A variable universe fuzzy navigation control method for unmanned watercraft towing conditions

Abstract

The present invention relates to the field of vessel control technology, and more specifically to a variable-universe fuzzy navigation control method for unmanned vessel towing conditions, comprising the following steps: S1: constructing a dynamic maneuvering response model for the vessel; S2: constructing a towing parameter model; S3: collecting the vessel's navigation data and calculating, based on the dynamic maneuvering response model and the towing parameter model, a stability index #imgabs0# of the vessel's maneuverability index, a turning index #imgabs1# of the vessel's maneuverability index, a dynamic maneuvering coefficient #imgabs2#, and basic control parameters; S4: designing a fuzzy controller and a parameter tuning module, wherein the fuzzy controller outputs an adjustment value, and the parameter tuning module integrates the adjustment value and basic control parameters to obtain PID parameters; S5: the PID controller outputs a navigation control rudder angle #imgabs3# based on the PID parameters and the navigation data. The present invention can achieve dynamic adaptive control of the vessel's maneuverability characteristics and towing tasks, with improved robustness and stability.

Description

Technical Field

[0001] The present invention relates to the technical field of boat control, and in particular to a variable universe fuzzy navigation control method for unmanned boat towing conditions. Background Art

[0002] With the continuous advancement of intelligent vessel technology, the use of towing mode to perform specific tasks has become increasingly popular, particularly in areas such as marine resource exploration, hydrological and meteorological observation, and maritime military operations, where it has become a standard operating mode. Vessel navigation control for towing missions is one of the key technologies driving the development of intelligent vessels, and its control effectiveness is directly related to the safety and efficiency of mission execution.

[0003] Boat towing missions differ from conventional navigation missions in that, under varying mission requirements, the length and depth of the payload must be dynamically adjusted based on the specific requirements of the towing mission. Furthermore, during the mission, the vessel is subject to environmental factors such as wind, waves, and currents. The vessel's motion, changes in payload, and environmental disturbances combine to form a complex dynamic system, making it extremely difficult to construct an accurate model of the vessel's maneuvering motion. Existing boat control methods are mostly designed for conventional navigation missions and fail to fully consider dynamic payload disturbances and real-time adjustments to mission requirements. Summary of the Invention

[0004] The present invention aims to address at least one of the technical problems existing in the related art. To this end, it provides a variable-universe fuzzy navigation control method for unmanned watercraft towing conditions. This method addresses the technical problem that existing watercraft control methods fail to fully account for dynamic load disturbances and real-time adjustments to mission requirements. This method improves the watercraft's maneuverability and dynamic adaptive control of towing missions, resulting in superior robustness and stability.

[0005] The present invention provides a variable universe fuzzy navigation control method for unmanned watercraft towing conditions, comprising the following steps:

[0006] S1: Based on the boat towing task, a dynamic maneuvering response model of the boat is constructed;

[0007] S2: Based on the real-time towing conditions, the towing parameter model of the boat is constructed with the towline length as the independent variable;

[0008] S3: Collecting navigation data of the boat, and calculating the stability index of the boat maneuverability index according to the dynamic maneuvering response model and the towing parameter model , turning index of ship maneuverability index , dynamic control coefficient and basic control parameters;

[0009] S4: Designing a fuzzy controller and a parameter tuning module, wherein the fuzzy controller outputs an adjustment value, and the parameter tuning module integrates the adjustment value and the basic control parameters to obtain a comprehensive control parameter;

[0010] S5: The PID controller outputs the navigation control rudder angle according to the comprehensive control parameters and navigation data .

[0011] A further improvement of the variable universe fuzzy navigation control method for unmanned watercraft towing conditions of the present invention is that the dynamic control response model in step S1 is ,in, is the bow angular velocity, is the bow angular acceleration, is the rudder angle, The stability index in the boat maneuverability index, It is the turning index in the boat maneuverability index.

