Cooperative orbiting guidance and preset performance control method for ship / aircraft in tailing detection task

Abstract

The application discloses a kind of follow detection task in method for machine / ship cooperative encircling guidance and preset performance control, the method includes the improvement ship unmanned aerial vehicle system's encircling mode, constructs dynamic encircling guidance strategy not only can realize the uniform angular velocity encircling of unmanned aerial vehicle, also can switch encircling radius according to the detection task demand anytime, and ensure that the whole encircling conversion process is in the smooth transition of velocity and acceleration two dimensions, according to ship and attitude error respectively define standard preset performance function, according to standard preset performance function constructs preset performance function dynamic compensation item to obtain position and attitude conversion error;According to position and attitude conversion error, the virtual control law of machine-ship system is constructed, to construct preset performance control law and adaptive law, high-precision control is realized under the condition of considering the nonlinear term of unknown model parameter, uncertain ocean environment interference and unknown actuator gain.

Description

Technical Field

[0001] This invention relates to the field of ship and unmanned aerial vehicle (UAV) motion control research technology, and in particular to a method for aircraft / ship cooperative orbital guidance and preset performance control in follow-up detection missions. Background Technology

[0002] The path tracking control system of the ship-UAV cooperative system consists of three subsystems: guidance, control, and navigation. The guidance system can automatically construct a reference signal based on the positional relationship between the current attitude of the ship-UAV system and the desired path; the control system can achieve effective convergence by stabilizing the error between the current attitude and the reference signal; and the navigation system can transmit the position and attitude information of the controlled object to the guidance and control systems through sensors.

[0003] Guidance and control are two crucial subsystems in path tracking control. Effective path tracking control fundamentally relies on robust guidance strategies to generate the trajectory. Among existing methods, line-of-sight (LOS) guidance is widely used due to its high computational efficiency; its working principle is to adjust the yaw angle based on target deviation. While LOS guidance performs well in stable environments, its performance is limited in dynamic environments, and it is sensitive to path curvature and unsuitable for orbital paths. Vector field guidance, by constructing a vector field, possesses excellent anti-interference capabilities. However, this method relies on accurate system models and environmental parameters, and it cannot be directly applied to USV-UAV heterogeneous systems because it requires constructing vector fields separately for the USV and UAV, making practical implementation challenging. Currently proposed three-dimensional mapping guidance methods can achieve three-dimensional cooperative guidance for the entire USV-UAV system, but require the UAV to maintain a fixed relative position with the USV. In terms of USV and UAV control, Preset Performance Control (PPC) has been widely studied and applied in recent years due to its accuracy advantages. The basic PPC method ensures that the system error is always within the preset boundaries through performance function design, while also accelerating the convergence speed. Some studies have proposed a fixed-time preset performance function (PPF) controller, which can enhance the error convergence rate and ensure that all tracking errors enter and remain within specified boundaries within a fixed time. Although PPC significantly improves the control accuracy and response speed of USV-UAV systems, its inherent vulnerability in dynamic environments remains a key limitation that needs to be addressed. Therefore, addressing the impact of environmental disturbances is an important direction for improvement when applying PPC. Numerous studies have combined PPC with disturbance observers, demonstrating that this hybrid approach can effectively suppress the instability effects of environmental disturbances while maintaining its transient performance characteristics. However, the aforementioned control schemes using a constant preset performance function (PPF) may encounter singular value problems under time-varying disturbances in real-world environments. Existing technologies propose event-triggered adaptive PPC, which dynamically updates the performance function when a reference signal tracking failure is detected through an event-driven mechanism. Existing technologies also include introducing dynamically adjustable preset performance functions as constraint boundary adjustment methods, which effectively solve singular value problems by adapting to external disturbances and measurement noise in real time. However, such PPFs continuously generate boundary changes throughout the control process, even under minimal environmental disturbances.

[0004] Based on the above analysis, the direct application of traditional guidance and control methods to ship-UAV surround detection missions has the following two main drawbacks:

[0005] 1) In the past, UAV-assisted ship navigation often adopted a synchronized ship-machine position mode. While this allowed the UAV to provide environmental information and warn of potential hazards, it failed to effectively utilize the high maneuverability of UAVs. In orbital detection, the UAV, while accompanying the ship, orbits around it at a uniform angular velocity. This fully leverages the advantages of the ship-machine cooperative system, expanding the UAV's monitoring range. Furthermore, the guidance design for orbital missions in ship-UAV heterogeneous systems must meet three key requirements: achieving coordinated guidance between the ship and UAV, ensuring the UAV orbits the ship at a uniform angular velocity, and adaptively and smoothly adjusting the UAV's orbital path radius based on mission requirements. However, using traditional LOS guidance methods during UAV radius conversion can easily cause abrupt changes in UAV speed and acceleration, leading to overshoot and affecting the stability of the UAV's actuators.

[0006] 2) Traditional preset performance control, in order to ensure control accuracy, has relatively stringent boundary designs, making it difficult to cope with sudden or large disturbances. Conversely, excessively large boundary designs can also slow down control response and further reduce control accuracy. In engineering practice, external environmental disturbances are variable and unpredictable, which can cause errors to exceed performance boundaries, leading to singularity problems or actuator input saturation. Furthermore, the actuator gain of ships and UAVs is often difficult to measure and varies with use (e.g., due to wear or load) and environment. Although treating actuator gain as a known constant may be a valid assumption in some simplified analyses, considering the variation and dynamic characteristics of actuator gain in actual control system design usually leads to better performance and system robustness. Summary of the Invention

[0007] This invention provides a method for aircraft / ship cooperative orbital guidance and preset performance control in follow-up detection missions to overcome the above-mentioned technical problems.

[0008] To achieve the above objectives, the technical solution of the present invention is as follows:

[0009] A method for aircraft / ship cooperative orbital guidance and preset performance control in a follow-up detection mission includes the following steps:

[0010] S1: Construct a nonlinear mathematical model for establishing a ship-machine system consisting of 1 ship and N drones. The nonlinear mathematical model includes a ship kinematics model and a drone kinematics model.

[0011] S2: Set the waypoint path and obtain the ship reference signal based on the preset virtual ship. Based on the ship reference signal, obtain the ship error according to the ship kinematic model. The ship error includes the ship position error and the ship attitude error.

[0012] S3: Based on the ship's kinematics model and the dynamic transformation strategy of the UAV's orbital radius, a virtual UAV is set up to obtain the UAV reference signal. Based on the UAV reference signal, the UAV error is obtained according to the UAV kinematics model. The UAV error includes the UAV position error and the UAV attitude error.

[0013] S4: Define a first standard preset performance function based on the ship's position error and the UAV's position error; construct a dynamic compensation term for the first preset performance function based on the first standard preset performance function to obtain the position conversion error; construct a first virtual control law for the ship-machine system based on the position conversion error.

