A variable structure unmanned surface vehicle control method based on a fuzzy controller

Abstract

The application discloses a variable structure unmanned ship control method based on a fuzzy controller, and comprises the following steps: obtaining a variable structure instruction, the variable structure being changed and generating a variable structure disturbance signal and a variable structure state signal, and sending the variable structure state signal to a first fuzzy controller; obtaining an initial motion speed V and an initial angular velocity W of a ship body through a sensor, and obtaining a moment transfer parameter and a resistance transfer parameter through ship body speed calculation; collecting the motion speed Vt and the angular velocity Wt of the ship body at a time t and generating a feedback signal; the application aims to provide a variable structure unmanned ship control method based on a fuzzy controller, which can describe the transfer parameter of the change of the damping of the unmanned ship according to the speed and the structure change of the unmanned ship, uses the moment transfer parameter and the resistance transfer parameter as the input quantity of the fuzzy controller, controls the ship body, uses a parallel fuzzy controller to control the unmanned ship, and realizes more accurate control effect.

Description

Technical Field

[0001] This invention relates to the field of unmanned surface vessel (USV) control technology, and in particular to a variable structure USV control method based on a fuzzy controller. Background Technology

[0002] Variable structure unmanned surface vessels (USVs) are a type of USV that can autonomously transform its form and perform multiple missions in different aquatic environments. Existing variable structure USVs use variable arresting net structures on both sides of the bow, meaning the opening angle is controllable and can adapt to various operating environments.

[0003] Currently, most control methods for variable structure unmanned surface vessels (USVs) employ fuzzy control. Traditional fuzzy control typically uses the target's error and error rate of change as inputs and employs a single-stage fuzzy controller, resulting in reduced control effectiveness. During the USV's movement, changes in the hull structure alter its motion characteristics, rendering the pre-set initial control scheme ineffective. Summary of the Invention

[0004] The purpose of this invention is to propose a variable structure unmanned surface vessel (USV) control method based on a fuzzy controller. This method can describe the damping changes of the USV based on the speed and structural changes of the USV. The torque transfer parameter and the resistance transfer parameter are used as inputs to the fuzzy controller to control the hull. By using a parallel fuzzy controller to control the USV, a more precise control effect can be achieved.

[0005] To achieve this objective, the present invention adopts the following technical solution: a variable structure unmanned surface vessel control method based on a fuzzy controller, comprising the following steps:

[0006] The variable structure command is obtained, the variable structure undergoes structural change and generates a variable structure disturbance signal and a variable structure state signal, and the variable structure state signal is sent to the first fuzzy controller.

[0007] The initial velocity V and initial angular velocity W of the hull are obtained by sensors, and the torque transfer parameters and drag transfer parameters are obtained by calculating the hull velocity.

[0008] The ship's velocity Vt and angular velocity Wt at time t are collected and a feedback signal is generated. The initial velocity V, initial angular velocity W and feedback signal are used to calculate the heading angle error e, and the heading angle error e is sent to the first fuzzy controller and the second fuzzy controller.

[0009] The first fuzzy controller generates a first signal, and the second fuzzy controller generates a second signal. The first and second signals are processed to obtain a hull adjustment signal. The hull adjustment signal and the variable structure disturbance signal are integrated to obtain the hydrodynamic force for hull movement.

[0010] Preferably, the formula for calculating the torque transfer parameter M is:

[0011] ;

[0012] Where k1 is the gain coefficient; θ1 is the deployment angle of the arresting net on the left side of the unmanned surface vessel, and θ r V1 is the deployment angle of the arresting net on the right side of the unmanned surface vessel; V1 is the hull speed on the left side of the unmanned surface vessel. r denoted as the starboard velocity of the unmanned surface vessel; L is the length of the arresting net; and d is the minimum distance between the left and right arresting nets.

