Guidance and gain adaptive control method for ship-air-sea collaborative mission for surround warning

Abstract

The present invention discloses a guidance and gain adaptive control method for a ship-air-sea collaborative mission for surround warning, comprising: constructing a mixed-order ship-UAV nonlinear system model; setting a waypoint path planning reference path to generate a ship reference signal; obtaining the real-time position of the ship and the surround radius of the UAV, and generating a UAV reference signal in combination with a virtual UAV; defining the kinematic error of the ship-UAV based on the reference signal and designing a virtual control law; introducing dynamic surface technology to obtain the dynamic surface signal of the virtual control law and define the dynamic error; using a radial basis function neural network and a minimum learning parameter technology to approximate the nonlinear terms of the ship-UAV system, introducing the MLP technology to simplify external interference, designing a preset performance control law and an adaptive law for actuator gain, and realizing path tracking control of the UAV under the surround warning mission. The present invention can ensure control accuracy, reduce the complexity of controller design, give full play to the maneuverability of the UAV, and achieve the goal of the ship and the UAV performing the surround warning mission.

Description

Technical Field

[0001] The present invention relates to the technical field of ship and unmanned aerial vehicle motion control, and in particular to a ship-air-sea collaborative mission guidance and gain adaptive control method for surround warning. Background Art

[0002] The path tracking control system implemented by the ship-UAV collaborative system consists of three subsystems: guidance, control, and navigation. The guidance system automatically generates a reference signal based on the positional relationship between the ship-UAV system's current attitude and the desired path. The control system achieves effective convergence by stabilizing the error between the current attitude and the reference signal. The navigation system transmits the position and attitude information of the controlled object to the guidance and control systems via sensors.

[0003] Guidance and control are two important subsystems in path-following control. In existing research, the most common Loss of Sight (LOS) guidance, while computationally simple, is significantly affected by ocean environmental disturbances and is unsuitable for guidance with large errors. Vector field guidance requires accurate data on ship or drone model parameters and is not adaptable to diverse environments. Traditional virtual guidance (such as LVS) often relies on time to divide the path into straight and turning segments for separate calculations. In control systems, a comprehensive control theory for the heterogeneous coordinated control of ship and drone systems has yet to be established. While 3D mapping guidance algorithms have established a guidance framework for drone-ship collaborative systems, they are not suitable for drone / ship circling surveillance missions due to the limitation of consistent trajectory alignment between the drone and ship. Furthermore, most literature treats actuator gains as simple constants. However, in engineering practice, accurate actuator gains are often difficult to obtain due to the varying parameters of the ship or drone and the varying environmental influences on actuator gains.

[0004] Based on the above analysis, the traditional guidance and control method directly applied to the ship-UAV surveillance mission has the following two main defects:

[0005] 1) In the past, drone-assisted ship navigation often employed a synchronized positioning scheme. This allowed the drone to provide information about the surrounding environment and warn of potential hazards. However, this collaborative approach failed to effectively utilize the drone's high maneuverability. In a surround surveillance scenario, the drone, while accompanying the ship, orbits at a constant angular velocity around the vessel. This fully leverages the advantages of the drone / ship collaborative system and expands the drone's monitoring range. However, surround surveillance missions require specific guidance methods, making traditional synchronized guidance with the ship unsuitable. Furthermore, traditional virtual guidance (such as LVS) requires calculating the timing of straight and turning segments before deriving a guidance reference signal. In practical navigation, this guidance strategy results in cumulative position and attitude errors at the transition point (straight-to-turn segment). This can affect path tracking accuracy in navigation situations with numerous waypoints, such as narrow waterways and island-reef areas.

[0006] 2) In engineering practice, actuator gains in ships and drones are often difficult to measure and can vary with use (e.g., due to wear or load) and the environment. While treating actuator gains as known constants may be a valid assumption in some simplified analyses, in actual control system design, considering the variations and dynamic characteristics of actuator gains generally leads to better performance and system robustness. To address this issue, designing adaptive gains is a common method used in ship control. However, due to the strong coupling nature of drone models, this approach cannot be directly applied to drone control. Summary of the Invention

[0007] The present invention provides a guidance and gain adaptive control method for ship-air-sea collaborative missions for surround surveillance, so as to overcome the technical problems that the existing ship-UAV collaborative system cannot be applied to UAVs for surround surveillance missions and cannot use adaptive gain instead of UAV actuator gain for control.

[0008] In order to achieve the above object, the technical solution of the present invention is:

[0009] A guidance and gain adaptive control method for ship-air-sea collaborative missions for surround alert, comprising:

[0010] S1: Construct a mixed-order ship-UAV nonlinear system model as the controlled object of the control law designed in the subsequent steps;

[0011] S2: Setting a waypoint path, using a virtual ship to plan a reference path based on the waypoint path, and generating a ship reference signal; obtaining the real-time position of the ship and the orbiting radius of the UAV, and generating a UAV reference signal in combination with the virtual UAV; the ship reference signal includes a ship position reference signal and a ship attitude reference signal;

[0012] S3: Based on the ship reference signal and the UAV reference signal, define the ship-UAV kinematic error and design the corresponding virtual control law to eliminate the kinematic error of the ship-UAV nonlinear system model; the kinematic error includes the ship-UAV position error and attitude error;

[0013] S4: Introducing dynamic surface technology to obtain the dynamic surface signal of the virtual control law, and defining a dynamic error based on the dynamic surface signal; the dynamic error includes a speed error and an angular velocity error between the ship and the UAV;

[0014] S5: Using radial basis function neural network and minimum learning parameter technology to simultaneously approximate the nonlinear terms of the ship-UAV nonlinear system model, and introducing MLP technology to simplify external interference;

[0015] S6: Using the approximated and simplified ship-UAV nonlinear system model and the dynamic error, design a preset performance control law and an adaptive law for actuator gains;

[0016] S7: Path tracking control of the UAV in the surrounding warning mission is achieved based on the preset performance control law and the adaptive law of actuator gain.

[0017] Furthermore, a mixed-order ship-UAV nonlinear system model is constructed, as shown in formulas (1)-(5).