[0014] S5: Introduce dynamic surface technology to reduce the order of the first virtual control law, obtain the dynamic surface signal of the first virtual control law to define the kinematic error of the ship system, and construct the first preset performance control law and adaptive law based on the kinematic error;

[0015] S6: Based on the first preset performance control law and adaptive law, obtain the reference roll angle signal and reference pitch angle signal of the UAV according to the UAV's yaw angle reference signal, and then obtain the roll angle error and pitch angle error. Combine the ship attitude error and the UAV attitude error to define a second standard preset performance function. Construct a dynamic compensation term for the second preset performance function according to the second standard preset performance function to obtain the attitude conversion error. Construct the second virtual control law of the ship-machine system according to the attitude conversion error.

[0016] S7: Introduce dynamic surface technology to reduce the order of the second virtual control law, obtain the dynamic surface signal of the second virtual control law to define the attitude error of the ship system, and construct the second preset performance control law and adaptive law based on the attitude error of the ship system.

[0017] S8: Based on the first preset performance control law and the adaptive law, and the second preset performance control law and the adaptive law, the aircraft / ship cooperative orbital guidance and preset performance control in the follow-up detection mission are realized.

[0018] Furthermore, the expression for the ship kinematics model described in S1 is:

[0019] (1)

[0020] (2)

[0021] In the formula: Represents the vectors of the ship's forward displacement, lateral drift displacement, and bow roll angle. , Represents the forward, lateral, and bow velocity vectors of the ship. ; The system nonlinear terms representing the ship's actions in the forward, lateral, and bow directions are... ; This indicates the additional mass of the vessel in the forward, lateral, and bow directions. ; Represents the gain matrix of the ship actuator and , This indicates the actuator gain at the ship's propeller speed and rudder angle; Indicates ship control input and , , These represent the ship's propeller speed and rudder angle, respectively. This represents the external disturbance forces / torques experienced by the ship in the forward, drift, and bow directions. ; express The first derivative; Represents the rotation matrix;

[0022] No. The kinematic model of the drone is as follows:

[0023] (3)

[0024] (4)

[0025] In the formula: The displacement vectors representing the forward, drift, and heave of the UAV are... , The vectors representing the roll, pitch, and yaw angles of the UAV are... ; This indicates that the drone is moving along a preset coordinate system. The velocity vector and rotational angular velocity vector of the axis and ; The system nonlinear terms acting in the directions of forward movement, drift, and heave of the UAV are expressed as follows: ; The system nonlinear terms acting in the roll, pitch, and yaw directions of the UAV are represented as follows: ; Represents the mass matrix of the drone and ; Indicates the drone along The rotational inertia matrix of the axis and ; This represents the actuator gain matrix of the UAV; This represents the position control input transformation matrix for the UAV. The transformation matrix represents the attitude control input of the UAV and , This indicates the distance from the center of the drone to each rotor; Indicates the drone control input; This represents the external disturbance force / torque experienced by the drone in the forward, drift, and heave directions. ; This represents the external disturbance forces / torques experienced by the drone in the roll, pitch, and yaw directions. ; Denotes the gravitational acceleration vector and It represents the acceleration due to gravity.

[0026] Furthermore, step S2 specifically includes the following steps:

[0027] S21: Set the waypoint path and the ship's turning radius. Select target points before and after each waypoint (excluding the starting and ending waypoints) according to the set turning radius. Based on a pre-set virtual ship real-time plan, obtain the ship's reference signal, the expression of which is:

[0028] (5)

[0029] (6)

[0030] In the formula: This indicates the virtual ship's forward distance, drift distance, and heading angle; This represents the forward speed and bow roll rate of the virtual ship; express The first derivative; This represents the lateral acceleration of the virtual ship; Indicates the azimuth angle of the virtual ship to the target point and ; This represents the azimuth angle of the virtual ship to the target point; This represents the distance from the virtual ship to the target point;

[0031] S22: Based on the ship reference signal, obtain the ship error according to the ship kinematic model, and obtain the azimuth angle from the real ship to the virtual ship based on the ship error. for:

[0032] (7)

[0033] (8)

[0034] In the formula: This indicates the ship's positional error, which includes the error in the ship's forward distance and the error in its lateral drift.

[0035] S23: Based on the bearing angle from the real ship to the virtual ship Obtaining ship attitude error for:

[0036] (9).

[0037] Furthermore, step S3 specifically includes the following steps:

[0038] S31: Construct a dynamic transformation strategy for the UAV's orbital radius, the expression of which is:

[0039] (10)

[0040] In the formula: Indicates the radius of the circle before conversion; This represents the desired encircling radius, i.e., the transformed encircling radius. Indicates the conversion time; Indicates the moment when the radius conversion begins; This represents the output of the UAV's dynamic orbiting radius transformation strategy. The dynamic orbiting guidance strategy proposed in this embodiment can not only achieve uniform angular velocity orbiting of the UAV, but also switch the orbiting radius at any time according to the needs of the detection mission, and ensure a smooth transition in both velocity and acceleration dimensions during the entire orbiting transformation process. This can improve the flexibility of the UAV in coordinating orbiting with ships, broaden the application scenarios, and achieve a dynamic balance between monitoring range and energy consumption.

[0041] S32: Based on the ship kinematics model and the dynamic transformation strategy of the UAV's orbital radius, the virtual UAV is set to obtain the UAV reference signal as follows:

[0042] (11)

[0043] In the formula: Indicates the forward and lateral distances of the virtual drone; Indicates the drone's orbital period; Indicates the azimuth angle of the ship to the virtual drone and This represents the initial value of the azimuth angle between the ship and the virtual drone; Indicates the expected lift-height of the drone;

[0044] S33: Obtain the UAV position error based on the UAV reference signal and the UAV kinematic model; and obtain the UAV pitch angle reference signal based on the UAV position error. for

[0045] (12)

[0046] (13)

[0047] In the formula: This refers to the positional error of the UAV, including the forward distance error, lateral distance error, and heave distance error.

[0048] S34: Based on the UAV's bow angle reference signal Obtaining UAV attitude error for:

[0049] (14).

[0050] Furthermore, step S4 specifically includes the following steps:

[0051] S41: Based on the ship's position error and the UAV's position error, define the first standard preset performance function, i.e., define the corresponding error. First standard preset performance function for:

[0052] (15)

[0053] (16)

[0054] In the formula: express The abbreviated form; and express The initial value and the final convergent value; Represents a positive constant;

[0055] S42: Define the dynamic compensation term of the first preset performance function, its expression is:

[0056] (17)

[0057] In the formula: Indicates corresponding error A general term; Indicates the design adjustment parameters, and satisfies ; Indicates dynamic compensation items; express The first derivative;

[0058] S43: Construct a position error transformation formula based on the dynamic compensation term and the first standard preset performance function to obtain the position transformation error. The position error conversion formula is as follows:

[0059] (18)

[0060] S44: Based on the position transformation error, the update law for the first set performance adaptive parameter is constructed as follows:

[0061] (19)

[0062] In the formula: This indicates the preset performance adaptive parameters; express The estimated value; This represents the update law for the performance adaptive parameters; Indicate design parameters; express The initial value;

[0063] S45: Based on the update law of the first performance adaptive parameter, the first virtual control law of the ship system is constructed as follows:

[0064] (20)

[0065] In the formula: Represents the first virtual control law Positive design parameters; Indicates design parameters.