[0012] A further improvement of the variable universe fuzzy navigation control method for unmanned watercraft towing conditions of the present invention is that the towing parameter model in step S2 is ,in, is the length of the towline, is the boat turning index function, is the boat stability index function, is the dimensionless stability index in the boat maneuverability index, It is the dimensionless conversion of the turning index in the boat maneuverability index;

[0013] is the boat turning index function and The boat stability index function is obtained through real ship maneuvering test, self-propelled model maneuvering test and numerical calculation ship maneuvering test. Specifically, ;

[0014] in, is the first model coefficient, is the second model coefficient, is the third model coefficient, is the fourth model coefficient, is the fifth model coefficient, is the sixth model coefficient.

[0015] The present invention is a further improvement of the variable universe fuzzy navigation control method for unmanned watercraft towing conditions in that the stability index in step S3 is , turning index of ship maneuverability index The calculation method is ;

[0016] in, is the boat speed, is the length of the boat;

[0017] Dynamic handling coefficient The calculation method is ;

[0018] in, is the stability index of the ship's maneuverability index, It is the turning index in the ship maneuverability index.

[0019] The present invention is a further improvement of the variable domain fuzzy navigation control method for unmanned boat towing working condition, in which the navigation data of the boat in step S3 includes the command heading. and real-time heading , collect the boat's command heading and real-time heading , calculate the heading deviation value , Course deviation change rate value , cumulative value of heading deviation , specifically: , , ,in, For the The heading deviation value at the moment, For the The heading deviation value at the moment, For the The heading deviation change rate value at the moment, For the The command heading at all times, For the Real-time heading at every moment;

[0020] The basic control parameters include the first basic control parameter , the second basic control parameter , the third basic control parameter , the calculation method is:

[0021]

[0022] in, is the natural frequency, is the damping ratio.

[0023] A further improvement of the variable universe fuzzy navigation control method for unmanned watercraft towing conditions of the present invention is that the design of the fuzzy controller in step S4 includes the following steps:

[0024] S41: Design input variables, output variables, domain, fuzzy sets, membership functions, and fuzzy rules of fuzzy controllers;

[0025] The input variables include heading deviation values , Course deviation change rate value , the first fuzzy inference input variable is , the second fuzzy inference input variable is ;

[0026] The output variables include a first output variable: The second output variable is , the third output variable is , the first fuzzy inference output variable is , the second fuzzy inference output variable is , the third fuzzy inference output variable is ;

[0027] variable The basic domain of ,variable The basic domain of ,variable The fuzzy domain is ,variable The fuzzy domain is , For variables The upper limit of the basic domain interval, For variables The upper limit of the basic domain interval, For variables The upper limit value of the fuzzy domain interval, For variables The upper limit value of the fuzzy universe interval;

[0028] The fuzzy set is {NB, NM, NS, ZO, PS, PM, PB}, where NB means negative large, NM means negative medium, NS means negative small, ZO means zero, PS means positive small, PM means positive medium, and PB means positive large;

[0029] The membership function adopts triangular function;

[0030] Design fuzzy rules, use Mamdani fuzzy reasoning, and use the centroid method to defuzzify;

[0031] S42: Designing the first scaling factor of the fuzzy controller based on the towing task , the second scaling factor , the first quantization factor , the second quantization factor , first scale factor , second scale factor , the third scale factor , dynamic factor , the first weight factor , the second weight factor , the third weight factor ;

[0032] Specifically:

[0033]

[0034] in, is the heading deviation value, is the heading deviation change rate value, for The upper limit of the basic domain interval, for The upper limit of the basic domain interval, for The upper limit value of the fuzzy domain interval, for The upper limit value of the fuzzy domain interval, is the first control parameter, is the second control parameter, is the third control parameter, is the fourth control parameter;

[0035] Dynamic Factor , specifically:

[0036]

[0037] in, Dynamic handling coefficient, is the control parameter.

[0038] A further improvement of the variable universe fuzzy navigation control method for unmanned watercraft towing conditions of the present invention is that S4 further includes the following steps:

[0039] The current heading deviation value and the heading deviation rate of change The first fuzzy inference input variable is calculated as and the second fuzzy inference input variable is , specifically:

[0040] , and then the fuzzy controller performs fuzzy reasoning to obtain the first fuzzy reasoning output variable: , the second fuzzy inference output variable is , the third fuzzy inference output variable is .

