Aircraft-ship cooperative path tracking method based on predicted LOS guidance law

Abstract

The invention discloses a ship-aircraft cooperative path tracking method based on a prediction LOS guidance law, and the method comprises the steps: building an improved LOS guidance law for predicting a path tracking error, and obtaining a ship target course, so as to obtain a ship course error; then, an unmanned aerial vehicle position control controller is established by obtaining an estimated value of the longitudinal speed of the ship and an estimated value representing the transverse speed of the ship and combining actual position coordinates of the ship; and meanwhile, through the established differentiator, a pitching control target and a rolling control target of the unmanned aerial vehicle are obtained, an unmanned aerial vehicle attitude controller is finally established, and in combination with an unmanned aerial vehicle position controller, aircraft-ship cooperative path tracking control is realized. According to the invention, the problem of large overshoot frequently appearing at the steering position of the aircraft-ship cooperative path tracking can be solved, the safety of the aircraft-ship cooperative operation can be improved, and the overshoot of the aircraft-ship cooperative path tracking at the steering position can be reduced.

Description

Technical Field

[0001] The present invention relates to the field of unmanned system collaborative control, and in particular to a machine-ship collaborative path tracking method based on predicted LOS guidance law. Background Art

[0002] With the rapid development of science and technology, people are increasingly interested in exploring unmanned systems in various fields. Specifically, current research is particularly focused on the cooperative control of drones and underactuated ships. The cooperative control between drones and underactuated ships can improve mission efficiency. Both systems have different characteristics: underactuated ships show excellent stability and high load capacity on the water, while drones provide a wide aerial view and extraordinary maneuverability. Reasonable task allocation enables underactuated ships and drones to jointly undertake complex tasks. The cooperative control method of drones and underactuated ships is applicable to various fields, including military, civil, scientific and environmental protection. The versatility of the machine-ship cooperative control system provides great application prospects in various fields. At present, the cooperative path tracking of the machine-ship often has a large overshoot at the turning point. The large overshoot means that the deviation from the designed route is large, which is unsafe. Therefore, in order to improve the safety of the cooperative operation of the machine-ship, it is necessary to reduce the overshoot of the cooperative path tracking of the machine-ship at the turning point. Summary of the invention

[0003] The present invention discloses a machine-ship cooperative path tracking technology method based on predicted LOS guidance law to overcome the above technical problems.

[0004] In order to achieve the above object, the technical solution of the present invention is:

[0005] A method for aircraft-ship collaborative path tracking based on predicted LOS guidance law comprises the following steps:

[0006] S1: Establish a mathematical model for cooperative control of aircraft and ship considering environmental interference;

[0007] S2. According to the actual position of the ship, an improved LOS guidance law for predicting the path tracking error is established to obtain the target heading of the ship according to the position of the preset turning point, and then obtain the heading error of the ship according to the actual heading of the ship;

[0008] S3, obtaining the ship's rudder angle according to the ship's heading error, and obtaining the ship's actual heading according to the ship-engine cooperative control mathematical model;

[0009] S4: obtaining an estimated value of the longitudinal speed of the ship and an estimated value representing the lateral speed of the ship according to the actual heading of the ship, so as to obtain the actual position coordinates of the ship according to the motor-ship cooperative control mathematical model;

[0010] S5. Establishing a UAV position control controller according to the actual position coordinates of the ship;

[0011] S6: Establish a differentiator to obtain the pitch control target and roll control target of the UAV, and establish a UAV attitude controller based on the pitch control target and roll control target; then, based on the UAV attitude controller and the UAV position controller, realize the tracking control of the aircraft-ship collaborative path.

[0012] Beneficial effects: The invention provides a method for cooperative path tracking of a motor-vessel based on a predictive LOS guidance law, establishes an improved LOS guidance law for predicting the path tracking error, so as to obtain the target heading of the ship according to the position of the preset turning point, and then obtain the heading error of the ship according to the actual heading of the ship; then obtain the rudder angle of the ship, so as to obtain the actual heading of the ship according to the motor-vessel cooperative control mathematical model, so as to obtain the estimated value of the longitudinal velocity of the ship and the estimated value representing the lateral velocity of the ship, and then according to the actual position coordinates of the ship; establish a UAV position control controller; at the same time, obtain the pitch control target and roll control target of the UAV through the established differentiator, and finally establish a UAV attitude controller, and combine the UAV position controller to realize the path tracking control of motor-vessel cooperation. The invention can solve the problem of large overshoot that often occurs in the cooperative path tracking of motor-vessel cooperation at the turning point, can improve the safety of the cooperative operation of motor-vessel cooperation, and reduce the overshoot of the cooperative path tracking of motor-vessel cooperation at the turning point. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces 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 creative labor.

[0014] Figure 1 The flow chart of the machine-ship collaborative control of the present invention is shown.

[0015] Figure 2 A schematic diagram of the coordinate system of the aircraft-vessel cooperative system in an embodiment of the present invention is shown.

[0016] Figure 3 The overall simulation schematic diagram of the aircraft-ship collaborative path control based on predicted LOS guidance in an embodiment of the present invention is shown.

[0017] Figure 4 A 3D schematic diagram of the aircraft-vessel collaborative path tracking in an embodiment of the present invention is shown.

[0018] Figure 5 A 2D schematic diagram of the aircraft-vessel collaborative path tracking in an embodiment of the present invention is shown.

[0019] Figure 6 The vehicle-vessel collaborative path tracking error in an embodiment of the present invention is demonstrated.

[0020] Figure 7 A schematic diagram of the rudder angle and the bow direction during the path tracking of an underactuated ship in an embodiment of the present invention is shown.

[0021] Figure 8 A schematic diagram of velocity estimation during path tracking of an underactuated ship in an embodiment of the present invention is shown.

[0022] Fig. 9 A schematic diagram of estimated values ​​of disturbances during path tracking of an underactuated ship in an embodiment of the present invention is shown.

[0023] Fig.10 A schematic diagram showing the weight changes of a radial basis function neural network used in the process of designing a ship controller in an embodiment of the present invention is shown.