[0013] Preferably, the formula for calculating the resistance transfer parameter N is:

[0014] N = k2(V l sinθ l +V r sinθ r );

[0015] Where k2 is the gain coefficient; θ1 is the deployment angle of the arresting net on the left side of the unmanned surface vessel, θ r V1 is the deployment angle of the arresting net on the right side of the unmanned surface vessel; V1 is the hull speed on the left side of the unmanned surface vessel. r The speed along the starboard side of the unmanned surface vessel;

[0016] Assume that the thrust f1 of the water on the left arresting net is linearly related to the velocity V1 of the unmanned surface vessel on the left side, and the thrust f1 of the water on the right arresting net is linearly related to the velocity V1 of the unmanned surface vessel on the left side. r Speed ​​V of the unmanned surface vessel on its starboard side r It is a linear relationship;

[0017] For different variable structure hulls, let:

[0018] M = f1(τ1, τ2, ..., τ) n ,V);

[0019] N = f2(τ1, τ2, ..., τ) n ,V);

[0020] Where f1 is the functional relationship between the torque transfer parameter M and the structural parameters, τ1, τ2, ..., τ n , where f1 represents the parameters corresponding to different variable structure hulls; f2 represents the functional relationship between the resistance transfer parameter N and the structural parameters.

[0021] Preferably, the total thrust F required by the unmanned surface vessel during the structural change process is obtained through calculation, and the calculation formula is as follows:

[0022] F = F1 + F2;

[0023] F2 = F pre +Fe ;

[0024] Where F1 is the control output thrust without considering structural changes; F2 is the control output thrust correction value under the influence of structural changes; F pre The prediction of F2 is based on the torque transfer parameter M and the drag transfer parameter N. Its magnitude and trend are determined by the drag transfer parameter and torque transfer parameter while the unmanned surface vessel (USV) performs variable structure maneuvers. e To adjust F based on actual error pre Corrections;

[0025] Substituting the formula for calculating total thrust F into the unmanned surface vessel control, we obtain:

[0026]

[0027]

[0028] Where F1 is the thrust required by the left propeller, F r F represents the thrust required by the right propeller. 11 The port thrust obtained from the ship's original control system, F r1 The starboard thrust obtained from the ship's original control system; F 12 and F r2 For correction; F lpre To predict the required port side correction force through structural changes, F rpre To predict the required starboard corrective force through structural changes; F le To adjust F according to the actual situation lpre The correction, F re To adjust F according to the actual situation rpre Corrections.

[0029] Preferably, control optimization is achieved through two fuzzy decision-making processes, where the torque transfer parameter M and the port side correction force F are... lpre and starboard correction force F rpre The correspondence is as follows:

[0030]

[0031] Drag transfer parameter N and port side correction force F lpre and starboard correction force F rpre The correspondence is as follows:

[0032] N = k n (F lpre +F rpre );

[0033] Where k m and k nFor the perturbation system with corresponding directional parameters.

[0034] Preferably, the fuzzy control is converted into linguistic variables to obtain a fuzzy set that expresses a specific fuzzy concept. The linguistic variables are divided into three types: large, medium, and small, accompanied by positive and negative signs.

[0035] Predict the port side correction force F based on the actual situation. lpre and starboard correction force F rpre The range of output voltage variation is set to [-12V, 12V], and the range of voltage correction variation is set to [-3V, 3V], corresponding to the term set {NB(-3), NM(-2), NS(-1), ZE(0), PS(1), PM(2), PB(3)}. The range of heading angle error e is set to [-60°, 60°], and the domain of error rate of change is set to [-1, 1]. Therefore, the formula for calculating the error rate of change ec is:

[0036]

[0037] Among them, e now For the current heading angle error, e last This represents the heading angle error at the previous sampling time.

[0038] Preferably, let x be any number in the intervals [a, b] and [b, c]. Calculate the membership degree of any number x relative to b, and obtain its membership degree corresponding to different values. Calculate the membership degree μ. b The formula for (x) is:

[0039] ;

[0040] Calculate the different values ​​of port side correction force Flpre, starboard side correction force Frpre, heading angle error e, and error change rate ec in different word sets to obtain the membership degree in the corresponding word set.

[0041] Preferably, fuzzy decision-making is used to assign each parameter to different word sets according to its membership degree, and to make corresponding decisions in the rule base based on the words corresponding to the input quantity;

[0042] The fuzzy rules of the first fuzzy controller are:

[0043] While keeping the hull structure unchanged, the motor input voltage is measured multiple times using sensors. Values ​​with large deviations are removed, and the average value is calculated. This average value is taken as the voltage output value under normal conditions.