[0066] Furthermore, S5 specifically includes the following steps:

[0067] S51: The dynamic surface technique is introduced to reduce the order of the first virtual control law, and the dynamic surface signal of the first virtual control law is obtained as follows:

[0068] (twenty one)

[0069] In the formula: Represents the first virtual control law Dynamic surface signals; This represents a time constant that is greater than zero. express The initial value; express The first derivative;

[0070] S52: Define the kinematic error of the ship-machine system based on the dynamic surface signal of the first virtual control law. Its expression is:

[0071] (twenty two)

[0072] S53: Taking the derivative of the kinematic error, we obtain the derivative of the kinematic error as follows:

[0073] (twenty three)

[0074] In the formula: Represents the vector form of the dynamic surface signal and ; Represents the error vector and = ; express The first derivative;

[0075] S54: Using MLP technology to analyze the system nonlinear term in the derivative of kinematic error. External interference After approximation and simplification, its expression is:

[0076] (twenty four)

[0077] In the formula: , express Neural network weight update law; Represents the Gaussian function; Indicates the approximation error; Indicates the approximation error The maximum value; A general term referring to interference from the external environment; Indicates intermediate parameters and ; express The norm;

[0078] S55: Obtain the actual control input and convert it into an adaptive law that combines a preset performance control law and actuator gain:

[0079] (25)

[0080] In the formula: Preset performance control laws representing the ship's forward direction, bow angle, and the UAV's forward direction, drift direction, heave direction, roll angle, pitch angle, and bow angle; , , , , , ; These represent the adaptive parameters. The estimated value is the adaptive law of actuator gain; express The initial value; Indicate design parameters; This represents the preset performance control law matrix;

[0081] S56: Based on the nonlinear mathematical model of the ship-machine system, and combining formulas (23) to (25), the first preset performance control law and adaptive law are constructed using MLP technology, coupling gain adaptive technology, and back stepping technology:

[0082] (26)

[0083] (27)

[0084] (28)

[0085] (29)

[0086] In the formula: This indicates a design parameter that is greater than zero. express Maximum value of external interference Estimated value; Indicates adaptive parameters and , express The estimated value; Represents intermediate variables and , Indicates a positive constant; express The initial value; express The first derivative; express The initial value.

[0087] Furthermore, step S6 specifically includes the following steps:

[0088] S61: Based on the first preset performance control law and adaptive law, obtain the reference roll angle signal of the UAV according to the UAV's yaw angle reference signal. Compared with the reference pitch angle signal for:

[0089] (30)

[0090] S62: Based on the reference roll angle signal and the reference pitch angle signal, the roll angle error and pitch angle error are obtained. Combining the ship's attitude error and the UAV's attitude error, the attitude error of the ship-machine system is defined as:

[0091] , , , (31)

[0092] S63: Define the second standard preset performance function, i.e., define the corresponding error, based on the attitude error of the ship system. The second standard preset performance function for:

[0093] (32)

[0094] In the formula: , express The initial value and the final convergent value; Represents a positive constant;

[0095] S64: The dynamic compensation term of the second preset performance function is constructed based on the second standard preset performance function as follows:

[0096] (33)

[0097] In the formula: Indicates corresponding error A general term; Indicates the design adjustment parameters, and satisfies ; Indicates dynamic compensation items; express The first derivative;

[0098] S65: Based on dynamic compensation terms The attitude error conversion formula is constructed by combining the second standard preset performance function to obtain the attitude conversion error. The attitude error conversion formula is as follows:

[0099] (34)

[0100] S66: The update law for the second preset performance adaptive parameters, constructed based on the attitude transformation error, is as follows:

[0101] (35)

[0102] In the formula: This indicates the preset performance adaptive parameters; yes The estimated value; This represents the update law for the performance adaptive parameters; Indicate design parameters; express The initial value;

[0103] S67: Based on the update law of the second performance adaptive parameter, the second virtual control law of the ship system is constructed as follows:

[0104] (36)

[0105] In the formula: Represents the second virtual control law The control parameters.

[0106] Furthermore, step S7 specifically includes the following steps:

[0107] S71: The dynamic surface technique is introduced to reduce the order of the second virtual control law, and the dynamic surface signal of the second virtual control law is obtained as follows:

[0108] (37)

[0109] In the formula: Represents the second virtual control law Dynamic surface signals; This represents a time constant that is greater than zero. express The initial value; express The first derivative;

[0110] S72: Define the attitude error of the ship system based on the dynamic surface signal of the second virtual control law. for:

[0111] (38)

[0112] S73: The derivative of the kinematic error is obtained by taking the derivative with respect to the kinematic error:

[0113] (39)

[0114] In the formula: Represents the vector form of the dynamic surface signal and ; Represents the error vector and ; express The first derivative;

[0115] S74: System nonlinearity of kinematic error derivative in S73 using MLP technique External interference After approximation and simplification, its expression is:

[0116] (40)

[0117] In the formula: , express Neural network weight update law; Represents the Gaussian function; Indicates the approximation error; express The maximum value; A general term referring to interference from the external environment; , express The norm;

[0118] S75: Based on the nonlinear mathematical model of the ship-machine system, and following the same technical principles as steps S55 to S56, the second preset performance control law and adaptive law are constructed using MLP technology, coupled gain adaptive technology, and back stepping technology:

[0119] (41)

[0120] (42)

[0121] (43)

[0122] (44)

[0123] In the formula: Indicates a design parameter that is greater than zero; Indicates the quality of the drone; express Maximum value of external interference Estimated value; Indicates adaptive parameters and ; express The estimated value; Represents intermediate variables and ; Indicates a positive constant; express The initial value; express The initial value; express The first derivative.

[0124] This invention provides a method for aircraft / ship cooperative orbital guidance and preset performance control in follow-up detection missions, with the following beneficial effects:

[0125] (1) By employing a dynamic transformation strategy for the UAV's orbiting radius, an adaptive reference signal is generated, enabling the UAV to smoothly adjust its orbiting radius around the ship according to the requirements of the reconnaissance mission. Simultaneously, a UAV reference signal is generated based on the actual position of the ship, ensuring a strict cooperative positioning relationship between the ship and the UAV, achieving smooth changes in both velocity and acceleration dimensions. This strategy enables real-time adjustment of the UAV's orbiting radius according to mission requirements and constructs a closed-loop feedback control system for the ship-machine system, maintaining a precise distance between the UAV's orbiting trajectory and the ship's motion state. This effectively enhances the collaborative operation capability and mission adaptability of multiple unmanned systems in complex environments, while suppressing the accumulation of positioning errors and strengthening the overall robustness of the system. It is suitable for various application scenarios such as maritime collaborative reconnaissance, target tracking, and joint operations.