[0024] Fig.11 A schematic diagram showing position and attitude changes during the path tracking of a drone in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] This embodiment introduces a method for aircraft-vessel collaborative path tracking based on a predicted LOS guidance law, including the following steps: Figure 1-3 As shown,

[0027] S1: Establish a mathematical model for cooperative control of aircraft and ship considering environmental interference;

[0028] Preferably, the aircraft-vessel cooperative control mathematical model is established as follows:

[0029]

[0030] Where: x s The coordinate of the actual position of the ship on the X-axis of the geodetic coordinate system; s Indicates the coordinate of the actual position of the ship on the Y axis of the geodetic coordinate system; ψ s Indicates the actual heading of the ship; u s Indicates the longitudinal speed of the ship; v sIndicates the transverse speed of the ship; r s represents the angular velocity of the ship; T represents the ship turning hysteresis index; K represents the ship turning ability index; δ s represents the rudder angle; f represents all unidentified disturbances affecting the system; f n is the inherent uncertainty parameter of the system internal model; f m represents external disturbance; x a The coordinate of the actual position of the drone on the x-axis of the earth coordinate system; a The coordinate of the actual position of the drone on the y-axis of the geodetic coordinate system; z a The coordinate of the actual position of the drone on the z-axis of the earth coordinate system; θ a Indicates the pitch angle of the drone; Indicates the rolling angle of the drone; ψ a Indicates the heading angle of the drone; m a represents the mass of the drone; g represents the acceleration due to gravity; I Y Indicates the moment of inertia of the drone on the Y axis of the drone coordinate system; I Z Indicates the moment of inertia of the drone on the Z axis of the drone coordinate system; I X Indicates the moment of inertia of the drone on the Z axis of the drone coordinate system; J r Represents the UAV rotor inertia; Ω r Indicates the total rotor speed of the drone; l a represents the wheelbase of the drone; b represents the lift coefficient; d a represents the drag coefficient; Ω1, Ω2, Ω3, and Ω4 represent the rotation speeds of the rotors on the rotor motors No. 1 to 4 of the drone respectively; F1, F2, F3, and F4 all represent the forces generated by the rotor motors of the drone, and in this embodiment, they are the forces generated by the rotor motors No. 1 to 4 of the drone; U1 represents the lift of the drone; U2 represents the yaw moment of the drone; U3 represents the pitch moment of the drone; U4 represents the rolling moment of the drone; K1, K2, and K3 are all the influence coefficients of air resistance; K4, K5, and K6 are all the influence coefficients of the gyro effect on the Euler angle; represents the first-order derivative; represents the second-order derivative;

[0031] Specifically, the drone of this embodiment is a 4-wing drone, so there are 4 rotor motors, namely rotor motors No. 1 to No. 4.

[0032] Specifically, this embodiment obtains the aircraft-ship coordinated route by designing the turning point, and the current turning point position information W i (x i ,y i ); when the horizontal coordinate of the current point is x i , the ordinate y of the current turning point i .

[0033] S2. According to the actual position of the ship, an improved LOS guidance law is established for predicting the path tracking error, so as to obtain the ship's target heading ψ according to the position of the preset turning point. sd , and then obtain the ship heading error ψ according to the actual heading of the ship se ;

[0034] Preferably, the ship heading error ψ se The acquisition method is as follows:

[0035] Specifically, the improved LOS guidance law established in this embodiment can obtain the ship target heading ψ sd , and reduce the overshoot of the ship during turning.

[0036] Specifically, in the improved LOS guidance law, the path tracked is determined by the next turning point W i+1 and the i-th preset turning point W i The actual position of the ship P(x s ,y s ) is the center of the circle, R is the distance from the center of the circle to the route, and W is the radius. i is the current turning point. los (x los ,y los ) is the starting point coordinate of the ship’s turning; ψ los is from the current P(x s ,y s ) to P los (x los ,y los ) and the direction of the X-axis of the geodetic coordinate system.

[0037] S21: Get the actual position P(x s ,y s ) and the starting point position P of the ship turning los (x los ,y los ) are as follows:

[0038]

[0039] Where: x los Indicates the horizontal coordinate of the starting point of the ship's turn; y los Indicates the ordinate of the starting point of the actual turning of the ship; R s Indicates the distance from the actual position of the ship to the preset route; x s The horizontal coordinate representing the actual position of the ship; y s The ordinate indicating the actual position of the ship; y irepresents the ordinate of the i-th preset turning point; x i represents the horizontal coordinate of the i-th preset turning point; i represents the serial number of the preset turning point; θ s Indicates the angle between the preset route corresponding to the ship's position and true north;

[0040] In order to ensure that equation (2) is valid, the ship's path tracking error y se Should always be less than the distance R from the ship's actual position to the preset route s Therefore, we make the following restriction: Let R s The value of the path tracking error y se and ship length L s The composition is as follows:

[0041] R s =c1|y se |+c2L s (3)

[0042] Where: c1 and c2 are both positive parameters; y se represents the path tracking error of the ship; L s Indicates the length of the ship; |y se | means y se The absolute value of

[0043] S22: According to the actual position of the ship, obtain the predicted value of the nth prediction step:

[0044] Specifically, the turning angle ψ of the starting point of the ship turning in this embodiment is los It is expressed as:

[0045]

[0046] Formula (4) is the traditional LOS guidance. When the ship reaches the turning point, due to its large inertia and time delay, overshoot will occur. Therefore, in order to avoid overshoot in the path following process and reduce the overshoot of the ship at the turning point, the path tracking error y of the ship in formula (4) is se The processing steps are as follows: First, add the prediction of the future path:

[0047]

[0048] Where: represents the predicted value of the nth prediction step, that is, the predicted value at time t+n; n represents the total number of prediction steps; t represents time; y s (t) represents the predicted position of the ship at time t; T1 represents the predicted sampling time; Indicates y s(t) is the discretization result; i represents the order number of the prediction step; k represents the discretization step size;

[0049] S23: According to the predicted value of the nth prediction step, the predicted tracking error of the nth prediction step is constructed as follows:

[0050]

[0051] In the formula, represents the prediction tracking error of the nth prediction step, that is, the prediction tracking error at time t+n; y sd (t+n) represents the expected position of the ship at time t+n;

[0052] Specifically, the total prediction tracking error is composed of the ship path tracking error at the current moment and the ship path tracking errors of n prediction steps.

