represent the output trajectory and expected path, output tracking error, output tracking error logarithmic scale, and control input generated by the adaptive zeroing gradient (AZG) controller for comparison; (i), (j), (k), (l) respectively represent the output trajectory and expected path, output tracking error, output tracking error logarithmic scale, and control input generated by the zeroing gradient (ZG) controller for comparison. The effectiveness and noise resistance of the IEZG controller proposed in this invention are verified by simulation experiments, and it shows excellent performance in solving the ship heading control problem.

[0085] S8, the optimization control process includes: updating the state variables of the system, mainly through iterative updating y(t) = x1(t), y(t) is the newly obtained yaw angle, and the error with the desired yaw angle is calculated. If there is no convergence, return to the error function setting step until the IEZG controller can make the residual error converge to or remain within the error limit globally and exponentially. or until the current number of iterations exceeds the preset maximum number of iterations, where any ω∈(0,1), represents the coefficient used to define the error boundary in the integral enhanced zeroing gradient controller. In this way, the optimized control input is obtained to achieve accurate control of the ship's heading.

[0086] This embodiment also provides a computing device, which is used to execute the ship heading control method.

[0087] In order to verify the effectiveness of the IEZG controller, the following experimental settings were carried out: MATLAB R2022a was run on a computer with an Intel(R) Core(TM) i5-9400F CPU (2.90GHz) and 16.00GB RAM to compare the performance of the IEZG controller, ZG controller, and AZG controller under constant noise and random noise. The noise was set to constant noise v(t) = 100 and random noise v(t) = 0.01×[-1,1]. The experimental parameters include initial conditions x1(0) = x2(0) = 0, u(0) = 100sin(2πt), controller parameters σ0 = 1, σ1 = 0.5, σ2 = 0, and the desired trajectory y d (t) = sin(0.2πt), and the total simulation time is T = 15 seconds. The evaluation indicators include trajectory tracking error, control input, response time, and steady-state error.

[0088] This embodiment further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is executed by a processor, the device where the storage medium is located is controlled to execute the ship heading control method provided in this embodiment.

[0089] Embodiment 2

[0090] The present invention also provides a ship heading control system, including: a model building module, a model conversion module, an adaptive step size establishment module, a measurement error introduction module and a target extraction module. The system is specifically implemented as follows:

[0091] Model building module, used to build a ship heading tracking model;

[0092] A model conversion module, used for converting the ship heading tracking model into a nonlinear system model by using nonlinear control theory;

[0093] An adaptive step size establishment module, used for setting an error function based on the nonlinear system model, and establishing an integral enhanced zeroing gradient controller based on the error function;

[0094] The measurement error introduction module is used to introduce the measurement error term that is resistant to noise disturbance, construct the error function of the integral enhanced zeroing gradient controller, and output the information at the future time;

[0095] The target extraction module is used to solve the problem by using the integral enhanced zeroing gradient controller, determine the optimal threshold by the optimization method, and complete the extraction of the ship heading information.

[0096] Embodiment 3

[0097] Since the ship is affected by external factors such as wind and waves during navigation, the ship may deviate from the predetermined course. In order to ensure safe and accurate navigation, it is necessary to effectively control the course of the ship. This embodiment provides an equation describing the relationship between the rudder angle and the yaw angular velocity, and the formula is as follows:

[0098]

[0099] Among them, x2(s) represents the representation of yaw angular velocity in the frequency domain, u(s) represents the representation of rudder angle in the frequency domain, K represents the gain constant, and T represents the time constant. This formula describes the relationship between rudder angle and yaw angular velocity in the case of small rudder angle.

[0100] Preferably, in order to describe the nonlinear effect under large rudder angle conditions, a new equation is introduced as follows:

[0101]

[0102] Among them, x2 represents the representation of the yaw angular velocity in the frequency domain, and u represents the control input, that is, the rudder angle. This formula comprehensively considers nonlinear factors, making it more accurate when operating at large rudder angles.

[0103] This formula takes nonlinear factors into consideration, making it more accurate when operating at large rudder angles.

[0104] Preferably, in order to achieve accurate heading control, the following state space equation is used to describe the dynamic behavior of the system, and the formula is as follows:

[0105]

[0106] Among them, x1(t) represents the yaw angle, x2(t) represents the yaw angular velocity, represents the yaw acceleration, u(t) represents the control input, i.e. the rudder angle, y(t) represents the system output, i.e. the yaw angle, σ0 represents the gain of the control input, σ1 represents the linear coefficient of the yaw velocity, and σ2 represents the nonlinear coefficient of the yaw velocity.

[0107] This set of equations describes the dynamic response of the ship and the influence of the control input on the heading. Through this state space representation, the controller can be designed and analyzed more effectively, thereby achieving precise control of the ship's heading and ensuring safe and stable navigation.