[0041] A further improvement of the variable universe fuzzy navigation control method for unmanned watercraft towing conditions of the present invention is that S4 further includes the following steps:

[0042] The fuzzy controller outputs the variable according to the first fuzzy inference: , the second fuzzy inference output variable is , the third fuzzy inference output variable is , dynamic factors , first scale factor , second scale factor , the third scale factor , output adjustment amount, the adjustment amount is the first regulation control parameter , the second adjustment control parameter , the third adjustment control parameter ;

[0043] Specifically: .

[0044] A further improvement of the variable universe fuzzy navigation control method for unmanned watercraft towing conditions of the present invention is that it further includes:

[0045] The parameter setting module adjusts the control parameters according to the first , the second adjustment control parameter , the third adjustment control parameter , the first basic control parameter , the second basic control parameter , the third basic control parameter , the first weight factor , the second weight factor , the third weight factor Output comprehensive control parameters, the comprehensive control parameters are as follows:

[0046]

[0047] in, is the first comprehensive control parameter, is the second comprehensive control parameter, is the third comprehensive control parameter.

[0048] A further improvement of the variable universe fuzzy navigation control method for unmanned watercraft towing conditions of the present invention is that step S5 is specifically:

[0049] The PID controller uses the current heading deviation value , Course deviation change rate value , cumulative value of heading deviation , the first comprehensive control parameter , the second comprehensive control parameter , the third comprehensive control parameter , and finally output the navigation control rudder angle , specifically: .

[0050] The present invention addresses the navigation control problem of boat towing tasks, fully utilizes the real-time information of the boat's navigation status and load operation status, and uses a variable universe fuzzy method for navigation control. The beneficial effects achieved are specifically manifested as follows:

[0051] (1) The dynamic maneuvering response model of a vessel involved in this invention is designed specifically for vessel towing missions. It integrates the towing load and the vessel into a unified model for analysis. By simplifying the complex multi-body motion response into a single, integrated maneuvering response model, this model not only reduces model complexity but also accurately describes the basic navigation state of a vessel towing mission.

[0052] (2) The boat towing parameter model proposed in this invention is also designed for boat towing tasks. The model uses the tow cable length as the independent variable and achieves the goal of dynamically adjusting the maneuvering response model through measurable parameters, thereby optimizing the towing process.

[0053] (3) The dynamic maneuvering coefficient designed in the present invention can dynamically adjust the fuzzy domain according to the real-time status of the towing task, ensuring the close coupling between the fuzzy control and the ship status. This method can improve the stability of the control system when the task conditions change.

[0054] (4) The basic parameters designed by the present invention ensure the basic stability of the boat navigation control and enhance the control stability of the system when facing unforeseen interference.

[0055] (5) The variable universe fuzzy navigation control method of the present invention integrates the information of the ship state, towing conditions and navigation control feedback. This method can realize dynamic adaptive control of the ship's maneuvering characteristics and towing tasks, thus having better robustness and stability.

[0056] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0058] Figure 1 It is a schematic diagram of the method principle of the present invention.

[0059] Figure 2 This is a comparison chart of the navigation control effects of the method of the present invention and the traditional method under the working condition of a boat towing task. DETAILED DESCRIPTION

[0060] To make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the present invention. Obviously, the embodiments described are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention.

[0061] The following combination Figure 1 The present invention describes a variable universe fuzzy navigation control method for unmanned watercraft towing conditions, comprising the following steps:

[0062] S1: Based on the boat towing task, a dynamic maneuvering response model of the boat is constructed. The dynamic maneuvering response model can accurately simulate the dynamic behavior of the boat during the towing task, helping to improve the boat's maneuverability under complex towing conditions and ensure navigation safety.

[0063] S2: Based on the real-time towing conditions, a towing parameter model of the boat is constructed with the tow cable length as the independent variable. The towing parameter model can reflect the impact of the towing conditions on the boat performance in real time, helping the control system to more accurately adjust the navigation strategy to adapt to different towing requirements and environmental conditions.

