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

Abstract

This invention relates to the field of multi-UAV cooperative control technology, providing an event-triggered cooperative control method and system for multi-quadrotor formation maneuvering. First, a dynamic model of the quadrotor UAVs in the inertial coordinate system is established in the distributed formation system. The synchronization error of the multi-quadrotor UAV cooperative formation is designed, and a preset performance control function is established to constrain the formation synchronization error, with corresponding equivalent error transformations performed. A dynamic event-triggered trajectory subsystem controller is designed; a robust attitude subsystem controller is designed; and a Lyapunov function is constructed to verify whether closed-loop system stability is achieved and whether the expected formation effect can be achieved. This invention can effectively constrain the synchronization error of the multi-quadrotor UAV formation system, reduce error overshoot, and improve the ability to cope with uncertainties, environmental interference, and trajectory changes, ensuring stable formation flight of the multi-quadrotor UAV system.

Description

Technical Field

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

[0002] In recent years, quadcopter drones have been widely used in numerous fields, with their usage scope and frequency showing a significant upward trend. From forest fire rescue to agricultural irrigation, from urban monitoring to military aid, quadcopter drones have provided strong support to various industries due to their flexibility and efficiency. However, with the continuous expansion of their application scope, how to effectively control these drones, especially to enable them to operate stably in formation and successfully carry out missions, has become an important problem that urgently needs to be solved.

[0003] Achieving formation control of quadcopter drones faces numerous challenges. One major difficulty is the pervasive presence of interference factors, which can arise from complex environmental conditions, electromagnetic interference, and other unpredictable external factors. Furthermore, quadcopter drones possess strong nonlinear coupling characteristics, meaning that the motion between their position and attitude is interconnected and difficult to predict precisely. Adding complexity, quadcopter drones are underactuated systems, meaning the relationship between their control inputs and motion states is not one-to-one, making precise control even more difficult. These factors combined can cause the drones to deviate from the desired formation, thereby compromising mission objectives. Notably, when multiple quadcopter drones fly in formation, a particularly critical and challenging issue is handling maneuvers with sudden trajectory changes. In such situations, each drone in the formation needs to react rapidly within a very short time to maintain formation stability and consistency. Trajectory changes can be triggered by various factors, such as sudden obstacle avoidance requirements, abrupt changes in target position, or rapid changes in the external environment. In these cases, coordination between drones is crucial, as a control error by any single drone can lead to chaos in the entire formation or even a collision. Therefore, developing and researching advanced control algorithms and technologies capable of coping with trajectory changes in interference environments is of great significance for improving the practicality and safety of multi-quadrotor UAV formations. Summary of the Invention

[0004] The purpose of this invention is to provide an event-triggered cooperative control method for multi-quadrotor formation maneuvering flight, aiming to solve the problems mentioned in the background art.

[0005] The present invention is implemented as follows: an event-triggered cooperative control method for multi-quadrotor formation maneuvering includes the following steps:

[0006] Establish a dynamic model of a quadrotor UAV in an inertial coordinate system within 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 cope with sudden changes in the reference trajectory to constrain the formation synchronization error, and perform corresponding equivalent error transformation;

[0007] Based on the dynamic model of the trajectory subsystem corresponding to each quadcopter UAV, and based on the equivalent error Command filtering backstepping control technology and neural network interference estimator were used to design the dynamic event triggering trajectory subsystem controller for each quadrotor UAV in the formation system. ;

[0008] Based on the dynamic model of the attitude subsystem corresponding to each quadrotor UAV, and using command filtering backstepping control technology and neural network disturbance estimator, a robust attitude subsystem controller for each quadrotor UAV in the formation system is designed. ;

[0009] Constructing Lyapunov functions This verifies whether the closed-loop system is stable and whether the expected formation effect can be achieved.

[0010] Another objective of this invention is to provide an event-triggered cooperative control system for multi-quadrotor formation maneuvering, used to implement the aforementioned event-triggered cooperative control method for multi-quadrotor formation maneuvering, comprising:

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

[0012] The trajectory controller design unit is used to design the trajectory controller based on the dynamic model of the trajectory subsystem corresponding to each quadcopter UAV and the equivalent error. Command filtering backstepping control technology and neural network interference estimator were used to design the dynamic event triggering trajectory subsystem controller for each quadrotor UAV in the formation system. ;

[0013] The attitude controller design unit is used to design robust attitude subsystem controllers for each quadrotor UAV in the formation system, based on the dynamic model of the attitude subsystem corresponding to each quadrotor UAV and using command filtering backstepping control technology and neural network disturbance estimator. ;