[0053] S24: Obtaining a total prediction tracking error according to the prediction tracking error of the nth prediction step;

[0054]

[0055] Where: y sE represents the total prediction tracking error; P is the weight of the ship path tracking error at the current moment; Q is the weight of the ship path tracking error of n prediction steps; Indicates the ship path tracking error at the current moment; Represents the forecast tracking error at time t+n;

[0056] S25: According to the actual position P(x s ,y s ) and the starting point position P of the ship turning los (x los ,y los ) and the total predicted tracking error, and based on the improved LOS guidance law, the target heading of the ship is obtained:

[0057]

[0058] Specifically, for the coordinated control of the ship and the aircraft path, the ship position information x s ,y s Transmitted to the UAV in real time as the UAV's tracking and control target.

[0059] Then, the ship heading error is expressed as follows:

[0060] ψ se =ψ s -ψ sd (9)

[0061] In the formula, ψ se represents the ship heading error, ψ s Indicates the actual heading of the ship, ψ sd Indicates the target heading of the ship.

[0062] S3, according to the ship heading error ψ se , obtain the ship's rudder angle, and obtain the ship's actual heading ψ according to the ship-engine cooperative control mathematical model s ;

[0063] Specifically, in this embodiment, according to the ship heading error, a heading tracking controller is designed based on a sliding mode control algorithm, a radial basis function neural network, and a speed observer to obtain the ship rudder angle, and the rudder angle and the ship speed are transmitted to a ship-machine collaborative mathematical model to obtain the ship's actual position and the ship's actual heading information, and the ship's actual position and the ship's actual heading information are updated to the UAV as the UAV position tracking control target;

[0064] Preferably, the ship rudder angle is obtained as follows:

[0065] Design the sliding surface based on nonlinear sliding mode and hyperbolic tangent function:

[0066]

[0067] Where c3 and c4 are positive parameters. Derivative of equation (10) with respect to time:

[0068]

[0069] Substitute equation (1) and equation (10) into equation (11):

[0070]

[0071] To ensure the stability of the sliding surface:

[0072]

[0073] Where: s represents the sliding surface; ψ se represents the ship heading error; c3 represents the hyperbolic tangent function gain; c4 represents the gain of the ship heading error; T represents the ship turning hysteresis index; K represents the ship turning ability index; f represents all unidentified disturbances affecting the system; ψ sd Indicates the target heading of the ship; r s Indicates the ship's turning angular velocity; δ s represents the controller output rudder angle, i.e., the ship rudder angle; c5 represents the sliding surface gain;

[0074] Substituting equation (12) into equation (13), the controller of the underactuated ship is finally constructed, and the rudder angle of the ship is obtained as:

[0075]

[0076] In the formula, δ s is the controller output rudder angle, i.e. the ship rudder angle; is the observation error of f; f represents all unidentified disturbances affecting the system; Yes s observation error; T represents the ship turning lag index; K represents the ship turning ability index; c3 represents the hyperbolic tangent function gain; c4 represents the gain of the ship heading error; c5 represents the sliding surface gain; ψ sd Indicates the target heading of the ship; r s Indicates the ship's turning angular velocity; ψ se Indicates the ship heading error.

[0077] Specifically, the ship's rudder angle is transmitted to the ship-engine collaborative mathematical model, so that the actual heading of the ship ψ can be obtained s .

[0078] S4: According to the actual heading of the ship ψ s , obtaining an estimated value of the longitudinal velocity of the ship and an estimated value representing the lateral velocity of the ship, so as to obtain the actual position coordinates of the ship according to the motor-ship cooperative control mathematical model;

[0079] Specifically, since all unidentified disturbances f affecting the system are difficult to observe directly, this embodiment estimates the disturbances according to the radial basis function neural network to obtain the observation error of f Radial basis function neural networks show powerful performance in dealing with nonlinear problems.

[0080]

[0081] In radial basis function neural networks: W s represents the ideal value of the weight; h(x) is the output of the radial basis function neural network; x is the input of the radial basis function neural network; κ represents the approximate error of the neural network, and its value can be constrained to be small enough; h j is the output of the jth Gaussian function in the hidden layer of the radial basis function neural network; c j b represents the coordinate vector of the Gaussian function; j represents the width vector; ||xc j || represents the distance between the neural network input and the coordinate vector of the Gaussian function;

[0082] Among them, the estimated value of f is as follows:

[0083]

[0084] in, It is W s The estimated value of .

[0085] In order to improve the performance of the radial basis function neural network approximation and improve the accuracy of the neural network, the inverse tangent function is used to update the weight values ​​because the inverse tangent function is strictly bounded and exhibits appropriate amplitude changes within its domain.

[0086]

[0087] In formula (17), s is the sliding surface, c6 and c7 are, where c6 is a positive parameter for coordinate compression to prevent excessive weight values ​​caused by large values ​​of s; c7 is a positive parameter for amplification of s to ensure that the weight value still shows a significant response even when s is relatively small, and τ is a weight performance adjustment parameter, which improves the approximation accuracy of the radial basis function neural network.

[0088] In the actual navigation process of the underactuated ship, the speed is not easy to obtain, so the speed is estimated by a nonlinear state observer. In this embodiment, the nonlinear state observer is used to estimate the speed variable u of the underactuated ship. s 、v s and r s .

[0089]

[0090] Among them, P s Represents the observed matrix, represented by x s ,y s , ψ s constitute; Indicates P s The estimated value of Indicates P s The estimated value of the derivative, Indicates P s The derivative of the derivative estimate; k p1 and k p2 are observer parameters that limit the magnitude of observation errors; ψ s Indicates the actual heading of the ship; x s Indicates the coordinate of the ship on the X-axis of the geodetic coordinate system; s Indicates the coordinate of the ship on the Y axis of the geodetic coordinate system;

[0091] And formula (18) can get P s The estimated value of and The estimated value of the speed is finally obtained according to formula (19):

[0092]

[0093] In formula (19), Indicated Estimated value, Indicated Estimated value; represents the longitudinal speed of the ship u s The estimated value of represents the ship's lateral velocity v s The estimated value of .

[0094] Specifically, the estimated speed value obtained by equation (19) is substituted into the ship-engine collaborative mathematical model, and the actual position coordinates of the ship (x s ,y s ).

