Aircraft / ship cooperative arrival navigation control method based on fuzzy observer

Abstract

The invention discloses a fuzzy observer-based aircraft / ship cooperative port entering navigation control method, which relates to the technical field of cooperative motion control, and comprises the following steps of: obtaining an azimuth angle from a ship to a virtual ship and an azimuth angle from an unmanned aerial vehicle to a virtual unmanned aerial vehicle by establishing a reference path of a virtual ship and a virtual unmanned aerial vehicle based on a speed quasi-control function; the rolling angle and the pitch angle of the virtual unmanned aerial vehicle are obtained; meanwhile, a feedforward input matrix of a non-linear model based on an event triggering mechanism is established, a fuzzy observer model is further established, the azimuth angle from the ship to the virtual ship, the azimuth angle from the unmanned aerial vehicle to the virtual unmanned aerial vehicle and the rolling angle and the pitch angle of the virtual unmanned aerial vehicle are combined, and control input matrixes of the unmanned aerial vehicle and the unmanned ship are obtained; and the aircraft / ship cooperative arrival navigation control based on the fuzzy observer is realized. According to the invention, various speed matching schemes can be carried out in different control stages according to the cooperative system, the system state signal can be observed and compensated, and the problem of noise disorder of the filtering signal is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of coordinated motion control, and in particular to a method for controlling aircraft / ship coordinated port entry navigation based on a fuzzy observer. Background Art

[0002] The maritime vessel motion control structure consists of two modules: guidance and control. For the USV-UAV heterogeneous collaborative formation system, the high-dimensional signal decoupling technology and actuator differences increase the complexity of the system control architecture. Existing research results often rely on the logical virtual ship-unmanned aerial vehicle (LVS-LVA) to generate reference signals and reference paths for guidance. The design and tuning of virtual speed-changing signals within the port area are still imperfect and challenging. For control systems, other existing results have cited event triggering mechanisms to convert continuous signals into step signals to alleviate channel resource loss. However, there is still broad research potential to address the issues of trigger signal instability and filter noise disturbance in heterogeneous speed-changing systems.

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

[0004] For the variable-speed navigation scenario of the collaborative system within the port area, the traditional guidance strategy will cause large tracking deviations during the phased braking operation, which to some extent violates the safe speed regulations within the port.

[0005] During speed change operations, the transmission of related control signals of the collaborative system is prone to communication disorder under the event trigger mechanism. The high deviation of the trigger ratio parameter makes the step signal unable to pass through the channel efficiently, thereby accelerating the wear of the actuator. Summary of the Invention

[0006] The present invention discloses a fuzzy observer-based aircraft / ship collaborative port entry navigation control method to overcome the above technical problems.

[0007] In order to achieve the above object, the technical solution of the present invention is:

[0008] A method for controlling aircraft / ship collaborative port entry navigation based on a fuzzy observer comprises the following steps:

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

[0010] S2: establishing a velocity pseudo-control function based on the nonlinear model;

[0011] S3: establishing a reference path of the virtual ship and the virtual UAV according to the speed simulation function;

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

[0013] S5: Based on the event trigger mechanism, establish 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 a fuzzy basis function matrix, and then a fuzzy observer model is established according to the feedforward input matrix to obtain an adaptive fuzzy logic function estimation value matrix;

[0015] S7: 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, the state information error of the UAV-UAV heterogeneous collaborative formation system is defined, and the fuzzy trigger gain is established according to the adaptive fuzzy logic function estimation value matrix; the control input matrix of the UAV and the UAV is obtained according to the adaptive fuzzy logic function estimation value matrix, realizing the aircraft / ship collaborative port entry navigation control based on the fuzzy observer.

[0016] Beneficial effects: The present invention provides a method for controlling the coordinated entry of aircraft / ship into the port based on a fuzzy observer. By establishing a reference path of a virtual ship based on a speed-simulated control function and a virtual drone, the azimuth angle from the ship to the virtual ship and the azimuth angle from the drone to the virtual drone, as well as the roll angle and pitch angle of the virtual drone, is obtained. At the same time, a feedforward input matrix of a nonlinear model based on an event trigger mechanism is established, and then a fuzzy observer model is established. The azimuth angle from the ship to the virtual ship and the azimuth angle from the drone to the virtual drone, as well as the roll angle and pitch angle of the virtual drone, is obtained to obtain the control input matrix of the drone and the unmanned ship, thereby realizing coordinated entry of aircraft / ship into the port based on a fuzzy observer. The present invention can perform various speed matching schemes according to the coordinated system in different operation stages through the speed-simulated control function, and observe and compensate for the system status signal through the fuzzy observer to solve the problem of noise disorder in the filtered signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0018] Figure 1 This is a flow chart of the aircraft / ship collaborative port entry navigation control method of the present invention;

[0019] Figure 2 A collaborative guidance three-dimensional information graph in an embodiment of the present invention;

[0020] Figure 3 Schematic diagram of the speed matching operation phase of the coordinated system speed change guidance in an embodiment of the present invention;

[0021] Figure 4 This is a core control architecture diagram of the aircraft / ship collaborative port entry navigation control system in an embodiment of the present invention;

[0022] Figure 5 This is a USV-UAV collaborative 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 in an embodiment of the present invention;

[0024] Figure 6b The USV turning torque control command and actual input in an embodiment of the present invention;

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

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

[0027] Figure 6e The UAV heave thrust control instructions and actual inputs in the embodiment of the present invention;

[0028] Figure 7a is an estimated value of the fuzzy trigger gain associated with the ship's forward degree of freedom and an estimated value of the fuzzy trigger gain associated with the ship's pitching degree of freedom in an embodiment of the present invention;

[0029] Figure 7b are the estimated values ​​of the fuzzy trigger gain associated with the forward degree of freedom of the UAV, the estimated values ​​of the fuzzy trigger gain associated with the drift degree of freedom of the UAV, and the estimated values ​​of the fuzzy trigger gain associated with the heave degree of freedom of the UAV in the embodiment of the present invention;

[0030] Figure 8 : A comparison diagram of USV tracking paths in an embodiment of the present invention;

[0031] Figure 9a This is a comparison diagram of triggering intervals of USV forward thrust control instructions in an embodiment of the present invention;