[0044] By altering the hull structure so that the calculated left and right predicted correction forces at various speeds correspond to the following values ​​in the fuzzy word set: positive (PB), positive (PM), positive small (PS), zero (ZE), negative small (NS), negative medium (NM), and negative large (NB), the hull is made to move forward in a straight line at zero speed. The input voltage of the corresponding motor is then measured by sensors. After multiple measurements and removing values ​​with large deviations, the average value is calculated. This average value is then subtracted from the voltage output value under normal conditions to obtain the predicted correction voltage of the corresponding motor under the corresponding conditions.

[0045] The fuzzy rules of the second fuzzy controller are:

[0046] When e is PB or e is PM, and ec is PM or ec is PB, the error is large and needs to be reduced quickly by correcting the voltage u. le for NB,u re For PB;

[0047] When e is PS or ZE, the voltage value should be adjusted according to the condition of ec.

[0048] When e is positive and ec is negative, the hull is reducing errors, and the output voltage is adjusted according to the reduction of errors.

[0049] When the error e is negative or positive, the hull is reducing the error, and the output voltage is adjusted accordingly based on the reduction of the error.

[0050] Preferably, both the first fuzzy controller and the second fuzzy controller use zero-order Sugeno inference for fuzzy inference. When defuzzifying the data obtained by the corresponding fuzzy controller, the maximum membership method is used, and the output value with the highest membership degree is taken as the control result.

[0051] One beneficial effect of the technical solution of this invention is that by estimating a force trend using structural changes and predicting the ship's motion trend using the estimation result, the control is optimized through fuzzy control based on this trend, thereby achieving rapid ship control. This invention does not require complete modification of the original controller, but rather optimizes it within the existing controller state, thus having a wide range of applications. By applying the prediction and optimization method proposed in this invention, the ship's motion trend can be estimated in advance, and control quantities can be adjusted accordingly to achieve more precise control. Attached Figure Description

[0052] Figure 1 This is a schematic diagram of the structure of an unmanned surface vessel according to an embodiment of the present invention;

[0053] Figure 2 This is a schematic diagram of the control process according to an embodiment of the present invention;

[0054] Figure 3This is a schematic diagram of the forces acting on an unmanned surface vessel according to an embodiment of the present invention;

[0055] Figure 4 This is an embodiment F of the present invention. lpre A diagram illustrating membership degree;

[0056] Figure 5 This is an embodiment F of the present invention. rpre A diagram illustrating membership degree;

[0057] Figure 6 This is a schematic diagram of the membership degree of one embodiment of the present invention;

[0058] Figure 7 This is a schematic diagram of the membership degree of an embodiment of the present invention;

[0059] Figure 8 This is an embodiment of the present invention. lpre A diagram of the rule table;

[0060] Figure 9 This is an embodiment of the present invention. rpre A diagram of the rule table;

[0061] Figure 10 This is an embodiment of the present invention. le A diagram of the rule table;

[0062] Figure 11 This is an embodiment of the present invention. re A diagram of the rule table. Detailed Implementation

[0063] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0064] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0065] In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0066] See Figures 1 to 11 As shown, a variable structure unmanned surface vessel control method based on a fuzzy controller includes the following steps:

[0067] The variable structure command is obtained, the variable structure undergoes structural change and generates a variable structure disturbance signal and a variable structure state signal, and the variable structure state signal is sent to the first fuzzy controller.

[0068] The initial velocity V and initial angular velocity W of the hull are obtained by sensors, and the torque transfer parameters and drag transfer parameters are obtained by calculating the hull velocity.

[0069] The ship's velocity Vt and angular velocity Wt at time t are collected and a feedback signal is generated. The initial velocity V, initial angular velocity W and feedback signal are used to calculate the heading angle error e, and the heading angle error e is sent to the first fuzzy controller and the second fuzzy controller.

[0070] The first fuzzy controller generates a first signal, and the second fuzzy controller generates a second signal. The first and second signals are processed to obtain a hull adjustment signal. The hull adjustment signal and the variable structure disturbance signal are integrated to obtain the hydrodynamic force for hull movement.