[0095] S5, according to the actual position coordinates (x s ,y s ), establish the drone position controller;

[0096] Preferably, the drone position control controller is established as follows:

[0097] First, the ship position (x s ,y s ) as the control target of the lateral position and longitudinal position of the UAV, that is, x ad =x s ,y ad =y s , and set the drone height control target z according to actual needs ad ; Secondly, according to the actual position and altitude information of the drone (x a ,y a ,z a ). Calculate the drone position and height error e ax ,e ay ,e az , where e ax =x a -x ad represents the lateral position error of the UAV, e ay =y a -y ad represents the longitudinal position error of the UAV, e az =z a -z ad Represents the height error of the drone. Finally, the PD controller is designed based on the obtained drone position error and height error. In the design process of the system control law and control parameters, the Hurwitz criterion of the secondary system is used to ensure the stability of the system. The details are as follows:

[0098] make:

[0099]

[0100] Where U 1x represents the transverse component of U1, U 1y represents the longitudinal component of U1, U 1z represents the vertical component of U1; U1 represents the lift of the UAV; θ a Indicates the pitch angle of the drone; Indicates the rolling angle of the drone; ψ a Indicates the heading angle of the drone;

[0101] According to the mathematical model of aircraft-ship cooperative control considering environmental interference, by substituting equation (1) into equation (20), the UAV position state model can be obtained:

[0102]

[0103] Where: x a Indicates the coordinate of the actual position of the drone on the x-axis of the geodetic coordinate system; y a Indicates the coordinate of the actual position of the drone on the y-axis of the geodetic coordinate system; z a Indicates the coordinate of the actual position of the drone on the z-axis of the geodetic coordinate system;

[0104] According to the Hurwitz criterion, the PD control method is used to design the lateral position control rate, and the UAV position controller can be obtained:

[0105]

[0106] e ax =x a -x ad

[0107] e ay =y a -y ad

[0108] e az =z a -z ad

[0109] x ad =x s

[0110] y ad =y s

[0111] In the formula, e ax represents the lateral error of the UAV; k px represents the proportional term coefficient of the UAV lateral position controller, k dxrepresents the integral coefficient of the UAV lateral position controller, e ay represents the longitudinal error of the UAV, e az represents the altitude error of the UAV, k py represents the proportional term coefficient of the UAV longitudinal position controller, k dy k represents the integral coefficient of the UAV longitudinal position controller. pz Represents the integral coefficient of the UAV altitude controller, k dz Represents the integral coefficient of the UAV altitude controller. ad The control target representing the lateral position of the UAV; y ad The control target represents the longitudinal position of the UAV; z ad Indicates the altitude control target of the drone; g indicates the acceleration of gravity;

[0112] Specifically, the output of the UAV lateral position controller, the output of the longitudinal position controller, and the output of the altitude controller are transmitted to the aircraft-ship cooperative control mathematical model considering environmental interference, and the actual lateral position x of the UAV can be obtained. a , current vertical position y a , current height z a , forming a control closed loop.

[0113] S6: Establish a differentiator to obtain the pitch control target and roll control target of the UAV, and establish a UAV attitude controller based on the pitch control target and roll control target; then, according to the UAV position controller, realize the tracking control of the aircraft-ship collaborative path.

[0114] According to the actual position coordinates of the ship and the actual heading of the ship, the UAV position and heading tracking control targets are set, and the UAV flight altitude is set, and the UAV pitch angle control target and roll angle control target are calculated. The PD algorithm is used to design the UAV position controller and UAV attitude controller under environmental interference, and the UAV roll attitude control target is solved by establishing a differential controller. and the pitch control target θ ad The continuity problem in the solving process is solved, and finally the attitude control of the UAV and the tracking of the ship's position are realized to achieve coordinated control of the UAV and the ship.

[0115] Specifically, the ship status information (x s ,,y s and ψ s ) as the position and heading tracking control target of the UAV, and set the UAV attitude pitch angle θ ad , Roll Angle and the drone height z adThe control target is to design the position and attitude controllers of the UAV respectively through the PD algorithm, and solve the problem that the attitude angle is discontinuous and cannot be derived by designing a differentiator. The output of the UAV controller is transmitted to the mathematical model of aircraft-ship cooperative control to obtain the actual position of the UAV (horizontal coordinate x a , vertical coordinate y a , height coordinate z a ) and attitude information (pitch angle θ a , Roll Angle and heading angle ψ a ), and finally realize the collaborative path tracking of aircraft and ship.

[0116] Specifically, in this embodiment, the quadcopter UAV control system is designed to consist of two levels of control loops: an outer loop and an inner loop. The outer loop is responsible for the position control of the UAV. The inner loop is used to control the attitude of the UAV. The entire control system adopts a dual-loop control strategy to design the flight control law, and designs the UAV position controller and the UAV attitude controller separately.

[0117] Preferably, the drone attitude controller is established as follows:

[0118] Due to the under-actuated characteristics of the quadrotor UAV control system, the position control target (x ad ,y ad ,z ad ) and heading control target can be obtained directly, but the attitude control target θ ad and Therefore, the pitch control target and roll control target will be calculated according to the characteristics of the quadrotor drone, that is, θ ad and Pitch control target θ ad and roll attitude control targets The calculation process is as follows:

[0119] According to the mathematical model of aircraft-ship cooperative control considering environmental interference, we can obtain: multiply the left and right sides of the sixth and seventh equations in equation (1) by sinψ a and cosψ a , and then add them together:

[0120]

[0121] Therefore, in formula (23), the two formulas are subtracted to obtain the variable

[0122]

[0123] Similarly, multiply the sixth and seventh equations in equation (1) by cosψa and sinψ a , and then add them together:

[0124]

[0125] Therefore, in equation (25), adding the two equations gives the variable θ ad :

[0126]

[0127] Where: x a The coordinate of the actual position of the drone on the x-axis of the earth coordinate system; a The coordinate of the actual position of the UAV on the y-axis of the geodetic coordinate system; ψ a Indicates the heading angle of the drone; θ ad Indicates the pitch control target; Indicates the rolling angle of the drone; m a Indicates the mass of the drone; K1 and K2 are the coefficients of air resistance; Indicates the roll control target;

[0128] Specifically, the actual heading ψ of the ship obtained in S3 is s As the current heading control target of the UAV, that is, ψ ad =ψ s The UAV roll angle control target is the result of formula (24) The UAV pitch angle control target is the result of equation (26) θ ad .