[0032] Figure 9b 1 is a comparison diagram of triggering intervals of USV turning torque control instructions in an embodiment of the present invention. DETAILED DESCRIPTION

[0033] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0034] This embodiment introduces a method for controlling aircraft / ship collaborative port entry based on fuzzy observer, including the following steps: Figure 1 and Figure 4 As shown:

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

[0036] Preferably, combined with matrix structure theory, the nonlinear model of the UAV-UAV ship heterogeneous cooperative formation system is expressed as follows:

[0037]

[0038] Where: η i Represents the one-dimensional state information matrix of the UAV and the ship, where η1 and η2 are both one-dimensional state information matrices of the UAV, η1=[x a ,y a ,z a ] T , x a ,y a ,z a They represent the forward displacement, lateral displacement, and lifting displacement of the UAV respectively, η2=[φ a ,θ a ,ψ a ] T ,φ a ,θ a ,ψ a They 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 They represent the forward displacement, drift displacement and bow angle of the ship in the inertial coordinate system respectively; v i Represents the two-dimensional state information matrix 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 They represent the forward speed, lateral speed, and lifting speed of the UAV in the body coordinate system, respectively, v2 = [p a ,q a ,r a ] T , where p a ,q a ,r a They represent the rolling angular velocity, pitching angular velocity, and yaw angular velocity of the UAV in the body coordinate system, ν3 is the two-dimensional state information matrix of the ship, ν3=[u s ,v s ,r s ] T ,u s ,v s ,r s Respectively represent the forward speed, drift speed and bow angular velocity of the ship; F i represents the nonlinear term matrix of the UAV and the UAV ship, where F1 and F2 represent the nonlinear term matrix on the UAV position loop and the attitude loop, respectively, and F3 represents the nonlinear term matrix of the ship; T represents transpose; represents the first-order differential; M i Represents the additional mass matrix, where M1 and M2 are the additional mass matrices of the UAV, M1=diag{m a ,m a ,m a}, m a Indicates the mass of the drone, M2=diag{I xx ,I yy ,I zz}, I xx ,I yy ,I zz They 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 centroid of the UAV as the origin: the horizontal axis ox of the three-dimensional coordinate system is along the direction of the UAV's forward freedom; the vertical axis oy of the three-dimensional coordinate system is along the direction of the UAV's drift freedom; the vertical axis oz of the three-dimensional coordinate system is along the direction of the UAV's heave freedom, and M3 is the additional mass matrix of the ship, M3=diag{m u ,m v ,m r};m u ,m v ,m r All represent the additional mass of the ship model; diag{·} represents the diagonal matrix; Indicates M i The inverse matrix of R i is the gain matrix of the UAV and the UAV. In this embodiment, R2 = diag{1, d, d}, R3 = diag{1, 1, 1}, d represents the diagonal diameter of the UAV; τ i Represents the control input matrix of the UAV and the unmanned ship, 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 represents the rotor force of the UAV rotor, τ φ ,τ θ ,τ ψ are the roll, pitch and yaw moments of the UAV, τ u ,τ r Indicates the forward thrust and turning moment of the ship; D i represents the disturbance force / torque caused by the external ocean environment disturbance, 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 They represent the external interference force / torque on the UAV in the forward, drift, and heave directions, respectively. wφ ,d wθ ,d wψ They represent the external interference force / torque on the UAV in the roll, pitch and yaw directions, d wu ,d wv ,d wr Respectively represent the external interference force / torque on the ship in the forward, drift and yaw directions; η1, η2, η3, v1, v2, v3, M1, M2, M3, R1, R2, R3, D1, D2, D3, i are all intermediate calculation parameters, among which, when i=1, 2, they are intermediate calculation parameters related to the UAV, and when i=3, they are intermediate calculation parameters related to the ship; Ji represents the transformation matrix;

[0039] in,

[0040]

[0041]

[0042] Where: k dx ,k dy ,k dz All represent positive parameter constants; q aj Indicates the angular velocity of the drone along the oy axis, p aj Indicates the angular velocity of the drone along the ox axis, r aj Indicates the angular velocity of the drone along the oz axis; d u1 Denotes the hydrodynamic damping coefficient related to the first power of the ship's forward speed, d v1 represents the hydrodynamic damping coefficient related to the first power of the ship's drift velocity, d r1 represents the hydrodynamic damping coefficient related to the first power of the ship's bow angular velocity; d u2 Denotes the hydrodynamic damping coefficient related to the square power of the ship's forward speed, d v2 represents the hydrodynamic damping coefficient related to the square power of the ship's drift speed, d r2 represents the hydrodynamic damping coefficient related to the second power of the ship's bow angular velocity; d u3 Denotes the hydrodynamic damping coefficient related to the third power of the ship's forward speed, d v3 represents the hydrodynamic damping coefficient related to the cubic power of the ship's drift velocity, d r3 represents the hydrodynamic damping coefficient related to the third power of the ship's bow angular velocity; |·| represents the absolute value;

[0043] S2: Establishing a speed pseudo-control function u based on the nonlinear model sl ;

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

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

[0046] Where: u sl represents the velocity pseudo-control function; κ1, k2, k3, k4, and κ5 all represent velocity correction parameters; t represents time; exp{·} represents an exponential function with the natural base e as the base;

[0047] S3: establishing a reference path of the virtual ship and the virtual UAV according to the speed simulation function;

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

[0049]

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

[0051] η 3l =[x sl ,y sl ,ψ sl ] T

[0052] Where: η 3l represents the one-dimensional state information matrix of the virtual ship, where η 3l =[x sl ,y sl ,ψ sl ] T , x sl ,y sl ,ψ sl They represent the expected forward distance, expected drift distance and expected heading angle of the virtual ship respectively; J 3l represents the transformation matrix of the virtual ship; v 3l represents the two-dimensional state information matrix of the virtual ship; v sl ,r sl They represent the drift speed and yaw angular velocity of the virtual ship respectively; u sl represents the velocity pseudo-control function;

[0053] in,

[0054]

[0055] Where: sl represents the desired heading angle of the virtual ship;

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

[0057] Specifically, this embodiment constructs a virtual UAV as shown in formula (6) based on the 3D mapping guidance strategy:

[0058]

[0059] Where: x al ,y al ,ψ al Respectively represent the forward distance, drift distance and heading angle of the virtual UAV; r slrepresents the yaw angular velocity of the virtual ship; in addition, the heave distance z of the virtual drone al The specific value is set by the user.