[0018]

[0019] In formula (1), H1=[x s ,y s ,ψ s ] T Indicates the ship's forward and sideways displacement and bow angle, H2=[x a ,y a ,z a ] T Indicates the forward, drift, and heave displacement of the UAV, H3=[φ a ,θ a ,ψ a ] T Indicates the roll, pitch and yaw angles of the UAV; I1=[u s ,v s ,r s ] T Indicates the ship's forward, drifting, and bow rolling speeds, I2=[u x ,u y ,u z ] T and I3=[p a ,q a ,r a ]T Indicates the speed and angular velocity of the drone along the ox, oy, oz axes in the coordinate system with the fuselage as the origin; F1 = [f u ,f v ,f r ] T , F2=[f x ,f y ,f z ] T and F3=[f φ ,f θ ,f ψ ] T It represents the nonlinear term of the ship-UAV system, and its specific expansion is shown in formula (3); J1 in formula (2) is an intermediate variable;

[0020] M1=diag[m u ,m v ,m r ] represents the additional mass of the ship in the forward, drifting and bow rolling directions, M2=diag[m a ,m a ,m a ] represents the mass of the drone, M3=diag[I xx ,I yy ,I zz ] represents the rotational inertia of the drone along the ox, oy, oz axis; C1=diag[C u ,0,C r ],C2=diag[k t ,k t ,k t ],C3=diag[k t ,k t ,k d ], are unknown system actuator gain matrices; where C u ,C r is the ship actuator gain, k t ,k d is the UAV actuator gain; T1=[n|n|,0,δ] T represents the ship control input, n and δ represent the ship propeller speed and rudder angle respectively, T2 and T3 are the UAV control inputs, which are specifically expanded as shown in formulas (4) and (5), where Ω is the UAV propeller rotor speed;

[0021] D1=[d wu ,d wv ,d wr ] T , D2=[d wx ,d wy ,d wz ]T and D3=[d wφ ,d wθ ,d wψ ] T G = [0, 0, g] represents the external interference force / torque on the ship in the forward, drift, and pitch directions, and on the UAV in the forward, drift, heave, roll, pitch, and pitch directions respectively. T represents the acceleration due to gravity;

[0022] In formula (3), R2=diag[R x ,R y ,R z ], R3=diag[l,l,1], both represent the fixed actuator gain matrix, where l is the distance from the center of the drone to each rotor; d ii1 ,d ii2 ,d ii3 ,ii=u,v,r represents the nonlinear damping term of the model; k dx ,k dy ,k dz It represents the rotational drag coefficient of the UAV along the ox, oy, oz axis;

[0023] f u , f v , f r They represent the nonlinear terms of the ship in the forward, drift and bow directions, respectively, and f x , f y , f z They represent the nonlinear terms of the UAV in the forward, drift, and heave directions, respectively, and f θ ,f θ ,f ψ They represent the nonlinear terms of the UAV in the roll, pitch and yaw directions, R x ,R y ,R z Represent the gain coefficients of the drone on ox, oy, and oz respectively.

[0024] Furthermore, setting a waypoint path, using a virtual ship to plan a reference path according to the waypoint path, and generating a ship reference signal; obtaining the real-time position of the ship and the UAV's orbiting radius, and generating a UAV reference signal in combination with the virtual UAV, including:

[0025] S21, set the waypoint path and the ship turning radius, and select the target point P according to the set turning radius before and after each waypoint except the starting point and the end point. inWk and P outWk ;

[0026] S22, using a virtual ship to plan a reference path according to the waypoint path, as shown in formula (6),

[0027]

[0028] Where x sd ,y sd ,ψ sd Indicates the forward, drift distance and heading angle of the virtual ship L1, that is, the ship position reference signal, u sd ,r sd represents the forward speed and yaw angular velocity of L1 ship, u sd Set according to actual situation, r sd The calculation formula is shown in formula (7),

[0029]

[0030] Where, L 1s is the distance from L1 ship to the target point, η s =ψ sd -ψ L1s , ψ L1s is the azimuth from L1 ship to the target point, η s Indicates the deviation angle between L1 ship and target point; a ls represents the lateral acceleration of the L1 ship;

[0031] The azimuth from the actual ship to the L1 ship is shown in formula (8),

[0032]

[0033] Where, ψ r Indicates the azimuth from the actual ship to the L1 ship, that is, the ship attitude reference signal, x se =x sd -x s ,y se =y sd -y s , indicating the ship's forward and drift distance errors;

[0034] S23, obtaining the real-time position of the ship and the circling radius of the drone;

[0035] S24, use the virtual drone to obtain the drone's reference signal, as shown in formula (9),

[0036]

[0037] Where x ad ,y ad ,ψ ad represents the forward and drift distances and heading angles of the virtual UAV, i.e. the reference signal of the UAV, T a is the set drone orbiting period, LR is the set UAV orbit radius, η a is the azimuth from the ship to the virtual drone, pp(0) its initial value, x ae =x ad -x a ,y ae =y ad -y a , is the UAV's forward and lateral distance error, z ad is the desired takeoff altitude of the drone.

[0038] Furthermore, based on the ship reference signal and the UAV reference signal, the ship-UAV kinematic error is defined, and the corresponding virtual control law is designed, including:

[0039] S31. Define the ship-UAV position error based on the ship reference signal and the UAV reference signal, as shown in formula (10):

[0040]

[0041] Where z se represents the ship position error, z ae Indicates the UAV heave distance error;

[0042] S32, deriving the position error between the ship and the UAV, as shown in formula (11),

[0043]

[0044] Where, ψ se =ψ r -ψ s is the ship's heading angle error;

[0045] S33. Define the preset performance function of the ship-UAV position error, as shown in formula (12),

[0046]

[0047] Where, ρ i ,i=u,x,y,z, is the preset performance function of position error, ρ i0 and ρ i∞ is ρ i The initial value and final convergence value of k αi is a constant;

[0048] Define the conversion error of the ship-UAV position error as shown in formula (13):

[0049]

[0050] Where, σ i ,i=u,x,y,z represents the conversion error of position error;

[0051] S34. Design the virtual control law of the ship-UAV position error, as shown in formula (14):

[0052]

[0053] Where k αu ,k αx ,k αy ,k αz is the virtual control law α u ,α x ,α y ,α z design parameters.

[0054] Furthermore, the kinematic error of the ship-UAV is defined and the corresponding virtual control law is designed, which also includes:

[0055] S35. Define the ship-UAV attitude error based on the ship reference signal and the UAV reference signal, as shown in formula (15):

[0056]

[0057] Where, ψ se Indicates the ship's bow angle error, φ ae represents the roll angle error of the UAV, θ ae represents the pitch angle error of the UAV, ψ ae Indicates the UAV bow angle error; φ ad represents the reference roll angle of the UAV, θ ad Indicates the reference roll and pitch angles of the drone;

[0058] S36, deriving the attitude error of the ship-UAV, as shown in formula (16),

[0059]

[0060] S37. Define a preset performance function of the ship-UAV attitude error, as shown in formula (17),

[0061] ρ j (t)=(ρ j0 -ρ j∞ )e -kjt +ρ j∞ (17)

[0062] Where, ρ j ,j=r,φ,θ,ψ, is the preset performance function of attitude error, ρ j0 and ρj∞ is ρ j The initial value and final convergence value of k αj is a constant;

[0063] Define the conversion error of the ship-UAV attitude error as shown in formula (18),

[0064]

[0065] Where, σ j ,j=r,φ,θ,ψ, represents the attitude error conversion error;

[0066] S38. Design the virtual control law of the ship-UAV attitude error, as shown in formula (19):

[0067]

[0068] Where k αr ,k αφ ,k αθ ,k αψ is the virtual control law α for attitude error r ,α φ ,α θ ,α ψ control parameters.