[0129] In formula (1), the actual attitude information of the UAV can be obtained Calculate the drone attitude error e aψ ,e aθ , e aψ =ψ a -ψ ad Represents the heading angle error of the UAV, e aθ =θ a -θ ad Indicates the pitch angle error of the drone, Indicates the roll angle error of the drone.

[0130] In order to simplify the controller design, the UAV attitude equation can be obtained by simplifying equation (1) according to the mathematical model of aircraft-ship cooperative control considering environmental interference as follows:

[0131]

[0132] Where: I YIndicates the moment of inertia of the drone on the Y axis of the drone coordinate system; I Z Indicates the moment of inertia of the drone on the Z axis of the drone coordinate system; I X represents the rotational inertia of the drone in the Z axis of the drone coordinate system; U2 represents the yaw moment of the drone; U3 represents the pitch moment of the drone; U4 represents the roll moment of the drone; l a Indicates the wheelbase of the drone; K4 and K5 are the coefficients of the gyro effect on the Euler angle; θ a Indicates the pitch angle of the drone; Indicates the rolling angle of the drone; ψ a Indicates the heading angle of the drone;

[0133] Then, according to the Hurwitz criterion, the UAV attitude controller is obtained as follows:

[0134]

[0135] Among them, e aψ =ψ a -ψ ad

[0136] e aθ =θ a -θ ad

[0137]

[0138] In the formula, U2 represents the yaw moment of the UAV; U3 represents the pitch moment of the UAV; U4 represents the rolling moment of the UAV; k pψ Represents the proportional term coefficient of the unmanned heading attitude controller, k pθ represents the proportional term coefficient of the unmanned pitch attitude controller, represents the proportional term coefficient of the unmanned roll attitude controller, k dψ represents the differential coefficient of the unmanned heading attitude controller, k dθ is the differential coefficient of the unmanned pitch attitude controller, Represents the proportional term coefficient of the unmanned roll attitude controller: e aψ Represents the heading angle error of the UAV; e aθ Indicates the pitch angle error of the drone; Represents the UAV roll angle error; θ ad Indicates the pitch control target; represents the rolling control target; ψ ad Indicates the heading control target;

[0139] Specifically, due to the rolling attitude control target of the UAV and the pitch control target θ adThe calculation process of involves the calculation of function derivatives, which usually requires the function to be continuous. The problem of function continuity can be solved by designing a differentiator. The differentiator can solve the problem of the derivative of discontinuous functions. By designing a differentiator, it is possible to avoid the requirement of the desired and θ ad Therefore, in order to improve the control accuracy of the system, this embodiment is designed to track and θ ad , while achieving as well as Calculation of value.

[0140] Preferably, the differentiator is established as follows:

[0141]

[0142] Where: x 1θ ,x 2θ ,x 3θ Respectively represent the ad , and The estimated value of θ is the pitch angle differentiator design parameter; v θ (t) represents the desired pitch angle θ of the drone ad The input signal to be differentiated; and Respectively express and The estimated value of . is the roll angle differentiator design parameter; Indicates the desired roll angle of the drone The input signal to be differentiated; y 1θ represents the desired pitch angular velocity output by the differentiator; y 2θ represents the desired pitch acceleration output by the differentiator; represents the desired roll angular velocity output by the differentiator; represents the desired roll angular acceleration output by the differentiator;

[0143] Specifically, the output of the drone position controller and the output of the drone attitude controller are transmitted to the aircraft-ship collaborative control mathematical model of formula (1), and the actual position and attitude of the drone can be obtained to form a control closed loop. The drone can track the position of the ship and control the attitude of the drone at the same time. Finally, the aircraft-ship collaborative path tracking is achieved.

[0144] This embodiment aims to achieve the coordinated path tracking of the motor-ship and reduce the overshoot at the turning point while taking into account the environmental interference as much as possible. The main technical problems to be solved are as follows: In view of the overshoot problem of the motor-ship cooperative system at the turning point under environmental interference, this embodiment proposes a LOS guidance algorithm based on position prediction. First, a mathematical model of the motor-ship cooperative path tracking is established, a reference path is designed, and the ship control target is obtained. By designing a LOS guidance law based on position prediction to reduce the overshoot at the turning point, the advantage of this method is that it can construct a total path error based on the position predicted at the next moment at the current moment, thereby reducing the path tracking error of the motor-ship cooperative path tracking. The heading tracking error is obtained according to the difference between the actual heading angle of the ship and the set heading angle. According to the ship heading tracking error, a heading tracking controller is designed based on sliding mode control and radial basis function neural network to obtain the ship rudder angle, and the rudder angle is transmitted to the motor-ship cooperative mathematical model to obtain the ship status information, and the ship status information is updated to the UAV as the UAV position tracking control target. The ship status information is used as the UAV position tracking control target, and the UAV attitude control target is set at the same time. The PD control rate is designed to realize the UAV position control and UAV attitude control. The discontinuous function differentiation problem in the UAV components is solved by designing a differentiator. The UAV controller output is transmitted to the ship-machine cooperative control mathematical model to obtain the actual position and attitude information of the UAV, and finally the ship-machine cooperative path tracking is realized. This has certain reference significance for the ship-machine cooperative path tracking control.

[0145] Specifically, a specific embodiment of the present invention is as follows:

[0146] For the design of the UAV controller, in the design process of the control law, its parameters are set using the Hurwitz criterion, so the UAV control system must be stable. Therefore, in this embodiment, only the stability of the position prediction LOS algorithm, radial basis function neural network, underactuated ship control algorithm and differentiator is analyzed.