[0060] S4: Based on the reference path of the virtual ship and the virtual drone, obtain the azimuth angle ψ from the ship to the virtual ship sv and the azimuth angle ψ from the UAV to the virtual UAV av , as shown in formula (7), based on the nonlinear decoupling technology, the rolling angle φ of the virtual drone is obtained av and pitch angle θ av , is solved by equation (8) using the control input and relative azimuth angle:

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

[0062]

[0063] Where, ψ sv represents the relative bearing angle from the ship to the virtual ship; ψ av Indicates the relative azimuth angle from the UAV to the virtual UAV; x se ,y se They are the ship's forward distance error and horizontal drift distance error respectively; x a ,y a Respectively represent the forward displacement and lateral displacement of the UAV; x al ,y al Respectively represent the forward distance and horizontal drift distance of the virtual drone; x ae ,y ae They are the forward distance error and the horizontal drift distance error of the UAV; x sl ,y sl They represent the expected forward distance and expected drift distance of the virtual ship respectively; x s ,y s They represent the forward displacement and lateral drift displacement of the ship in the inertial coordinate system respectively;

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

[0065]

[0066] Where: φ av represents the roll angle of the virtual drone; θ av represents the pitch angle of the virtual drone; ψ avrepresents the relative azimuth angle from the UAV to the virtual UAV; τ x represents the rotor force of the UAV rotor in the forward direction; τ y represents the rotor force of the UAV rotor in the drift direction; τ z It represents the rotor force of the UAV rotor in the heave direction;

[0067] Specifically, in order to facilitate subsequent control design calculations, the guidance signals for LVS and LVA are organized as follows:

[0068] 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 ;

[0069] Where: η 1v Represents the one-dimensional position information matrix related to the virtual drone; η 2v Represents the one-dimensional attitude information matrix related to the virtual drone; η 3v represents the one-dimensional state information matrix related to the virtual ship; ψ sv Indicates the relative bearing angle from the ship to the virtual ship; z al represents (the expected heave distance of the virtual ship);

[0070] S6: Based on the event trigger mechanism, establish feedforward input a (·) , as shown in formula (10), to establish the feedforward input matrix of the nonlinear model;

[0071] Preferably, the event triggering mechanism is expressed using the following formula:

[0072] t l+1 =inf{t>t l ||a (·) (t l )-τ (·) (t)|>d (·) τ (·) (t)} (9)

[0073] Where: inf{·} represents the lower bound of the set; |·| represents the absolute value; d (·) is the threshold parameter, satisfying d (·) ∈(0,1).

[0074] The formula used to obtain the feedforward input is as follows:

[0075] τ (·) (t) = a (·) (t ι ),t∈[t ι ,t ι+1 ] (10)

[0076] Where: τ (·) (t) represents the control input; t ι Indicates the time when the event trigger starts; a (·) represents feedforward input; t ι+1 Indicates the time when the event trigger ends; t indicates time;

[0077] Consider two situations triggered by the control signal:

[0078] τ (·) (t)≥0,|a (·) (t ι )-τ (·) (t)|≤d (·) τ (·) (t),

[0079] a (·) (t ι )-τ (·) (t) = p (·) d (·) τ (·) (t),p (·) ∈[-1,1];

[0080] τ (·) (t)<0,|a (·) (t ι )-τ (·) (t)|≤-d (·) τ (·) (t),

[0081] α (·) (t ι )-τ (·) (t) = p (·) d (·) τ (·) (t),p (·) ∈[-1,1]

[0082] From the above analysis, we can get:

[0083]

[0084] Where: p (·) is the threshold value.

[0085] S6: Based on the fuzzy logic system, the nonlinear term matrix in the nonlinear system is approximated to obtain the fuzzy basis function matrix Φ i , and then establish a fuzzy observer model based on the feedforward input matrix to obtain the adaptive fuzzy logic function estimation value matrix

[0086] Preferably, the adaptive fuzzy logic function estimation matrix is ​​obtained Here’s how:

[0087] S61: Based on the fuzzy logic system FLS, the nonlinear term matrix in the nonlinear system is approximated as follows:

[0088] F i =W i Φ i +E i (12)

[0089] Where: W i Represents the self-feedforward function matrix, where ω x ,ω y ,ω z They represent the adaptive fuzzy logic function related to the UAV's forward freedom, the adaptive fuzzy logic function related to the UAV's drift freedom, and the adaptive fuzzy logic function related to the UAV's heave freedom, ω φ ,ω θ ,ω ψ They represent the adaptive fuzzy logic function related to the roll degree of freedom of the UAV, the adaptive fuzzy logic function related to the pitch degree of freedom of the UAV, and the adaptive fuzzy logic function related to the bow degree of freedom of the UAV, ω u ,ω v ,ω r They represent the adaptive fuzzy logic function related to the ship's forward freedom, the adaptive fuzzy logic function related to the ship's drifting freedom, and the adaptive fuzzy logic function related to the ship's pitching freedom respectively; Φ i represents the fuzzy basis function matrix, where

[0090] They represent the fuzzy basis functions related to the UAV's forward freedom, the fuzzy basis functions related to the UAV's drift freedom, and the fuzzy basis functions related to the UAV's heave freedom. They represent the fuzzy basis functions related to the roll degree of freedom of the UAV, the fuzzy basis functions related to the pitch degree of freedom of the UAV, and the fuzzy basis functions related to the bow degree of freedom of the UAV, respectively. They represent the fuzzy basis functions related to the ship's forward freedom, the fuzzy basis functions related to the ship's drifting freedom, and the fuzzy basis functions related to the ship's pitching freedom respectively; E i Represents 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 They represent the fuzzy approximation error related to the UAV's forward freedom, the fuzzy approximation error related to the UAV's drift freedom, and the fuzzy approximation error related to the UAV's heave freedom, ε φ ,ε θ ,ε ψ They represent the fuzzy approximation error related to the roll degree of freedom of the UAV, the fuzzy approximation error related to the pitch degree of freedom of the UAV, and the fuzzy approximation error related to the bow degree of freedom of the UAV, respectively. u ,ε v ,ε r They represent the fuzzy approximation error related to the ship's forward freedom, the fuzzy approximation error related to the ship's drifting freedom, and the fuzzy approximation error related to the ship's pitching freedom, respectively. i The upper bound of E iM ;