[0069] Furthermore, the dynamic surface technology is introduced to obtain the dynamic surface signal of the virtual control law, and the dynamic error is defined according to the dynamic surface signal, including:

[0070] S41. Introducing dynamic surface technology, the derivatives of the virtual control law of position error and the derivatives of the virtual control law of attitude error are reduced to obtain the dynamic surface error of position error and the dynamic surface error of attitude error, as shown in formulas (20) and (21).

[0071]

[0072] In formula (20), β i is α i Dynamic surface signal, ∈ i is the time constant, and the dynamic surface error of the position error is y i =α i -β i , α i A virtual control law representing the position error;

[0073] In formula (21), β j is α j Dynamic surface signal, ∈ j is the time constant, and the dynamic surface error of the attitude error is y j =α j-β j , α j A virtual control law representing the attitude error;

[0074] S42. Define the ship-UAV velocity error and perform the derivative, as shown in formulas (22) and (23),

[0075]

[0076] Where u se ,u xe ,u ye ,u ze represents the ship's forward movement, the UAV's forward movement, drift and heave speed errors, B2 = [β x ,β y ,β z ] T Dynamic surface signal matrix representing position error;

[0077] Define the angular velocity error between the ship and the UAV and perform the derivative, as shown in formulas (24) and (25),

[0078]

[0079] Where r se ,p ae ,q ae ,r ae represents the angular velocity error of ship's bow, UAV's roll, pitch and bow; B3 = [β φ ,β θ ,β ψ ] T , representing the dynamic surface signal matrix of the attitude error.

[0080] Furthermore, the radial basis function neural network and the minimum learning parameter technique are used to simultaneously approximate the nonlinear terms of the ship-UAV nonlinear system model, and the MLP technique is introduced to simplify the external interference, as shown in formulas (26) and (27),

[0081] f i -d wi ≤Θ i1 Θ i2 -c i S i W i (26)

[0082] f j -d wj ≤Θ j1 Θ j2 -c j S j W j(27)

[0083] Where, f i and f j represents the nonlinear term of the ship-UAV nonlinear system model, Θ i2 =1+‖S i ‖+1,Θ j2 =1+‖S j ‖+1,Θ i1 ,Θ j1 ,Θ i2 ,Θ j2 is the intermediate variable in the minimum learning parameter technique;

[0084] A i represents f i The neural network weight update law, A j represents f j The neural network weight update law, c i ,c j A i ,A j Norm of S i and S j represents the Gaussian function, ε i and ε j represents the approximation error, is the approximation error ε i The maximum value of is the approximation error ε j The maximum value, d mi It is the external environment interference wi The maximum value, d mj It is the external environment interference wj The maximum value of W i ,W j represents the intermediate variable, as shown in formula (28),

[0085]

[0086] Furthermore, the ship-UAV nonlinear system model after approximation and simplification and the dynamic error are used to design a preset performance control law and an adaptive law of actuator gain, including:

[0087] S61, obtain the actual control input, and convert the actual control input into a preset performance control law and an adaptive law of actuator gain, as shown in formula (29),

[0088]

[0089] Where, τ l,l=u,r,x,y,z,φ,λ,ψ are the preset performance control laws of the ship's forward direction, yaw angle, UAV's forward direction, drift, heave direction, roll, pitch and yaw angle; n is the ship's propeller speed; δ is the ship's rudder angle;

[0090] λ2=[λ kt ,λ kt ,λ kt ] T ,λ3=[λ kt ,λ kt ,λ kd ] T , They are λ cu ,λ cr ,λ kt ,λ kd The estimated value of λ cu ,λ cr ,λ kt ,λ kd They represent the adaptive parameters of the actuator gain, i.e., the adaptive law, and represents the matrix of actuator gains, τ2 and τ3 represent the preset performance control law matrices;

[0091] S62. Using the simplified ship-UAV nonlinear system model, formulas (23) and (29), the MLP technology, coupling gain adaptation technology, and backstepping method are introduced to design the preset performance control law of the speed error and the adaptive law of the actuator gain, as shown in formulas (30)-(32).

[0092]

[0093]

[0094] Where k i ,Γ Cu1 ,Γ Cu2 , is the design parameter, ω i represents the adaptive parameter introduced by the minimum learning parameter, Yes i estimated value of; Ψ i Represents ω i The robust damping term, where ρ i1 ,ρ i2 is a constant; τ u ,τ x ,τ y ,τz represents the preset performance control law for the speed error, represents the derivative of the adaptive parameter;

[0095] S63. Using the simplified ship-UAV nonlinear system model and formulas (25) and (29), the MLP technology, coupling gain adaptation technology, and backstepping method are introduced to design the preset performance control law of the angular velocity error and the adaptive law of the actuator gain, as shown in formulas (33)-(35).

[0096]

[0097] Where k j ,Γ Cr1 ,Γ Cr2 ,Γ kt1 ,Γ kt2 ,Γ kd1 ,Γ kd2 , is the design parameter, Yes j The estimated value of ω j The robust damping term, where ρ j1 ,ρ j2 is a constant; τ j Denotes the preset performance control law for the angular velocity error, ω j Adaptive parameters introduced for the minimum learning parameter technique, Derivative of the adaptive parameter representing the actuator gain.

[0098] Furthermore, the nonlinear decoupling technology is used to solve formula (30) to obtain the reference roll angle and pitch angle of the UAV, as shown in formula (36).

[0099]

[0100] Where, φ ad represents the reference roll angle of the UAV, θ ad Indicates the reference pitch angle of the drone.

[0101] Beneficial Effects: The present invention provides a guidance and gain adaptive control method for ship-air-sea collaborative missions for surround alerting. The method obtains the reference signal of the ship and the reference signal of the UAV for surround alerting. Compared with the traditional UAV surveillance mode in which the UAV is fixed above or in front of the ship, the surround alerting method can give full play to the maneuverability of the UAV and has the advantages of being less susceptible to interference, having a large alert range, and having no blind spots.

[0102] Considering the maneuverability of the ship-UAV heterogeneous system, a virtual control law for the ship-UAV kinematic error is designed. The radial basis function neural network and the minimum learning parameter technology are combined to simultaneously perform online approximation of the model structure uncertainties of the ship-UAV collaborative system and the external environmental disturbances. In addition, considering the problem of actuator gain uncertainty in actual navigation, an adaptive law for actuator gain and a preset performance control law are designed. While ensuring control accuracy, the complexity of the controller design is reduced, and the goal of the ship and UAV performing the surround warning mission is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0103] 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.

