Event triggering cooperative control method and system for multi-quadrotor formation maneuvering flight

Abstract

The invention is suitable for the technical field of multi-unmanned aerial vehicle cooperative control, and provides a multi-quadrotor formation maneuvering flight event triggering cooperative control method and system, and the method comprises the steps: firstly building a kinetic model of a quadrotor unmanned aerial vehicle in a distributed formation system under an inertial coordinate system, designing a multi-quadrotor unmanned aerial vehicle cooperative formation synchronization error, and carrying out the cooperative formation of the quadrotor unmanned aerial vehicle; establishing a preset performance control function to constrain the formation synchronization error, and performing corresponding equivalent error transformation; designing a dynamic event trigger track subsystem controller; designing a robust attitude subsystem controller; and constructing a Lyapunov function, and verifying whether a closed-loop system is stable or not and whether an expected formation effect can be achieved or not. The method can effectively restrain the synchronization error of the multi-quadrotor unmanned aerial vehicle formation system, reduces the error overshoot, improves the coping capability for uncertainty, environmental interference and abrupt trajectory change, and guarantees the stable formation flight of the multi-quadrotor unmanned aerial vehicle system.

Description

Technical Field

[0001] The present invention belongs to the technical field of multi - UAV cooperative control, and particularly relates to an event - triggered cooperative control method and system for multi - quadrotor formation maneuvering flight. Background Technique

[0002] In recent years, quadrotor UAVs have been widely used in many fields, and their scope of use and frequency have shown a significant growth trend. From forest fire rescue to agricultural irrigation, from urban surveillance to military assistance, quadrotor UAVs provide strong support for various industries with their flexibility and efficiency. However, with the continuous expansion of the application scope, how to effectively control these UAVs, especially to make them operate stably in formation and successfully execute tasks, has become an important problem to be solved urgently.

[0003] The realization of quadrotor UAV formation control faces many difficulties. One of the main difficulties is the widespread existence of interference factors, which may come from complex environmental conditions, electromagnetic signal interference, and other unpredictable external factors. In addition, quadrotor UAVs themselves have strong non - linear coupling characteristics, which means that the motion between the position and attitude of quadrotor UAVs is interrelated and difficult to accurately predict. More complicatedly, the quadrotor UAV itself is an under - actuated system, and the relationship between its control input and motion state is not one - to - one, which makes precise control more difficult. The combined effect of these factors may cause the aircraft to deviate from the required formation pattern, thereby damaging the achievement of the mission objective. It is worth mentioning that when multiple quadrotor UAVs fly in formation, a particularly critical and challenging problem is to cope with maneuvering flight actions in the form of trajectory mutations. In this case, each UAV in the formation needs to make a quick response within a very short time to maintain the stability and consistency of the formation. Trajectory mutations may be caused by various factors, such as sudden obstacle avoidance requirements, sudden changes in target positions, or rapid changes in the external environment. In this situation, the cooperation between UAVs is crucial because any control error of a single UAV may lead to the chaos of the entire formation and even cause collision accidents. Therefore, developing and researching advanced control algorithms and technologies with the ability to cope with trajectory mutations in an interference environment is of great significance for improving the practicality and safety of multi - quadrotor UAV formations. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide an event - triggered cooperative control method for multi - quadrotor formation maneuvering flight, aiming to solve the problems proposed in the above background technique.

[0005] The embodiments of the present invention are implemented as follows. The event - triggered cooperative control method for multi - quadrotor formation maneuvering flight includes the following steps:

[0006] Establish the dynamic model of a quadrotor UAV in the inertial coordinate system in a distributed formation system, and design the synchronization error of multi-quadrotor UAV cooperative formation , establish a preset performance control function with the ability to handle sudden changes in the reference trajectory to constrain the formation synchronization error, and perform corresponding equivalent error transformation;

[0007] According to the dynamic model of the trajectory subsystem corresponding to each quadrotor UAV, and based on the equivalent error , command-filtered backstepping control technology, and neural network disturbance estimator, design the dynamic event-triggered trajectory subsystem controller for each quadrotor UAV in the formation system ;

[0008] According to the dynamic model of the attitude subsystem corresponding to each quadrotor UAV, and based on the command-filtered backstepping control technology and neural network disturbance estimator, design the robust attitude subsystem controller for each quadrotor UAV in the formation system ;

[0009] Construct a Lyapunov function , verify whether the closed-loop system is stable and whether the expected formation effect can be achieved.