[0091] S62: Introducing the fuzzy trigger ratio parameter ζ i , which is expressed as follows:

[0092]

[0093] Where: i 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 represents the fuzzy trigger ratio parameter related to the UAV's forward freedom of movement, ζ y represents the fuzzy trigger ratio parameter related to the UAV's drift freedom, ζz represents the fuzzy trigger ratio parameter related to the UAV's heave and sink degrees of freedom; ζ φ represents the fuzzy trigger ratio parameter related to the UAV's roll freedom, ζ θ represents the fuzzy trigger ratio parameter related to the pitch freedom of the UAV, ζ ψ represents the fuzzy trigger ratio parameter related to the UAV's bow pitch degree of freedom; ζ u ,ζ r They represent the fuzzy trigger ratio parameters related to the ship's forward freedom and the fuzzy trigger ratio parameters related to the ship's pitching freedom respectively; A i Represents 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 They represent the feedforward input in the UAV's forward freedom direction, the feedforward input in the UAV's drift freedom direction, and the feedforward input in the UAV's heave freedom direction, respectively. φ ,a θ ,a ψ They represent the feedforward input in the roll degree of freedom, the pitch degree of freedom, and the bow degree of freedom, respectively. u ,a r They represent the feedforward input in the direction of the ship's forward freedom and the feedforward input in the direction of the ship's pitching freedom respectively; ζ1, ζ2, ζ3, A1, A2, and A3 are all intermediate calculation parameters;

[0094] S63: Establish a fuzzy observer model, which is expressed as follows:

[0095]

[0096] Where: and Both represent the system state observation matrix, and Both represent the state observation error matrix; J i represents the transformation matrix; A represents the estimated value matrix of the fuzzy trigger ratio parameter; i represents the feedforward input matrix; Represents the adaptive fuzzy logic function estimation matrix; represents the observer parameter matrix associated with the one-dimensional state information, where δ ax ,δ ay ,δ az They represent the observer parameters related to the UAV's forward distance, the observer parameters related to the UAV's drift distance, and the observer parameters related to the UAV's heave distance, respectively. φ ,δ θ , They represent the observer parameters related to the roll angle of the UAV, the observer parameters related to the pitch angle of the UAV, and the observer parameters related to the bow angle of the UAV, respectively. x ,δ y , They 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; represents the observer parameter matrix related to the two-dimensional state information, where δ uax ,δ uay ,δ uaz They represent the observer parameters related to the forward speed of the UAV, the observer parameters related to the drift speed of the UAV, and the observer parameters related to the heave speed of the UAV, respectively. p ,δ q , They represent the observer parameters related to the roll angular velocity of the UAV, the observer parameters related to the pitch angular velocity of the UAV, and the observer parameters related to the bow angular velocity of the UAV, respectively. u ,δ v , They represent the observer parameters related to the ship's forward speed, the observer parameters related to the ship's lateral drift speed, and the observer parameters related to the ship's bow angular velocity respectively;

[0097] S7: 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 angle and pitch angle of the virtual UAV, the state information error of the UAV-UAV heterogeneous cooperative formation system is defined to establish the fuzzy trigger gain g according to the adaptive fuzzy logic function estimation value matrix. i ; To estimate the value matrix based on the adaptive fuzzy logic function The control input matrix of the UAV and the unmanned ship is obtained to realize the coordinated port entry control of the UAV / ship based on the fuzzy observer.

[0098] Preferably, the method for obtaining the control input matrix of the UAV and the unmanned ship is as follows:

[0099] S71: Define the state information error of the UAV-UAV ship heterogeneous cooperative formation system as follows:

[0100] η ie =η i -η iv ,i=1,2,3 (15)

[0101] Where: η ie Represents the one-dimensional state information error matrix of the UAV and the ship; η i Represents the one-dimensional state information matrix of the UAV and the ship; η iv A matrix representing state information related to the virtual drone and the virtual ship;

[0102] in,

[0103] η 1v =[x al ,y al ,z al ] T

[0104] η 2v =[φ av ,θ av ,ψ av ]T

[0105] η 3v =[x sl ,y sl ,ψ sv ] T

[0106] Where: η 1v Represents the one-dimensional position information matrix related to the virtual drone; η 2v Represents the one-dimensional attitude information matrix related to the virtual drone; η 3v represents the one-dimensional state information matrix related to the virtual ship; ψ sv Indicates the relative bearing angle from the ship to the virtual ship; z al represents the expected heave distance of the virtual ship;

[0107] Specifically, this embodiment is based on the characteristics of the three-degree-of-freedom dynamic model of an underactuated ship. It lacks lateral propulsion, and the position loop torque only acts in the forward direction. In order to centralize and unify the subsequent control design, the position torque is integrated in the forward direction according to the characteristics of the matrix. The resulting position error is expressed as follows:

[0108] p se =‖η 3e (x se ,y se )‖2 (16)

[0109] Where: p se represents the integrated position error of the ship; η 3e represents the one-dimensional state error matrix of the ship; x se Indicates the ship's forward distance error; y se represents the ship's drift distance error; ‖·‖2 represents the Euclidean 2 norm;

[0110] The state error of the ship is obtained, which is expressed as follows:

[0111] η 3e =[p se -l Δ ,0,ψ se ] T (17)

[0112] Where: l Δ is a positive constant used to ensure that the virtual ship is always in front of the real ship; ψ se Indicates the ship's heading angle error.