[0104] Figure 1 A flow chart of the method for guidance and gain adaptive control of the ship-air-sea collaborative mission for surround alert provided by the present invention;

[0105] Figure 2 A framework diagram for generating guidance reference signals for L1 ships;

[0106] Figure 3 Generate a guidance reference signal framework diagram for using a virtual UAV;

[0107] Figure 4 This is the trajectory diagram of the ship-UAV surrounding warning path tracking of the present invention;

[0108] Figure 5 Schematic diagram of propeller speed and rudder angle of the ship of the present invention;

[0109] Figure 6 Schematic diagram of the rotor speed of the UAV of the present invention;

[0110] Figure 7 Schematic diagram of the ship position error and attitude error of the present invention;

[0111] Figure 8 Schematic diagram of the position error of the UAV of the present invention;

[0112] Figure 9 Schematic diagram of the attitude error of the UAV of the present invention. DETAILED DESCRIPTION

[0113] 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.

[0114] This embodiment provides a guidance and gain adaptive control method for ship-air-sea collaborative missions for surround warning, such as Figure 1 Shown, including:

[0115] A guidance and gain adaptive control method for ship-air-sea collaborative missions for surround alert, comprising:

[0116] S1: Construct a mixed-order ship-UAV nonlinear system model as the controlled object of the control law designed in the subsequent steps;

[0117] S2: Setting a waypoint path, using a virtual ship to plan a reference path based on the waypoint path, and generating a ship reference signal; obtaining the real-time position of the ship and the orbiting radius of the UAV, and generating a UAV reference signal in combination with the virtual UAV; the ship reference signal includes a ship position reference signal and a ship attitude reference signal;

[0118] S3: Based on the ship reference signal and the UAV reference signal, define the ship-UAV kinematic error and design the corresponding virtual control law to eliminate the kinematic error of the ship-UAV nonlinear system model; the kinematic error includes the ship-UAV position error and attitude error;

[0119] S4: Introducing dynamic surface technology to obtain the dynamic surface signal of the virtual control law, and defining a dynamic error based on the dynamic surface signal; the dynamic error includes a speed error and an angular velocity error between the ship and the UAV;

[0120] S5: Using radial basis function neural network and minimum learning parameter technology to simultaneously approximate the nonlinear terms of the ship-UAV nonlinear system model, and introducing MLP technology to simplify external interference;

[0121] S6: Using the approximated and simplified ship-UAV nonlinear system model and the dynamic error, design a preset performance control law and an adaptive law for actuator gains;

[0122] S7: Path tracking control of the UAV in the surrounding warning mission is achieved based on the preset performance control law and the adaptive law of the actuator gain.

[0123] Specifically, first, a mixed-order ship-UAV nonlinear system model is constructed as the controlled object of the control law designed in the subsequent steps, in preparation for the approximation of nonlinear terms in the subsequent steps, and then the design of preset performance control laws and adaptive laws; secondly, a waypoint path is set, and a virtual ship is used to plan a reference path according to the waypoint path, and a ship reference signal is generated; the real-time position of the ship and the UAV's circling radius are obtained, and a UAV reference signal is generated in combination with the virtual UAV to realize the UAV's circling warning mission, meet the requirements of navigation practice, and have higher guidance accuracy; based on the ship reference signal and the UAV reference signal, the ship-UAV kinematic error is defined, and the corresponding virtual control law is designed to eliminate the kinematic error of the ship-UAV nonlinear system model, so as to achieve accurate tracking of the ship-UAV and return the path to the reference path. ; Secondly, the dynamic surface technology is introduced to obtain the dynamic surface signal of the virtual control law, and the dynamic error is defined according to the dynamic surface signal, which can reduce the load in the calculation process and improve the processing efficiency; the radial basis function neural network and the minimum learning parameter technology are used to simultaneously approximate the nonlinear terms of the ship-UAV nonlinear system model, and the MLP technology is introduced to simplify the external interference, which can reduce the adaptive parameters in the controller, which is beneficial to the maneuverability of the heterogeneous system and improve the efficiency of the UAV's coordinated surveillance of ships; finally, the ship-UAV nonlinear system model after approximation and simplification and the dynamic error are used to design the preset performance control law and the adaptive law of the actuator gain, so as to achieve the control effect of controlling the error within the budget range when the actuator gain is uncertain, and realize the path tracking control of the UAV under the surrounding surveillance task.

[0124] In a specific embodiment, a mixed-order ship-UAV nonlinear system model is constructed as the controlled object of the control law designed in the subsequent steps. The model is shown in formulas (37)-(41):

[0125]

[0126]

[0127] In formula (37), H1=[x s ,y s ,ψ s ] T Indicates the ship's forward and sideways displacement and bow angle, H2=[x a ,y a ,z a ] T Indicates the forward, drift, and heave displacement of the UAV, H3=[φ a ,θ a ,ψ a ] TIndicates the roll, pitch and yaw angles of the UAV; I1=[u s ,v s ,r s ] T Indicates the ship's forward, drifting, and pitching speeds, I2=[u x ,u y ,u z ] T and I3=[p a ,q a ,r a ] T Indicates the speed and angular velocity of the drone along the ox, oy, oz axes in the coordinate system with the fuselage as the origin; F1 = [f u ,f v ,f r ] T , F2=[f x ,f y ,f z ] T and F3=[f φ ,f θ ,f ψ ] T represents the nonlinear term of the ship-UAV system, which is specifically expanded as shown in formula (39); J1 in formula (38) is an intermediate variable;

[0128] M1=diag[m u ,m v ,m r ] represents the additional mass of the ship in the forward, drifting and bow rolling directions, M2=diag[m a ,m a ,m a ] represents the mass of the drone, M3=diag[I xx ,I yy ,I zz ] represents the rotational inertia of the drone along the ox, oy, oz axis; C1=diag[C u ,0,C r ],C2=diag[k t ,k t ,k t ],C3=diag[k t ,k t ,k d ], are unknown system actuator gain matrices; where C u ,C r is the ship actuator gain, k t ,k d For UAV actuator gains; in engineering practice, they are often uncertain;

[0129] T1=[n|n|,0,δ] T represents the ship control input, n and δ represent the ship propeller speed and rudder angle respectively, T2 and T3 are the UAV control inputs, and the specific expansion is shown in formulas (40) and (41), where Ω is the UAV propeller rotor speed;

[0130] D1=[d wu ,d wv ,d wr ] T , D2=[d wx ,d wy ,d wz ] T and D3=[d wφ ,d wθ ,d wψ ] T Respectively represent the external interference forces / torques on the ship in the forward, drift, and pitch directions, and on the UAV in the forward, drift, heave, roll, pitch, and pitch directions. Here, it is assumed that the interference has a maximum value and a supremum; G = [0, 0, g] T represents the acceleration due to gravity;

[0131] In formula (39), R2=diag[R x ,R y ,R z ], R3=diag[l,l,1], both represent the fixed actuator gain matrix, where l is the distance from the center of the drone to each rotor; d ii1 ,d ii2 ,d ii3 ,ii=u,v,r represents the nonlinear damping term of the model; k dx ,k dy ,k dz It represents the rotational drag coefficient of the UAV along the ox, oy, oz axis;

[0132] f u , f v , f r They represent the nonlinear terms of the ship in the forward, drift and bow directions, respectively, and f x , f y , f z They represent the nonlinear terms of the UAV in the forward, drift, and heave directions, respectively, and f φ ,f θ ,f ψ They represent the nonlinear terms of the UAV in the roll, pitch and yaw directions, R x ,R y ,R z Represent the gain coefficients of the drone on ox, oy, and oz respectively.