[0071] This invention describes the damping changes of an unmanned surface vessel (USV) based on its speed and structural variations. It obtains torque and resistance transfer parameters from the hull speed and uses these parameters as inputs to a fuzzy controller to control the hull. By using two parallel fuzzy controllers (a first and a second fuzzy controller) to control the USV, the invention can estimate the hull's motion trend in advance and make targeted corrections, thereby achieving more precise control.

[0072] This invention estimates a force trend by utilizing structural changes in an unmanned surface vessel (USV) and predicts the vessel's motion trend using the estimation results. Based on this trend, fuzzy control is used for optimized control to achieve rapid response. Compared to existing controllers, this invention achieves control optimization by adding correction values, without requiring a complete modification of the original controller. Instead, optimization is performed within the existing controller's state, thus broadening its applicability.

[0073] In this invention, the target is a variable-structure unmanned surface vessel (USV) with a movable barrier net at its bow for collecting surface debris. The variable-structure USV is a catamaran configuration, with controllable opening angles for the barrier nets on both bows. During navigation, changes in the hull structure alter the vessel's motion characteristics, rendering a fixed control scheme ineffective. This invention optimizes control and predicts motion trends through structural changes in the USV, achieving more precise control.

[0074] Preferably, the formula for calculating the torque transfer parameter M is:

[0075] ;

[0076] Where k1 is the gain coefficient, which is determined based on the actual variable structure, characteristics, and unmanned surface vessel type; θ l θ is the deployment angle of the arresting net on the left side of the unmanned surface vessel. rV represents the deployment angle of the arresting net on the right side of the unmanned surface vessel, in degrees. l V is the port side velocity of the unmanned surface vessel. r d is the speed of the unmanned surface vessel on its starboard side, in m / s; L is the length of the arresting net, in m; d is the minimum distance between the arresting nets on the left and right sides.

[0077] The change in the opening angle of the barrier net affects the hull, including increasing rearward drag and restricting its movement during forward motion; or generating forces in different directions, causing hull attitude deviations. These effects can reduce the control accuracy of the fuzzy controller. However, the hull's structure and properties are controllable, known, and predictable. This invention uses torque transfer parameters M and resistance transfer parameters N to simulate these effects, thereby adjusting the hull to achieve more precise control. Specifically, the hull velocity V is acquired through sensors, and the corresponding torque transfer parameters and resistance transfer parameters are calculated according to formulas. Simultaneously, the torque transfer parameter M is limited to the range [-60, 60], with units of (N·m), and the resistance transfer parameter N is limited to the range [-60, 60], with units of (N).

[0078] Specifically, the formula for calculating the resistance transfer parameter N is as follows:

[0079] N = k2(V l sinθ l +V r sinθ r );

[0080] Where k2 is the gain coefficient, determined based on the actual variable structure form, characteristics, and unmanned surface vessel type; θ1 is the deployment angle of the arresting net on the left side of the unmanned surface vessel, θ r V represents the deployment angle of the arresting net on the right side of the unmanned surface vessel, in degrees. l V is the port side velocity of the unmanned surface vessel. r The velocity along the starboard side of the unmanned surface vessel is expressed in m / s.

[0081] To simplify the calculation, assume that the left-side barrier net is subjected to a thrust f from the water. l With respect to the port side hull speed V of the unmanned surface vessel l The relationship is linear; the barrier net on the right experiences a thrust f from the water. r Speed ​​V of the unmanned surface vessel on its starboard side r It is a linear relationship;

[0082] For different variable structure hulls, let:

[0083] M = f1(τ1, τ2, ..., τ) n ,V);

[0084] N = f2(τ1, τ2, ..., τ) n ,V);

[0085] Among them, f l Let M be the functional relationship between the torque transfer parameter M and the structural parameters, τ1, τ2, ..., τ n , where f1 represents the parameters corresponding to different variable structure hulls; f2 represents the functional relationship between the resistance transfer parameter N and the structural parameters.

[0086] By indirectly predicting the approximate changes in the hull and the potential impact of structural alterations, guidance can be provided for appropriate adjustments.