[0064] S3: Collecting navigation data of the boat, and calculating the stability index of the boat maneuverability index according to the dynamic maneuvering response model and the towing parameter model , turning index of ship maneuverability index , dynamic control coefficient As well as basic control parameters, it can comprehensively evaluate the maneuverability of the boat and help improve the accuracy and stability of navigation control;

[0065] S4: Design a fuzzy controller and a parameter tuning module. The fuzzy controller outputs an adjustment value. The parameter tuning module integrates the adjustment value and the basic control parameters to obtain a comprehensive control parameter. The control parameter can be dynamically adjusted according to the real-time working conditions. The adjustment value output by the fuzzy controller can modify the navigation control strategy in real time. The parameter tuning module can integrate these adjustment values ​​and the basic control parameters to obtain a more optimized comprehensive control parameter.

[0066] S5: The PID controller outputs the navigation control rudder angle according to the comprehensive control parameters and navigation data , which can achieve precise control of the boat's navigation posture. The stability and reliability of the PID controller ensure the continuity and accuracy of navigation control, which helps to improve the overall performance of the boat in towing tasks.

[0067] In a preferred embodiment of the variable universe fuzzy navigation control method for unmanned watercraft towing conditions of the present invention, the dynamic control response model in step S1 is ,in, is the bow angular velocity, is the bow angular acceleration, is the rudder angle, The stability index in the boat maneuverability index, It is the turning index in the boat maneuverability index.

[0068] Optimally, this dynamic maneuvering response model accurately reflects the maneuvering characteristics of the unmanned vessel under towing conditions. Bow angular velocity and bow angular acceleration are key parameters describing the dynamic steering behavior of the vessel, and they exhibit a complex nonlinear relationship with the rudder angle. The stability index and turning index within the vessel's maneuverability index characterize the vessel's stability and turning performance, respectively, during steering. Accurately modeling and analyzing these parameters can further optimize navigation control strategies and enhance the maneuverability and stability of the unmanned vessel during towing missions.

[0069] Furthermore, the drag parameter model in step S2 is ,in, is the length of the towline, is the boat turning index function, is the boat stability index function, is the dimensionless stability index in the boat maneuverability index, It is the dimensionless conversion of the turning index in the boat maneuverability index;

[0070] is the boat turning index function and The boat stability index function is obtained through real ship maneuvering test, self-propelled model maneuvering test and numerical calculation ship maneuvering test. Specifically, ;

[0071] in, is the first model coefficient, is the second model coefficient, is the third model coefficient, is the fourth model coefficient, is the fifth model coefficient, is the sixth model coefficient.

[0072] Optimally, the towing parameter model fully considers the actual maneuverability of the boat by identifying its turning and stability index functions, making the navigation control strategy more consistent with the boat's actual motion patterns. This helps improve the adaptability and robustness of the unmanned boat under complex towing conditions, ensuring stable navigation under various conditions. Furthermore, each coefficient in the model, such as the first through sixth model coefficients, has been carefully fitted and optimized to ensure its accuracy and practicality. This enables the navigation control strategy to dynamically adjust to changes in towing parameters under different conditions, further improving the unmanned boat's navigation stability and control accuracy.

[0073] Specifically, the stability index in step S3 , turning index of ship maneuverability index The calculation method is ;

[0074] in, is the boat speed, is the length of the boat;

[0075] Dynamic handling coefficient The calculation method is ;

[0076] in, is the stability index of the ship's maneuverability index, It is the turning index in the ship maneuverability index.

[0077] Specifically, the navigation data of the boat in step S3 includes the command heading and real-time heading , collect the boat's command heading and real-time heading , calculate the heading deviation value , Course deviation change rate value , cumulative value of heading deviation , specifically: , , ,in, For the The heading deviation value at the moment, For the The heading deviation value at the moment, For the The heading deviation change rate value at the moment, For the The command heading at all times, For the Real-time heading at every moment;

[0078] The basic control parameters include the first basic control parameter , the second basic control parameter , the third basic control parameter ,

[0079] The PID controller now uses the current heading deviation value , Course deviation change rate value , cumulative value of heading deviation , the first basic control parameter , the second basic control parameter , the third basic control parameter , will output a leading rudder angle , specifically: ;

[0080] Will Substitution The characteristic equation of the closed-loop system model is obtained as follows: , by correlating the natural frequencies and damping ratio To construct the coefficient relationship, ensure that the roots of the characteristic equation have negative real parts, and obtain the first basic control parameter in step S3 , the second basic control parameter , the third basic control parameter The value of

[0081] in, is the natural frequency, is the damping ratio.