[0010] Another object of the embodiments of the present invention is to provide an event-triggered cooperative control system for multi-quadrotor formation maneuvering flight, which is used to implement the event-triggered cooperative control method for the above-mentioned multi-quadrotor formation maneuvering flight, including:

[0011] A model construction unit, which is used to establish the dynamic model of a quadrotor UAV in the inertial coordinate system in a distributed formation system, design the synchronization error of multi-quadrotor UAV cooperative formation , establish a preset performance control function with the ability to handle sudden changes in the reference trajectory to constrain the formation synchronization error, and perform corresponding equivalent error transformation;

[0012] A trajectory controller design unit, which is used to design the dynamic event-triggered trajectory subsystem controller for each quadrotor UAV in the formation system according to the dynamic model of the trajectory subsystem corresponding to each quadrotor UAV, and based on the equivalent error , command-filtered backstepping control technology, and neural network disturbance estimator ;

[0013] An attitude controller design unit, which is used to design the robust attitude subsystem controller for each quadrotor UAV in the formation system according to the dynamic model of the attitude subsystem corresponding to each quadrotor UAV, and based on the command-filtered backstepping control technology and neural network disturbance estimator ;

[0014] A verification unit, which is used to construct a Lyapunov function , verify whether the closed-loop system is stable and whether the expected formation effect can be achieved.

[0015] The event-triggered cooperative control method for multi-quadrotor formation maneuvering flight provided by the embodiments of the present invention can effectively constrain the synchronization error of the multi-quadrotor UAV formation system, reduce the error overshoot, and at the same time improve the ability to cope with uncertainties, environmental disturbances, and trajectory mutations; Secondly, an event-triggered controller based on dynamic parameters is designed to achieve aperiodic control of the multi-quadrotor UAV formation. Different from the traditional fixed-parameter static event-triggered control method, it can dynamically adjust the time interval of event triggering according to the system operation state, which not only ensures the stability of the system, but also significantly reduces the communication traffic and computational load, thereby improving the efficiency and flexibility of formation control; Finally, to cope with the uncertainty problems during formation flight, a neural network disturbance estimator is introduced in the controller design process to estimate and compensate the disturbance online. At the same time, an attitude loop robust controller is constructed for each quadrotor UAV in the formation system to further enhance the ability of the formation system to cope with uncertainties, disturbances, and trajectory mutations, and ensure the stable formation flight of the multi-quadrotor UAV system. Description of the Drawings

[0016] Figure 1 is the flowchart of the event-triggered cooperative control method for multi-quadrotor formation maneuvering flight provided by the embodiments of the present invention;

[0017] Figure 2 is the schematic diagram of the distributed formation motion trajectory of multi-quadrotor UAVs in the inertial coordinate system provided by the embodiments of the present invention;

[0018] Figure 3 is the schematic diagram of the position response curve of each quadrotor UAV provided by the embodiments of the present invention;

[0019] Figure 4 is the schematic diagram of the formation synchronization error curve of each quadrotor UAV provided by the embodiments of the present invention;

[0020] Figure 5 is the schematic diagram of the control input and trigger time interval of the quadrotor UAV (i = 1) provided by the embodiments of the present invention;

[0021] Figure 6 is the schematic diagram of the control input and trigger time interval of the quadrotor UAV (i = 2) provided by the embodiments of the present invention;

[0022] Figure 7 is the schematic diagram of the control input and trigger time interval of the quadrotor UAV (i = 3) provided by the embodiments of the present invention;

[0023] Figure 8It is a structural block diagram of an event-triggered cooperative control system for multi-quadrotor formation maneuvering flight provided by an embodiment of the present invention. Detailed implementation manners

[0024] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0025] The following describes the specific implementation of the present invention in detail with reference to specific embodiments.