[0133] In this scheme, a mixed-order ship-UAV nonlinear system model is constructed as the controlled object of the control law designed in the subsequent steps. At the same time, the nonlinear terms are obtained to prepare for the approximation of the nonlinear terms in the subsequent steps, and then to design the preset performance control law and adaptive law.

[0134] In a specific embodiment, a waypoint path is set, a reference path is planned based on the waypoint path using a virtual ship, and a ship reference signal is generated; the real-time position of the ship and the orbiting radius of the drone are obtained, and a solution for generating a drone reference signal in combination with the virtual drone is:

[0135] S21, such as Figure 2 As shown, the virtual guidance generates a ship reference signal that needs to be processed by the virtual ship L1 and generate a smooth reference path. First, a waypoint path consisting of several waypoints and a ship turning radius are set. The target point R is selected according to the set turning radius before and after each waypoint except the starting point and the end point. inWk and P outWk , when L1 ship reaches a target point, it switches to the next target point tracking; among them, the starting point does not select the target point, and the end point is the target point selected by the previous waypoint; in the figure, W k-1 ,W k ,W k+1 There are three consecutive waypoints, waypoint W k Select the target point P according to the set turning radius inWk and P outWk , L1 ship is now at P inWk is the target point, and after tracking, it switches to the next target point. Wk and y Wk It's W k The coordinates of x target and y target is the target point P at this time inWk Coordinates, L 1s is the distance from L1 ship to the target point, η s =ψ sd -ψ L1s , ψ L1s is the azimuth from L1 ship to the target point, η s represents the deviation angle between L1 ship and target point, ψ sd Indicates the heading angle of L1 ship.

[0136] S22, using a virtual ship to plan a reference path according to the waypoint path, as shown in formula (42),

[0137]

[0138] Where x sd ,ysd ,ψ sd Indicates the forward, drift distance and heading angle of the virtual ship L1, that is, the ship position reference signal, u sd ,r sd represents the forward speed and yaw angular velocity of L1 ship, u sd Set according to actual situation, r sd The calculation formula is shown in formula (44),

[0139]

[0140] Where, L 1s is the distance from L1 ship to the target point, η s =ψ sd -ψ L1s , ψ L1s is the azimuth from L1 ship to the target point, η s Indicates the deviation angle between L1 ship and target point; a ls represents the lateral acceleration of the L1 ship;

[0141] The azimuth from the actual ship to the L1 ship is shown in formula (45),

[0142]

[0143] Where, ψ r Indicates the azimuth from the actual ship to the L1 ship, that is, the ship attitude reference signal, x se =x sd -x s ,y se =y sd -y s , indicating the ship's forward and drift distance errors;

[0144] S23, such as Figure 3 As shown, the real-time position of the ship and the orbiting radius of the drone are obtained. The three-dimensional diagram on the left side of the figure is the motion trajectory of the ship-drone system within one orbiting cycle of the drone. The upper right corner is a diagram of the drone state variables, and the lower right corner is a top-down plan view of the trajectory diagram of the ship-drone system within one orbiting cycle of the drone. R is the set UAV orbit radius, η a is the bearing angle from the ship to the virtual drone;

[0145] S24. Use the virtual drone to obtain the drone's reference signal, as shown in formula (46),

[0146]

[0147] Where x ad ,y ad ,ψad represents the forward and drift distances and heading angles of the virtual UAV, i.e. the reference signal of the UAV, T a is the drone's orbiting period set by the user, L R is the drone's orbit radius set by the user, η a is the azimuth from the ship to the virtual drone, pp(0) its initial value, x ae =x ad -x a ,y ae =y ad -y a , is the UAV's forward and lateral distance error, z ad It is the expected rise and fall height of the UAV, and the specific value is set by the user.

[0148] In this solution, a virtual L1 vessel plans a reference path based on set waypoints and generates guidance signals. The L1 vessel generates reference signals based on the distance between the vessel and the target point, rather than time, which better meets practical navigation requirements and improves guidance accuracy. To ensure the drone maintains an appropriate distance from the vessel during navigation (satisfying the warning range but not exceeding the communication range), the drone's reference signal is calculated from the vessel's real-time position and a set orbiting radius, enabling the drone to orbit the vessel at a uniform angular velocity.

[0149] In a specific embodiment, based on the ship reference signal and the UAV reference signal, the ship-UAV kinematic error is defined, and the corresponding virtual control law is designed to eliminate the kinematic error of the ship-UAV nonlinear system model. The solution is:

[0150] S31. Define the ship-UAV position error based on the ship reference signal and the UAV reference signal, as shown in formula (47):

[0151]

[0152] Where z se represents the ship position error, z ae Indicates the UAV heave distance error;

[0153] S32, deriving the position error between the ship and the UAV, as shown in formula (48),

[0154]

[0155] Where, ψ se =ψ r -ψ s is the ship's heading angle error;

[0156] S33. Define the preset performance function of the ship-UAV position error, as shown in formula (49),

[0157]

[0158] Where, ρ i ,i=u,x,y,z, is the preset performance function of position error, ρ i0 and ρ i∞ is ρ i The initial value and final convergence value of k αi is a positive constant;

[0159] Define the conversion error of the ship-UAV position error as shown in formula (50):

[0160]

[0161] Where, σ i ,i=u,x,y,x represents the conversion error of position error;

[0162] S34. To stabilize the position error, a virtual control law for the position error of the ship-UAV is designed, as shown in formula (51):

[0163]

[0164] Where k αu ,k αx ,k αy ,k αz is the virtual control law α u ,α x ,α y ,α z Positive design parameters;

[0165] S35. In order to control the current attitude of the ship-UAV to converge to the reference attitude, the attitude error of the ship-UAV is defined according to the ship reference signal and the UAV reference signal, as shown in formula (52):

[0166]

[0167] Where, ψ se Indicates the ship's bow angle error, φ ae represents the roll angle error of the UAV, θ ae represents the pitch angle error of the UAV, ψ ae Indicates the UAV bow angle error; φ ad represents the reference roll angle of the UAV, θ ad Indicates the reference roll and pitch angles of the drone;

[0168] S36, deriving the attitude error of the ship-UAV, as shown in formula (53),

[0169]

[0170] S37. Define the preset performance function of the ship-UAV attitude error, as shown in formula (54),

[0171] ρ j (t)=(ρ j0 -ρ j∞ )e -kjt +ρ j∞ (54)

[0172] Where, ρ j ,j=r,φ,θ,ψ, is the preset performance function of attitude error, ρ j0 and ρ j∞ is ρ j The initial value and final convergence value of k αj is a positive constant;

[0173] Define the conversion error of the ship-UAV attitude error as shown in formula (55),

[0174]

[0175] Where, σ j ,j=r,φ,θ,ψ, represents the attitude error conversion error;

[0176] S38. In order to stabilize the attitude error, a virtual control law for the attitude error of the ship-UAV is designed, as shown in formula (56):

[0177]

[0178] Where k αr ,k αφ ,k αθ ,k αψ The virtual control law α is the attitude error greater than zero r ,α φ ,α θ ,α ψ control parameters.