[0108] Preferably, in order to control and monitor the output error, an error function about the real-time output and the expected output is first constructed as follows:

[0109] z1(t)=y(t)-y d (t)

[0110] Among them, y d(t) represents the expected output, that is, the expected yaw angle, and z1(t) is the output error.

[0111] Preferably, based on the zeroing dynamic method, the output is adjusted by the error evolution formula:

[0112]

[0113] get:

[0114]

[0115] In order to obtain the control input u(t), the control strategy needs to be further adjusted. Considering that the control input variable u(t) has not yet been introduced into the controller, the auxiliary error variable z2(t) is defined as follows:

[0116]

[0117] A control strategy similar to that of z1(t) is adopted for z2(t), and the formula is as follows:

[0118]

[0119] Its specific expression is:

[0120]

[0121] By rearranging the above formula, the specific expression of the control input u(t) is as follows:

[0122]

[0123] Preferably, in order to avoid the control variable u(t) falling into singularity, the control problem is transformed into a minimization root-finding problem by constructing a square error function, and the energy function form is as follows:

[0124]

[0125] in,

[0126]

[0127] Preferably, the gradient descent method is used to solve the controller to avoid the control variable u(t) falling into singularity. The controller formula is as follows:

[0128]

[0129] Preferably, in order to solve the problem of insufficient control accuracy under small disturbances, an error integral term is introduced to construct an integrated enhanced zeroing gradient (IEZG) controller, and the formula is as follows:

[0130]

[0131] For comparison, the adaptive zeroing gradient (AZG) controller is introduced, and the formula is as follows:

[0132]

[0133] Among them, λ represents the adaptive coefficient, g(t) is the gradient of the system performance indicator function.

[0134] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A ship heading control method, characterized in that: The following steps are involved: Construct a nonlinear system model for ship heading control; constructing an error function based on the nonlinear system model; and adjusting the error function through an error evolution formula; Based on the adjusted error function and the nonlinear system model, a control input expression is obtained; An integral enhanced zeroing gradient controller is constructed based on a control input expression, the integral enhanced zeroing gradient controller is solved to obtain a control input, and an optimized control input is obtained through iterative optimization.

2. The ship heading control method according to claim 1, characterized in that: The process of constructing a nonlinear system model for ship heading control includes: constructing a ship heading tracking model, transforming the ship heading tracking model through nonlinear control theory, and introducing noise disturbance interference terms to obtain a nonlinear system model for ship heading control.

3. The ship heading control method according to claim 1, characterized in that: The error function is the error function between the real-time output and the expected output of the nonlinear system model, which is expressed as follows: z1(t)=y(t)-y d (t) Among them, y d (t) represents the expected output, y(t) represents the system output, and z1(t) represents the output error.

4. The ship heading control method according to claim 3, characterized in that: The control input expression is: Among them, x1(t) represents the yaw angle, x2(t) represents the yaw angular velocity; u(t) represents the control input, that is, the rudder angle; y(t) represents the system output, that is, the yaw angle; represents the second-order derivative of the system output, It represents the first-order derivative of the system output, σ0 represents the gain of the control input; σ1 represents the linear coefficient of the yaw angular velocity; σ2 represents the nonlinear coefficient of the yaw angular velocity, and θ represents the adjustment factor.

5. The ship heading control method according to claim 4, characterized in that: The process of building an integral-enhanced zeroing gradient controller includes: The error integral term is introduced to improve the zeroing gradient controller and obtain the integral enhanced zeroing gradient controller. The expression of the integral enhanced zeroing gradient controller is as follows: Among them, α>0, represents the convergence parameter, β represents the integral coefficient, is the first-order derivative of the control input.

6. A ship heading control method according to claim 1, characterized in that: The optimization control process includes: iteratively updating the state variables of the system, where the state variables include the yaw angle and the acceleration of the yaw angle, calculating the error between the yaw angle output by the system and the expected yaw angle based on the updated state variables, and if the error does not converge, returning to the setting step of the error function until the error converges or the current number of iterations exceeds the preset maximum number of iterations to obtain the optimized control input.

7. A ship heading tracking system, characterized in that: Model building module, used to build a ship heading tracking model; A model conversion module, used for converting the ship heading tracking model into a nonlinear system model by using nonlinear control theory; An adaptive step size establishment module, used for setting an error function based on the nonlinear system model, and establishing an integral enhanced zeroing gradient controller based on the error function; The measurement error introduction module is used to introduce the measurement error term that is resistant to noise disturbance, construct the error function of the integral enhanced zeroing gradient controller, and output the information at the future time; The target extraction module is used to solve the problem by using the integral enhanced zeroing gradient controller, determine the optimal threshold by the optimization method, and complete the extraction of the ship heading information.

8. A computing device, characterized in that: Used to execute a ship heading control method as described in any one of claims 1-6.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute a ship heading control method as described in any one of claims 1 to 6.

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