[0126] (2) An improved adaptive preset performance control is constructed to address the vulnerability of preset performance control. This enhances the robustness of the system by actively compensating for performance degradation caused by external disturbances. The invention integrates a self-adjusting performance function to ensure the system's autonomous stability recovery capability. At the same time, adaptive parameters are embedded in the virtual control law to avoid the computational complexity caused by the differential operation of transformation error. This technical solution effectively improves the anti-interference performance and real-time computing efficiency of the control system in complex environments and is suitable for navigation missions that require high-precision control. In addition, considering the uncertainty of actuator gain in actual navigation, coupling gain adaptive technology is introduced into the gain of all actuators in the ship-UAV system. The preset performance robust adaptive control law is constructed in combination with preset performance control, thereby achieving the control effect of keeping the error within the budget range under the condition of uncertain actuator gain. Attached Figure Description

[0127] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0128] Figure 1 This is a flowchart of the aircraft / ship cooperative orbiting guidance and preset performance control method in the follow-up detection mission of the present invention;

[0129] Figure 2 This is a framework diagram of the guidance and control algorithm for the ship-UAV system in this embodiment;

[0130] Figure 3 This embodiment utilizes Schematic diagram of guidance reference signal generation for ships and virtual drones;

[0131] Figure 4This is a simulation diagram of the ship-UAV detection path tracking trajectory in this embodiment;

[0132] Figure 5 This is a simulation diagram of ship error in this embodiment;

[0133] Figure 6 This is a simulation diagram of the UAV error in this embodiment;

[0134] Figure 7 This is a simulation diagram of the ship control input in this embodiment. Detailed Implementation

[0135] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0136] This embodiment provides a method for aircraft / ship cooperative orbital guidance and preset performance control in a follow-and-detection mission, such as... Figures 1 to 2 As shown, the steps include:

[0137] S1: Construct a nonlinear mathematical model for establishing a ship-machine system consisting of 1 ship and N drones. The nonlinear mathematical model includes a ship kinematics model and a drone kinematics model.

[0138] Specifically, the expression for the ship kinematics model is:

[0139] (1)

[0140] ,

[0141] (2)

[0142] In the formula: Represents the vectors of the ship's forward displacement, lateral drift displacement, and bow roll angle. , Represents the forward, lateral, and bow velocity vectors of the ship. ; The system nonlinear terms representing the ship's actions in the forward, lateral, and bow directions are... ; This indicates the additional mass of the vessel in the forward, lateral, and bow directions. ; Represents the gain matrix of the ship actuator and , This indicates the actuator gain at the ship's propeller speed and rudder angle; Indicates ship control input and , , These represent the ship's propeller speed and rudder angle, respectively. This represents the external disturbance forces / torques experienced by the ship in the forward, drift, and bow directions. ; express The first derivative; Represents the rotation matrix;

[0143] ,

[0144] in, This represents the nonlinear damping term of the model;

[0145] No. The kinematic model of the drone is as follows:

[0146] (3)

[0147] (4)

[0148] , ,

[0149] In the formula: The displacement vectors representing the forward, drift, and heave of the UAV are... , The vectors representing the roll, pitch, and yaw angles of the UAV are... ; This indicates that the drone is moving along a preset coordinate system. The velocity vector and rotational angular velocity vector of the axis and ; The system nonlinear terms acting in the directions of forward movement, drift, and heave of the UAV are expressed as follows: ; The system nonlinear terms acting in the roll, pitch, and yaw directions of the UAV are represented as follows: ; Represents the mass matrix of the drone and ; Indicates the drone along The rotational inertia matrix of the axis and ; This represents the actuator gain matrix of the UAV; The position control input transformation matrix of the UAV is represented and ; The transformation matrix represents the attitude control input of the UAV and , This indicates the distance from the center of the drone to each rotor; Indicates the drone control input; This represents the external disturbance force / torque experienced by the drone in the forward, drift, and heave directions. ; This represents the external disturbance forces / torques experienced by the drone in the roll, pitch, and yaw directions. ; Denotes the gravitational acceleration vector and Represents gravitational acceleration;

[0150] ,

[0151] ,

[0152] In the formula: Indicates the drone along The coefficient of rotational resistance of the shaft;

[0153] S2: Set the waypoint path and obtain the ship reference signal based on the preset virtual ship. Based on the ship reference signal, obtain the ship error according to the ship kinematic model. The ship error includes the ship position error and the ship attitude error, and specifically includes the following steps:

[0154] S21: Set the waypoint path and vessel turning radius; select target points before and after each waypoint (excluding the starting and ending waypoints) according to the set vessel turning radius; and obtain vessel reference signals in real time based on the preset virtual vessel planning, such as... Figure 3 As shown in the figure, the dynamic orbital guidance in this embodiment requires the generation of a ship reference signal through... Ships (virtual ships) To achieve this, the user needs to set a waypoint path consisting of several waypoints, and select target points before and after each waypoint according to the set turning radius. and ,when After the ship reaches one target point, it switches to tracking the next target point. The specific calculation method for generating ship reference signals for real-time ship planning is as follows:

[0155] (5)

[0156] (6)

[0157] In the formula: This indicates the virtual ship's forward distance, drift distance, and heading angle; This represents the forward speed and bow roll rate of the virtual ship; express The first derivative; This represents the lateral acceleration of the virtual ship; Indicates the azimuth angle of the virtual ship to the target point and ; This represents the azimuth angle of the virtual ship to the target point; This represents the distance from the virtual ship to the target point;

[0158] S22: Based on the ship reference signal, obtain the ship error according to the ship kinematic model, and obtain the azimuth angle from the real ship to the virtual ship based on the ship error. for:

[0159] (7)

[0160] (8)

[0161] In the formula: This indicates the ship's positional error, which includes the error in the ship's forward distance and the error in its lateral drift.