[0026] As Figure 1 shown, it is a flowchart of an event-triggered cooperative control method for multi-quadrotor formation maneuvering flight provided by an embodiment of the present invention, including the following steps:

[0027] S1. Establish a dynamic model characterizing the trajectory subsystem and attitude subsystem characteristics of a quadrotor UAV with external unknown disturbances in an inertial coordinate system, design the cooperative formation synchronization error of multi-quadrotor UAVs , establish a preset performance control function with the ability to cope with reference trajectory mutations to constrain the formation synchronization error, and perform corresponding equivalent error transformation, and then enter step S2;

[0028] In the specific design and implementation of the above step S1, for each quadrotor UAV in the formation system, the following operations are performed:

[0029] S11. Based on the number of quadrotor UAVs in the distributed formation , , establish a dynamic model of the follower quadrotor UAV in an inertial coordinate system for each quadrotor UAV;

[0030]

[0031] Among them, and respectively represent the position and Euler angles of the -th quadrotor in the inertial coordinate system and the body coordinate system; is the mass of the -th quadrotor UAV; and are aerodynamic damping matrices; is the strong coupling relationship existing between translational and rotational dynamics; , represents the gravitational acceleration; is the control input , , and is the thrust of the propeller of a quadrotor UAV; represents the control torque, , , , , where and respectively represent the distance from each propeller to the centroid of the quadrotor UAV and the moment coefficient; is a positive definite inertia matrix; and represent unknown bounded environmental disturbances.

[0032] The detailed model parameters of the quadrotor UAV are: , , , , , , ;

[0033] The dynamic model of the quadrotor UAV is arranged in the following form:

[0034]

[0035] where, , and respectively represent the velocity and angular velocity of the th quadrotor UAV in the inertial coordinate system and the body coordinate system; represents the virtual control input of the trajectory subsystem; and are the model perturbations caused by the inaccuracies of the aerodynamic damping matrix and ; , and respectively represent , and ;

[0036] S12, design the cooperative formation synchronization error of the th quadrotor UAV:

[0037]

[0038] where, is the cooperative formation synchronization error of the th quadrotor UAV, represents the set of real numbers, is the 3-dimensional set of real numbers; Represents the communication weight value between the th quadrotor and the th quadrotor; For the weight matrix between the quadrotor UAV and the virtual leader; Represents the position of the th quadrotor in the inertial coordinate system; Represents the expected position deviation between the th quadrotor UAV and the virtual leader, ; Is the preset virtual leader expected trajectory;

[0039] S13. Establish the following improved preset performance control function to make the cooperative formation synchronization error Satisfy :

[0040]

[0041]

[0042] Among them, , , , , , and Are design parameters, ; Is constructed by the following auxiliary function:

[0043]

[0044] Among them, And Represent the system state, , Is the initial state; , And Are design parameters; Represents the number of sampling times; Represents the sampling interval; Is the acceleration factor; Represents the time-optimal control synthesis function described as follows:

[0045]

[0046] S14. Perform equivalent error transformation on the cooperative formation synchronization error To obtain the transformed error variables as follows:

[0047]

[0048] Among them ;

[0049] S2. According to the dynamic models of the trajectory subsystems corresponding to each quadrotor UAV, and based on the equivalent error , command filtering backstepping control technology, and neural network disturbance estimator, design the dynamic event-triggered trajectory subsystem controllers for each quadrotor UAV in the formation system , which are used to control the trajectory subsystems of each quadrotor UAV respectively, so that each quadrotor UAV can form a stable formation configuration in the interference environment and track the expected trajectory of the preset virtual leader with trajectory mutations, and then enter step S3;

[0050] In the specific design and implementation of the above step S2, the following operations are carried out:

[0051] S21. Design the virtual controller of the position loop in the trajectory subsystem:

[0052]

[0053] Among them, represents the virtual control signal of the position loop of the th quadrotor UAV; the variable , , ; , where , , and represent positive constants; is the equivalent error vector; ; represents the speed of the th quadrotor UAV; is the th quadrotor UAV's degree; represents 's first derivative, is 's first derivative;

[0054] S22. Establish the dynamic event-triggered position loop controllers in the corresponding trajectory subsystems of each quadrotor in the formation system as follows:

[0055]

[0056]

[0057]

[0058] Among them, is the virtual control input of the trajectory subsystem, is the Euler angles of the th quadrotor UAV in the body coordinate system, represents the control input; is the measurement error, is the intermediate continuous control signal; ; , and are design parameters, represents the absolute value; , where , , and are design parameters; is the neural network weight, represents the estimated value of; is the activation function; is the input vector of the neural network disturbance estimator; is the velocity tracking error, represents the th quadrotor UAV's velocity in the inertial coordinate system, represents the output signal after passing through the command filter; is the norm of the matrix; and are constructed in the following form:

[0059]

[0060] where, , , and represent positive constants; is the initial value, satisfying ; satisfies ; , is a positive constant, represents the set of real numbers, and the design formulas of the command filter and the neural network disturbance estimator are as follows:

[0061]

[0062]

[0063] where, and represent the filter output, and the initial value of the filter is set to , ; , is a design parameter; denotes the estimated value of denotes the speed of the th quadrotor UAV, denotes the estimation error, ; is the neural network weight matrix, denotes the estimated value of is the input vector of the neural network disturbance estimator; is the gain matrix; is the adaptive gain matrix, denotes the correction parameter matrix.

[0064] S3. According to the attitude subsystem dynamic models corresponding to each quadrotor UAV, and based on the command filtering backstepping control technology and the neural network disturbance estimator, design the robust attitude subsystem controller for each quadrotor UAV in the formation system , which is used to control the stable operation of the quadrotor UAV attitude subsystem in the interference environment, and then enter step S4;

[0065] In the specific design and implementation of the above step S3, the following operations are performed:

[0066] S31. Perform an inverse transformation on the coupling relationship between the trajectory subsystem and the attitude subsystem of the quadrotor UAV to obtain the desired instruction of the attitude loop :

[0067]

[0068]

[0069] where, is the strong coupling relationship existing between translation and rotation dynamics, , where denotes the gravitational acceleration, denotes the th quadrotor UAV mass; denotes the control input, , , and are the thrusts of the quadrotor UAV propellers; is the yaw angle command, which is a freely adjustable variable and can be set by the ground station;

[0070] S32. Construct the Virtual Controller for the Attitude Angle Loop of a Quadrotor UAV :

[0071]

[0072] represents the attitude angle tracking error, represents the Euler angles of the th quadrotor; , where , , is a positive constant;

[0073] S33. Design the robust controller for the angular velocity loop, i.e., the actual control quantity is as follows:

[0074]

[0075] where, represents the actual control quantity, which is the control torque of the quadrotor UAV, , , , where and respectively represent the distance from each propeller to the center of mass of the quadrotor UAV and the moment coefficient, is a positive definite inertia matrix; , is a diagonal matrix with all elements being positive constants; is filtered signal; is the neural network weight, is estimated value; is the activation function; is the input vector of the neural network disturbance estimator; is the angular velocity tracking error, represents the th quadrotor UAV's angular velocity in the body coordinate system, is the output signal of the command filter; represents the Filippov solution of the following designed differential equation:

[0076]

[0077] where, is the robust feedback gain;

[0078] S34. The design formulas for the command filter and the neural network disturbance estimator are as follows:

[0079]

[0080]

[0081] Among them, , and are diagonal matrices with all elements being positive constants; , are design parameters; is 's estimated value, represents the estimation error;

[0082] S4. Construct a Lyapunov function , and use the Lyapunov stability theory to prove that the designed control method can achieve the stability of the closed-loop system and can achieve the expected formation effect at the same time;

[0083] In the specific design and implementation of the above step S3, the following operations are performed:

[0084] S41. Construct a Lyapunov function :

[0085]

[0086] Among them, is the equivalent error; represents the velocity tracking error; is the velocity state estimation error; and represent the weight estimation error, and are the ideal weight matrices; and are the adaptive gain matrices; represents the attitude angle tracking error, is the angular velocity tracking error; is the attitude angular velocity estimation error; and are the auxiliary function variables, is 's Filippov solution, is the neural network reconstruction error;

[0087] S42. Take the derivative of with respect to time and simplify to get:

[0088]

[0089] That is, the system is closed-loop stable, among which, , Correspond to the variables in the above formula in sequence The coefficient before Denote the minimum eigenvalue Denote the maximum eigenvalue; , And Is the upper bound of the filter filtering error And Is the upper bound of the neural network reconstruction error And Is the ideal weight matrix Is the design parameter And Is the correction parameter matrix;

[0090] Furthermore, it can be obtained that:

[0091]

[0092] That is, the system can achieve the expected formation effect, where , Denote the set of real numbers Is Dimensional set of real numbers; , Denote the Expected position deviation between the [[th]] quadrotor and the virtual leader Is Dimensional set of real numbers Is a 3-dimensional set of real numbers; , And Are the design parameters of the improved preset performance function; Is Upper bound of; Denote Minimum eigenvalue of Is the Laplacian matrix of the communication topology , , , Is Dimensional set of real numbers.

