A ship / ship cooperative approach navigation control method based on fuzzy observer

Abstract

The application discloses a kind of based on fuzzy observer's machine / ship cooperation entry navigation control method, it is related to cooperative motion control technical field, by establishing the reference path of virtual ship based on speed pseudo-control function and virtual unmanned aerial vehicle, the azimuth angle of ship to virtual ship and the azimuth angle of unmanned aerial vehicle to virtual unmanned aerial vehicle are acquired, and the roll angle and pitch angle of virtual unmanned aerial vehicle;While establishing the feedforward input matrix of non-linear model based on event trigger mechanism, and then establish fuzzy observer model, in combination with the azimuth angle of ship to virtual ship and the azimuth angle of unmanned aerial vehicle to virtual unmanned aerial vehicle, the control input matrix of unmanned aerial vehicle and unmanned ship is acquired, realizes the machine / ship cooperation entry navigation control based on fuzzy observer.The application can carry out various speed matching scheme according to the different manipulation stages of cooperative system, and can observe and compensate for system state signal, solve the problem of filter signal noise disorder.

Description

Technical Field

[0001] This invention relates to the field of cooperative motion control technology, and in particular to a machine / ship cooperative port entry navigation control method based on a fuzzy observer. Background Technology

[0002] The maritime vessel motion control structure consists of two modules: guidance and control. For the USV-UAV heterogeneous cooperative formation system, the high-dimensional signal decoupling technology and actuator differences increase the complexity of the system control architecture. Current research indicates that guidance systems often rely on logical virtual ships-unmanned aerial vehicles (LVS-LVA) to generate reference signals and paths for guidance. However, the design and tuning of virtual variable-speed signals within port areas still face imperfections and significant challenges. As for the control system, existing research has employed event-triggered mechanisms to transform continuous signals into step signals to mitigate channel resource loss. However, addressing the instability of trigger signals and the resulting filtering noise in heterogeneous variable-speed systems remains a promising area for further research.

[0003] Based on the above analysis, the USV-UAV path tracking control algorithm based on 3D mapping guidance has the following two main shortcomings in the collaborative control task between the two:

[0004] In the case of variable speed navigation of the coordinated system within the port area, the traditional guidance strategy will cause a large tracking deviation when braking is carried out in stages, which to some extent violates the safe speed regulations within the port.

[0005] During speed change operation, the transmission of control signals related to the cooperative system is prone to communication disorder under the event triggering mechanism. The high deviation of the trigger ratio parameter prevents the step signal from passing through the channel efficiently, thereby accelerating the wear of the actuator. Summary of the Invention

[0006] This invention discloses a machine / ship cooperative port entry navigation control method based on a fuzzy observer to overcome the above-mentioned technical problems.

[0007] To achieve the above objectives, the technical solution of the present invention is as follows:

[0008] A method for coordinated port entry navigation control based on a fuzzy observer for aircraft / ships includes the following steps:

[0009] S1: Establish a nonlinear model of the heterogeneous cooperative formation system of UAV-unmanned vessel;

[0010] S2: Establish a velocity control function based on the aforementioned nonlinear model;

[0011] S3: Based on the speed control function, establish the reference path of the virtual ship and the virtual drone;

[0012] S4: Based on the reference path of the virtual ship and the virtual drone, obtain the azimuth angle from the ship to the virtual ship and the azimuth angle from the drone to the virtual drone, so as to obtain the roll angle and pitch angle of the virtual drone;

[0013] S5: Based on the event-triggered mechanism, establish a feedforward input to build the feedforward input matrix of the nonlinear model;

[0014] S6: Based on the fuzzy logic system, the nonlinear term matrix in the nonlinear system is approximated to obtain the fuzzy basis function matrix. Then, based on the feedforward input matrix, a fuzzy observer model is established to obtain the adaptive fuzzy logic function estimation matrix.

[0015] S7: Define the state information error of the UAV-unmanned vessel heterogeneous cooperative formation system based on the azimuth angle from the ship to the virtual ship, the azimuth angle from the UAV to the virtual UAV, and the roll and pitch angles of the virtual UAV. Establish fuzzy trigger gain based on the adaptive fuzzy logic function estimation matrix. Obtain the control input matrix of the UAV and the unmanned vessel based on the adaptive fuzzy logic function estimation matrix to realize the ship / vessel cooperative port entry navigation control based on the fuzzy observer.

[0016] Beneficial Effects: This invention provides a fuzzy observer-based aircraft / ship cooperative port entry navigation control method. By establishing a reference path for a virtual ship and a virtual UAV based on a velocity-based simulated control function, it obtains the azimuth angles from the ship to the virtual ship and from the UAV to the virtual UAV, as well as the roll and pitch angles of the virtual UAV. Simultaneously, it establishes a feedforward input matrix for a nonlinear model based on an event-triggered mechanism, and then establishes a fuzzy observer model. Combining the azimuth angles from the ship to the virtual ship and from the UAV to the virtual UAV, as well as the roll and pitch angles of the virtual UAV, it obtains the control input matrices for the UAV and the unmanned vessel, thus achieving fuzzy observer-based aircraft / ship cooperative port entry navigation control. This invention, through the velocity-based simulated control function, can implement various speed matching schemes according to different operation stages of the cooperative system, and through the fuzzy observer, it observes and compensates for system state signals, solving the problem of noise disturbance in the filtered signal. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of the machine / ship cooperative port entry navigation control method of the present invention;

[0019] Figure 2 This is a three-dimensional information diagram of cooperative guidance in an embodiment of the present invention;

[0020] Figure 3 This is a schematic diagram of the variable speed guidance speed matching control stage of the cooperative system in an embodiment of the present invention;

[0021] Figure 4 This is a diagram of the core control architecture of the machine / ship cooperative port entry navigation control system in this embodiment of the invention;

[0022] Figure 5 This is a USV-UAV cooperative port entry path tracking trajectory diagram in an embodiment of the present invention;

[0023] Figure 6a The USV forward thrust control command and actual input are as follows in this embodiment of the invention;

[0024] Figure 6b The USV turning torque control command and actual input are shown in the embodiments of the present invention.

[0025] Figure 6c The UAV forward thrust control command and actual input in the embodiments of the present invention;

[0026] Figure 6d The UAV drift thrust control command and actual input in the embodiments of the present invention;

[0027] Figure 6e The UAV heave thrust control command and actual input in the embodiments of the present invention;

[0028] Figure 7a These are the estimated values ​​of the fuzzy triggering gain related to the ship's forward degree of freedom and the estimated values ​​of the fuzzy triggering gain related to the ship's bow degree of freedom in the embodiments of the present invention.

[0029] Figure 7b These are the estimated values ​​of the fuzzy triggering gain related to the forward degree of freedom of the UAV, the estimated values ​​of the fuzzy triggering gain related to the lateral drift degree of freedom of the UAV, and the estimated values ​​of the fuzzy triggering gain related to the heave degree of freedom of the UAV in the embodiments of the present invention.