[0113]

[0114] Where: represents the virtual control law; v ie Indicates speed error;

[0115] A virtual control law is established to stabilize the derivatives of the one-dimensional state information error matrix of the UAV and the ship. The virtual control law is established as follows:

[0116] According to the backstepping design control method, the virtual control law is defined Satisfaction but:

[0117]

[0118] Where: represents the positive definite parameter matrix of the virtual control law; η ie Represents the one-dimensional state information error matrix of the UAV and the ship; represents the observer parameter matrix associated with the one-dimensional state information; represents the state observation error matrix;

[0119] In the subsequent design and calculation of the derivative of the virtual control law, in order to avoid the influence of complexity explosion, a reduced-order filter is designed by referring to the DSC theory. The formula used is as follows:

[0120]

[0121] Where: represents the positive definite time constant matrix, represents the filter error; represents a dynamic surface filter; Represents the initial value of the dynamic surface filter; Indicates the initial value of the virtual control rate;

[0122] The derivative of the filter and its error is obtained to satisfy equation (21).

[0123]

[0124] Where: A negative variable representing the virtual control rate, where and satisfy ‖·‖ represents the Euclidean norm;

[0125] S72: Establish fuzzy trigger gain, the method is as follows:

[0126] First, the velocity error v ie Taking the derivative, we get:

[0127]

[0128] Where: i represents the fuzzy trigger ratio parameter;

[0129] Then, establish the fuzzy trigger gain g i :

[0130]

[0131] S73: Obtain the control input matrix of the UAV and the UAV. The formula used is as follows:

[0132]

[0133] Where: Indicates g i The estimated value of g i represents the fuzzy trigger gain; where, They represent the estimated value of the fuzzy trigger gain related to the UAV's forward degree of freedom, the estimated value of the fuzzy trigger gain related to the UAV's drift degree of freedom, and the estimated value of the fuzzy trigger gain related to the UAV's heave degree of freedom. They represent the estimated value of the fuzzy trigger gain associated with the UAV's roll degree of freedom, the estimated value of the fuzzy trigger gain associated with the UAV's pitch degree of freedom, and the estimated value of the fuzzy trigger gain associated with the UAV's yaw degree of freedom. They represent the estimated value of the fuzzy trigger gain related to the ship's forward degree of freedom and the estimated value of the fuzzy trigger gain related to the ship's pitching degree of freedom, respectively. represents a positive definite parameter matrix.

[0134] Preferably, the fuzzy trigger gain g i Adaptive law and adaptive fuzzy logic function estimation matrix The adaptive law is expressed as follows:

[0135] Through the backstepping design control method, the design gain adaptive law and fuzzy adaptive law are (24).

[0136]

[0137] Where: Γ i ,λ i , L i , μ i They 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; v ie Represents the two-dimensional state error matrix of the UAV and the ship; An initial value matrix representing the estimated value of the fuzzy trigger gain; Represents the initial value matrix of the adaptive fuzzy logic function estimation value; Y3 represents the transition matrix of 3 rows and 3n columns;

[0138] In order to facilitate the design of the adaptive law, the transition matrix is ​​defined as follows:

[0139]

[0140] Wherein: Y0 represents the n-dimensional transition column vector, Y1 represents the 3-dimensional transition column vector, Y2 represents the 3n-dimensional transition column vector, and n represents the dimension of the vector.

[0141] The fuzzy control strategy for the port-area speed change control system proposed in this embodiment mainly includes the following features:

[0142] (1) Aiming at the demand of coordinated speed-changing operation in the port area, a step-by-step speed-changing function (speed pseudo-control function) is designed for the LVS-LVA guidance system, i.e., the virtual ship-virtual UAV guidance system. The speed pseudo-control function has an exponential decreasing trend over time. According to the operation planning of the coordinated system, the engineering practice in the port is divided into three speed matching stages, i.e., braking and deceleration-low-speed drifting-preparation for berthing, as shown in the following figure. Figure 3 shown.

[0143] (2) Combining the event trigger mechanism and the nonlinear dynamics of the system, an event-triggered fuzzy observer model is designed based on the mathematical model of the heterogeneous system. This model integrates the system state information and the trigger threshold information to observe and process the system, and timely compensates for the signal deviation caused by unstable factors.

[0144] (3) Considering that the signal trigger ratio parameter has desynchronization effect on the control signal in the variable speed heterogeneous system, an adaptive fuzzy trigger gain is designed and introduced through the robust adaptive boundary compensation technology to maintain the stable transmission of the control signal while ensuring the robustness of the overall closed-loop system.

[0145] In order to verify the effectiveness and superiority of the control algorithm of this embodiment under the background of speed change operation in the port, path tracking simulation experiments and comparative tests were carried out on the relevant simulation platform. Among them, the designed speed correction parameters are set to [κ1,κ2,κ3,κ4,κ5] = [2, -3*10 -3 ,0.3,5*10 -3 ,2], the expected angular velocity r of the USV-UAV cooperative system sl The design is as shown in formula (26).

[0146]

[0147] The initial guidance signal for the cooperative system is designed to be As for the control loop initial signal, it is

[0148]

[0149] Figure 5 Figure 2 shows a schematic diagram of the collaborative system performing variable-speed braking path tracking maneuvers within a simulated port area. It can be seen that the reference path generated by variable-speed guidance planning also demonstrates good tracking performance when the USV and UAV perform collaborative tracking, thanks to the high precision and effectiveness of the algorithm proposed in this embodiment. Figure 6a-Figure 6e The data effects of the control trigger signal under the event trigger mechanism are demonstrated. Based on the differences of the actuators in the heterogeneous collaborative system, the control instructions are converted into actual inputs by using the servo system and acted on each actuator in real time. Figure 7a and Figure 7b This illustrates the signal fluctuation trend based on the adaptive fuzzy gain update law of this embodiment. Clearly, its gradual convergence and stabilization around zero over time demonstrates the ability of the correlated fuzzy observation system to handle the noise disturbances caused by high-frequency trigger signals, thus demonstrating the gradual stabilization of trigger signal transmission within the system.

[0150] To further highlight the advantages and practicality of the algorithm proposed in this embodiment for trigger signal processing, a comparative experiment on a USV's ship rotation loop was conducted, comparing the algorithm in this embodiment with control algorithms from existing research results. In this comparative experiment, the relevant speed change functions were kept consistent, and the run time was set to 400 seconds. Figure 8 The figure shows the comparison of the tracking paths run by the two algorithms. It is not difficult to see that the tracking accuracy of this algorithm is higher than that of the compared algorithm because the tracking path under this algorithm has a higher fit with the reference path. Figure 9a and Figure 9b This experiment demonstrates a comparison of signal trigger intervals under the same simulation control scenario using different algorithms. The orange portion represents the algorithm in this embodiment, while the green portion represents the comparison algorithm. It is clearly observed that, compared to existing control algorithms, this algorithm significantly extends the average trigger interval and reduces the trigger frequency. This experimental result provides substantial evidence that the fuzzy control strategy employed can effectively mitigate signal desynchronization issues caused by heterogeneous coordination differences and channel resource depletion caused by high-frequency signal triggering under rapidly changing control conditions.