[0082] By accurately calculating and reasonably setting these parameters, the control system can maintain stable performance under various working conditions, while improving the system's response speed and accuracy, providing more reliable technical support for the autonomous navigation of unmanned vessels.

[0083] Furthermore, the design of the fuzzy controller in step S4 includes the following steps:

[0084] S41: Design input variables, output variables, domain, fuzzy sets, membership functions, and fuzzy rules of fuzzy controllers;

[0085] The input variables include heading deviation values , Course deviation change rate value , the first fuzzy inference input variable is , the second fuzzy inference input variable is ;

[0086] The output variables include a first output variable: The second output variable is , the third output variable is , the first fuzzy inference output variable is , the second fuzzy inference output variable is , the third fuzzy inference output variable is ;

[0087] variable The basic domain of ,variable The basic domain of ,variable The fuzzy domain is ,variable The fuzzy domain is , For variables The upper limit of the basic domain interval, For variables The upper limit of the basic domain interval, For variables The upper limit value of the fuzzy domain interval, For variables The upper limit value of the fuzzy universe interval;

[0088] The fuzzy set is {NB, NM, NS, ZO, PS, PM, PB}, where NB means negative large, NM means negative medium, NS means negative small, ZO means zero, PS means positive small, PM means positive medium, and PB means positive large;

[0089] The membership function adopts triangular function;

[0090] Design fuzzy rules, use Mamdani fuzzy reasoning (Mamdani fuzzy reasoning is a widely used reasoning method in the field of fuzzy logic, proposed by Egyptian scholar Ebrahim Mamdani in 1975), and use the center of gravity method to defuzzify;

[0091] S42: Designing the first scaling factor of the fuzzy controller based on the towing task , the second scaling factor , the first quantization factor , the second quantization factor , first scale factor , second scale factor , the third scale factor , dynamic factor , the first weight factor , the second weight factor , the third weight factor ;

[0092] Specifically:

[0093]

[0094] in, is the heading deviation value, is the heading deviation change rate value, for The upper limit of the basic domain interval, for The upper limit of the basic domain interval, for The upper limit value of the fuzzy universe interval, for The upper limit value of the fuzzy domain interval, is the first control parameter, is the second control parameter, is the third control parameter, is the fourth control parameter;

[0095] The expansion factor construction method of the fuzzy controller is designed according to the towing task. The expansion factor of the fuzzy controller is designed in the form of a mathematical function to intuitively represent the expansion and contraction degree of the domain range, so as to achieve the effect of dynamic adjustment of the fuzzy domain following error.

[0096] Dynamic Factor , specifically:

[0097]

[0098] in, Dynamic handling coefficient, is the control parameter.

[0099] Preferably, by carefully designing the input variables, output variables, domain, fuzzy sets, membership functions and fuzzy rules of the fuzzy controller, the flexibility and adaptability of the control system can be significantly improved, so that the control system can more accurately handle the complex navigation conditions of the unmanned boat under towing conditions and effectively deal with various uncertainties and interferences. The use of fuzzy sets and membership functions enables the control system to handle changes in input variables more smoothly, avoiding sudden changes and instability in traditional control methods. The scaling factor, quantization factor, proportional factor and dynamic factor of the fuzzy controller are designed according to the specific requirements of the towing task, further enhancing the robustness and adaptability of the control system. The introduction of these factors enables the control system to dynamically adjust the control strategy and output according to different navigation states and towing task requirements, thereby achieving more accurate and stable navigation control.

[0100] Furthermore, S4 further includes the following steps:

[0101] The navigation data of the boat is calculated according to step S3, and the current heading deviation value is and the heading deviation rate of change The first fuzzy inference input variable is calculated as and the second fuzzy inference input variable is , specifically:

[0102] , and then the fuzzy controller performs fuzzy reasoning to obtain the first fuzzy reasoning output variable: , the second fuzzy inference output variable is , the third fuzzy inference output variable is .