[0030] Figure 8 This is a comparison diagram of the USV tracking paths in the embodiments of the present invention;

[0031] Figure 9a This is a comparison diagram of the USV forward thrust control command trigger interval in an embodiment of the present invention;

[0032] Figure 9b This is a comparison diagram of the trigger intervals of the USV turning torque control command in an embodiment of the present invention. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] This embodiment introduces a machine / ship cooperative port entry navigation control method based on a fuzzy observer, including the following steps: Figure 1 and Figure 4 As shown:

[0035] S1: Establish a nonlinear model of the heterogeneous cooperative formation system of UAV-UAV;

[0036] Preferably, based on matrix structure theory, the nonlinear model of the UAV-unmanned vessel heterogeneous cooperative formation system is expressed as follows:

[0037]

[0038] In the formula: η i This represents a one-dimensional state information matrix for both the UAV and the ship, where η1 and η2 are both one-dimensional state information matrices for the UAV, and η1 = [x a ,y a ,z a ] T x a ,y a ,z a These represent the forward, lateral, and vertical displacements of the UAV, respectively, η2=[φ a ,θ a ,ψ a ] T , φ a ,θ a ,ψ a These represent the roll angle, pitch angle, and yaw angle of the UAV, respectively, and η3 is the one-dimensional state information matrix of the ship, η3=[x s ,y s ,ψ s ] T x s ,y s ,ψ s These represent the ship's forward displacement, lateral drift, and bow angle in the inertial coordinate system, respectively; v i Represents the two-dimensional state information matrices of the UAV and the ship, where v1 and ν2 are both two-dimensional state information matrices of the UAV, v1 = [u ax ,uay ,u az ] T u ax ,u ay ,u az Let v2 represent the forward velocity, lateral velocity, and vertical velocity of the UAV in volume coordinates, respectively. a ,q a ,r a ] T , where p a ,q a ,r a Let ν3 represent the roll angular velocity, pitch angular velocity, and yaw angular velocity of the UAV in the body coordinate system, respectively, and let ν3 be the two-dimensional state information matrix of the ship, where ν3 = [u s ,v s ,r s ] T u s ,v s ,r s These represent the ship's forward speed, drift speed, and bow roll rate, respectively; F i Let F1 and F2 represent the nonlinear terms of the UAV and unmanned vessel, respectively, and let F3 represent the nonlinear terms of the UAV on the position loop and attitude loop, respectively; T represents the transpose of the UAV. M represents the first-order differential; i Let M1 and M2 be the additional mass matrices of the UAV, where M1 = diag{m a ,m a ,m a}, m a The mass of the drone is represented by M2 = diag{I xx ,I yy ,I zz}, I xx ,I yy ,I zz Let x, y, and oz represent the rotational inertia of the UAV along the ox, oy, and oz axes, respectively. In this embodiment, a three-dimensional coordinate system is established with the UAV's centroid as the origin: the horizontal axis ox of the three-dimensional coordinate system is along the direction of the UAV's forward degree of freedom; the vertical axis oy is along the direction of the UAV's lateral drift degree of freedom; and the vertical axis oz is along the direction of the UAV's heave degree of freedom. M3 is the ship's added mass matrix, M3 = diag{m u ,m v ,m r};m u ,m v ,m r All represent the model's added mass of the ship; diag{·} represents a diagonal matrix; M represents i The inverse matrix of R; i This is the gain matrix for the drone and the unmanned surface vessel. In this embodiment, R2 = diag{1,d,d}, R3 = diag{1,1,1}, where d represents the diagonal diameter of the drone; τ i This represents the control input matrix for the drone and the unmanned surface vessel, where τ1 = [τ f ,τ f ,τ f ,] T ,τ2=[τ φ ,τ θ ,τ ψ ,] T ,τ3=[τ u ,0,τ r ,] T ; where τ f τ represents the rotor force of the UAV rotor. φ ,τ θ ,τ ψ These represent the roll, pitch, and yaw moments of the UAV, respectively. u ,τ r D represents the ship's forward thrust and turning moment; i This represents the disturbance force / torque caused by external marine environmental disturbances, where D1 = [d wx ,d wy ,d wz ] T D2 = [d wφ ,d wθ ,d wψ ] T D3 = [d wu ,d wv ,d wr ] T d wx ,d wy ,d wz These represent the external disturbance forces / torques experienced by the UAV in the forward, drift, and heave directions, respectively. wφ ,d wθ ,d wψ These represent the external disturbance forces / torques experienced by the UAV in the roll, pitch, and yaw directions, respectively. wu ,d wv ,d wr These represent the external disturbance forces / torques experienced by the ship in the forward, lateral, and bow directions, respectively; η1, η2, η3, v1, v2, v3, M1, M2, M3, R1, R2, R3, D1, D2, D3, and i are all intermediate calculation parameters. When i = 1 or 2, these are intermediate calculation parameters related to the UAV; when i = 3, these are intermediate calculation parameters related to the ship itself. Ji Represents the transformation matrix;

[0039] in,

[0040]

[0041]

[0042] In the formula: k dx ,k dy ,k dz Both represent positive parameter constants; q aj p represents the angular velocity of the UAV along the oy axis. aj The angular velocity r of the UAV along the ox axis represents the rotational velocity of the UAV. aj d represents the angular velocity of the UAV along the oz axis. u1 d represents the hydrodynamic damping coefficient that is related to the first power of the ship's forward speed. v1 The hydrodynamic damping coefficient, d, represents the first power correlation between the ship's drift velocity and the hydrodynamic damping coefficient. r1 The hydrodynamic damping coefficient is the first power related to the ship's bow roll rate; d u2 d represents the hydrodynamic damping coefficient related to the second power of the ship's forward speed. v2 d represents the hydrodynamic damping coefficient related to the second power of the ship's drift velocity. r2 The hydrodynamic damping coefficient is the factor relating to the square power of the ship's bow roll rate; d u3 d represents the hydrodynamic damping coefficient related to the cube of the ship's forward speed. v3 d represents the hydrodynamic damping coefficient related to the cubic power of the ship's drift velocity. r3 The coefficient representing the hydrodynamic damping factor related to the cube power of the ship's bow roll rate; |·| represents the absolute value;

[0043] S2: Establish the velocity fitting control function u based on the aforementioned nonlinear model. sl ;

[0044] Preferably, the speed control function is established as follows:

[0045] u sl =κ1exp{κ2t+κ3sin(κ4t)}+κ5 (3)

[0046] In the formula: u sl κ1, k2, k3, k4, and κ5 represent the speed control function; t represents time; and exp{·} represents an exponential function with the natural base e.

[0047] S3: Based on the speed control function, establish the reference path of the virtual ship and the virtual drone;

[0048] Preferably, such as Figure 2 As shown, the reference path of the virtual ship is generated as shown in equation (4).