[0087] Preferably, the total thrust F required by the unmanned surface vessel during the structural change process is obtained through calculation, and the calculation formula is as follows:

[0088] F = F1 + F2;

[0089] F2 = F pre +F e ;

[0090] Where F1 is the control output thrust without considering structural changes; F2 is the control output thrust correction value under the influence of structural changes; F pre The prediction of F2 is based on the torque transfer parameter M and the drag transfer parameter N. Its magnitude and trend are determined by the drag transfer parameter and torque transfer parameter while the unmanned surface vessel (USV) performs variable structure maneuvers. e To adjust F based on actual error pre Corrections;

[0091] Substituting the formula for calculating total thrust F into the unmanned surface vessel control, we obtain:

[0092]

[0093]

[0094] Among them, F l F is the thrust required by the left propeller. r F represents the thrust required by the right propeller. l1 The port thrust obtained from the ship's original control system, F r1 The starboard thrust obtained from the ship's original control system; F l2 and F r2 For correction; F lpre To predict the required port side correction force through structural changes, F rpre To predict the required starboard corrective force through structural changes; F le To adjust F according to the actual situation lpre The correction, F reTo adjust F according to the actual situation rpre Corrections.

[0095] This patent addresses the issue of variable-structure hulls. The proposed adjustment scheme involves adding a correction factor to the existing controller to compensate for the effects of structural changes, thereby achieving adaptive control of the hull structure and improving its operational stability and reliability. The advantage of this method is that it eliminates the need to redesign the hull controller, as the existing controller is already well-suited for situations where the hull structure remains unchanged.

[0096] Specifically, control optimization is achieved through two fuzzy decision-making processes, involving the torque transfer parameter M and the port side correction force F. lpre and starboard correction force F rpre The correspondence is as follows:

[0097]

[0098] Drag transfer parameter N and port side correction force F lpre and starboard correction force F rpre The correspondence is as follows:

[0099] N = k n (F lpre +F rpre );

[0100] Where k m and k n For perturbation systems with corresponding directional parameters, this is treated as a hyperparameter in this invention.

[0101] The first fuzzy controller, that is, the decision made by the fuzzy controller using F lpre and F rpre The two input quantities are used to make a fuzzy decision to select the corresponding propulsion motor voltage increment; this decision-making method is open-loop control. The second fuzzy controller, i.e., the main fuzzy controller, makes a fuzzy decision based on the heading angle error e and the error change rate ec, and corrects the propulsion motor voltage increment obtained from the fuzzy controller's decision; this decision-making method is closed-loop control. The corrected propulsion motor voltage increment is the required propulsion motor voltage correction increment.

[0102] Preferably, the fuzzy control is converted into linguistic variables to obtain a fuzzy set that expresses a specific fuzzy concept. The linguistic variables are divided into three types: large, medium, and small, accompanied by positive and negative signs.

[0103] Predict the port side correction force F based on the actual situation. lpre and starboard correction force F rpreThe range of output voltage variation is set to [-12V, 12V], and the range of voltage correction variation is set to [-3V, 3V], corresponding to the term set {NB(-3), NM(-2), NS(-1), ZE(0), PS(1), PM(2), PB(3)}. The range of heading angle error e is set to [-60°, 60°], and the domain of error rate of change is set to [-1, 1]. Therefore, the formula for calculating the error rate of change ec is:

[0104]

[0105] Among them, e now For the current heading angle error, e last This represents the heading angle error at the previous sampling time.

[0106] For the input variable, the predicted port side correction force F from the port and starboard motors. lpre and starboard correction force F rpre Assuming the port side correction force F lpre and starboard correction force F rpre Within the interval [-60N, 60N], we divide it into intervals [-60, -40], [-40, -20], [-20, 0], [0, 20], [20, 40], and [40, 60], corresponding to the word sets {positive large (PB), positive medium (PM), positive small (PS), zero (ZE), negative small (NS), negative medium (NM), and negative large (NB)}. Similarly, the input heading angle error e, taking clockwise as positive, divides its interval into [-60, -40], [-40, -20], [-20, 0], [0, 20], [20, 40], and [40, 60], corresponding to the word sets {positive large (PB), positive medium (PM), positive small (PS), zero (ZE), negative small (NS), negative medium (NM), and negative large (NB)}. The heading angle error change rate ec interval is also divided into [-1, -0.6], [-0.6, -0.3], [-0.3, 0], [0, 0.3], [0.3, 0.6], [0.6, 1], which correspond to the word sets {positive large (PB), positive medium (PM), positive small (PS), zero (ZE), negative small (NS), negative medium (NM), negative large (NB)}.