[0151] In summary, this embodiment has the following two beneficial effects in the field of sea-air coordinated control:

[0152] 1) The exponential speed function designed in this embodiment, i.e., the speed pseudo-control function, improves the braking efficiency of the collaborative system when entering the port. The planned speed control strategy can ensure that the system maintains a safe speed while preparing for braking for berthing at the port. Compared with the existing technology, this embodiment can macro-control the maneuverability of the collaborative system in port and marine engineering practice, such as Figure 5 , combined with the optimization algorithm of related signal compensation, a closed-loop control process system of preset virtual signal-signal filtering and transmission-control signal output can be realized in terms of ship motion control.

[0153] 2) The fuzzy observer model of this embodiment, based on an event-triggered mechanism and dynamic indicators of the system mathematical model, implements a stabilization and compensation measure for the transmission disturbances of the cooperative system's trigger signals in variable-speed scenarios. By simulating external environmental interference and conducting path tracking simulations of the cooperative system on a relevant platform, a series of experimental data results validate the significant advantages of the algorithm designed in this embodiment in addressing channel resource depletion caused by high-frequency signal triggering.

[0154] In summary, the present invention provides a method for cooperative port entry control of aircraft / ship based on a fuzzy observer. By establishing a reference path of a virtual ship and a virtual drone based on a speed-simulated control function, the azimuth angle from the ship to the virtual ship and the azimuth angle from the drone to the virtual drone, as well as the roll angle and pitch angle of the virtual drone, is obtained. At the same time, a feedforward input matrix of a nonlinear model based on an event trigger mechanism is established, and then a fuzzy observer model is established. The azimuth angle from the ship to the virtual ship and the azimuth angle from the drone to the virtual drone, as well as the roll angle and pitch angle of the virtual drone, is obtained to obtain the control input matrix of the drone and the unmanned ship, thereby realizing cooperative port entry control of aircraft / ship based on a fuzzy observer. Through the speed-simulated control function, the present invention can perform various speed matching schemes according to the cooperative system at different operation stages, and observe and compensate for the system state signal through the fuzzy observer. In addition, the present invention can introduce a fuzzy logic system (FLS) and an adaptive fuzzy trigger gain to address the problem of filter signal noise disorder caused by the uncertainty in the nonlinear system and the low synchronization of the trigger threshold. At the same time, the dynamic surface control technology (DSC) and backstepping design control method are combined to improve the tracking accuracy and efficiency of the system while ensuring steady signal transmission.

[0155] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements 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 method for controlling aircraft / ship collaborative port entry based on fuzzy observer, characterized in that: The steps include: S1: Establish a nonlinear model of the UAV-UAV heterogeneous cooperative formation system; S2: establishing a velocity pseudo-control function based on the nonlinear model; S3: establishing a reference path of the virtual ship and the virtual UAV according to the speed simulation function; S4: Based on the reference path of the virtual ship and the virtual UAV, obtaining an azimuth angle from the ship to the virtual ship and an azimuth angle from the UAV to the virtual UAV, so as to obtain a roll angle and a pitch angle of the virtual UAV; S5: Based on the event trigger mechanism, establish 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 a fuzzy basis function matrix, and then a fuzzy observer model is established according to the feedforward input matrix to obtain an adaptive fuzzy logic function estimation value matrix; S7: 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, the state information error of the UAV-UAV heterogeneous collaborative formation system is defined, and the fuzzy trigger gain is established according to the adaptive fuzzy logic function estimation value matrix; the control input matrix of the UAV and the UAV is obtained according to the adaptive fuzzy logic function estimation value matrix, realizing the aircraft / ship collaborative port entry navigation control based on the fuzzy observer.