[0147] According to the point-line distance formula, the path error of the underactuated ship can be calculated as follows:

[0148]

[0149] Where A, B and C are the parameters of the straight line expression of the reference path; x s and s is the position coordinate of the underactuated ship, θ s It represents the angle between the preset route corresponding to the ship's position and due north. The Lyapunov function is designed as follows:

[0150]

[0151] In the formula, V represents the designed Yapunov function, y serepresents the path tracking error of the ship, s represents the sliding surface, ξ and λ both represent positive parameters, and W s represents the ideal value of the weight, It is W s The estimated value, P s Represents the observed matrix, represented by x s ,y s , ψ s constitute; Indicates P s The estimated value of Indicates P s The estimated value of the derivative, Indicates P s The derivative of

[0152] Derivative (31) with respect to time and substitute (12), (18), (30) into (31):

[0153]

[0154] Where U s Indicates the ship's ground speed, Obviously, Since the inverse tangent function is strictly bounded, a suitable positive parameter c6<2 / π is chosen so that s-c6arctan(c7s) and s have the same sign. When s>0, s-c6arctan(c7s)>0 Will increase to Therefore w<0; when s<0, s-c6arctan(c7s)<0 at the same time will be reduced to Therefore w<0. μs is greater than c5s 2 , much smaller, so μs-c5s 2 < 0. Since the hyperbolic tangent function is strictly bounded, choosing an appropriate positive parameter k p2 <1 / μ can make d2 and Same number; that is, When d2>0. In formula (32), the derivative of the term is less than zero, then will decrease until d1<0, i.e. d1d2<0. Similarly, d3 is bounded and is an order of magnitude smaller, so there exists a real number ε such that d1d2+d3<ε. At this point, the derivative of V can be re-expressed as:

[0155]

[0156] Therefore, when The system is globally asymptotically stable.

[0157] when When , the system error is constrained to be less than , and the system is stable in the Lyapunov sense. p1 represents the observer parameter that limits the observation error amplitude; ε represents the real number that makes d1d2+d3<ε; μ, w, ​​d1, d2, d3 all represent intermediate calculation parameters; k p1 represents the observer parameter that limits the magnitude of the observation error;

[0158] Table 1 Main parameters of the ship

[0159]

[0160] Table 2 Main parameters of UAV

[0161]

[0162]

[0163] In order to verify the effectiveness of the algorithm designed in this invention, a simulation experiment was carried out using Matlab. The specific parameters of the ship are shown in Table 1. The model in formula (1) is used as the ship simulation model. Table 2 shows the specific parameters of the drone. Regarding environmental interference, the wind force (torque) X in formula (1) sw , Y sw and N sw As shown below:

[0164]

[0165] In formula (35), ρ a is the air density, α W is the relative wind direction angle, U R is the relative wind speed, L s is the length of the underdriven vessel, F s and S s fenb is the front and side projection area above the waterline of the underdriven ship, C sx , C sy and C sn are wind pressure (torque) coefficients respectively. In this embodiment, the second-order wave force and moment are regarded as wave disturbances, as shown below:

[0166]

[0167] In formula (36), X wave Indicates the longitudinal wave interference of the ship; Y wave Indicates the lateral wave interference of the ship; N wave Represents the moment of the ship receiving wave disturbance; ρ srepresents the density of seawater; α represents the wavelength of the waves; χ represents the amplitude of the waves; λ w are the density of seawater; C sx ,C sy and C sn Indicates the longitudinal and lateral encounter angles and drift force (torque) coefficients;

[0168] The interference of wind interference to drones is expressed as follows:

[0169]

[0170] In formula (37), t represents the simulation time. Z Indicates the effect of wind disturbance on the height of the drone, represents the effect of wind disturbance on the roll angle of the UAV, D θ The influence of wind disturbance on the pitch angle of the UAV and D ψ Indicates the impact of wind interference on the heading of the drone. η represents the wind interference gain; l1 represents the wind interference longitudinal adjustment coefficient; l2 represents the wind interference roll angle adjustment coefficient; l3 represents the wind interference pitch adjustment coefficient; l4 represents the wind interference heading adjustment coefficient;

[0171] The controller parameters are set as follows: The parameters of the predicted LOS guidance law are: c1 = 2, c2 = 2.5 in equation (3), Q = 0.1, P = 1 in equation (7). In the ship controller part: In equation (11), the parameters are c3 = 0.21, c4 = 0.2; in equation (13), c5 = 0.2; in equation (17), c6 = 0.05, c7 = 50; in equation (15), b = 0.21, c = [-1, -0.5, 0, 0.5, 1; -1, -0.5, 0, 0.5, 1]. In equation (18), the parameters of the speed observer are k p1 =0.9,k p2 =0.75. In equation (1), the air resistance parameters are set as K1=K2=K3=0.01, K4=K5=K6=0.012. The controller parameters of the UAV controller are set as follows: The controller parameters of the UAV position control law are k in equation (22) px =k dx =5k py =k dy =k pz =k dz =5. The controller parameters of the UAV attitude control law are shown in equation (28): pψ =k dψ =15, In formula (37), the wind disturbance of the UAV is set to η = 0.6, [l1, l2, l3, l4] = [0.7, 0.5, 0.6, 0.8]. Simulation comparison tests are carried out for different prediction steps of LOS position prediction. The path following simulation results are shown in Figure 5 and Figure 6 shown.

[0172] Specifically, the simulation results are as follows: Figure 4-Figure 11 As shown, Figure 4-6 The collaborative path following effect of the machine-ship system is demonstrated. Figure 4 and Figure 5 The 3D graphs of the coordinated path following of the vehicle-ship control and the projection of the drone to X are given for different prediction values ​​(np). E O E Y E 2D path following plot on a plane. In the figure, three different prediction values, namely np=40, 30 and 20, are depicted. Figure 5 It shows that when the prediction value np = 40, the cooperative path following accuracy of the vehicle-ship system is the highest and the overshoot is the smallest. Figure 6 a and Figure 6 b shows the absolute values ​​of the path following errors of the ship and the UAV over time. se represents the following error of the underactuated ship, y ae Indicates the following error of the drone. When the predicted value is 40, the path error y se and ae is minimized, and the overshoot of the cooperative path following of the aircraft-ship system is reduced. However, increasing the prediction value will increase the system calculation time. In addition, after reaching a certain value, further increasing the prediction value will not significantly affect the path following performance. Therefore, a larger prediction value is not optimal for improving control performance.

[0173] Figure 7-9 is the state of the ship during the simulation, when the radius of the circle changes with the steering angle. Figure 7 The curves showing the changes of the ship's rudder angle and heading over time are shown. Figure 8 The results of the nonlinear state observer designed by the present invention and the estimated value are shown. It can be seen that the difference between the observed value and the actual value is very small, indicating that the nonlinear observer designed by the present invention has high accuracy in path following control.

[0174] Fig.10 shows the RBF neural network's estimate of the total disturbance. Fig.10It can be seen that the estimated value has a satisfactory overall following effect on the actual disturbance value f, and there is an estimation error at the sudden change of the disturbance f, but it is within an acceptable range. Fig.10 The neural network weights W are shown i Curve over time.