[0107] Preferably, let x be any number in the intervals [a, b] and [b, c]. Calculate the membership degree of any number x relative to b, and obtain its membership degree corresponding to different values. Calculate the membership degree μ. b The formula for (x) is:

[0108] ;

[0109] Calculate the different values ​​of port side correction force Flpre, starboard side correction force Frpre, heading angle error e, and error change rate ec in different term sets, and obtain the membership degree in the corresponding term set. The membership degree results for each term set are shown in the figure. Figures 4 to 7 As shown.

[0110] Specifically, fuzzy decision-making is used to assign each parameter to different word sets based on its membership degree, and to make corresponding decisions in the rule base based on the words corresponding to the input quantities. The fuzzy decision-making method adopts the "if A and B, then C" decision scheme, assigning each parameter to different word sets based on its membership degree, and making corresponding decisions in the rule base based on the words corresponding to these two input quantities.

[0111] The first fuzzy controller, namely the fuzzy rule design of the fuzzy controller, was determined through experimental methods. The voltage output value of the zero (ZE) state at different speeds during the operation of the unmanned surface vessel was obtained through multiple experiments.

[0112] The fuzzy rules of the first fuzzy controller are:

[0113] While keeping the hull structure unchanged, i.e., with the predicted left and right correction forces at zero, the ship moves forward. The input voltage of the motor is measured multiple times by sensors, values ​​with large deviations are removed, and the average value is taken as the voltage output value under normal conditions.

[0114] By altering the hull structure to make the calculated left and right predicted correction forces at various speeds correspond to the following values ​​in the fuzzy word set: positive (PB), positive (PM), positive small (PS), zero (ZE), negative small (NS), negative medium (NM), and negative large (NB), respectively, the hull is made to move forward in a straight line at zero speed. The input voltage of the corresponding motor is then measured using sensors. After multiple measurements and removing values ​​with large deviations, the average value is calculated. This average value is then subtracted from the voltage output value under normal conditions to obtain the predicted correction voltage of the corresponding motor under the corresponding conditions. Figure 8 , Figure 9 As shown;

[0115] The fuzzy rules of the master fuzzy controller are designed to mimic the way the operator operates, and the degree of control is adjusted according to the difference in the ship's course and the speed of change.

[0116] The fuzzy rules of the second fuzzy controller are:

[0117] When e is PB or e is PM, and ec is PM or ec is PB, the error is large and needs to be reduced quickly by correcting the voltage u. le for NB,u re For PB;

[0118] When e is PS or ZE, the voltage value should be adjusted according to the condition of ec.

[0119] When e is positive and ec is negative, the hull is reducing errors, and the output voltage is adjusted according to the reduction of errors.

[0120] When the error e is negative or positive, the hull is reducing the error, and the output voltage is adjusted accordingly based on this reduction. Figure 10 , Figure 11 As shown.

[0121] Preferably, both the first and second fuzzy controllers employ zero-order Sugeno inference for fuzzy reasoning. When defuzzifying the data obtained by the corresponding fuzzy controllers, the maximum membership method is used, taking the output value with the highest membership degree as the control result, thereby achieving more accurate hull control. Specifically, the Sugeno inference method is a fuzzy logic-based inference method capable of performing fuzzy inference over multiple input variables and outputting a definite value. In the application of this patent, we construct an appropriate rule base, such as... Figure 8 , Figure 9 As shown, the fuzzy inference process required for hull control is implemented. At the same time, when applying the maximum membership degree method for defuzzification, we take the output value with the highest membership degree as the control result, thereby achieving more accurate hull control.

[0122] Taking a fuzzy controller as an example, let's assume F lpre =-15, F rpre =35. According to Figure 4 It can be seen that F lpre The membership degree for NS is 0.75, and the membership degree for ZE is 0.25. According to... Figure 5 It can be seen that F rpre The membership degree for PS is 0.25, and the membership degree for PM is 0.75. According to the rules in Table 1, [u] lpre35 (0.75∧0.25), u lpre25 (0.75∧0.75), u lpre34 (0.25∧0.25), u lpre24 (0.25∧0.75)]=[u lpre35 (0.75), u lpre25 (0.25), u lpre34 (0.25), U lpre24 [0.25], ∧ is an operator in fuzzy inference, taking the intersection; when two membership degrees intersect, the smaller value is taken. U lpre35 (0.75∧0.25), u lpre35 The value in parentheses represents the membership degree of that output value. According to the maximum membership degree method, the maximum membership degree is u. 1pre35If (0.75), then the output is u. lpre35 The same applies to the master fuzzy controller.