[0162] S23: Based on the bearing angle from the real ship to the virtual ship Obtaining ship attitude error for:

[0163] (9)

[0164] S3: Based on the ship's kinematics model and the UAV's orbital radius dynamic transformation strategy, a virtual UAV is set up to obtain the UAV reference signal. Based on the UAV reference signal and the UAV kinematics model, the UAV error is obtained. The UAV error includes the UAV position error and the UAV attitude error, specifically including the following steps:

[0165] S31: Construct a dynamic transformation strategy for the UAV's orbital radius, the expression of which is:

[0166] (10)

[0167] In the formula: Indicates the radius of the circle before conversion; This represents the desired encircling radius, i.e., the transformed encircling radius. Indicates the conversion time; Indicates the moment when the radius conversion begins (i.e., the time required for the conversion process; the specific value is set by the user). This represents the output of the UAV's orbital radius dynamic transformation strategy;

[0168] S32: Based on the ship kinematics model and the dynamic transformation strategy of the UAV's orbital radius, the virtual UAV is set to obtain the UAV reference signal as follows:

[0169] (11)

[0170] In the formula: Indicates the forward and lateral distances of the virtual drone; Indicates the drone's orbital period; Indicates the azimuth angle of the ship to the virtual drone and This represents the initial value of the azimuth angle between the ship and the virtual drone; Indicates the expected lift-height of the drone;

[0171] S33: Obtain the UAV position error based on the UAV reference signal and the UAV kinematic model; and obtain the UAV pitch angle reference signal based on the UAV position error. for

[0172] (12)

[0173] (13)

[0174] In the formula: This refers to the positional error of the UAV, including the forward distance error, lateral distance error, and heave distance error.

[0175] S34: Based on the UAV's bow angle reference signal Obtaining UAV attitude error for:

[0176] (14).

[0177] This embodiment also includes obtaining the derivative of the position error of the ship-unmanned aerial vehicle system, i.e., the ship-machine system:

[0178] ;

[0179] S4: Define a first standard preset performance function based on the ship's position error and the UAV's position error; construct a dynamic compensation term for the first preset performance function based on the first standard preset performance function to obtain the position conversion error; construct the first virtual control law of the ship-machine system based on the position conversion error, specifically including the following steps:

[0180] S41: Based on the ship's position error and the UAV's position error, define the first standard preset performance function, i.e., define the corresponding error. First standard preset performance function for:

[0181] (15)

[0182] (16)

[0183] In the formula: express The abbreviated form; and express The initial value and the final convergent value; Represents a positive constant;

[0184] S42: Define the dynamic compensation term of the first preset performance function, its expression is:

[0185] (17)

[0186] In the formula: Indicates corresponding error A general term; Indicates the design adjustment parameters, and satisfies ; Indicates dynamic compensation items; express The first derivative;

[0187] S43: Enhance system robustness by actively compensating the boundary when the external environment changes abruptly, that is, construct the position error transformation formula based on the dynamic compensation term and the first standard preset performance function to obtain the position transformation error. The position error conversion formula is as follows:

[0188] (18)

[0189] S44: Based on the position transformation error, the update law for the first set performance adaptive parameter is constructed as follows:

[0190] (19)

[0191] In the formula: This indicates the preset performance adaptive parameters; express The estimated value; This represents the update law, or growth rate, of the performance adaptive parameter. Indicate design parameters; for The initial value;

[0192] S45: To stabilize positional errors Based on the update law of the first performance adaptive parameters, the first virtual control law of the ship system is constructed as follows:

[0193] (20)

[0194] In the formula: Represents the first virtual control law Positive design parameters; Indicate design parameters;

[0195] S5: Introduce dynamic surface technology to reduce the order of the first virtual control law, obtain the dynamic surface signal of the first virtual control law to define the kinematic error of the ship-machine system, and construct the first preset performance control law and adaptive law based on the kinematic error. The specific steps include:

[0196] S51: The virtual controller will cause a large computational load in the subsequent differentiation. Therefore, dynamic surface technique is introduced to reduce the order of the derivative of the virtual controller. That is, dynamic surface technique is introduced to reduce the order of the first virtual control law. The dynamic surface signal of the first virtual control law is obtained as follows:

[0197] (twenty one)

[0198] In the formula: Represents the first virtual control law Dynamic surface signals; This represents a time constant that is greater than zero. express The initial value; express The first derivative, and the dynamic surface error ;

[0199] S52: Define the kinematic error of the ship-machine system based on the dynamic surface signal of the first virtual control law. Its expression is:

[0200] (twenty two)

[0201] S53: Taking the derivative of the kinematic error, we obtain the derivative of the kinematic error as follows:

[0202] (twenty three)

[0203] In the formula: Represents the vector form of the dynamic surface signal and ; Represents the error vector and = ; express The first derivative;

[0204] S54: Using MLP technology to analyze the system nonlinear term in the derivative of kinematic error. External interference Approximation simplification, i.e., nonlinear terms It can be used to perform online approximation using radial basis function neural networks, and introduces MLP technology to simplify and approximate nonlinear terms and external environmental disturbances. Its expression is:

[0205] (twenty four)

[0206] In the formula: , express Neural network weight update law; Represents the Gaussian function; Indicates the approximation error; Indicates the approximation error The maximum value; A general term referring to interference from the external environment; Indicates intermediate parameters and ; express The norm;

[0207] S55: Obtain the actual control input and convert it into an adaptive law that combines a preset performance control law and actuator gain:

[0208] (25)

[0209] In the formula: Preset performance control laws representing the ship's forward direction, bow angle, and the UAV's forward direction, drift direction, heave direction, roll angle, pitch angle, and bow angle; , , , , , ; These represent the adaptive parameters. The estimated value is the adaptive law of actuator gain; express The initial value; Indicate design parameters; This represents the preset performance control law matrix;

[0210] S56: Based on the nonlinear mathematical model of the ship-machine system, and combining formulas (23) to (25), the first preset performance control law and adaptive law are constructed using MLP technology, coupling gain adaptive technology, and back stepping technology:

[0211] (26)

[0212] (27)

[0213] (28)

[0214] (29)

[0215] In the formula: This indicates a design parameter that is greater than zero. express Maximum value of external interference Estimated value; Indicates adaptive parameters and , express The estimated value; Represents intermediate variables and , Indicates a positive constant; express The initial value; express The first derivative; express The initial value.

[0216] S6: Based on the first preset performance control law and adaptive law, obtain the reference roll angle signal and reference pitch angle signal of the UAV according to the UAV's yaw angle reference signal, and then obtain the roll angle error and pitch angle error. Combine the ship attitude error and the UAV attitude error to define a second standard preset performance function. Construct a dynamic compensation term for the second preset performance function according to the second standard preset performance function to obtain the attitude conversion error. Construct the second virtual control law of the ship-machine system according to the attitude conversion error.