[0049]

[0050] v 3l =[u sl ,v sl ,r sl ] T

[0051] η 3l =[x sl ,y sl ,ψ sl ] T

[0052] In the formula: η 3l This represents a one-dimensional state information matrix for a virtual ship, where η 3l =[x sl ,y sl ,ψ sl ] T x sl ,y sl ,ψ sl J represents the expected forward distance, expected drift distance, and expected heading angle of the virtual ship, respectively; 3l The transformation matrix representing the virtual ship; v 3l A two-dimensional state information matrix representing a virtual ship; v sl ,r sl These represent the drift speed and bow roll rate of the virtual ship, respectively; u sl Represents the velocity control function;

[0053] in,

[0054]

[0055] In the formula: ψ sl This represents the desired heading angle of the virtual ship;

[0056] Preferably, the virtual drone is established as follows;

[0057] Specifically, in this embodiment, a virtual drone is constructed as shown in equation (6) based on a 3D mapping guidance strategy:

[0058]

[0059] In the formula: x al ,y al ,ψ al These represent the forward distance, drift distance, and heading angle of the virtual drone, respectively; r slThis represents the bow roll rate of the virtual ship; additionally, it represents the heave distance z of the virtual drone. al The user sets the specific value.

[0060] S4: Based on the reference path of the virtual ship and the virtual UAV, obtain the azimuth angle ψ from the ship to the virtual ship. sv The azimuth angle ψ from the drone to the virtual drone av As shown in equation (7), the roll angle φ of the virtual drone is obtained based on the nonlinear decoupling technique. av and pitch angle θ av Equation (8) is used to solve the problem using the control input and relative azimuth angle:

[0061] Preferably, the azimuth angle ψ between the ship and the virtual ship is obtained. sv The azimuth angle ψ from the drone to the virtual drone av The formula used is as follows:

[0062]

[0063] In the formula, ψ sv Indicates the relative bearing angle between the ship and the virtual ship; ψ av Indicates the relative azimuth angle between the drone and the virtual drone; x se ,y se These are the ship's forward distance error and lateral drift distance error, respectively; x a ,y a These represent the forward and lateral displacements of the UAV, respectively; x al ,y al These represent the forward distance and lateral drift distance of the virtual drone, respectively; x ae ,y ae These are the forward distance error and the lateral drift distance error of the drone; x sl ,y sl These represent the expected forward distance and expected lateral drift distance of the virtual ship, respectively; x s ,y s These represent the forward displacement and lateral drift displacement of the ship in the inertial coordinate system, respectively.

[0064] Preferably, the roll angle φ of the virtual drone is obtained based on nonlinear decoupling technology. av and pitch angle θ av as follows:

[0065]

[0066]

[0067] Where: φ av θ represents the roll angle of the virtual drone. avIndicates the pitch angle of the virtual drone; ψ av τ represents the relative azimuth angle between the drone and the virtual drone. x τ represents the rotor force of the UAV rotor in the forward direction; y τ represents the rotor force of the UAV rotor in the lateral drift direction. z This represents the rotor force of the UAV rotor in the direction of heave;

[0068] Specifically, to facilitate subsequent control design calculations, the guidance signals for LVS and LVA are organized as follows:

[0069] η 1v =[x al ,y al ,z al ] T η 2v =[φ av ,θ av ,ψ av ] T η 3v =[x sl ,y sl ,ψ sv ] T ;

[0070] In the formula: η 1v Represents a one-dimensional location information matrix related to a virtual drone; η 2v Represents a one-dimensional attitude information matrix related to the virtual drone; η 3v Represents a one-dimensional state information matrix related to the virtual ship; ψ sv Indicates the relative bearing angle between the ship and the virtual ship; z al Indicates (the expected heave distance of the virtual ship);

[0071] S6: Based on the event-triggered mechanism, establish a feedforward input a (·) As shown in equation (10), the feedforward input matrix of the nonlinear model is established;

[0072] Preferably, the event triggering mechanism is represented by the following formula:

[0073] t l+1 =inf{t>t l ||a (·) (t l )-τ (·) (t)|>d (·) τ (·) (t)} (9)

[0074] In the formula: inf{·} denotes the infimum of the set; |·| denotes the absolute value; d (·)It is a threshold parameter that satisfies d (·) ∈(0,1).

[0075] The formula used to obtain the feedforward input is as follows:

[0076] τ (·) (t)=a (·) (t ι ),t∈[t ι ,t ι+1 (10)

[0077] In the formula: τ (·) (t) represents the control input; t ι Indicates the start time of the event trigger; a (·) Indicates feedforward input; t ι+1 Indicates the time when the event was triggered and ended; t represents time.

[0078] Considering the two scenarios triggered by the control signal:

[0079] τ (·) (t)≥0,|a (·) (t ι )-τ (·) (t)|≤d (·) τ (·) (t),

[0080] a (·) (t ι )-τ (·) (t)=p (·) d (·) τ (·) (t),p (·) ∈[-1,1];

[0081] τ (·) (t)<0,|a (·) (t ι )-τ (·) (t)|≤-d (·) τ (·) (t),

[0082] α (·) (t ι )-τ (·) (t)=p (·) d (·) τ (·) (t),p (·) ∈[-1,1]

[0083] Based on the above analysis, we can conclude that:

[0084]

[0085] In the formula: p (·) It is a small threshold.

[0086] S6: Based on fuzzy logic systems, approximate the nonlinear term matrix in nonlinear systems to obtain the fuzzy basis function matrix Φ. i Then, based on the feedforward input matrix, a fuzzy observer model is established to obtain the adaptive fuzzy logic function estimation matrix.

[0087] Preferably, the adaptive fuzzy logic function estimation matrix is ​​obtained. The method is as follows:

[0088] S61: Based on the fuzzy logic system FLS, the nonlinear term matrix of the nonlinear system is approximated as follows:

[0089] F i =W i Φ i +E i (12)

[0090] In the formula: W i Let the self-feedforward function matrix be denoted as , where ω x ,ω y ,ω z Let ω represent the adaptive fuzzy logic functions related to the forward degree of freedom of the UAV, the adaptive fuzzy logic functions related to the lateral drift degree of freedom of the UAV, and the adaptive fuzzy logic functions related to the heave degree of freedom of the UAV, respectively. φ ,ω θ ,ω ψ Let ω represent the adaptive fuzzy logic functions related to the UAV's roll degree of freedom, the adaptive fuzzy logic functions related to the UAV's pitch degree of freedom, and the adaptive fuzzy logic functions related to the UAV's yaw degree of freedom, respectively. u ,ω v ,ω r Let Φ represent the adaptive fuzzy logic functions related to the ship's forward degree of freedom, the adaptive fuzzy logic function related to the ship's lateral drift degree of freedom, and the adaptive fuzzy logic function related to the ship's bow roll degree of freedom, respectively; i Let represent the fuzzy basis function matrix, where

[0091] Let represent the fuzzy basis functions related to the forward degree of freedom of the UAV, the fuzzy basis functions related to the lateral drift degree of freedom of the UAV, and the fuzzy basis functions related to the heave degree of freedom of the UAV, respectively. Let represent the fuzzy basis functions related to the UAV's roll degree of freedom, the fuzzy basis functions related to the UAV's pitch degree of freedom, and the fuzzy basis functions related to the UAV's yaw degree of freedom, respectively. Let E represent the fuzzy basis functions related to the ship's forward degree of freedom, the fuzzy basis functions related to the ship's lateral drift degree of freedom, and the fuzzy basis functions related to the ship's bow roll degree of freedom, respectively; i Let E1 represent the fuzzy approximation error matrix, where E1 = [ε x ,ε y ,ε z ] T E2=[ε φ ,ε θ ,ε ψ ] T E3 = [ε u ,ε v ,ε r ] T , ε x ,ε y ,ε z Let ε represent the fuzzy approximation errors related to the forward degree of freedom of the UAV, the fuzzy approximation errors related to the lateral drift degree of freedom of the UAV, and the fuzzy approximation errors related to the heave degree of freedom of the UAV, respectively. φ ,ε θ ,ε ψ Let ε represent the fuzzy approximation errors related to the UAV's roll degree of freedom, the fuzzy approximation errors related to the UAV's pitch degree of freedom, and the fuzzy approximation errors related to the UAV's yaw degree of freedom, respectively. u ,ε v ,ε r E represents the fuzzy approximation error related to the ship's forward degree of freedom, the fuzzy approximation error related to the ship's lateral drift degree of freedom, and the fuzzy approximation error related to the ship's bow roll degree of freedom, respectively. i The upper bound is E iM ;