[0123] In the description of this specification, references to terms such as "embodiment," "example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0124] The technical principles of the present invention have been described above with reference to specific embodiments. These descriptions are merely for explaining the principles of the invention and should not be construed as limiting the scope of protection of the invention in any way. Based on this explanation, those skilled in the art can readily conceive of other specific embodiments of the invention without inventive effort, and these embodiments will all fall within the scope of protection of the present invention.

Claims

1. A variable structure unmanned surface vessel control method based on a fuzzy controller, characterized in that, Includes the following steps: The variable structure command is obtained, the variable structure undergoes a structural change and generates a variable structure disturbance signal and a variable structure state signal. The variable structure state signal is sent to the first fuzzy controller. The variable structure is the structure in which the variable arresting nets on both sides of the unmanned surface vessel perform the deployment angle adjustment. The variable structure undergoes a structural change as the variable arresting nets on both sides of the unmanned surface vessel perform the deployment angle adjustment. The initial velocity V and initial angular velocity W of the hull are obtained by sensors, and the torque transfer parameters and drag transfer parameters are obtained by calculating the hull velocity. The ship's velocity Vt and angular velocity Wt at time t are collected and a feedback signal is generated. The initial velocity V, initial angular velocity W and feedback signal are used to calculate the heading angle error e, and the heading angle error e is sent to the first fuzzy controller and the second fuzzy controller. The first fuzzy controller generates a first signal, and the second fuzzy controller generates a second signal. The first and second signals are processed to obtain a hull adjustment signal. The hull adjustment signal and the variable structure disturbance signal are integrated to obtain the hydrodynamic force for hull movement.

2. The variable structure unmanned surface vessel control method based on a fuzzy controller according to claim 1, characterized in that, The formula for calculating the torque transfer parameter M is: ； Where k1 is the gain coefficient; θ l θ is the deployment angle of the arresting net on the left side of the unmanned surface vessel. r V is the deployment angle of the arresting net on the right side of the unmanned surface vessel; l V is the port side velocity of the unmanned surface vessel. r denoted as the starboard velocity of the unmanned surface vessel; L is the length of the arresting net; and d is the minimum distance between the left and right arresting nets.

3. The variable structure unmanned surface vessel control method based on a fuzzy controller according to claim 2, characterized in that, The formula for calculating the resistance transfer parameter N is: N=k2(V l sinθ l +V r sinθ r ); Where k2 is the gain coefficient; θ l θ is the deployment angle of the arresting net on the left side of the unmanned surface vessel. r V is the deployment angle of the arresting net on the right side of the unmanned surface vessel; l V is the port side velocity of the unmanned surface vessel. r The speed along the starboard side of the unmanned surface vessel; Assume the barrier net on the left is subjected to a thrust f from the water. l With respect to the port side hull speed V of the unmanned surface vessel l The relationship is linear; the barrier net on the right experiences a thrust f from the water. r Speed ​​V of the unmanned surface vessel on its starboard side r It is a linear relationship; For different variable structure hulls, let: ； ； Among them, f l This represents the functional relationship between the torque transfer parameter M and the structural parameters. , where f1 represents the parameters corresponding to different variable structure hulls; f2 represents the functional relationship between the resistance transfer parameter N and the structural parameters.

4. The variable structure unmanned surface vessel control method based on a fuzzy controller according to claim 3, characterized in that, The total thrust F required by the unmanned surface vessel during the structural change process is obtained through calculation. The calculation formula is as follows: ； ； Where F1 is the control output thrust without considering structural changes; F2 is the control output thrust correction value under the influence of structural changes; F pre The prediction of F2 is based on the torque transfer parameter M and the drag transfer parameter N. Its magnitude and trend are determined by the drag transfer parameter and torque transfer parameter while the unmanned surface vessel (USV) performs variable structure maneuvers. e To adjust F based on actual error pre Corrections; Substituting the formula for calculating total thrust F into the unmanned surface vessel control, we obtain: ； ； Among them, F l F is the thrust required by the left propeller. r F represents the thrust required by the right propeller. l1 The port thrust obtained from the ship's original control system, F r1 The starboard thrust obtained from the ship's original control system; F l2 and F r2 For correction; F lpre To predict the required port side correction force through structural changes, F rpre To predict the required starboard corrective force through structural changes; F le To adjust F according to the actual situation lpre The correction, F re To adjust F according to the actual situation rpre Corrections.