[0179] In this embodiment, the position error and attitude error of the ship are obtained, and a virtual control law is designed to eliminate the error, so that accurate tracking of the ship-UAV can be achieved and the path can be returned to the reference path.

[0180] In a specific embodiment, the dynamic surface technology is introduced to obtain the dynamic surface signal of the virtual control law. The solution for defining the dynamic error according to the dynamic surface signal is:

[0181] S41. The virtual controller will cause a large computational load problem in the subsequent derivation. Therefore, the dynamic surface technology is introduced to reduce the order of the derivative of the virtual control law of the position error and the derivative of the virtual control law of the attitude error to obtain the dynamic surface error of the position error and the dynamic surface error of the attitude error, as shown in formulas (57) and (58).

[0182]

[0183] In formula (57), β i is α i Dynamic surface signal, ∈ i is a time constant greater than zero, and the dynamic surface error of the position error is y i =α i -β i , α i A virtual control law representing the position error;

[0184] In formula (58), β j is α j Dynamic surface signal, ∈ j is a time constant greater than zero, and the dynamic surface error of the attitude error is y j =α j -β j , α j A virtual control law representing the attitude error;

[0185] S42. Define the ship-UAV velocity error and perform the derivative, as shown in formulas (59) and (60),

[0186]

[0187] Where u se ,u xe ,u ye ,u ze represents the ship's forward movement, the UAV's forward movement, drift and heave speed errors, B2 = [β x ,β y ,β z ] T Dynamic surface signal matrix representing position error;

[0188] Define the angular velocity error between the ship and the UAV and perform the derivative, as shown in formulas (61) and (62),

[0189]

[0190]

[0191] Where r se ,pae ,q ae ,r ae represents the angular velocity error of ship's bow, UAV's roll, pitch and bow; B3 = [β φ ,β θ ,β ψ ] T , representing the dynamic surface signal matrix of the attitude error.

[0192] In this solution, dynamic surface technology is introduced to reduce the order of virtual control law, which can reduce the load in the calculation process and improve processing efficiency.

[0193] In a specific embodiment, radial basis function neural network and minimum learning parameter technology are used to simultaneously approximate the nonlinear terms of the ship-UAV nonlinear system model, and MLP technology is introduced to simplify external interference, as shown in formulas (63) and (64),

[0194] f i -d wi ≤Θ i1 Θ i2 -c i S i W i (63)

[0195] f j -d wj ≤Θ j1 Θ j2 -c j S j W j (64)

[0196] Where, f i and f j represents the nonlinear term of the ship-UAV nonlinear system model, Θ i2 =1+‖S i ‖+1,Θ j2 =1+‖S j ‖+1,Θ i1 ,Θ j1 ,Θ i2 ,Θ j2 is the intermediate variable in the minimum learning parameter technique;

[0197] A i represents f i The neural network weight update law, A j represents f j The neural network weight update law, c i ,c j A i ,Aj Norm of S i and S j represents the Gaussian function, ε i and ε j represents the approximation error, is the approximation error ε i The maximum value of is the approximation error ε j The maximum value, d mi It is the external environment interference wi The maximum value, d mj It is the external environment interference wj The maximum value of W i ,W j represents the intermediate variable, as shown in formula (65),

[0198]

[0199] In this scheme, a neural network system and a minimum learning parameter strategy are combined to deal with the structural uncertainties and external environmental interference in the ship-UAV collaborative system. At the same time, a controller is designed for the ship-UAV system to reduce the adaptive parameters in the controller, which is beneficial to the maneuverability of the heterogeneous system and improves the efficiency of UAV collaborative surveillance of ships.

[0200] In a specific embodiment, the scheme for designing a preset performance control law and an adaptive law for actuator gain using the approximated simplified ship-UAV nonlinear system model and the dynamic error is:

[0201] S61, obtain the actual control input, and convert the actual control input into the preset performance control law and the adaptive law of the actuator gain, as shown in formula (66),

[0202]

[0203] Where, τ l ,l=u,r,x,y,z,φ,θ,ψ are the preset performance control laws of the ship's forward direction, yaw angle, UAV's forward direction, drift, heave direction, roll, pitch and yaw angle; n is the ship's propeller speed; δ is the ship's rudder angle;

[0204] λ2=[λ kt ,λ kt ,λ kt ] T ,λ3=[λ kt ,λ kt ,λ kd ] T , They are λ cu ,λ cr ,λ kt ,λ kd The estimated value of λ cu ,λ cr ,λ kt ,λ kd They represent the adaptive parameters of the actuator gain, i.e., the adaptive law, and represents the matrix of actuator gains, τ2 and τ3 represent the preset performance control law matrices;

[0205] S62. Using the simplified ship-UAV nonlinear system model, formulas (60) and (66), the MLP technology, coupling gain adaptation technology, and backstepping method are introduced to design the preset performance control law of the speed error and the adaptive law of the actuator gain, as shown in formulas (67)-(69).

[0206]

[0207] Where k i ,Γ Cu1 ,Γ Cu2 , is a design parameter greater than zero, ω i represents the adaptive parameter introduced by the minimum learning parameter, Yes i estimated value of; Ψ i Represents ω i The robust damping term, where ρ i1 ,ρ i2 is a positive constant; τ u ,τ x ,τ y ,τ z represents the preset performance control law for the speed error, represents the derivative of the adaptive parameter;

[0208] The nonlinear decoupling technology is used to solve formula (67) to obtain the reference roll angle and pitch angle of the UAV, as shown in formula (70).

[0209]

[0210] Where, φ ad represents the reference roll angle of the UAV, θ ad Indicates the reference pitch angle of the UAV;

[0211] S63. Using the simplified ship-UAV nonlinear system model and formulas (62) and (66), the MLP technology, coupling gain adaptation technology, and backstepping method are introduced to design the preset performance control law of the angular velocity error and the adaptive law of the actuator gain, as shown in formulas (71)-(73).

[0212]

[0213] Where k j ,Γ Cr1 ,Γ Cr2 ,Γ kt1 ,Γ kt2 ,Γ kd1 ,Γ kd2 , is a design parameter greater than zero, Yes j The estimated value of ω j The robust damping term, where ρ j1 ,ρ j2 is a constant; τ j Denotes the preset performance control law for the angular velocity error, ω j Adaptive parameters introduced for the minimum learning parameter technique, Derivative of the adaptive parameter representing the actuator gain.