[0103] Preferably, the first fuzzy inference output variable is the adjustment amount for the heading control gain, the second fuzzy inference output variable is the heading control deviation adjustment coefficient, and the third fuzzy inference output variable is the heading control deviation rate adjustment coefficient. By calculating these three fuzzy inference output variables, the adjusted heading control parameters can be obtained, thereby achieving precise control of the unmanned watercraft's heading. Based on the accumulated heading deviation value, the domain of the fuzzy controller is adjusted to improve the control system's response speed and stability, thereby enhancing the control system's accuracy and robustness.

[0104] Furthermore, S4 further includes the following steps:

[0105] The fuzzy controller outputs the variable according to the first fuzzy inference: , the second fuzzy inference output variable is , the third fuzzy inference output variable is , dynamic factors , first scale factor , second scale factor , the third scale factor , output adjustment amount, the adjustment amount is the first regulation control parameter , the second adjustment control parameter , the third adjustment control parameter ;

[0106] Specifically: .

[0107] Specifically, it also includes:

[0108] The parameter setting module adjusts the control parameters according to the first , the second adjustment control parameter , the third adjustment control parameter , the first basic control parameter , the second basic control parameter , the third basic control parameter , the first weight factor , the second weight factor , the third weight factor Output comprehensive control parameters, the comprehensive control parameters are as follows:

[0109]

[0110] in, is the first comprehensive control parameter, is the second comprehensive control parameter, is the third comprehensive control parameter.

[0111] Through three independent fuzzy inference processes, the fuzzy controller accurately infers the control variables for the unmanned vessel's heading, speed, and depth. This improves control flexibility and better adapts to the unmanned vessel's navigation requirements under complex towing conditions. By outputting adjustment variables—the first, second, and third adjustment control parameters—the fuzzy controller can perform preliminary adjustments to the unmanned vessel's navigation state, laying the foundation for subsequent precise control. The introduction of dynamic and proportional factors further enhances the fuzzy controller's adaptability. The dynamic factor adjusts to the dynamic changes in the current operating conditions, ensuring that the control strategy remains synchronized with the actual situation. The proportional factor can be flexibly set to meet different control requirements, enabling precise adjustment of control accuracy. The combined effect of these factors makes the fuzzy controller more robust and stable in the navigation control of unmanned vessels.

[0112] Preferably, the parameter tuning module's comprehensive computational capabilities combine the fuzzy controller's initial adjustment results with basic control parameters, while also taking into account the importance of different control parameters in the integrated control. This comprehensive computational approach not only improves control accuracy but also enables comprehensive monitoring and adjustment of the unmanned vessel's navigational state. By outputting the first, second, and third integrated control parameters, the parameter tuning module ensures the unmanned vessel maintains a stable navigational state during towing operations, enhancing navigational safety and reliability.

[0113] Specifically, step S5 is as follows:

[0114] The PID controller uses the current heading deviation value , Course deviation change rate value , cumulative value of heading deviation , the first comprehensive control parameter , the second comprehensive control parameter , the third comprehensive control parameter , and finally output the navigation control rudder angle , specifically: , a feedback control algorithm is adopted, which uses the heading deviation value, heading deviation change rate value, and heading deviation cumulative value as algorithm input and outputs the navigation control rudder angle.

[0115] The present invention addresses the navigation control problem of boat towing tasks, fully utilizes the real-time information of the boat's navigation status and load operation status, and uses a variable universe fuzzy method for navigation control. The beneficial effects achieved are specifically manifested as follows:

[0116] (1) The dynamic maneuvering response model of a vessel involved in this invention is designed specifically for vessel towing missions. It integrates the towing load and the vessel into a unified model for analysis. By simplifying the complex multi-body motion response into a single, integrated maneuvering response model, this model not only reduces model complexity but also accurately describes the basic navigation state of a vessel towing mission.

[0117] (2) The boat towing parameter model proposed in this invention is also designed for boat towing tasks. The model uses the tow cable length as the independent variable and achieves the goal of dynamically adjusting the maneuvering response model through measurable parameters, thereby optimizing the towing process.