5. The variable structure unmanned surface vessel control method based on a fuzzy controller according to claim 4, characterized in that, Control optimization is achieved through two fuzzy decision-making processes, involving the torque transfer parameter M and the port side correction force F. lpre and starboard correction force F rpre The correspondence is as follows: ； Drag transfer parameter N and port side correction force F lpre and starboard correction force F rpre The correspondence is as follows: ； Where k m and k n This represents the perturbation coefficient for the corresponding directional parameter.

6. The variable structure unmanned surface vessel control method based on a fuzzy controller according to claim 5, characterized in that, The fuzzy control is converted into linguistic variables to obtain a fuzzy set that expresses a specific fuzzy concept. The linguistic variables are divided into three types: large, medium, and small, and are accompanied by positive and negative signs. Predict the port side correction force F based on the actual situation. lpre and starboard correction force F rpre The range of output voltage variation is set to [-12V, 12V], and the range of voltage correction variation is set to [-3V, 3V], corresponding to the term set {NB(-3), NM(-2), NS(-1), ZE(0), PS(1), PM(2), PB(3)}. The range of heading angle error e is set to [-60°, 60°], and the domain of error rate of change is [-1, 1]. The formula for calculating the error rate of change ec is: ； in, For the current heading angle error, This represents the heading angle error at the previous sampling time.

7. The variable structure unmanned surface vessel control method based on a fuzzy controller according to claim 6, characterized in that, Let x be any number in the intervals [a, b] and [b, c]. Calculate the membership degree of any number x with respect to b, and obtain the membership degree corresponding to different values. The formula is: ； Calculate the different values ​​of port side correction force Flpre, starboard side correction force Frpre, heading angle error e, and error change rate ec in different word sets to obtain the membership degree in the corresponding word set.

8. The variable structure unmanned surface vessel control method based on a fuzzy controller according to claim 7, characterized in that, Fuzzy decision-making is used to assign each parameter to different word sets based on its membership degree, and to make corresponding decisions in the rule base based on the words corresponding to the input quantity. The fuzzy rules of the first fuzzy controller are: While keeping the hull structure unchanged, the motor input voltage is measured multiple times using sensors. Values ​​with large deviations are removed, and the average value is calculated. This average value is taken as the voltage output value under normal conditions. By altering the hull structure so that the calculated left and right predicted correction forces at various speeds correspond to the following values ​​in the fuzzy word set: positive large (PB), positive medium (PM), positive small (PS), zero (ZE), negative small (NS), negative medium (NM), and negative large (NB), the hull is made to move forward in a straight line at zero speed. The input voltage of the corresponding motor is then measured by sensors. After multiple measurements and removing values ​​with large deviations, the average value is calculated. This value is then subtracted from the voltage output value under normal conditions to obtain the predicted correction voltage of the corresponding motor under the corresponding conditions. The fuzzy rules of the second fuzzy controller are: When e is PB or e is PM, and ec is PM or ec is PB, the error is large and needs to be reduced quickly by correcting the voltage u. le for NB,u re For PB; When e is PS or ZE, the voltage value should be adjusted according to the condition of ec. When e is positive and ec is negative, the hull is reducing errors, and the output voltage is adjusted according to the reduction of errors. When the error e is negative or positive, the hull is reducing the error, and the output voltage is adjusted accordingly based on the reduction of the error.

9. A variable structure unmanned surface vessel control method based on a fuzzy controller according to claim 8, characterized in that, Both the first and second fuzzy controllers use zero-order Sugeno inference for fuzzy inference. When defuzzifying the data obtained by the corresponding fuzzy controllers, the maximum membership method is used, and the output value with the highest membership degree is taken as the control result.

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