2. The method for controlling aircraft / ship collaborative port entry based on fuzzy observer according to claim 1 is characterized in that: In S6, the method for obtaining the adaptive fuzzy logic function estimation value matrix is ​​as follows: S61: Based on the fuzzy logic system, the nonlinear term matrix in the nonlinear system is approximated as follows: F i =W i F i +E i Where: W i Represents the self-feedforward function matrix, where ω x ,ω y ,ω z They represent the adaptive fuzzy logic function related to the UAV's forward freedom, the adaptive fuzzy logic function related to the UAV's drift freedom, and the adaptive fuzzy logic function related to the UAV's heave freedom, ω φ ,ω θ ,ω ψ They represent the adaptive fuzzy logic function related to the roll degree of freedom of the UAV, the adaptive fuzzy logic function related to the pitch degree of freedom of the UAV, and the adaptive fuzzy logic function related to the bow degree of freedom of the UAV, ω u ,ω v ,ω r They represent the adaptive fuzzy logic function related to the ship's forward freedom, the adaptive fuzzy logic function related to the ship's drifting freedom, and the adaptive fuzzy logic function related to the ship's pitching freedom respectively; Φ i represents the fuzzy basis function matrix, where They represent the fuzzy basis functions related to the UAV's forward freedom, the fuzzy basis functions related to the UAV's drift freedom, and the fuzzy basis functions related to the UAV's heave freedom. They represent the fuzzy basis functions related to the roll degree of freedom of the UAV, the fuzzy basis functions related to the pitch degree of freedom of the UAV, and the fuzzy basis functions related to the bow degree of freedom of the UAV, respectively. They represent the fuzzy basis functions related to the ship's forward freedom, the fuzzy basis functions related to the ship's drifting freedom, and the fuzzy basis functions related to the ship's pitching freedom respectively; E i Represents 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 They represent the fuzzy approximation error related to the UAV's forward freedom, the fuzzy approximation error related to the UAV's drift freedom, and the fuzzy approximation error related to the UAV's heave freedom, ε φ ,ε θ ,ε ψ They represent the fuzzy approximation error related to the roll degree of freedom of the UAV, the fuzzy approximation error related to the pitch degree of freedom of the UAV, and the fuzzy approximation error related to the bow degree of freedom of the UAV, respectively. u ,ε v ,ε r They represent the fuzzy approximation error related to the ship's forward freedom, the fuzzy approximation error related to the ship's drifting freedom, and the fuzzy approximation error related to the ship's pitching freedom, respectively. i The upper bound of E iM ; S62: Introducing the fuzzy trigger ratio parameter ζ i , which is expressed as follows: Where: i 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 represents the fuzzy trigger ratio parameter related to the UAV's forward freedom of movement, ζ y represents the fuzzy trigger ratio parameter related to the UAV's drift freedom, ζ z represents the fuzzy trigger ratio parameter related to the UAV's heave and sink degrees of freedom; ζ φ represents the fuzzy trigger ratio parameter related to the UAV's roll freedom, ζ θ represents the fuzzy trigger ratio parameter related to the pitch freedom of the UAV, ζ ψ represents the fuzzy trigger ratio parameter related to the UAV's bow pitch degree of freedom; ζ u ,ζ r They represent the fuzzy trigger ratio parameters related to the ship's forward freedom and the fuzzy trigger ratio parameters related to the ship's pitching freedom respectively; A i Represents 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 They represent the feedforward input in the UAV's forward freedom direction, the feedforward input in the UAV's drift freedom direction, and the feedforward input in the UAV's heave freedom direction, respectively. φ ,a θ ,a ψ They represent the feedforward input in the roll degree of freedom, the pitch degree of freedom, and the bow degree of freedom, respectively. u ,a r They represent the feedforward input in the direction of the ship's forward freedom and the feedforward input in the direction of the ship's pitching freedom respectively; ζ1, ζ2, ζ3, A1, A2, and A3 are all intermediate calculation parameters; S63: Establish a fuzzy observer model, which is expressed as follows: Where: and Both represent the system state observation matrix, and Both represent the state observation error matrix; J i represents the transformation matrix; A represents the estimated value matrix of the fuzzy trigger ratio parameter; i represents the feedforward input matrix; Represents the adaptive fuzzy logic function estimation matrix; represents the observer parameter matrix associated with the one-dimensional state information, where δ ax ,δ ay ,δ az They represent the observer parameters related to the UAV's forward distance, the observer parameters related to the UAV's drift distance, and the observer parameters related to the UAV's heave distance. They represent the observer parameters related to the roll angle of the UAV, the observer parameters related to the pitch angle of the UAV, and the observer parameters related to the bow angle of the UAV, respectively. They 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; represents the observer parameter matrix related to the two-dimensional state information, where δ uax ,δ uay ,δ uaz They represent the observer parameters related to the forward speed of the UAV, the observer parameters related to the drift speed of the UAV, and the observer parameters related to the heave speed of the UAV. They represent the observer parameters related to the roll angular velocity of the UAV, the observer parameters related to the pitch angular velocity of the UAV, and the observer parameters related to the bow angular velocity of the UAV, respectively. They represent the observer parameters related to the ship's forward speed, the observer parameters related to the ship's lateral drift speed, and the observer parameters related to the ship's bow angular velocity, respectively.

3. The method for controlling aircraft / ship collaborative port entry based on fuzzy observer according to claim 1, characterized in that: In S1, the nonlinear model of the UAV-UAV heterogeneous cooperative formation system is expressed as follows: Where: η i Represents the one-dimensional state information matrix of the UAV and the ship; v i Represents the two-dimensional state information matrix 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 They represent the forward speed, lateral speed, and lifting speed of the UAV in the body coordinate system, respectively, v2 = [p a ,q a ,r a ] T , where p a ,q a ,r a They represent the rolling angular velocity, pitching angular velocity, and yaw angular velocity of the UAV in the body coordinate system, 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 Respectively represent the forward speed, drift speed and bow angular velocity of the ship; F i represents the nonlinear term matrix of the UAV and the UAV ship, where F1 and F2 represent the nonlinear term matrix on the UAV position loop and the attitude loop, respectively, and F3 represents the nonlinear term matrix of the ship; T represents transpose; represents the first-order differential; M i Represents the additional mass matrix, where M1 and M2 are the additional mass matrices of the UAV, M3 is the additional mass matrix of the ship, M3=diag{m u ,m v ,m r };m u ,m v ,m r All represent the additional mass of the ship model; diag{·} represents the diagonal matrix; Indicates M i The inverse matrix of R i is the gain matrix of the UAV and the UAV, τ i represents the control input matrix of the UAV and the unmanned ship; D i Represents the interference force / torque caused by the external ocean environment interference; i is an intermediate calculation parameter, among which, when i=1, 2, it is an intermediate calculation parameter related to the UAV, and when i=3, it is an intermediate calculation parameter related to the ship; J i represents the transformation matrix; in, Where: k dx ,k dy ,k dz All represent positive parameter constants; q aj Indicates the angular velocity of the drone along the oy axis, p aj Indicates the angular velocity of the drone along the ox axis, r aj Indicates the angular velocity of the drone along the oz axis; d u1 Denotes the hydrodynamic damping coefficient related to the first power of the ship's forward speed, d v1 represents the hydrodynamic damping coefficient related to the first power of the ship's drift velocity, d r1 represents the hydrodynamic damping coefficient related to the first power of the ship's bow angular velocity; d u2 Denotes the hydrodynamic damping coefficient related to the square power of the ship's forward speed, d v2 represents the hydrodynamic damping coefficient related to the square power of the ship's drift speed, d r2 represents the hydrodynamic damping coefficient related to the second power of the ship's bow angular velocity; d u3 Denotes the hydrodynamic damping coefficient related to the third power of the ship's forward speed, d v3 represents the hydrodynamic damping coefficient related to the cubic power of the ship's drift velocity, d r3 represents the hydrodynamic damping coefficient related to the cube of the ship's bow angular velocity; |·| represents the absolute value.

4. According to the method for controlling aircraft / ship coordinated port entry based on fuzzy observer in claim 3, the speed pseudo-control function is established as follows: u sl =k1exp{κ2t+k3sin(κ4t)}+κ5 Where: u sl represents the velocity pseudo-control function; k1, k2, k3, κ4, and κ5 all represent velocity correction parameters; t represents time; and exp{·} represents an exponential function with the natural base e as its base.