[0092] S62: Introduce the fuzzy trigger ratio parameter ζ i , means as follows:

[0093]

[0094] In the formula: ζ i This represents the fuzzy trigger ratio parameter, where ζ1 = diag{ζ x ,ζ y ,ζ z},ζ2=diag{ζ φ ,ζ θ ,ζ ψ},ζ3=diag{ζ u ,0,ζ r};ζx ζ represents the fuzzy trigger ratio parameter related to the forward degrees of freedom of the UAV. y ζ represents the fuzzy trigger ratio parameter related to the drone's drift degree of freedom. z This represents the fuzzy trigger ratio parameter related to the heave-descent degrees of freedom of the UAV; ζ φ ζ represents the fuzzy trigger ratio parameter related to the yaw degree of freedom of the UAV. θ ζ represents the fuzzy trigger ratio parameter related to the pitch degree of freedom of the UAV. ψ This represents the fuzzy trigger ratio parameter related to the bow degree of freedom of the UAV; ζ u ,ζ r These represent the fuzzy trigger ratio parameters related to the ship's forward degree of freedom and the fuzzy trigger ratio parameters related to the ship's yaw degree of freedom, respectively; A i Let A1 represent the feedforward input matrix of the nonlinear model, where A1 = [a x ,a y ,a z ] T A2 = [a φ ,a θ ,a ψ ] T A3 = [a u ,0,a r ] T a x ,a y ,a z These represent the feedforward inputs in the forward degree of freedom direction, the feedforward inputs in the lateral degree of freedom direction, and the feedforward inputs in the heave degree of freedom direction, respectively. φ ,a θ ,a ψ These represent the feedforward inputs in the roll, pitch, and yaw directions of the UAV, respectively. u ,a r These represent the feedforward inputs in the forward degree of freedom direction and the feedforward inputs in the yaw degree of freedom direction, respectively; ζ1, ζ2, ζ3, A1, A2, and A3 are all intermediate calculation parameters;

[0095] S63: Establish a fuzzy observer model, represented as follows:

[0096]

[0097] In the formula: and Each represents a system state observation matrix. and Both represent the state observation error matrix; J i Represents the transformation matrix; A matrix representing the estimated values ​​of the fuzzy trigger ratio parameter; i Represents the feedforward input matrix; This represents the matrix of estimated values ​​for the adaptive fuzzy logic function; This represents the observer parameter matrix related to the one-dimensional state information, where, δ ax ,δ ay ,δ az These represent the observer parameters related to the UAV's forward distance, the observer parameters related to the UAV's lateral drift distance, and the observer parameters related to the UAV's heave distance, respectively. δ φ ,δ θ , δ represents the observer parameters related to the UAV's roll angle, the observer parameters related to the UAV's pitch angle, and the observer parameters related to the UAV's yaw angle, respectively. x ,δ y , These represent the observer parameters related to the ship's forward distance, the observer parameters related to the ship's drift distance, and the observer parameters related to the ship's bow roll angle, respectively. The observer parameter matrix represents the information related to the two-dimensional state. δ uax ,δ uay ,δ uaz δ represents the observer parameters related to the UAV's forward speed, the observer parameters related to the UAV's drift speed, and the observer parameters related to the UAV's heave speed, respectively. p ,δ q , δ represents the observer parameters related to the UAV's roll rate, the observer parameters related to the UAV's pitch rate, and the observer parameters related to the UAV's yaw rate, respectively. u ,δ v , These represent the observer parameters related to the ship's forward speed, the observer parameters related to the ship's drift speed, and the observer parameters related to the ship's bow roll rate, respectively.

[0098] S7: Based on the azimuth angle between the ship and the virtual ship, the azimuth angle between the UAV and the virtual UAV, and the roll and pitch angles of the virtual UAV, define the state information error of the UAV-unmanned ship heterogeneous cooperative formation system, and establish the fuzzy triggering gain g based on the adaptive fuzzy logic function estimation matrix. i ; to estimate the value matrix based on the adaptive fuzzy logic function Acquire the control input matrices of UAVs and unmanned vessels to achieve coordinated port navigation control based on fuzzy observers.

[0099] Preferably, the method for obtaining the control input matrix of the UAV and the unmanned surface vessel is as follows:

[0100] S71: Define the state information error of the UAV-Unmanned Vessel heterogeneous cooperative formation system as follows:

[0101] η ie =η i -η iv i = 1, 2, 3 (15)

[0102] In the formula: η ie Represents the one-dimensional state information error matrix for UAVs and ships; η i A one-dimensional state information matrix representing the state of drones and ships; η iv A matrix representing the state information related to virtual drones and virtual ships;

[0103] in,

[0104] η 1v =[x al ,y al ,z al ] T

[0105] η 2v =[φ av ,θ av ,ψ av ]T

[0106] η 3v =[x sl ,y sl ,ψ sv ] T

[0107] In the formula: η 1v Represents a one-dimensional location information matrix related to the virtual drone; η 2v Represents a one-dimensional attitude information matrix related to the virtual drone; η 3v Represents a one-dimensional state information matrix related to the virtual ship; ψ sv Indicates the relative bearing angle between the ship and the virtual ship; z al This represents the expected heave distance of the virtual ship;

[0108] Specifically, this embodiment is based on the characteristics of an underactuated ship three-degree-of-freedom dynamic model, which lacks lateral thrust and whose position loop torque only acts in the forward direction. In order to unify the subsequent control design, the position torque is integrated into the forward direction according to the characteristics of the matrix. The resulting position error is expressed as follows:

[0109] p se =‖η3e (x se ,y se )‖2 (16)

[0110] In the formula: p se Indicates the integrated position error of the ship; η 3e This represents the one-dimensional state error matrix of the ship; x se Indicates the error in the ship's forward distance; y se ‖·‖2 represents the lateral drift distance error of the ship; ‖·‖2 represents the Euclidean 2-norm;

[0111] The ship's state error is obtained as follows:

[0112] η 3e =[p se -l Δ ,0,ψ se ] T (17)

[0113] In the formula: l Δ ψ is a positive constant used to ensure that the virtual ship always lies ahead of the real ship. se This indicates the error in the bow roll angle of the ship.