[0093] As Figure 8 Shown, it is the structural frame of the event-triggered cooperative control system for multi-quadrotor formation maneuvering flight provided by an embodiment of the present invention, including:

[0094] Model construction unit 100, used to establish the dynamic model of the quadrotor UAV in the inertial coordinate system in the distributed formation system, and design the multi-quadrotor UAV cooperative formation synchronization error , a preset performance control function with the ability to handle sudden changes in the reference trajectory is established to constrain the formation synchronization error, and corresponding equivalent error transformation is performed;

[0095] The trajectory controller design unit 200 is used to design the dynamic event-triggered trajectory subsystem controllers for the respective quadrotor UAVs in the formation system according to the dynamic models of the trajectory subsystems corresponding to the respective quadrotor UAVs and based on the equivalent error , command-filtered backstepping control technology, and neural network disturbance estimator; ;

[0096] The attitude controller design unit 300 is used to design the robust attitude subsystem controllers for the respective quadrotor UAVs in the formation system according to the dynamic models of the attitude subsystems corresponding to the respective quadrotor UAVs and based on the command-filtered backstepping control technology and neural network disturbance estimator; ;

[0097] The verification unit 400 is used to construct a Lyapunov function to verify whether the closed-loop system is stable and whether the expected formation effect can be achieved.

[0098] Applying the above control method to the actual situation, consider a distributed formation system consisting of a virtual leader and three follower quadrotor UAVs:

[0099] The expected trajectory of the virtual leader is set as: , enabling the virtual leader to navigate along the expected trajectory and guiding the triangular formation composed of three quadrotor UAVs to follow the virtual leader's trajectory for formation flight;

[0100] The expected position deviation between each quadrotor UAV and the virtual leader is set as , , ; the disturbances suffered by each quadrotor UAV are set as , and ; the initial positions of each quadrotor UAV are set as: , , ;

[0101] The design parameters of the quadrotor UAV in the formation system are set as shown in Table 1:

[0102] Table 1

[0103]

[0104] Through experiments, the distributed formation motion trajectories of multiple quadrotor UAVs in the inertial coordinate system are as Figure 2 shown, and the position state output curves of each UAV are asFigure 3 As shown, the preset performance control effect of the formation synchronization error is as follows Figure 4 As shown, in the formation system, the quadrotor UAV The control input and trigger time interval are respectively as follows Figure 5 、 6 、Figure 7. It can be seen that the formation synchronization error always remains within the expected performance range and finally stabilizes within the predetermined steady-state error range. The preset performance control method proposed in the embodiment of the present invention has an adaptive ability, which can alleviate the overshoot and oscillation of the control input to a certain extent, especially when the trajectory of the virtual leader changes suddenly during the steady state period.

[0105] In summary, an event-triggered cooperative control method for multi-quadrotor formation maneuvering flight provided by the embodiment of the present invention first establishes a quadrotor UAV dynamics model including unknown external disturbances to accurately describe the dynamic behavior of the UAV in a complex environment. On this basis, an improved preset performance function with the ability to cope with sudden changes in the reference trajectory is designed to constrain the formation synchronization error to ensure the stability and accuracy of formation flight; further, the quadrotor UAV dynamics model is decomposed into a trajectory and an attitude two subsystems, and a dynamic event-triggered trajectory subsystem controller and a robust attitude subsystem controller based on the command filtering backstepping technique are designed respectively to achieve precise control of the position and attitude of the quadrotor UAV; among them, the construction of the dynamic event-triggered position subsystem controller optimizes the data transmission strategy and reduces unnecessary communication, thus saving network resources; in addition, a neural network disturbance estimator is introduced to estimate and compensate the unknown disturbances in the system in real time and improve the disturbance rejection ability of the system.