5. According to the method for controlling aircraft / ship coordinated port entry based on fuzzy observer in 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 Where: η 3l Represents the one-dimensional state information matrix of the virtual ship, where η 3l =[x sl ,y sl ,ψ sl ] T , x sl ,y sl ,ψ sl They represent the expected forward distance, expected drift distance and expected heading angle of the virtual ship respectively; J 3l represents the transformation matrix of the virtual ship; v 3l represents the two-dimensional state information matrix of the virtual ship; v sl ,r sl They represent the drift speed and yaw angular velocity of the virtual ship respectively; u sl represents the velocity pseudo-control function; in, Where: sl represents the desired heading angle of the virtual ship; The virtual drone is established as follows; Where: x al ,y al ,ψ al Respectively represent the forward distance, drift distance and heading angle of the virtual UAV; r sl Indicates the yaw angular velocity of the virtual ship.

6. According to the method for controlling aircraft / ship coordinated port entry based on fuzzy observers in claim 5, in step S4, the formulas used to obtain the azimuth angle from the ship to the virtual ship and the azimuth angle from the drone to the virtual drone 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 Where, ψ sv represents the relative bearing angle from the ship to the virtual ship; ψ av Indicates the relative azimuth angle from the UAV to the virtual UAV; x se ,y se They are the ship's forward distance error and horizontal drift distance error respectively; x a ,y a Respectively represent the forward displacement and lateral displacement of the UAV; x al ,y al Respectively represent the forward distance and horizontal drift distance of the virtual drone; x ae ,y ae They are the forward distance error and the horizontal drift distance error of the UAV; x sl ,y sl They represent the expected forward distance and expected drift distance of the virtual ship respectively; x s ,y s They represent the forward displacement and lateral drift displacement of the ship in the inertial coordinate system respectively; Get the roll angle and pitch angle of the virtual drone as follows: Where: φ av represents the roll angle of the virtual drone; θ av represents the pitch angle of the virtual drone; ψ av represents the relative azimuth angle from the UAV to the virtual UAV; τ x represents the rotor force of the UAV rotor in the forward direction; τ y represents the rotor force of the UAV rotor in the drift direction; τ z It represents the rotor force of the UAV rotor in the heave direction.

7. The method for controlling aircraft / ship coordinated port entry based on fuzzy observer according to claim 1, wherein 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)} Where: inf{·} represents the lower bound of the set; |·| represents the absolute value; d (·) is the threshold parameter; The formula used to obtain the feedforward input is as follows: τ (·) (t)=a (·) (t ι ),t∈[t ι ,t ι+1 ] Where: τ (·) (t) represents the control input; t ι Indicates the time when the event trigger starts; a (·) represents feedforward input; t ι+1 Indicates the time when the event trigger ends; t indicates time; Consider two situations 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] Where: p (·) is the threshold value.

8. According to the method for controlling coordinated aircraft / ship port entry based on fuzzy observers in claim 2, in step S7, the method for obtaining the control input matrix of the UAV and the UAV is as follows: S71: Define the state information error of the UAV-UAV ship heterogeneous cooperative formation system as follows: or ie =the i -or iv ,i=1,2,3 Where: η ie Represents the one-dimensional state information error matrix of the UAV and the ship; η i Represents the one-dimensional state information matrix of the UAV and the ship; η iv A matrix representing state information related to the virtual drone and the virtual ship; 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 Where: η 1v Represents the one-dimensional position information matrix related to the virtual drone; η 2v Represents the one-dimensional attitude information matrix related to the virtual drone; η 3v represents the one-dimensional state information matrix related to the virtual ship; ψ sv Indicates the relative bearing angle from the ship to the virtual ship; z al represents the expected heave distance of the virtual ship; Where: represents the virtual control law; v ie Indicates speed error; Where: represents the positive definite parameter matrix of the virtual control law; η ie Represents the one-dimensional state information error matrix of the UAV and the ship; represents the observer parameter matrix associated with the one-dimensional state information; represents the state observation error matrix; Where: represents the positive definite time constant matrix, represents the filter error; represents a dynamic surface filter; Represents the initial value of the dynamic surface filter; Indicates the initial value of the virtual control rate; Where: A negative variable representing the virtual control rate, where and satisfy ‖·‖ represents the Euclidean norm; S72: Establish fuzzy trigger gain, the method is as follows: First, the velocity error v ie Taking the derivative, we get: Where: i represents the fuzzy trigger ratio parameter; Then, establish the fuzzy trigger gain: S73: Obtain the control input matrix of the UAV and the UAV. The formula used is as follows: Where: Indicates g i The estimated value of g i represents the fuzzy trigger gain; where, They represent the estimated value of the fuzzy trigger gain related to the UAV's forward degree of freedom, the estimated value of the fuzzy trigger gain related to the UAV's drift degree of freedom, and the estimated value of the fuzzy trigger gain related to the UAV's heave degree of freedom. They represent the estimated value of the fuzzy trigger gain associated with the UAV's roll degree of freedom, the estimated value of the fuzzy trigger gain associated with the UAV's pitch degree of freedom, and the estimated value of the fuzzy trigger gain associated with the UAV's yaw degree of freedom. They represent the estimated value of the fuzzy trigger gain related to the ship's forward degree of freedom and the estimated value of the fuzzy trigger gain related to the ship's pitching degree of freedom, respectively. represents a positive definite parameter matrix.

9. According to the method of claim 1 for controlling coordinated aircraft / ship port entry based on a fuzzy observer, the adaptive law of the fuzzy trigger gain and the adaptive law of the adaptive fuzzy logic function estimation value matrix are expressed as follows: Where: Γ i ,λ i , L i , μ i They 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; v ie Represents the two-dimensional state error matrix of the UAV and the ship; An initial value matrix representing the estimated value of the fuzzy trigger gain; Represents the initial value matrix of the adaptive fuzzy logic function estimation value; Y3 represents the transition matrix of 3 rows and 3n columns; in, Wherein: Y0 represents the n-dimensional transition column vector, Y1 represents the 3-dimensional transition column vector, Y2 represents the 3n-dimensional transition column vector, and n represents the dimension of the vector.

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