[0114]

[0115] In the formula: Represents a virtual control law; v ie Indicates speed error;

[0116] A virtual control law is established to stabilize the derivative of the one-dimensional state information error matrix of the UAV and the ship. The virtual control law is established as follows:

[0117] Based on the backstepping control design method, a virtual control law is defined. Make satisfy but:

[0118]

[0119] In the formula: The positive definite parameter matrix representing the virtual control law; η ie Represents the one-dimensional state information error matrix of drones and ships; This represents the observer parameter matrix related to one-dimensional state information. Represents the state observation error matrix;

[0120] In the subsequent design and calculation of the derivative of the virtual control law, to avoid the impact of complexity explosion, a reduced-order filter is designed with reference to DSC theory. The formula used is as follows:

[0121]

[0122] In the formula: Represents the positive definite time constant matrix. Indicates filter error; Indicates a dynamic surface filter; This represents the initial value of the dynamic surface filter; Indicates the initial value of the virtual control law;

[0123] The derivatives of the filter and its error are obtained and satisfy equation (21).

[0124]

[0125] In the formula: The variable representing the negative value of the virtual control law, where, and satisfy ‖·‖ represents the Euclidean norm;

[0126] S72: Establish fuzzy trigger gain, as follows:

[0127] First, regarding the speed error v ie Taking the derivative, we get:

[0128]

[0129] In the formula: ζ i This represents the fuzzy trigger ratio parameter;

[0130] Then, establish the fuzzy trigger gain g. i :

[0131]

[0132] S73: Obtain the control input matrix for the UAV and unmanned surface vessel using the following formula:

[0133]

[0134] In the formula: G represents i The estimated value, g i Indicates the fuzzy trigger gain; where, , representing the estimated values ​​of the fuzzy triggering gain related to the forward degree of freedom of the UAV, the estimated values ​​of the fuzzy triggering gain related to the yaw degree of freedom of the UAV, and the estimated values ​​of the fuzzy triggering gain related to the heave degree of freedom of the UAV, respectively. , representing the estimated values ​​of the fuzzy triggering gain related to the UAV's roll degree of freedom, the estimated values ​​of the fuzzy triggering gain related to the UAV's pitch degree of freedom, and the estimated values ​​of the fuzzy triggering gain related to the UAV's tumble degree of freedom, respectively. , representing the estimated values ​​of the fuzzy triggering gain related to the ship's forward degree of freedom, and the estimated values ​​of the fuzzy triggering gain related to the ship's bow degree of freedom, respectively. This represents a positive definite parameter matrix.

[0135] Preferably, the fuzzy triggering gain g i The adaptive law and the estimated value matrix of the adaptive fuzzy logic function The adaptive law is expressed as follows:

[0136] By using the backstepping design control method, the gain adaptive law and fuzzy adaptive law are designed as (24).

[0137]

[0138] In the formula: Γ i , λ i L i μ i Let v represent the positive definite parameter matrix of the gain adaptive law, the intermediate parameter matrix of the gain adaptive law, the positive definite parameter matrix of the fuzzy adaptive law, and the intermediate parameter matrix of the fuzzy adaptive law, respectively; ie A two-dimensional state error matrix representing the UAV and the ship; The initial value matrix represents the fuzzy trigger gain estimate; Y1 represents the initial value matrix of the adaptive fuzzy logic function estimate; Y2 represents the 3-row, 3n-column transition matrix.

[0139] To facilitate the design of the adaptive law, the transition matrix is ​​defined as follows:

[0140]

[0141] In the formula: Y0 represents an n-dimensional transition column vector, Y1 represents a 3-dimensional transition column vector, Y2 represents a 3n-dimensional transition column vector, and n represents the dimension of the vector.

[0142] The fuzzy control strategy for port area transmission control systems proposed in this embodiment mainly includes the following characteristics:

[0143] (1) To address the needs of coordinated speed control in port areas, a graded speed control function (speed simulation function) was designed for the LVS-LVA guidance system, i.e., the virtual ship-virtual UAV guidance system. The speed simulation function exhibits an exponential decreasing trend over time. Based on the coordinated system's control plan, its port engineering practice is divided into three speed matching stages: braking deceleration, low-speed drifting, and berthing preparation. Figure 3 As shown.

[0144] (2) Based on the mathematical model of the heterogeneous system, an event-triggered fuzzy observer model was designed, combining the event triggering mechanism and the nonlinear dynamics of the system. This model integrates the observation and processing of system state information and trigger threshold information to compensate for signal deviations caused by unstable factors in a timely manner.

[0145] (3) Considering that the signal trigger ratio parameter has desynchronization with the control signal in the variable speed heterogeneous system, an adaptive fuzzy trigger gain is designed and introduced through robust adaptive boundary compensation technology to ensure the robustness of the overall closed-loop system while maintaining the stable transmission of the control signal.

[0146] To verify the effectiveness and superiority of the control algorithm in this embodiment under the background of variable speed operation within the port, path tracking simulation experiments and comparative experiments were conducted on a relevant simulation platform. The designed speed correction parameters were set as [κ1,κ2,κ3,κ4,κ5]=[2,-3*10 -3 0.3,5*10 -3 [2], the desired angular velocity r of the USV-UAV cooperative system sl The design is as shown in equation (26).

[0147]

[0148] In this experiment, the initial guidance signal for the cooperative system was designed as follows: As for the initial signal of the control loop, it is

[0149]

[0150] Figure 5 The diagram illustrates the path tracking maneuver of the cooperative system performing variable speed braking within the simulated port area. It can be observed that the reference path obtained from the variable speed guidance planning also exhibits good tracking performance during USV-UAV cooperative tracking, thanks to the high precision and effectiveness of the algorithm proposed in this embodiment. Figures 6a-6e This demonstrates the data effects of control trigger signals under an event-triggered mechanism. Based on the differences in actuators within a heterogeneous collaborative system, a servo system is used to convert control commands into actual inputs and apply them to each actuator in real time. Figure 7a and Figure 7bThe signal fluctuation trend based on the adaptive fuzzy gain update law of this embodiment is explained. Clearly, its ability to gradually converge and stabilize in the neighborhood of zero based on the time-varying trend demonstrates that the correlated fuzzy observation system can handle noise disturbances caused by high-frequency trigger signals, thus illustrating the characteristic of gradually stabilizing trigger signal transmission within the system.

[0151] To further highlight the advantages and practicality of the algorithm proposed in this embodiment for trigger signal processing, a comparative experiment was conducted on a USV's ship swivel loop, comparing the algorithm of this embodiment with control algorithms in existing research. In this comparative experiment, the relevant speed change functions were kept consistent, and the running time was set to 400s. Figure 8 This section compares the tracking path performance of the two algorithms. It's clear that the tracking path achieved by this algorithm has a higher fit to the reference path, resulting in higher tracking accuracy than the compared algorithm. Figure 9a and Figure 9b This paper demonstrates the comparative effects of different operating algorithms on the signal triggering interval in the same simulated control scenario. The orange section represents the algorithm used in this embodiment, and the green section represents the comparison algorithms. It is clearly observed that, compared to existing control algorithms, this algorithm significantly extends the average triggering interval and reduces the triggering frequency. This experimental result provides substantial evidence that the adopted fuzzy control strategy can effectively alleviate the signal desynchronization problem caused by heterogeneous coordination differences and the channel resource depletion problem caused by high-frequency signal triggering under rapid-change manipulation conditions.