[0106] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An event-triggered collaborative control method for multi-quadrotor formation maneuvering flight, characterized in that: The following steps are involved: Establish the dynamic model of the quadrotor UAV in the distributed formation system in the inertial coordinate system, and design the synchronization error of the multi-quadrotor UAV collaborative formation , establish a preset performance control function capable of coping with sudden changes in the reference trajectory to constrain the formation synchronization error, and perform corresponding equivalent error transformation; According to the dynamic model of the trajectory subsystem corresponding to each quadrotor drone, and based on the equivalent error , command filtering backstepping control technology, neural network interference estimator, design of dynamic event-triggered trajectory subsystem controller corresponding to each quadrotor drone in the formation system ; According to the dynamic model of the attitude subsystem corresponding to each quadrotor UAV, and based on the command filtering backstepping control technology and neural network interference estimator, the robust attitude subsystem controller corresponding to each quadrotor UAV in the formation system is designed. ; Constructing Lyapunov functions , verify whether the closed-loop system is stable and whether the expected formation effect can be achieved.

2. The event-triggered collaborative control method for multi-quadrotor formation maneuvering flight according to claim 1, characterized in that: The dynamic model of the quad-rotor UAV in the distributed formation system in the inertial coordinate system is established, and the synchronization error of the multi-quad-rotor UAV collaborative formation is designed. , establishing a preset performance control function capable of coping with sudden changes in the reference trajectory to constrain the formation synchronization error, and performing the corresponding equivalent error transformation steps, specifically including: Based on the number of quadrotor drones in distributed formation , , Corresponding to each quadrotor drone, a dynamic model of the follower quadrotor drone in the inertial coordinate system is established; Design Synchronization error of the coordinated formation of four quadrotor drones: in, For the The synchronization error of the coordinated formation of four quadrotor drones, represents the set of real numbers, is a 3-dimensional real number set; Indicates The quadcopter and The communication weight between the four rotors; For the The weight matrix between the quadrotor drone and the virtual navigator; Indicates The position of the quadrotor in the inertial coordinate system; Indicates The expected position deviation between the quadrotor drone and the virtual navigator, ; To preset the expected trajectory of the virtual navigator; The following improved preset performance control function is established to make the collaborative formation synchronization error satisfy : in, , , , , ,and is the design parameter, ; Constructed by the following auxiliary functions: in, and Indicates the system status. , is the initial state; , and is the design parameter; represents the number of sampling times; represents the sampling interval; is the acceleration factor; Express the fastest control synthesis function as follows: Synchronization error of coordinated formation Perform equivalent error transformation and obtain the transformed error variable as follows: in .

3. The event-triggered collaborative control method for multi-quadrotor formation maneuvering flight according to claim 2, characterized in that: The dynamic model of the trajectory subsystem corresponding to each quadrotor drone and based on the equivalent error , command filtering backstepping control technology, neural network interference estimator, design of dynamic event-triggered trajectory subsystem controller corresponding to each quadrotor drone in the formation system The steps include: Design a virtual controller for the position loop in the trajectory subsystem: in, Indicates Virtual control signal for the position loop of a quadrotor drone; variable , , ; ,in , ,and represents a positive constant; is the equivalent error vector; ; Indicates The speed of the quadcopter; For the The speed of a quadcopter drone; express The first derivative of for The first derivative of ; The dynamic event trigger position loop controller corresponding to each quadrotor trajectory subsystem in the formation system is established as follows: in, is the virtual control input of the trajectory subsystem, It is The Euler angle of a quadrotor drone in the body coordinate system, represents control input; is the measurement error, is the intermediate continuous control signal; The moment when the event is triggered; ; , and is the design parameter, Indicates absolute value; ,in , ,and is the design parameter; is the neural network weight, express An estimated value of is the activation function; is the input vector of the neural network disturbance estimator; is the speed tracking error, Indicates The speed of the quadrotor drone in the inertial coordinate system, express Output signal after command filter; is the norm of the matrix; and The structure is as follows: in, , , and represents a positive constant; is the initial value, satisfying ; satisfy ; , is a normal number, represents a real number set; the design formulas of the command filter and the neural network interference estimator are as follows: in, and Represents the filter output, and the initial value of the filter is set to , ; , is the design parameter; express The estimated value of Indicates The speed of a quadcopter drone, represents the estimation error, ; is the neural network weight matrix, express An estimated value of is the input vector of the neural network disturbance estimator; is the gain matrix; is the adaptive gain matrix, represents the correction parameter matrix.