[0217] The specific steps include:

[0218] S61: Based on the first preset performance control law and adaptive law, nonlinear decoupling technology is used for calculation, and the reference roll angle signal of the UAV is obtained according to the UAV's yaw angle reference signal. Compared with the reference pitch angle signal for:

[0219] (30)

[0220] S62: To control the ship-UAV's current attitude to converge to the reference attitude, the roll angle error and pitch angle error are obtained based on the reference roll angle signal and the reference pitch angle signal. Combining the ship's attitude error and the UAV's attitude error, the attitude error of the ship-machine system is defined as:

[0221] , , , (31)

[0222] Taking its derivative, we get:

[0223] ;

[0224] S63: Define the second standard preset performance function, i.e., define the corresponding error, based on the attitude error of the ship system. The second standard preset performance function for:

[0225] (32)

[0226] In the formula: , express The initial value and the final convergent value; Represents a positive constant;

[0227] S64: The dynamic compensation term of the second preset performance function is constructed based on the second standard preset performance function as follows:

[0228] (33)

[0229] In the formula: Indicates corresponding error A general term; Indicates the design adjustment parameters, and satisfies ; Indicates dynamic compensation items; express The first derivative;

[0230] S65: Based on dynamic compensation terms The attitude error conversion formula is constructed by combining the second standard preset performance function to obtain the attitude conversion error. The attitude error conversion formula is as follows:

[0231] (34)

[0232] S66: The update law for the second preset performance adaptive parameters, constructed based on the attitude transformation error, is as follows:

[0233] (35)

[0234] In the formula: This indicates the preset performance adaptive parameters; yes The estimated value; This represents the update law for the performance adaptive parameters; Indicate design parameters; express The initial value;

[0235] S67: To stabilize attitude errors Based on the update law of the second performance adaptive parameter, the second virtual control law of the ship system is constructed as follows:

[0236] (36)

[0237] In the formula: Represents the second virtual control law Control parameters;

[0238] S7: Introduce dynamic surface technology to reduce the order of the second virtual control law, obtain the dynamic surface signal of the second virtual control law to define the attitude error of the ship system, and construct the second preset performance control law and adaptive law based on the attitude error of the ship system. The specific steps include:

[0239] S71: To avoid the computational explosion problem caused by the attitude virtual controller in subsequent differentiation, a dynamic surface technique is introduced. That is, the dynamic surface technique is used to reduce the order of the second virtual control law, and the dynamic surface signal of the second virtual control law is obtained as follows:

[0240] (37)

[0241] In the formula: Represents the second virtual control law Dynamic surface signals; This represents a time constant that is greater than zero. express The initial value; express The first derivative; and the dynamic surface error ;

[0242] S72: Define the attitude error of the ship system based on the dynamic surface signal of the second virtual control law. for:

[0243] (38)

[0244] S73: The derivative of the kinematic error is obtained by taking the derivative with respect to the kinematic error:

[0245] (39)

[0246] In the formula: Represents the vector form of the dynamic surface signal and ; Represents the error vector and ; express The first derivative;

[0247] S74: System nonlinearity of kinematic error derivative in S73 using MLP technique External interference To simplify the approximation, the nonlinear term in this embodiment... It can be used to perform online approximation using radial basis function neural networks, and introduces MLP technology to simplify and approximate nonlinear terms and external environmental disturbances. Its expression is:

[0248] (40)

[0249] In the formula: , express Neural network weight update law; Represents the Gaussian function; Indicates the approximation error; express The maximum value; A general term referring to interference from the external environment; , express The norm;

[0250] S75: Based on the nonlinear mathematical model of the ship-machine system, and following the same technical principles as steps S55 to S56, the second preset performance control law and adaptive law are constructed using MLP technology, coupled gain adaptive technology, and back stepping technology:

[0251] (41)

[0252] (42)

[0253] (43)

[0254] (44)

[0255] In the formula: Indicates a design parameter that is greater than zero; Indicates the quality of the drone; express Maximum value of external interference Estimated value; Indicates adaptive parameters and ; express The estimated value; Represents intermediate variables and ; Indicates a positive constant; express The initial value; express The initial value; express The first derivative;

[0256] S8: Based on the first preset performance control law and the adaptive law, and the second preset performance control law and the adaptive law, the aircraft / ship cooperative orbital guidance and preset performance control in the follow-up detection mission are realized.

[0257] The method described in this embodiment addresses the two aforementioned shortcomings of ship-UAV cooperative systems in engineering practice. It designs a dynamic orbital guidance system and, based on this, an adaptive preset performance control method that maintains control accuracy under conditions of unknown actuator gain and fluctuating external environmental disturbances. The constructed dynamic orbital guidance strategy can build a ship reference path based on set waypoints and obtain the ship's reference signal. The UAV reference signal needs to be calculated based on the actual ship position information and the orbital radius required by the mission. Simultaneously, during the mission, the UAV's orbital radius can be smoothly adjusted according to detection requirements to achieve different emphases on energy saving and detection range. To address the vulnerability of preset performance control, the method in this embodiment constructs an adaptive preset performance control law. Its core idea is to enhance system robustness by actively compensating for boundaries when the external environment changes abruptly. An adaptive parameter is embedded in the design of the virtual control law, directly eliminating the differential component of the transformation error, thereby reducing computational complexity and solving the real-time bottleneck caused by nonlinear transformations in traditional PPC. Considering the uncertainty of actuator gain, the mathematical model of the UAV was decoupled and a coupled adaptive gain was designed to calculate all control commands of the ship-UAV system to specific actuator commands.

[0258] To verify the performance of the method described in this embodiment in a UAV-based ship circumnavigation monitoring task, a simulation experiment was conducted on the MATLAB platform. The controlled objects were an underactuated ship and four rotary-wing UAVs. A waypoint path consisting of four ship waypoints was selected, and between the second and third waypoints, two UAVs needed to reduce their circumnavigation radius. The UAV circumnavigation radii were 250m and 180m, the circumnavigation period was 50s, and the radius conversion process took 5s. The initial state of the controlled object was as follows: ,

[0259] ,

[0260] ,

[0261] , .like Figures 4 to 7 The results are shown below, based on the simulation results of ship-UAV cooperative search under sea state 4 on the MATLAB simulation platform. Figure 4 The path tracking trajectory curve representing the ship-UAV collaborative detection mission, from Figure 4 As can be seen, the reference path between the ship and the UAV is obtained based on a dynamic orbital guidance algorithm, enabling the UAV to move at a constant angular velocity around the ship at a fixed radius throughout the entire trajectory, and to smoothly transition when the orbital radius changes. Figures 5 to 6 The figure shows the position and attitude errors of the ship-UAV cooperative system. Under the preset performance control, all system errors eventually converge within the expected minimum range. The UAV position error exhibits a periodic variation because, during the orbiting mission, it needs to orbit the ship at a constant angular velocity while following it. This causes the UAV's desired speed to accelerate when moving in the same direction as the ship and decelerate when moving in the opposite direction, resulting in a periodic change in the UAV's speed. The unavoidable lag between the guidance signal and the actual control of the UAV ultimately leads to a periodically varying position error. However, under the proposed control algorithm, the final error still converges within the expected range. Figure 7 The diagram shows the control inputs of the ship, taking into account the actuator gain of the system. All control commands are accurate to the specific actuator commands.

[0262] Combining existing technologies, controller design, and simulation experiments, the method described in this embodiment has the following two beneficial effects in the field of ship-UAV cooperative orbiting:

[0263] 1) The orbiting mode of the ship-UAV system has been improved. The proposed dynamic orbiting guidance strategy not only enables the UAV to orbit at a uniform angular velocity, but also allows for real-time switching of the orbiting radius according to the needs of the detection mission, ensuring a smooth transition in both velocity and acceleration dimensions throughout the orbiting transition process. The method described in this embodiment can improve the flexibility of UAVs in coordinating orbiting with ships, broaden application scenarios, and achieve a dynamic balance between monitoring range and energy consumption.