[0152] In summary, this embodiment has the following two beneficial effects in the field of sea-air coordinated control:

[0153] 1) The exponential variable speed function designed in this embodiment, i.e., the speed control function, improves the braking efficiency of the cooperative system when entering the port. The planned variable speed guidance strategy ensures that the system maintains a safe speed while preparing for berthing at the port. Compared with the prior art, this embodiment can macroscopically control the maneuverability of the cooperative system in port marine engineering practices, such as... Figure 5 Furthermore, by combining relevant signal compensation optimization algorithms, a closed-loop control process system can be realized in ship motion control, consisting of preset virtual signal - signal filtering and transmission - control signal output.

[0154] 2) The fuzzy observer model in this embodiment, based on the event triggering mechanism and the dynamic indicators of the system's mathematical model, implements stabilization compensation measures for signal transmission disturbances in the cooperative system under variable speed scenarios. Path tracking simulation experiments were conducted on a relevant platform to simulate external environmental interference. A series of experimental data results verified the significant advantages of the algorithm designed in this embodiment in solving the channel resource depletion problem caused by high-frequency signal triggering.

[0155] In summary, the present invention provides a ship / machine cooperative port entry navigation control method based on a fuzzy observer. This method establishes a reference path for a virtual ship and a virtual UAV based on a velocity-controlled pseudo-function, obtaining the azimuth angles from the ship to the virtual ship and from the UAV to the virtual UAV, as well as the roll and pitch angles of the virtual UAV. Simultaneously, it establishes a feedforward input matrix for a nonlinear model based on an event-triggered mechanism, and then establishes a fuzzy observer model. Combining the azimuth angles from the ship to the virtual ship and from the UAV to the virtual UAV, as well as the roll and pitch angles of the virtual UAV, it obtains the control input matrices for the UAV and the unmanned vessel, thus achieving ship / machine cooperative port entry navigation control based on a fuzzy observer. This invention, through the velocity-controlled pseudo-function, can implement various speed matching schemes according to different maneuvering stages of the cooperative system. It also uses a fuzzy observer to observe and compensate for system state signals. Furthermore, it introduces a fuzzy logic system (FLS) and adaptive fuzzy trigger gain to address the noise disturbance in the filtered signal caused by uncertainties and low synchronization of trigger thresholds in nonlinear systems. Meanwhile, by combining Dynamic Surface Control (DSC) technology and backstepping design control methods, the tracking accuracy and efficiency of the system can be improved while ensuring steady signal transmission.

[0156] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A machine / ship cooperative port entry navigation control method based on a fuzzy observer, characterized in that, Includes the following steps: S1: Establish a nonlinear model of the heterogeneous cooperative formation system of UAV-unmanned vessel; S2: Establish a velocity control function based on the aforementioned nonlinear model; S3: Based on the speed control function, establish the reference path of the virtual ship and the virtual drone; S4: Based on the reference path of the virtual ship and the virtual drone, obtain the azimuth angle from the ship to the virtual ship and the azimuth angle from the drone to the virtual drone, so as to obtain the roll angle and pitch angle of the virtual drone; S5: Based on the event-triggered mechanism, establish a feedforward input to build the feedforward input matrix of the nonlinear model; S6: Based on the fuzzy logic system, the nonlinear term matrix in the nonlinear system is approximated to obtain the fuzzy basis function matrix. Then, based on the feedforward input matrix, a fuzzy observer model is established to obtain the adaptive fuzzy logic function estimation matrix. S7: Define the state information error of the UAV-unmanned vessel heterogeneous cooperative formation system based on the azimuth angle from the ship to the virtual ship, the azimuth angle from the UAV to the virtual UAV, and the roll and pitch angles of the virtual UAV. Establish fuzzy trigger gain based on the adaptive fuzzy logic function estimation matrix. Obtain the control input matrix of the UAV and the unmanned vessel based on the adaptive fuzzy logic function estimation matrix to realize the ship / vessel cooperative port entry navigation control based on the fuzzy observer.