[0175] Fig.11 They respectively represent the changes of the drone's state over time. Fig.11 (a), (b), and (c) show that the UAV is able to track the location information of the ship and its height is stable at the set value. Fig.11 (d), (e), and (f) show that the roll angle is usually stable at 0° and the fluctuation is within an acceptable range, the pitch angle is stable at 0°, and the heading angle tracks the heading angle of the ship.

[0176] The present embodiment proposes a vehicle-vessel collaborative path tracking control method that takes into account marine environmental disturbances, which can be used in marine research. The proposed vehicle-vessel collaborative control method can realize vehicle-vessel collaborative path following control:

[0177] 1. Combining the robustness of the ship controller with the UAV PD control can control the ship-machine collaborative system to accurately track the reference path in the presence of time-varying disturbances (such as wind and waves).

[0178] 2. The proposed predicted LOS algorithm can reduce the overshoot of the vehicle-ship cooperative path at the turning point.

[0179] 3. The proposed high-order differentiator can accurately obtain the differential signal and solve the problem of discontinuous differentiation.

[0180] 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for aircraft-ship collaborative path tracking based on predicted LOS guidance law, characterized in that: The steps include: S1: Establish a mathematical model for cooperative control of aircraft and ship considering environmental interference; S2. According to the actual position of the ship, an improved LOS guidance law for predicting the path tracking error is established to obtain the target heading of the ship according to the position of the preset turning point, and then obtain the heading error of the ship according to the actual heading of the ship; S3, obtaining the ship's rudder angle according to the ship's heading error, and obtaining the ship's actual heading according to the ship-engine cooperative control mathematical model; S4: obtaining an estimated value of the longitudinal speed of the ship and an estimated value representing the lateral speed of the ship according to the actual heading of the ship, so as to obtain the actual position coordinates of the ship according to the motor-ship cooperative control mathematical model; S5. Establishing a UAV position control controller according to the actual position coordinates of the ship; S6: Establish a differentiator to obtain the pitch control target and roll control target of the UAV, and establish a UAV attitude controller based on the pitch control target and roll control target; then, based on the UAV attitude controller and the UAV position controller, realize the tracking control of the aircraft-ship collaborative path.

2. The method for aircraft-vessel collaborative path tracking based on predicted LOS guidance law according to claim 1 is characterized in that: The method for obtaining the ship heading error is as follows: S21: Get the actual position P(x s ,y s ) and the starting point position P of the ship turning los (x los ,y los ) are as follows: Where: x los Indicates the horizontal coordinate of the starting point of the ship's turn; y los Indicates the ordinate of the starting point of the actual turning of the ship; R s Indicates the distance from the actual position of the ship to the preset route; x s The horizontal coordinate representing the actual position of the ship; y s The ordinate indicating the actual position of the ship; y i represents the ordinate of the i-th preset turning point; x i represents the horizontal coordinate of the i-th preset turning point; i represents the serial number of the preset turning point; θ s Indicates the angle between the preset route corresponding to the ship's position and true north; in, R s =c1|y se |+c2L s Where: c1 and c2 are both positive parameters; y se represents the path tracking error of the ship; L s Indicates the length of the ship; |y se | means y se The absolute value of S22: According to the actual position of the ship, obtain the predicted value of the nth prediction step: Where: represents the predicted value of the nth prediction step, that is, the predicted value at time t+n; n represents the total number of prediction steps; t represents time; y s (t) represents the predicted position of the ship at time t; T1 represents the predicted sampling time; Indicates y s (t) is the discretization result; i represents the order number of the prediction step; k represents the discretization step size; S23: According to the predicted value of the nth prediction step, the predicted tracking error of the nth prediction step is constructed as follows: In the formula, represents the prediction tracking error of the nth prediction step, that is, the prediction tracking error at time t+n; y sd (t+n) represents the expected position of the ship at time t+n; S24: Obtaining a total prediction tracking error according to the prediction tracking error of the nth prediction step; Where: y sE represents the total prediction tracking error; P is the weight of the ship path tracking error at the current moment; Q is the weight of the ship path tracking error of n prediction steps; Indicates the ship path tracking error at the current moment; Represents the forecast tracking error at time t+n; S25: According to the actual position P(x s ,y s ), the starting point position P of the ship's turning los (x los ,y los ) and the total predicted tracking error, the target heading of the ship is obtained: ψ sd =arctan2(y los -y s , x los -x s ) =θ s +arcsin(y sE / R s ) Then, the ship heading error is expressed as follows: ψ se =ψ s -ψ sd In the formula, ψ se represents the ship heading error, ψ s Indicates the actual heading of the ship, ψ sd Indicates the target heading of the ship.

3. The method for aircraft-vessel collaborative path tracking based on predicted LOS guidance law according to claim 1, characterized in that: The mathematical model of the aircraft-vessel cooperative control is established as follows: Where: x s The coordinate of the actual position of the ship on the X-axis of the geodetic coordinate system; s Indicates the coordinate of the actual position of the ship on the Y axis of the geodetic coordinate system; ψ s Indicates the actual heading of the ship; u s Indicates the longitudinal speed of the ship; v s Indicates the transverse speed of the ship; r s represents the angular velocity of the ship; T represents the ship turning hysteresis index; K represents the ship turning ability index; δ s represents the rudder angle; f represents all unidentified disturbances affecting the system; f n is the inherent uncertainty parameter of the system internal model; f m represents external disturbance; x a The coordinate of the actual position of the drone on the x-axis of the earth coordinate system; a The coordinate of the actual position of the drone on the y-axis of the geodetic coordinate system; z a The coordinate of the actual position of the drone on the z-axis of the earth coordinate system; θ a Indicates the pitch angle of the drone; Indicates the rolling angle of the drone; ψ a Indicates the heading angle of the drone; m a represents the mass of the drone; g represents the acceleration due to gravity; I Y Indicates the moment of inertia of the drone on the Y axis of the drone coordinate system; I Z Indicates the moment of inertia of the drone on the Z axis of the drone coordinate system; I X Indicates the moment of inertia of the drone on the Z axis of the drone coordinate system; J r Represents the UAV rotor inertia; Ω r Indicates the total rotor speed of the drone; l a represents the wheelbase of the drone; b represents the lift coefficient; d a represents the drag coefficient; Ω1, Ω2, Ω3, Ω4 represent the rotation speed of the rotors on the UAV rotor motors 1 to 4 respectively; F1, F2, F3, F4 represent the forces generated by the UAV rotor motors; U1 represents the lift of the UAV; U2 represents the yaw moment of the UAV; U3 represents the pitch moment of the UAV; U4 represents the rolling moment of the UAV; K1, K2, and K3 are the coefficients of air resistance; K4, K5, and K6 are the coefficients of gyroscopic effect on Euler angles represents the first-order derivative; represents the second-order derivative.