[0264] 2) The designed control law incorporates an adaptive preset performance control and introduces a preset performance adaptive parameter to achieve high-precision control under conditions considering system nonlinearities of unknown model parameters, uncertain marine environmental disturbances, and unknown actuator gains. A simulation experiment of ship-UAV air-sea cooperative search was conducted in a simulated marine environment to verify the effectiveness of the guidance strategy and control algorithm proposed in this embodiment. Simulation results show that the algorithm exhibits excellent performance in control accuracy and control response speed, while maintaining the stability of the preset performance control under sudden environmental disturbances.

[0265] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for aircraft / ship cooperative orbital guidance and preset performance control in a follow-and-detection mission, characterized in that, The specific steps include: S1: Construct a nonlinear mathematical model for establishing a ship-machine system consisting of 1 ship and N drones. The nonlinear mathematical model includes a ship kinematics model and a drone kinematics model. S2: Set the waypoint path and obtain the ship reference signal based on the preset virtual ship. Based on the ship reference signal, obtain the ship error according to the ship kinematic model. The ship error includes the ship position error and the ship attitude error. S3: Based on the ship's kinematics model and the dynamic transformation strategy of the UAV's orbital radius, a virtual UAV is set up to obtain the UAV reference signal. Based on the UAV reference signal, the UAV error is obtained according to the UAV kinematics model. The UAV error includes the UAV position error and the UAV attitude error. The specific steps include: S31: Construct a dynamic transformation strategy for the UAV's orbital radius, the expression of which is: （10） In the formula: Indicates the radius of the circle before conversion; This represents the desired encircling radius, i.e., the transformed encircling radius. Indicates the conversion time; Indicates the moment when the radius conversion begins; This represents the output of the UAV's orbital radius dynamic transformation strategy; S32: Based on the ship kinematics model and the dynamic transformation strategy of the UAV's orbital radius, the virtual UAV is set to obtain the UAV reference signal as follows: （11） In the formula: Indicates the forward and lateral distances of the virtual drone; Indicates the drone's orbital period; Indicates the azimuth angle of the ship to the virtual drone and This represents the initial value of the azimuth angle between the ship and the virtual drone; Indicates the expected lift-height of the drone; S33: Obtain the UAV position error based on the UAV reference signal and the UAV kinematic model; and obtain the UAV pitch angle reference signal based on the UAV position error. for （12） （13） In the formula: This refers to the positional error of the drone, including the forward distance error, lateral distance error, and heave distance error. S34: Based on the UAV's bow angle reference signal Obtaining UAV attitude error for: （14） S4: Define a first standard preset performance function based on the ship's position error and the UAV's position error; construct a dynamic compensation term for the first preset performance function based on the first standard preset performance function to obtain the position conversion error; construct a first virtual control law for the ship-machine system based on the position conversion error. S5: Introduce dynamic surface technology to reduce the order of the first virtual control law, obtain the dynamic surface signal of the first virtual control law to define the kinematic error of the ship system, and construct the first preset performance control law and adaptive law based on the kinematic error; S6: Based on the first preset performance control law and adaptive law, obtain the reference roll angle signal and reference pitch angle signal of the UAV according to the UAV's yaw angle reference signal, and then obtain the roll angle error and pitch angle error. Combine the ship attitude error and the UAV attitude error to define a second standard preset performance function. Construct a dynamic compensation term for the second preset performance function according to the second standard preset performance function to obtain the attitude conversion error. Construct the second virtual control law of the ship-machine system according to the attitude conversion error. S7: Introduce dynamic surface technology to reduce the order of the second virtual control law, obtain the dynamic surface signal of the second virtual control law to define the attitude error of the ship system, and construct the second preset performance control law and adaptive law based on the attitude error of the ship system. S8: Based on the first preset performance control law and the adaptive law, and the second preset performance control law and the adaptive law, the aircraft / ship cooperative orbital guidance and preset performance control in the follow-up detection mission are realized.

2. The method for aircraft / ship cooperative orbiting guidance and preset performance control in a follow-and-detection mission according to claim 1, characterized in that, The expression for the ship kinematics model described in S1 is: （1） （2） In the formula: Represents the vectors of the ship's forward displacement, lateral drift displacement, and bow roll angle. , Represents the forward, lateral, and bow velocity vectors of the ship. ; The system nonlinear terms representing the ship's actions in the forward, lateral, and bow directions are... ; This indicates the additional mass of the vessel in the forward, lateral, and bow directions. ; Represents the gain matrix of the ship actuator and , This indicates the actuator gain at the ship's propeller speed and rudder angle; Indicates ship control input and , , These represent the ship's propeller speed and rudder angle, respectively. This represents the external disturbance forces / torques experienced by the ship in the forward, drift, and bow directions. ; express The first derivative; Represents the rotation matrix; No. The kinematic model of the drone is as follows: （3） （4） In the formula: The displacement vectors representing the forward, drift, and heave of the UAV are... , The vectors representing the roll, pitch, and yaw angles of the UAV are... ; This indicates that the drone is moving along a preset coordinate system. The velocity vector and rotational angular velocity vector of the axis and ; The system nonlinear terms acting in the directions of forward movement, drift, and heave of the UAV are expressed as follows: ; The system nonlinear terms acting in the roll, pitch, and yaw directions of the UAV are represented as follows: ; Represents the mass matrix of the drone and ; Indicates the drone along The rotational inertia matrix of the axis and ; This represents the actuator gain matrix of the UAV; This represents the position control input transformation matrix for the UAV. The transformation matrix represents the attitude control input of the UAV and , This indicates the distance from the center of the drone to each rotor; Indicates the drone control input; This represents the external disturbance force / torque experienced by the drone in the forward, drift, and heave directions. ; This represents the external disturbance forces / torques experienced by the drone in the roll, pitch, and yaw directions. ; Denotes the gravitational acceleration vector and It represents the acceleration due to gravity.