2. The method for coordinated port entry navigation control based on a fuzzy observer according to claim 1, characterized in that, In step S6, the method for obtaining the adaptive fuzzy logic function estimation matrix is ​​as follows: S61: Based on fuzzy logic systems, the nonlinear term matrix of a nonlinear system is approximated as follows: F i =W i F i +E i In the formula: W i Let the self-feedforward function matrix be denoted as , where ω x ,ω y ,ω z Let ω represent the adaptive fuzzy logic functions related to the forward degree of freedom of the UAV, the adaptive fuzzy logic functions related to the lateral drift degree of freedom of the UAV, and the adaptive fuzzy logic functions related to the heave degree of freedom of the UAV, respectively. φ ,ω θ ,ω ψ Let ω represent the adaptive fuzzy logic functions related to the UAV's roll degree of freedom, the adaptive fuzzy logic functions related to the UAV's pitch degree of freedom, and the adaptive fuzzy logic functions related to the UAV's yaw degree of freedom, respectively. u ,ω v ,ω r Let Φ represent the adaptive fuzzy logic functions related to the ship's forward degree of freedom, the adaptive fuzzy logic function related to the ship's lateral drift degree of freedom, and the adaptive fuzzy logic function related to the ship's bow roll degree of freedom, respectively; i Let represent the fuzzy basis function matrix, where Let represent the fuzzy basis functions related to the forward degree of freedom of the UAV, the fuzzy basis functions related to the lateral drift degree of freedom of the UAV, and the fuzzy basis functions related to the heave degree of freedom of the UAV, respectively. Let represent the fuzzy basis functions related to the UAV's roll degree of freedom, the fuzzy basis functions related to the UAV's pitch degree of freedom, and the fuzzy basis functions related to the UAV's yaw degree of freedom, respectively. Let E represent the fuzzy basis functions related to the ship's forward degree of freedom, the fuzzy basis functions related to the ship's lateral drift degree of freedom, and the fuzzy basis functions related to the ship's bow roll degree of freedom, respectively; i Let E1 represent the fuzzy approximation error matrix, where E1 = [ε x ,ε y ,ε z ] T E2=[ε φ ,ε θ ,ε ψ ] T E3 = [ε u ,ε v ,ε r ] T , ε x ,ε y ,ε z Let ε represent the fuzzy approximation errors related to the forward degree of freedom of the UAV, the fuzzy approximation errors related to the lateral drift degree of freedom of the UAV, and the fuzzy approximation errors related to the heave degree of freedom of the UAV, respectively. φ ,ε θ ,ε ψ Let ε represent the fuzzy approximation errors related to the UAV's roll degree of freedom, the fuzzy approximation errors related to the UAV's pitch degree of freedom, and the fuzzy approximation errors related to the UAV's yaw degree of freedom, respectively. u ,ε v ,ε r E represents the fuzzy approximation error related to the ship's forward degree of freedom, the fuzzy approximation error related to the ship's lateral drift degree of freedom, and the fuzzy approximation error related to the ship's bow roll degree of freedom, respectively. i The upper bound is E iM ; S62: Introduce the fuzzy trigger ratio parameter ζ i , means as follows: In the formula: ζ i This represents the fuzzy trigger ratio parameter, where ζ1 = diag{ζ x ,ζ y ,ζ z },ζ2=diag{ζ φ ,ζ θ ,ζ ψ },ζ3=diag{ζ u ,0,ζ r };ζ x ζ represents the fuzzy trigger ratio parameter related to the forward degrees of freedom of the UAV. y ζ represents the fuzzy trigger ratio parameter related to the drone's drift degree of freedom. z This represents the fuzzy trigger ratio parameter related to the heave-descent degrees of freedom of the UAV; ζ φ ζ represents the fuzzy trigger ratio parameter related to the yaw degree of freedom of the UAV. θ ζ represents the fuzzy trigger ratio parameter related to the pitch degree of freedom of the UAV. ψ This represents the fuzzy trigger ratio parameter related to the bow degree of freedom of the UAV; ζ u ,ζ r These represent the fuzzy trigger ratio parameters related to the ship's forward degree of freedom and the fuzzy trigger ratio parameters related to the ship's yaw degree of freedom, respectively; A i Let A1 represent the feedforward input matrix of the nonlinear model, where A1 = [a x ,a y ,a z ] T A2 = [a φ ,a θ ,a ψ ] T A3 = [a u ,0,a r ] T a x ,a y ,a z These represent the feedforward inputs in the forward degree of freedom direction, the feedforward inputs in the lateral degree of freedom direction, and the feedforward inputs in the heave degree of freedom direction, respectively. φ ,a θ ,a ψ These represent the feedforward inputs in the roll, pitch, and yaw directions of the UAV, respectively. u ,a r These represent the feedforward inputs in the forward degree of freedom direction and the feedforward inputs in the yaw degree of freedom direction, respectively; ζ1, ζ2, ζ3, A1, A2, and A3 are all intermediate calculation parameters; S63: Establish a fuzzy observer model, represented as follows: In the formula: and Each represents a system state observation matrix. and Both represent the state observation error matrix; J i Represents the transformation matrix; A matrix representing the estimated values ​​of the fuzzy trigger ratio parameter; i Represents the feedforward input matrix; This represents the matrix of estimated values ​​for the adaptive fuzzy logic function; This represents the observer parameter matrix related to the one-dimensional state information, where, δ ax ,δ ay ,δ az These represent the observer parameters related to the UAV's forward distance, the observer parameters related to the UAV's lateral drift distance, and the observer parameters related to the UAV's heave distance, respectively. These represent the observer parameters related to the UAV's roll angle, the observer parameters related to the UAV's pitch angle, and the observer parameters related to the UAV's yaw angle, respectively. These represent the observer parameters related to the ship's forward distance, the observer parameters related to the ship's drift distance, and the observer parameters related to the ship's bow roll angle, respectively. The observer parameter matrix represents the information related to the two-dimensional state. δ uax ,δ uay ,δ uaz These represent the observer parameters related to the UAV's forward speed, the observer parameters related to the UAV's drift speed, and the observer parameters related to the UAV's heave speed, respectively. These represent the observer parameters related to the UAV's roll rate, pitch rate, and yaw rate, respectively. These represent the observer parameters related to the ship's forward speed, the observer parameters related to the ship's drift speed, and the observer parameters related to the ship's bow roll rate, respectively.

3. The aircraft / ship cooperative port entry navigation control method based on a fuzzy observer according to claim 1, characterized in that, In S1, the nonlinear model of the UAV-unmanned vessel heterogeneous cooperative formation system is expressed as follows: In the formula: η i A one-dimensional state information matrix representing unmanned aerial vehicles (UAVs) and ships; v i Represents the two-dimensional state information matrices of the UAV and the ship, where v1 and ν2 are both two-dimensional state information matrices of the UAV, v1 = [u ax ,u ay ,u az ] T u ax ,u ay ,u az Let v2 represent the forward velocity, lateral velocity, and vertical velocity of the UAV in volume coordinates, respectively. a ,q a ,r a ] T , where p a ,q a ,r a These represent the roll angular velocity, pitch angular velocity, and yaw angular velocity of the UAV in the body coordinate system, respectively. v3 is the two-dimensional state information matrix of the ship, v3 = [u s ,v s ,r s ] T u s ,v s ,r s These represent the ship's forward speed, drift speed, and bow roll rate, respectively; F i Let F1 and F2 represent the nonlinear terms of the UAV and unmanned vessel, respectively, and let F3 represent the nonlinear terms of the UAV on the position loop and attitude loop, respectively; T represents the transpose of the UAV. M represents the first-order differential; i Let M1 and M2 be the added mass matrices of the UAV, and M3 be the added mass matrix of the ship. M3 = diag{m u ,m v ,m r };m u ,m v ,m r All represent the model's added mass of the ship; diag{·} represents a diagonal matrix; M represents i The inverse matrix of R; i It is the gain matrix for drones and unmanned ships, τ i D represents the control input matrix for drones and unmanned surface vessels; i This represents the disturbance force / torque caused by external marine environmental disturbances; i are all intermediate calculation parameters, where when i = 1, 2 are intermediate calculation parameters related to UAVs, and when i = 3 are intermediate calculation parameters related to ships; J i Represents the transformation matrix; in, In the formula: k dx ,k dy ,k dz Both represent positive parameter constants; q aj p represents the angular velocity of the UAV along the oy axis. aj The angular velocity r of the UAV along the ox axis represents the rotational velocity of the UAV. aj d represents the angular velocity of the UAV along the oz axis. u1 d represents the hydrodynamic damping coefficient that is related to the first power of the ship's forward speed. v1 The hydrodynamic damping coefficient, d, represents the first power correlation between the ship's drift velocity and the hydrodynamic damping coefficient. r1 The hydrodynamic damping coefficient is the first power related to the ship's bow roll rate; d u2 d represents the hydrodynamic damping coefficient related to the second power of the ship's forward speed. v2 d represents the hydrodynamic damping coefficient related to the second power of the ship's drift velocity. r2 The hydrodynamic damping coefficient is the factor relating to the square power of the ship's bow roll rate; d u3 d represents the hydrodynamic damping coefficient related to the cube of the ship's forward speed. v3 d represents the hydrodynamic damping coefficient related to the cubic power of the ship's drift velocity. r3 The value represents the hydrodynamic damping coefficient related to the cube of the ship's bow roll rate; |·| represents the absolute value.

4. The ship / machine cooperative port entry navigation control method based on a fuzzy observer according to claim 3, wherein the speed control function is established as follows: u sl =k1exp{κ2t+k3sin(κ4t)}+κ5 In the formula: u sl denoted as the speed control function; k1, k2, k3, κ4, and κ5 all represent speed correction parameters; t represents time; exp{·} represents an exponential function with the natural base e.