[0214] In this scheme, considering the problem of actuator gain uncertainty in actual navigation, a coupled gain adaptation is designed for all actuator gains of the ship-UAV system. Combined with the preset performance control, a preset performance robust adaptive control law and an adaptive law are designed to achieve the control effect of controlling the error within the budget range when the actuator gain is uncertain.

[0215] In order to perform the collaborative task of the drone surrounding the ship, this embodiment conducted a simulation experiment on the MATLAB platform. A waypoint path consisting of 6 ship waypoints was selected, where the drone's circle radius was 200m and the cycle was 50s. The initial state of the controlled object was

[0216] [x s (0),y s (0),ψ s (0),u s (0),v s (0),r s (0),x a (0),y a (0),z a (0),ψ a (0),φa (0),θ a (0),u x (0),u y (0),u z (0),p a (0),q a (0), r a(0)]

[0217] =[-5m,605m,0deg,0m / s,0m / s,0rad / s,165m,705m,105m,0deg,0deg,0deg,0m / s,0m / s,0m / s,0rad / s,0rad / s,0rad / s]

[0218] Figure 4-Figure 9 The following are the simulation results of ship-UAV collaborative search under level 4 sea conditions simulated on the MATLAB simulation platform:

[0219] Figure 4 Represents the path tracking trajectory curve of the ship-UAV collaborative circling surveillance mission, Figure 4 (a) is a three-dimensional trajectory diagram, (b) is a plane trajectory diagram, Figure 4 It can be seen that the reference path of the ship-UAV is obtained according to the surrounding virtual guidance algorithm, which enables the UAV to move at a constant angular speed within a fixed radius around the ship throughout the entire process. In addition, compared with a single automatic system of a ship or a UAV, since the reference signal of the UAV is generated in real time according to the position of the ship, the present invention can achieve the coordinated tracking of the ship and the UAV to the reference signal at the desired speed.

[0220] Figure 5 and Figure 6 It represents the control input of the ship-UAV collaborative system, taking into account the actuator gains of the ship-UAV system. All control commands are accurate to the specific actuator commands.

[0221] Figure 7 、 Figure 8 and Figure 9The position error and attitude error of the ship-UAV collaborative system are shown in the figure. It can be found that under the preset performance control, all system errors can eventually converge within the desired minimum value range. The UAV position error will produce a periodic change. This is because the circling warning task requires the UAV to follow the ship while circling the ship at a uniform angular speed. This causes the UAV's expected speed to accelerate when in the same direction as the ship and decelerate when in the opposite direction during each circling cycle, resulting in a periodic change in the UAV's speed. There is an unavoidable lag time from the guidance signal to the actual control of the UAV, which will eventually cause the UAV to have a periodic position error. However, under the proposed control algorithm, the final error can still converge within the desired range. Combined with the existing technology, controller design and simulation experiments, the present invention has the following two beneficial effects in the field of ship-UAV collaborative circling warning:

[0222] 1) The present invention improves the existing fixed-position following alert mode of the ship-UAV system. The proposed surround virtual guidance strategy will be able to generate a ship reference signal based on the ship waypoints set in the mission, and then further autonomously plan the UAV path and ensure that the UAV always maintains a fixed distance range from the ship to meet the surround alert mission requirements. The speed of the ship and the UAV are related, which can ensure the collaborative mission of the ship-UAV. The present invention can combine the neural network system and the minimum learning parameter strategy to deal with the structural uncertainties and external environmental interference in the ship-UAV collaborative system, and at the same time design a controller for the ship-UAV system to reduce the adaptive parameters in the controller, which is beneficial to the maneuverability of the heterogeneous system. The present invention can improve the efficiency of UAV collaborative alerting of ships.

[0223] 2) The control law designed in this invention combines preset performance control with coupled adaptive gains to achieve high-precision control while accounting for system nonlinearities of unknown model parameters, uncertain ocean environmental disturbances, and unknown actuator gains. Simulation experiments of a ship-UAV air-sea collaborative search in a simulated ocean environment verified the effectiveness of the proposed guidance strategy and control algorithm. Simulation results demonstrate that the algorithm exhibits excellent control accuracy and response speed.