[0118] (3) The dynamic maneuvering coefficient designed in the present invention can dynamically adjust the fuzzy domain according to the real-time status of the towing task, ensuring the close coupling between the fuzzy control and the ship status. This method can improve the stability of the control system when the task conditions change.

[0119] (4) The basic parameters designed by the present invention ensure the basic stability of the boat navigation control and enhance the control stability of the system when facing unforeseen interference.

[0120] (5) The variable universe fuzzy navigation control method described in the present invention integrates the information of the ship state, towing conditions and navigation control feedback. This method can realize dynamic adaptive control of the ship's maneuvering characteristics and towing tasks, thereby having better robustness and stability.

[0121] The variable universe fuzzy navigation control method of the present invention is compared with the traditional representative control algorithm. Figure 2As shown in the figure, the control effects of the boat towing task under no-load condition, half-load condition (towing length 500m), and full-load condition (towing length 1000m) are shown. The real-time heading tracking curves of the control method of the present invention (the present technical method) and the traditional PID method (PID method) are shown. The command heading is a constant value of 30°, and the horizontal axis is time (0-300 seconds). The traditional PID method has a large overshoot, the heading fluctuation amplitude is about 8°, and it takes about 250 seconds to enter a stable state; while the control method of the present invention responds quickly, with only a small overshoot and a heading fluctuation amplitude of about 8°. The amplitude of the fluctuation is about 5°, the convergence trend is smooth, and it stabilizes in about 150 seconds. Although the control method of the present invention (the present technical method) and the traditional PID method (PID method) can ultimately track the command heading, the control method of the present invention has significant advantages in dynamic response speed and stability, demonstrating more precise heading control capabilities. By comparison, the control method of the present invention effectively suppresses the heading deviation caused by load changes through load compensation and adaptive adjustment, overcomes the influence of the nonlinear characteristics of the towing system, and can still achieve high-precision and high-robustness heading control under various working conditions. The variable domain fuzzy navigation control method of the present invention for boat towing tasks has better control effect and has consistent effect under various towing task conditions.

[0122] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A variable universe fuzzy navigation control method for unmanned watercraft towing conditions, characterized by: The steps include: S1: Based on the boat towing task, a dynamic maneuvering response model of the boat is constructed; The dynamic control response model is ,in, is the bow angular velocity, is the bow angular acceleration, is the rudder angle, The stability index in the boat maneuverability index, It is the turning index in the boat maneuverability index; S2: Based on the real-time towing conditions, the towing parameter model of the boat is constructed with the towline length as the independent variable; The drag parameter model is ,in, is the length of the towline, is the boat turning index function, is the boat stability index function, is the dimensionless stability index in the boat maneuverability index, It is the dimensionless conversion of the turning index in the boat maneuverability index; is the boat turning index function and The boat stability index function is obtained through real ship maneuvering test, self-propelled model maneuvering test and numerical calculation ship maneuvering test. Specifically, ; in, is the first model coefficient, is the second model coefficient, is the third model coefficient, is the fourth model coefficient, is the fifth model coefficient, is the sixth model coefficient; S3: Collecting navigation data of the boat, and calculating the stability index of the boat maneuverability index according to the dynamic maneuvering response model and the towing parameter model , turning index of ship maneuverability index , dynamic control coefficient and basic control parameters; S4: Designing a fuzzy controller and a parameter tuning module, wherein the fuzzy controller outputs an adjustment value, and the parameter tuning module integrates the adjustment value and the basic control parameters to obtain a comprehensive control parameter; S5: The PID controller outputs the navigation control rudder angle according to the comprehensive control parameters and navigation data .

2. The variable universe fuzzy navigation control method for unmanned watercraft towing conditions according to claim 1 is characterized in that: Stability index in step S3 , turning index of ship maneuverability index The calculation method is ; in, is the boat speed, is the length of the boat; Dynamic handling coefficient The calculation method is ; in, is the stability index of the ship's maneuverability index, It is the turning index in the ship maneuverability index.