5. In the machine / ship cooperative port entry navigation control method based on a fuzzy observer according to claim 4, in step S4, the reference path of the virtual ship is established as follows: v 3l =[u sl ,v sl ,r sl ] T or 3l =[x sl ,y sl ,ψ sl ] T In the formula: η 3l This represents a one-dimensional state information matrix of a virtual ship, where... η 3l =[x sl ,y sl ,ψ sl ] T x sl ,y sl ,ψ sl J represents the expected forward distance, expected drift distance, and expected heading angle of the virtual ship, respectively; 3l The transformation matrix representing the virtual ship; v 3l A two-dimensional state information matrix representing a virtual ship; v sl ,r sl These represent the drift speed and bow roll rate of the virtual ship, respectively; u sl Represents the velocity control function; in, In the formula: ψ sl This represents the desired heading angle of the virtual ship; The virtual drone is established as follows; In the formula: x al ,y al ,ψ al These represent the forward distance, drift distance, and heading angle of the virtual drone, respectively; r sl This represents the bow roll rate of the virtual ship.

6. In the ship / machine cooperative port entry navigation control method based on a fuzzy observer according to claim 5, the formulas used in step S4 to obtain the azimuth angle from the ship to the virtual ship and the azimuth angle from the UAV to the virtual UAV are as follows: x se =x s -x sl ,and se / and s -and sl x ae =x a -x al ,and ae / and a -and al In the formula, ψ sv Indicates the relative bearing angle between the ship and the virtual ship; ψ av Indicates the relative azimuth angle between the drone and the virtual drone; x se ,y se These are the ship's forward distance error and lateral drift distance error, respectively; x a ,y a These represent the forward and lateral displacements of the UAV, respectively; x al ,y al These represent the forward distance and lateral drift distance of the virtual drone, respectively; x ae ,y ae These are the forward distance error and the lateral drift distance error of the drone; x sl ,y sl These represent the expected forward distance and expected lateral drift distance of the virtual ship, respectively; x s ,y s These represent the forward displacement and lateral drift displacement of the ship in the inertial coordinate system, respectively. The roll and pitch angles of the virtual drone are obtained as follows: Where: φ av θ represents the roll angle of the virtual drone. av Indicates the pitch angle of the virtual drone; ψ av τ represents the relative azimuth angle between the drone and the virtual drone. x τ represents the rotor force of the UAV rotor in the forward direction; y τ represents the rotor force of the UAV rotor in the lateral drift direction. z This represents the rotor force of the drone's rotor in the direction of lift.

7. In the machine / ship cooperative port entry navigation control method based on a fuzzy observer according to claim 1, in step S5, the event triggering mechanism is expressed by the following formula: t ι+1 =inf{t>t ι ||a (·) (t ι )-τ (·) (t)|>d (·) τ (·) (t)} In the formula: inf{·} denotes the infimum of the set; |·| denotes the absolute value; d (·) It is a threshold parameter; The formula used to obtain the feedforward input is as follows: τ (·) (t)=a (·) (t ι ),t∈[t ι ,t ι+1 ] In the formula: τ (·) (t) represents the control input; t ι Indicates the start time of the event trigger; a (·) Indicates feedforward input; t ι+1 Indicates the time when the event was triggered and ended; t represents time. Considering the two scenarios triggered by the control signal: t (·) (t)≥0,|a (·) (t ι )-t (·) (t)|≤d (·) t (·) (t), a (·) (t ι )-τ (·) (t)=p (·) d (·) τ (·) (t),p (·) ∈[-1,1]; t (·) (t)<0,|a (·) (t ι )-t (·) (t)|≤-d (·) t (·) (t), a (·) (t ι )-τ (·) (t)=p (·) d (·) τ (·) (t),p (·) ∈[-1,1] In the formula: p (·) It is a small threshold.

8. In the machine / ship cooperative port entry navigation control method based on a fuzzy observer according to claim 2, the method for obtaining the control input matrices of the UAV and the unmanned vessel in step S7 is as follows: S71: Define the state information error of the UAV-Unmanned Vessel heterogeneous cooperative formation system as follows: or ie =the i -or iv ,i=1,2,3 In the formula: η ie Represents the one-dimensional state information error matrix of drones and ships; η i A one-dimensional state information matrix representing drones and ships; η iv A matrix representing the state information related to virtual drones and virtual ships; in, η 1v [x al ,y al ,z al ] T or 2v =[φ av ,i av ,ψ av ] T or 3v =[x sl ,y sl ,ψ sv ] T In the formula: η 1v Represents a one-dimensional location information matrix related to a virtual drone; η 2v Represents a one-dimensional attitude information matrix related to the virtual drone; η 3v Represents a one-dimensional state information matrix related to the virtual ship; ψ sv Indicates the relative bearing angle between the ship and the virtual ship; z al This represents the expected heave distance of the virtual ship; In the formula: Represents a virtual control law; v ie Indicates speed error; In the formula: The positive definite parameter matrix representing the virtual control law; η ie Represents the one-dimensional state information error matrix of drones and ships; This represents the observer parameter matrix related to one-dimensional state information. Represents the state observation error matrix; In the formula: Represents the positive definite time constant matrix. Indicates filter error; Indicates a dynamic surface filter; This represents the initial value of the dynamic surface filter; Indicates the initial value of the virtual control law; In the formula: The variable representing the negative value of the virtual control law, where, and satisfy ‖·‖ represents the Euclidean norm; S72: Establish fuzzy trigger gain, as follows: First, regarding the speed error v ie Taking the derivative, we get: In the formula: ζ i This represents the fuzzy trigger ratio parameter; Then, establish the fuzzy trigger gain: S73: Obtain the control input matrix for the UAV and unmanned surface vessel using the following formula: In the formula: G represents i The estimated value, g i Indicates the fuzzy trigger gain; where, , representing the estimated values ​​of the fuzzy triggering gain related to the forward degree of freedom of the UAV, the estimated values ​​of the fuzzy triggering gain related to the yaw degree of freedom of the UAV, and the estimated values ​​of the fuzzy triggering gain related to the heave degree of freedom of the UAV, respectively. , representing the estimated values ​​of the fuzzy triggering gain related to the UAV's roll degree of freedom, the estimated values ​​of the fuzzy triggering gain related to the UAV's pitch degree of freedom, and the estimated values ​​of the fuzzy triggering gain related to the UAV's tumble degree of freedom, respectively. , representing the estimated values ​​of the fuzzy triggering gain related to the ship's forward degree of freedom, and the estimated values ​​of the fuzzy triggering gain related to the ship's bow degree of freedom, respectively. This represents a positive definite parameter matrix.

9. The ship / machine cooperative port entry navigation control method based on a fuzzy observer according to claim 1, wherein the adaptive law of the fuzzy trigger gain and the adaptive law of the adaptive fuzzy logic function estimation matrix are expressed as follows: In the formula: Γ i , λ i L i μ i Let v represent the positive definite parameter matrix of the gain adaptive law, the intermediate parameter matrix of the gain adaptive law, the positive definite parameter matrix of the fuzzy adaptive law, and the intermediate parameter matrix of the fuzzy adaptive law, respectively; ie A two-dimensional state error matrix representing the UAV and the ship; The initial value matrix represents the fuzzy trigger gain estimate; Y1 represents the initial value matrix of the adaptive fuzzy logic function estimate; Y2 represents the 3-row, 3n-column transition matrix. in, In the formula: Y0 represents an n-dimensional transition column vector, Y1 represents a 3-dimensional transition column vector, Y2 represents a 3n-dimensional transition column vector, and n represents the dimension of the vector.

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