[0224] 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 guidance and gain adaptive control method for ship-air-sea collaborative missions for surround alert, characterized in that: include: S1: Construct a hybrid-order ship-UAV nonlinear system model as the controlled object of the control law designed in the subsequent steps. Construct a hybrid-order ship-UAV nonlinear system model as shown in formulas (1)-(5). （1） （2） （3） （4） （5） In formula (1), Indicates the ship's forward and sideways displacement and bow angle, Indicates the forward, horizontal drift, and heave displacement of the drone. Indicates the roll, pitch and yaw angles of the drone; Indicates the ship's forward, drifting and pitching speeds. and Indicates that the drone is moving along the coordinate system with the fuselage as the origin. The speed and angular velocity of the axis; , and represents the nonlinear term of the ship-UAV system, which is specifically expanded as shown in formula (3); formula (2) is an intermediate variable; It indicates the additional mass of the ship in the forward, sideways and yaw directions. Indicates the quality of the drone, Indicates that the drone is following The rotational inertia of the shaft; , , , are all unknown system actuator gain matrices; is the ship actuator gain, Gain for the drone actuator; represents the ship control input, and Represent the ship's propeller speed and rudder angle respectively, and is the UAV control input, which is specifically expanded as shown in formulas (4) and (5), where is the UAV propeller rotor speed; , and They represent the external interference forces / torques on the ship in the forward, drift, and pitch directions, and on the UAV in the forward, drift, heave, roll, pitch, and pitch directions respectively; represents the acceleration due to gravity; In formula (3), , , both represent fixed actuator gain matrices, where is the distance from the center of the drone to each rotor; represents the nonlinear damping term of the model; Indicates that the drone is following The rotational resistance coefficient of the shaft; , , are the nonlinear terms of the ship in the forward, drift and bow directions, , , They represent the nonlinear terms of the UAV in the forward, drift, and heave directions, Respectively represent the nonlinear terms of the UAV in the roll, pitch and yaw directions, Respectively indicate that the drone is The gain coefficient on ; S2: Set a waypoint path, use a virtual ship to plan a reference path based on the waypoint path, and generate a ship reference signal; obtain the real-time position of the ship and the drone's orbiting radius, and generate a drone reference signal in combination with the virtual drone; the ship reference signal includes a ship position reference signal and a ship attitude reference signal. The specific steps are as follows: S21. Set the waypoint path and the ship turning radius, and select the target point before and after each waypoint except the starting point and the end point according to the set turning radius. and ; S22, using the virtual ship to plan a reference path according to the waypoint path, as shown in formula (6), （6） Where, Represents a virtual ship The ship's forward and drift distance and heading angle, that is, the ship's position reference signal, express The ship's forward speed and yaw angular velocity, Set according to actual situation. The calculation formula is shown in formula (7). （7） Where, yes The distance from the ship to the target point, , yes The bearing from the ship to the target point, express Deviation angle between the ship and the target point; express The transverse acceleration of the ship; Actual ship arrival The ship's azimuth is shown in formula (8): （8） Where, Indicates actual ship arrival The ship's azimuth, that is, the ship's attitude reference signal, , indicating the ship's forward and drift distance errors; S23, obtaining the real-time position of the ship and the circling radius of the drone; S24. Use the virtual drone to obtain the drone's reference signal, as shown in formula (9), （9） Where, Indicates the forward and drift distances and heading angles of the virtual UAV, i.e. the reference signals of the UAV. is the set drone orbiting cycle, is the set drone's orbit radius, is the azimuth from the ship to the virtual drone, Its initial value, , is the UAV's forward and lateral distance error, is the desired lift altitude of the drone; S3: Based on the ship reference signal and the UAV reference signal, define the ship-UAV kinematic error and design the corresponding virtual control law to eliminate the kinematic error of the ship-UAV nonlinear system model. The kinematic error includes the position error and attitude error of the ship-UAV. The specific steps are as follows: S31. Define the ship-UAV position error based on the ship reference signal and the UAV reference signal, as shown in formula (10): （10） Where, represents the ship position error, Indicates the UAV heave distance error; S32, deriving the position error between the ship and the UAV, as shown in formula (11), （11） Where, is the ship's heading angle error; S33. Define the preset performance function of the ship-UAV position error, as shown in formula (12), （12） Where, , is the preset performance function of position error, and for The initial value and final converged value of is a constant; Define the conversion error of the ship-UAV position error as shown in formula (13): （13） Where, Conversion error representing position error; S34. Design the virtual control law of the ship-UAV position error, as shown in formula (14): （14） Where, Virtual control law Design parameters; S35. Define the ship-UAV attitude error based on the ship reference signal and the UAV reference signal, as shown in formula (15): （15） Where, Indicates the ship's heading angle error, represents the roll angle error of the UAV, represents the pitch angle error of the UAV, Indicates the UAV heading angle error; represents the reference roll angle of the UAV, Indicates the reference pitch angle of the UAV; S36, deriving the attitude error of the ship-UAV, as shown in formula (16), （16） S37. Define the preset performance function of the ship-UAV attitude error, as shown in formula (17), （17） Where, , is the preset performance function of the attitude error, and for The initial value and final converged value of is a constant; Define the conversion error of the ship-UAV attitude error as shown in formula (18), （18） Where, , represents the attitude error conversion error; S38. Design the virtual control law of the ship-UAV attitude error, as shown in formula (19), （19） Where, is the virtual control law for attitude error Control parameters of S4: Introduce dynamic surface technology to obtain the dynamic surface signal of the virtual control law, and define the dynamic error based on the dynamic surface signal; the dynamic error includes the speed error and angular velocity error of the ship-UAV. The specific steps are as follows: S41. Introducing dynamic surface technology, the derivatives of the virtual control law of position error and the derivatives of the virtual control law of attitude error are reduced to obtain the dynamic surface error of position error and the dynamic surface error of attitude error, as shown in formulas (20) and (21). （20） （21） In formula (20), yes The dynamic surface signal, is the time constant, and the dynamic surface error of the position error is , A virtual control law representing the position error; In formula (21), yes The dynamic surface signal, is the time constant, and the dynamic surface error of the attitude error is , A virtual control law representing the attitude error; S42. Define the ship-UAV velocity error and perform the derivative, as shown in formulas (22) and (23), （22） （23） Where, Indicates the ship's forward movement, the UAV's forward movement, drift and heave speed errors, Dynamic surface signal matrix representing position error; Define the angular velocity error between the ship and the UAV and perform the derivative, as shown in formulas (24) and (25), （24） （25） Where, Indicates the angular velocity errors of ship pitch, UAV roll, and heading; , represents the dynamic surface signal matrix of the attitude error; S5: The nonlinear terms of the ship-UAV nonlinear system model are approximated by using radial basis function neural network and minimum learning parameter technology, and the MLP technology is introduced to simplify the external interference, as shown in formulas (26) and (27). （26） （27） Where, and represents the nonlinear term of the ship-UAV nonlinear system model, , , , , is the intermediate variable in the minimum learning parameter technique; express The neural network weight update law, express The neural network weight update law, for The norm of and represents the Gaussian function, and represents the approximation error, is the approximation error The maximum value of is the approximation error The maximum value of It is external environmental interference The maximum value of It is external environmental interference The maximum value of represents the intermediate variable, as shown in formula (28), （28）； S6: Using the simplified ship-UAV nonlinear system model and the dynamic error, design a preset performance control law and an adaptive law for actuator gains. The specific steps are as follows: S61, obtain the actual control input, and convert the actual control input into the preset performance control law and the adaptive law of the actuator gain, as shown in formula (29), （29） Where, Preset performance control laws for ship heading, bow angle, UAV heading, drift, heave direction, roll, pitch and bow angle; Indicates the propeller speed of the ship; Indicates the rudder angle of the ship; , , , , , ; They are estimated value of; They represent the adaptive parameters of the actuator gain, i.e., the adaptive law, and The matrix representing the actuator gains, and represents the preset performance control law matrix; S62. Using the simplified ship-UAV nonlinear system model, formulas (23) and (29), the MLP technology, coupling gain adaptation technology, and backstepping method are introduced to design the preset performance control law of the speed error and the adaptive law of the actuator gain, as shown in formulas (30)-(32). （30） （31） （32） Where, is the design parameter, , represents the adaptive parameter introduced by the minimum learning parameter, yes estimated value of; , express The robust damping term of is a constant; represents the preset performance control law for the speed error, represents the derivative of the adaptive parameter; S63. Using the simplified ship-UAV nonlinear system model, formulas (25) and (29), the MLP technology, coupling gain adaptation technology, and backstepping method are introduced to design the preset performance control law of the angular velocity error and the adaptive law of the actuator gain, as shown in formulas (33)-(35). （33） （34） （35） Where, is the design parameter, , yes The estimated value of ,for The robust damping term of is a constant; represents the preset performance control law for the angular velocity error, Adaptive parameters introduced for the minimum learning parameter technique, , , The derivative of the adaptive parameter representing the actuator gain; S7: Path tracking control of the UAV in the surrounding warning mission is achieved based on the preset performance control law and the adaptive law of actuator gain.

2. The method for ship-air-sea collaborative mission guidance and gain adaptive control for surround alert according to claim 1 is characterized in that: The nonlinear decoupling technology is used to solve formula (30) to obtain the reference roll angle and pitch angle of the UAV, as shown in formula (36). （36） Where, represents the reference roll angle of the UAV, Indicates the reference pitch angle of the drone.

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