3. The variable universe fuzzy navigation control method for unmanned watercraft towing conditions according to claim 2 is characterized in that: The navigation data of the boat in step S3 includes the command heading and real-time heading , collect the boat's command heading and real-time heading , calculate the heading deviation value , Course deviation change rate value , cumulative value of heading deviation , specifically: , , ,in, For the The heading deviation value at the moment, For the The heading deviation value at the moment, For the The heading deviation change rate value at the moment, For the The command heading at all times, For the Real-time heading at every moment; The basic control parameters include the first basic control parameter , the second basic control parameter , the third basic control parameter , the calculation method is: in, is the natural frequency, is the damping ratio.

4. The variable universe fuzzy navigation control method for unmanned watercraft towing conditions according to claim 3 is characterized in that: The design of the fuzzy controller in step S4 includes the following steps: S41: Design input variables, output variables, domain, fuzzy sets, membership functions, and fuzzy rules of fuzzy controllers; The input variables include heading deviation values , Course deviation change rate value , the first fuzzy inference input variable is , the second fuzzy inference input variable is ; The output variables include a first output variable: The second output variable is , the third output variable is , the first fuzzy inference output variable is , the second fuzzy inference output variable is , the third fuzzy inference output variable is ; variable The basic domain of ,variable The basic domain of ,variable The fuzzy domain is ,variable The fuzzy domain is , For variables The upper limit of the basic domain interval, For variables The upper limit of the basic domain interval, For variables The upper limit value of the fuzzy domain interval, For variables The upper limit value of the fuzzy universe interval; The fuzzy set is {NB, NM, NS, ZO, PS, PM, PB}, where NB means negative large, NM means negative medium, NS means negative small, ZO means zero, PS means positive small, PM means positive medium, and PB means positive large; The membership function adopts triangular function; Design fuzzy rules, use Mamdani fuzzy reasoning, and use the centroid method to defuzzify; S42: Designing the first scaling factor of the fuzzy controller based on the towing task , the second scaling factor , the first quantization factor , the second quantization factor , first scale factor , second scale factor , the third scale factor , dynamic factor , the first weight factor , the second weight factor , the third weight factor ; Specifically: in, is the heading deviation value, is the heading deviation change rate value, for The upper limit of the basic domain interval, for The upper limit of the basic domain interval, for The upper limit value of the fuzzy domain interval, for The upper limit value of the fuzzy domain interval, is the first control parameter, is the second control parameter, is the third control parameter, is the fourth control parameter; Dynamic Factor , specifically: in, Dynamic handling coefficient, is the control parameter.

5. The variable universe fuzzy navigation control method for unmanned watercraft towing conditions according to claim 4 is characterized in that: S4 also includes the following steps: The current heading deviation value and the heading deviation rate of change The first fuzzy inference input variable is calculated as and the second fuzzy inference input variable is , specifically: , and then the fuzzy controller performs fuzzy reasoning to obtain the first fuzzy reasoning output variable: , the second fuzzy inference output variable is , the third fuzzy inference output variable is .

6. The variable universe fuzzy navigation control method for unmanned watercraft towing conditions according to claim 5 is characterized in that: S4 also includes the following steps: The fuzzy controller outputs the variable according to the first fuzzy inference: , the second fuzzy inference output variable is , the third fuzzy inference output variable is , dynamic factors , first scale factor , second scale factor , the third scale factor , output adjustment amount, the adjustment amount is the first regulation control parameter , the second adjustment control parameter , the third adjustment control parameter ; Specifically: .

7. The variable universe fuzzy navigation control method for unmanned watercraft towing conditions according to claim 6 is characterized in that: Also includes: The parameter setting module adjusts the control parameters according to the first , the second adjustment control parameter , the third adjustment control parameter , the first basic control parameter , the second basic control parameter , the third basic control parameter , the first weight factor , the second weight factor , the third weight factor Output comprehensive control parameters, the comprehensive control parameters are as follows: in, is the first comprehensive control parameter, is the second comprehensive control parameter, is the third comprehensive control parameter.

8. The variable universe fuzzy navigation control method for unmanned watercraft towing conditions according to claim 7 is characterized in that: Step S5 is specifically as follows: The PID controller uses the current heading deviation value , Course deviation change rate value , cumulative value of heading deviation , the first comprehensive control parameter , the second comprehensive control parameter , the third comprehensive control parameter , and finally output the navigation control rudder angle , specifically: .

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