4. The method for aircraft-vessel collaborative path tracking based on predicted LOS guidance law according to claim 1, characterized in that: The ship rudder angle is obtained as follows: Where: s is the controller output rudder angle, i.e. the ship rudder angle; is the observation error of f; f represents all unidentified disturbances affecting the system; Yes s observation error; T represents the ship turning lag index; K represents the ship turning ability index; c3 represents the hyperbolic tangent function gain; c4 represents the gain of the ship heading error; c5 represents the sliding surface gain; ψ sd Indicates the target heading of the ship; r s Indicates the ship's turning angular velocity; ψ se Indicates the ship heading error.

5. The method for aircraft-vessel collaborative path tracking based on predicted LOS guidance law according to claim 1, characterized in that: The estimated value of the longitudinal velocity of the ship and the estimated value representing the transverse velocity of the ship are obtained as follows: Where: Indicated Estimated value, Indicated Estimated value; represents the longitudinal speed of the ship u s The estimated value of represents the ship's lateral velocity v s The estimated value of .

6. The method for aircraft-vessel collaborative path tracking based on predicted LOS guidance law according to claim 1, characterized in that: The method for establishing the drone position control controller is as follows: make: Where U 1x represents the transverse component of U1, U 1y represents the longitudinal component of U1, U 1z represents the vertical component of U1; U1 represents the lift of the UAV; θ a Indicates the pitch angle of the drone; Indicates the rolling angle of the drone; ψ a Indicates the heading angle of the drone; According to the mathematical model of aircraft-ship cooperative control considering environmental interference, the UAV position state model is obtained: Where: x a Indicates the coordinate of the actual position of the drone on the x-axis of the geodetic coordinate system; y a Indicates the coordinate of the actual position of the drone on the y-axis of the geodetic coordinate system; z a Indicates the coordinate of the actual position of the drone on the z-axis of the geodetic coordinate system; According to the Hurwitz criterion, the PD control method is used to obtain the UAV position controller as follows: e ax =x a -x ad and ay =and a -and ad yes az =with a -with ad x ad =x s and ad =and s In the formula, e ax represents the lateral error of the UAV; k px represents the proportional term coefficient of the UAV lateral position controller, k dx represents the integral coefficient of the UAV lateral position controller, e ay represents the longitudinal error of the UAV, e az represents the altitude error of the UAV, k py represents the proportional term coefficient of the UAV longitudinal position controller, k dy represents the integral coefficient of the UAV longitudinal position controller; k pz Represents the integral coefficient of the UAV altitude controller, k dz Represents the integral coefficient of the UAV altitude controller; x ad The control target representing the lateral position of the UAV; y ad The control target represents the longitudinal position of the UAV; z ad Indicates the altitude control target of the UAV; g indicates the acceleration of gravity.

7. The method for aircraft-vessel collaborative path tracking based on predicted LOS guidance law according to claim 1, characterized in that: The UAV attitude controller is established as follows: According to the mathematical model of ship-engine cooperative control considering environmental interference, we can get: then, Similarly, then, Where: x a The coordinate of the actual position of the drone on the x-axis of the earth coordinate system; a The coordinate of the actual position of the UAV on the y-axis of the geodetic coordinate system; ψ a Indicates the heading angle of the drone; θ ad Indicates the pitch control target; Indicates the rolling angle of the drone; m a Indicates the mass of the drone; K1 and K2 are the coefficients of air resistance; Indicates the roll control target; Simplifying the mathematical model of aircraft-ship cooperative control considering environmental interference, the attitude equation of the UAV is obtained as follows: Where: I Y Indicates the moment of inertia of the drone on the Y axis of the drone coordinate system; I Z Indicates the moment of inertia of the drone on the Z axis of the drone coordinate system; I X represents the rotational inertia of the drone in the Z axis of the drone coordinate system; U2 represents the yaw moment of the drone; U3 represents the pitch moment of the drone; U4 represents the roll moment of the drone; l a Indicates the wheelbase of the drone; K4 and K5 are the coefficients of the gyro effect on the Euler angle; θ a Indicates the pitch angle of the drone; Indicates the rolling angle of the drone; ψ a Indicates the heading angle of the drone; Then, according to the Hurwitz criterion, the UAV attitude controller is obtained as follows: among them, e aψ =ψ a -ψ ad e aθ =θ a -θ ad In the formula, U2 represents the yaw moment of the UAV; U3 represents the pitch moment of the UAV; U4 represents the rolling moment of the UAV; k pψ represents the proportional term coefficient of the unmanned heading attitude controller, k pθ represents the proportional term coefficient of the unmanned pitch attitude controller, represents the proportional term coefficient of the unmanned roll attitude controller, k dψ represents the differential coefficient of the unmanned heading attitude controller, k dθ is the differential coefficient of the unmanned pitch attitude controller, Represents the proportional term coefficient of the unmanned roll attitude controller: e aψ Represents the heading angle error of the UAV; e aθ Indicates the pitch angle error of the drone; Represents the UAV roll angle error; θ ad Indicates the pitch control target; represents the rolling control target; ψ ad Indicates the heading control target.

8. The method for aircraft-vessel collaborative path tracking based on predicted LOS guidance law according to claim 1, characterized in that: The differentiator is established as follows: Where: x 1θ ,x 2θ ,x 3θ Respectively represent the ad , and The estimated value of θ is the pitch angle differentiator design parameter; v θ (t) represents the desired pitch angle θ of the drone ad The input signal to be differentiated; and Respectively express and An estimated value of is the roll angle differentiator design parameter; Indicates the desired roll angle of the drone The input signal to be differentiated; y 1θ represents the desired pitch angular velocity output by the differentiator; y 2θ represents the desired pitch acceleration output by the differentiator; represents the desired roll angular velocity output by the differentiator; represents the desired roll acceleration at the differentiator output.

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