4. The event-triggered collaborative control method for multi-quadrotor formation maneuvering flight according to claim 3, characterized in that: According to the attitude subsystem dynamics model corresponding to each quadrotor drone, and based on the command filtering backstepping control technology and the neural network interference estimator, a robust attitude subsystem controller corresponding to each quadrotor drone in the formation system is designed. The steps include: Perform an inverse transformation on the coupling relationship between the trajectory subsystem and the attitude subsystem of the quadrotor drone to obtain the expected instructions for the attitude loop : in, is the strong coupling relationship between translational and rotational dynamics. ,in represents the acceleration due to gravity, Indicates The mass of the quadcopter; represents the control input, , , and is the thrust of the propeller of the quadcopter drone; is the yaw angle command; Constructing the formation system A virtual controller for the attitude angle loop of a quadrotor drone : represents the attitude angle tracking error, Indicates Euler angles of a quadrotor; ,in , , is a positive constant; Design the robust controller of the angular velocity loop, that is, the actual control quantity as follows: in, Indicates the actual control quantity, which is the control torque of the quadrotor drone. , , ,in and Respectively represent the distance and torque coefficient from each propeller to the center of mass of the quadrotor drone, is a positive definite inertia matrix; , is a diagonal matrix whose elements are all positive constants; for The filtered signal; is the neural network weight, for An estimated value of is the activation function; is the input vector of the neural network disturbance estimator; is the angular velocity tracking error, Indicates The angular velocity of the quadrotor drone in the body coordinate system, is the command filter output signal; Represent the Filipov solution of the following designed differential equation: in, is the robust feedback gain; The design formulas of command filter and neural network disturbance estimator are as follows: in, , and is a diagonal matrix whose elements are all positive constants; , is the design parameter; yes The estimated value of Represents the estimation error.

5. The event-triggered collaborative control method for multi-quadrotor formation maneuvering flight according to claim 4, characterized in that: The construction of the Lyapunov function , the steps to verify whether the closed-loop system is stable and whether the expected formation effect can be achieved are as follows: Constructing Lyapunov functions : in, is the equivalent error; Indicates the speed tracking error; is the speed state estimation error; and represents the weight estimation error, and is the ideal weight matrix; and is the adaptive gain matrix; represents the attitude angle tracking error, is the angular velocity tracking error; is the attitude angular velocity estimation error; and is the auxiliary function variable, yes The Filippov solution of is the neural network reconstruction error; Will Taking the derivative with respect to time and simplifying, we obtain: That is, the system is closed-loop stable, where , Corresponding to the above variables The coefficient before represents the minimum eigenvalue, represents the maximum eigenvalue; , and is the upper bound of the filter error, and is the upper bound of the neural network reconstruction error, and is the ideal weight matrix, is the design parameter, and is the correction parameter matrix; When you get: That is, the system can achieve the expected formation effect, among which, , represents the set of real numbers, for dimensional real number set; , Indicates The expected position deviation between the quadrotor and the virtual navigator, for dimensional real number set, is a 3-dimensional real number set; , and It is a design parameter that improves a preset performance function; for The upper bound of express The minimum eigenvalue of is the Laplace matrix of the communication topology, , , , for dimensional set of real numbers.

6. An event-triggered coordinated control system for multi-quadrotor formation maneuvering flight, used to implement the event-triggered coordinated control method for multi-quadrotor formation maneuvering flight as described in any one of claims 1-5, characterized in that: include: Model building unit, used to establish the dynamic model of the quad-rotor UAV in the distributed formation system in the inertial coordinate system, and design the synchronization error of the multi-quad-rotor UAV collaborative formation , establish a preset performance control function capable of coping with sudden changes in the reference trajectory to constrain the formation synchronization error, and perform corresponding equivalent error transformation; The trajectory controller design unit is used to design the trajectory subsystem according to the dynamic model of each quadrotor drone and based on the equivalent error , command filtering backstepping control technology, neural network interference estimator, design of dynamic event-triggered trajectory subsystem controller corresponding to each quadrotor drone in the formation system ; The attitude controller design unit is used to design the robust attitude subsystem controller corresponding to each quadrotor UAV in the formation system based on the attitude subsystem dynamics model corresponding to each quadrotor UAV, command filtering backstepping control technology, and neural network interference estimator. ; Verification unit, used to construct Lyapunov function , verify whether the closed-loop system is stable and whether the expected formation effect can be achieved.

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