3. The method for aircraft / ship cooperative orbiting guidance and preset performance control in a follow-and-detection mission according to claim 2, characterized in that, S2 specifically includes the following steps: S21: Set the waypoint path and the ship's turning radius. Select target points before and after each waypoint (excluding the starting and ending waypoints) according to the set turning radius. Based on a pre-set virtual ship real-time plan, obtain the ship's reference signal, the expression of which is: （5） （6） In the formula: This indicates the virtual ship's forward distance, drift distance, and heading angle; This represents the forward speed and bow roll rate of the virtual ship; express The first derivative; This represents the lateral acceleration of the virtual ship; Indicates the azimuth angle of the virtual ship to the target point and ; This represents the azimuth angle of the virtual ship to the target point; This represents the distance from the virtual ship to the target point; S22: Based on the ship reference signal, obtain the ship error according to the ship kinematic model, and obtain the azimuth angle from the real ship to the virtual ship based on the ship error. for: （7） （8） In the formula: This indicates the ship's positional error, which includes the error in the ship's forward distance and the error in its lateral drift. S23: Based on the bearing angle from the real ship to the virtual ship Obtaining ship attitude error for: （9）。 4. The method for aircraft / ship cooperative orbiting guidance and preset performance control in a follow-and-detection mission according to claim 3, characterized in that, S4 specifically includes the following steps: S41: Based on the ship's position error and the UAV's position error, define the first standard preset performance function, i.e., define the corresponding error. First standard preset performance function for: （15） （16） In the formula: express The abbreviated form; and express The initial value and the final convergent value; Represents a positive constant; S42: Define the dynamic compensation term of the first preset performance function, its expression is: （17） In the formula: Indicates corresponding error A general term; Indicates the design adjustment parameters, and satisfies ; Indicates dynamic compensation items; express The first derivative; S43: Construct a position error transformation formula based on the dynamic compensation term and the first standard preset performance function to obtain the position transformation error. The position error conversion formula is as follows: （18） S44: Based on the position transformation error, the update law for the first set performance adaptive parameter is constructed as follows: （19） In the formula: This indicates the preset performance adaptive parameters; express The estimated value; This represents the update law for the performance adaptive parameters; Indicate design parameters; express The initial value; S45: Based on the update law of the first performance adaptive parameter, the first virtual control law of the ship system is constructed as follows: （20） In the formula: Represents the first virtual control law Positive design parameters; Indicates design parameters.

5. The method for aircraft / ship cooperative orbiting guidance and preset performance control in a follow-and-detection mission according to claim 4, characterized in that, S5 specifically includes the following steps: S51: The dynamic surface technique is introduced to reduce the order of the first virtual control law, and the dynamic surface signal of the first virtual control law is obtained as follows: （21） In the formula: Represents the first virtual control law Dynamic surface signals; This represents a time constant that is greater than zero. express The initial value; express The first derivative; S52: Define the kinematic error of the ship-machine system based on the dynamic surface signal of the first virtual control law. Its expression is: （22） S53: Taking the derivative of the kinematic error, we obtain the derivative of the kinematic error as follows: （23） In the formula: Represents the vector form of the dynamic surface signal and ; Represents the error vector and = ; express The first derivative; S54: Using MLP technology to analyze the system nonlinear term in the derivative of kinematic error. External interference After approximation and simplification, its expression is: （24） In the formula: , express Neural network weight update law; Represents the Gaussian function; Indicates the approximation error; Indicates the approximation error The maximum value; A general term referring to interference from the external environment; Indicates intermediate parameters and ; express The norm; S55: Obtain the actual control input and convert it into an adaptive law that combines a preset performance control law and actuator gain: （25） In the formula: Preset performance control laws representing the ship's forward direction, bow angle, and the UAV's forward direction, drift direction, heave direction, roll angle, pitch angle, and bow angle; , , , , , ; These represent the adaptive parameters. The estimated value is the adaptive law of actuator gain; express The initial value; Indicate design parameters; This represents the preset performance control law matrix; S56: Based on the nonlinear mathematical model of the ship-machine system, and combining formulas (23) to (25), the first preset performance control law and adaptive law are constructed using MLP technology, coupling gain adaptive technology, and back stepping technology: （26） （27） （28） （29） In the formula: This indicates a design parameter that is greater than zero. express Maximum value of external interference Estimated value; Indicates adaptive parameters and , express The estimated value; Represents intermediate variables and , Indicates a positive constant; express The initial value; express The first derivative; express The initial value.

6. The method for aircraft / ship cooperative orbiting guidance and preset performance control in a follow-and-detection mission according to claim 5, characterized in that, S6 specifically includes the following steps: S61: Based on the first preset performance control law and adaptive law, obtain the reference roll angle signal of the UAV according to the UAV's yaw angle reference signal. Compared with the reference pitch angle signal for: （30） S62: Based on the reference roll angle signal and the reference pitch angle signal, the roll angle error and pitch angle error are obtained. Combining the ship's attitude error and the UAV's attitude error, the attitude error of the ship-machine system is defined as: ， ， , （31） S63: Define the second standard preset performance function, i.e., define the corresponding error, based on the attitude error of the ship system. The second standard preset performance function for: （32） In the formula: , express The initial value and the final convergent value; Represents a positive constant; S64: The dynamic compensation term of the second preset performance function is constructed based on the second standard preset performance function as follows: （33） In the formula: Indicates corresponding error A general term; Indicates the design adjustment parameters, and satisfies ; Indicates dynamic compensation items; express The first derivative; S65: Based on dynamic compensation terms The attitude error conversion formula is constructed by combining the second standard preset performance function to obtain the attitude conversion error. The attitude error conversion formula is as follows: （34） S66: The update law for the second preset performance adaptive parameters, constructed based on the attitude transformation error, is as follows: （35） In the formula: This indicates the preset performance adaptive parameters; yes The estimated value; This represents the update law for the performance adaptive parameters; Indicate design parameters; express The initial value; S67: Based on the update law of the second performance adaptive parameter, the second virtual control law of the ship system is constructed as follows: （36） In the formula: Represents the second virtual control law The control parameters.

7. The method for aircraft / ship cooperative orbiting guidance and preset performance control in a follow-and-detection mission according to claim 6, characterized in that, S7 specifically includes the following steps: S71: The dynamic surface technique is introduced to reduce the order of the second virtual control law, and the dynamic surface signal of the second virtual control law is obtained as follows: （37） In the formula: Represents the second virtual control law Dynamic surface signals; This represents a time constant that is greater than zero. express The initial value; express The first derivative; S72: Define the attitude error of the ship system based on the dynamic surface signal of the second virtual control law. for: （38） S73: The derivative of the kinematic error is obtained by taking the derivative with respect to the kinematic error: （39） In the formula: Represents the vector form of the dynamic surface signal and ; Represents the error vector and ; express The first derivative; S74: System nonlinearity of kinematic error derivative in S73 using MLP technology External interference After approximation and simplification, its expression is: （40） In the formula: , express Neural network weight update law; Represents the Gaussian function; Indicates the approximation error; express The maximum value; A general term referring to interference from the external environment; , express The norm; S75: Based on the nonlinear mathematical model of the ship-machine system, and following the same technical principles as steps S55 to S56, the second preset performance control law and adaptive law are constructed using MLP technology, coupled gain adaptive technology, and back stepping technology: （41） （42） （43） （44） In the formula: Indicates a design parameter that is greater than zero; Indicates the quality of the drone; express Maximum value of external interference Estimated value; Indicates adaptive parameters and ; express The estimated value; Represents intermediate variables and ; Indicates a positive constant; express The initial value; express The initial value; express The first derivative.

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