[0014] Verification unit, used to construct Lyapunov functions This verifies 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 provided in this invention can effectively constrain the synchronization error of multi-quadrotor UAV formation systems, reduce error overshoot, and improve the ability to cope with uncertainties, environmental interference, and trajectory changes. Secondly, a dynamic parameter-based event-triggered controller is designed to achieve non-periodic control of the multi-quadrotor UAV formation. Unlike traditional fixed-parameter static event-triggered control methods, this method can dynamically adjust the event triggering time interval according to the system's operating state. This not only ensures system stability but also significantly reduces communication traffic and computational load, thereby improving the efficiency and flexibility of formation control. Finally, to address uncertainties during formation flight, a neural network interference estimator is introduced into the controller design to estimate and compensate for interference online. Simultaneously, a robust attitude loop controller is constructed for each quadrotor UAV in the formation system, further enhancing the formation system's ability to cope with uncertainties, interference, and trajectory changes, ensuring stable formation flight of the multi-quadrotor UAV system. Attached Figure Description

[0016] Figure 1 A flowchart illustrating the event-triggered cooperative control method for multi-quadrotor formation maneuvering provided in this embodiment of the invention;

[0017] Figure 2 A schematic diagram of the distributed formation motion trajectory of multiple quadrotor UAVs in an inertial coordinate system provided in an embodiment of the present invention;

[0018] Figure 3 This is a schematic diagram of the position response curves of various quadcopter UAVs provided in the embodiments of the present invention;

[0019] Figure 4 This is a schematic diagram of the synchronization error curves of each quadcopter UAV formation provided in an embodiment of the present invention;

[0020] Figure 5 This is a schematic diagram of the control input and trigger time interval of a quadcopter UAV (i=1) provided in an embodiment of the present invention;

[0021] Figure 6 This is a schematic diagram of the control input and trigger time interval of a quadcopter UAV (i=2) provided in an embodiment of the present invention;

[0022] Figure 7 This is a schematic diagram of the control input and trigger time interval of a quadcopter UAV (i=3) provided in an embodiment of the present invention;

[0023] Figure 8This is a structural block diagram of an event-triggered cooperative control system for multi-quadrotor formation maneuvering provided in an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the 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 merely illustrative and not intended to limit the invention.

[0025] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

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

[0027] S1. Establish a dynamic model in the inertial coordinate system to characterize the trajectory and attitude subsystems of a quadrotor UAV with unknown external disturbances, and design the synchronization error of multi-quadrotor UAV cooperative formation. Establish a preset performance control function with the ability to cope with sudden changes in the reference trajectory to constrain the formation synchronization error, and perform corresponding equivalent error transformation, and then proceed to step S2;

[0028] In the specific design and implementation of step S1 above, for each quadcopter UAV in the formation system, the operation is as follows:

[0029] S11. Number of quadcopter drones in distributed formation , , For each quadrotor UAV, establish a dynamic model of the follower quadrotor UAV in the inertial coordinate system;

[0030]

[0031] in, and , respectively representing the first The position and Euler angles of the quadcopter in the inertial coordinate system and the body coordinate system; It is the first The mass of a quadcopter drone; and The aerodynamic damping matrix; It is a strong coupling relationship between translational and rotational dynamics; , Represents gravitational acceleration; To control input , , and For the thrust of the quadcopter drone's propeller; Indicates control torque. , , , ,in and These represent the distance from each propeller to the center of mass of the quadcopter and the torque coefficient, respectively. It is a positive definite inertia matrix; and It represents unknown bounded environmental disturbances.

[0032] Detailed parameters for the quadcopter drone model are as follows: , , , , , , ;

[0033] The dynamic model of the quadcopter UAV is presented in the following form:

[0034]

[0035] in, , and They represent the first The velocity and angular velocity of the quadcopter UAV in the inertial coordinate system and the body coordinate system; This represents the virtual control input of the trajectory subsystem; and It is composed of aerodynamic damping matrix and Model perturbations caused by inaccuracies; , and They represent , and ;

[0036] S12, Design No. Synchronization error in coordinated formation of quadcopter drones:

[0037]

[0038] in, For the first Synchronization error in the coordinated formation of quadcopter drones Represents the set of real numbers. It is a 3-dimensional set of real numbers; Indicates the first The quadcopter and the first Communication weights between quadcopters; For the first The weight matrix between the quadcopter drone and the virtual navigator; Indicates the first The position of the quadcopter in the inertial coordinate system; Indicates the first The expected position deviation between the quadcopter drone and the virtual navigator ; To pre-determine the desired trajectory of the virtual navigator;

[0039] S13. Establish the following improved preset performance control function to reduce the synchronization error of the cooperative formation. satisfy :

[0040]

[0041]

[0042] in, , , , , ,and For design parameters, ; Constructed by the following auxiliary function:

[0043]

[0044] in, and Indicates the system status. , This is the initial state; , and For design parameters; Represents the number of samples; Indicates the sampling interval; It is the acceleration factor; The following represents the fastest control synthesis function:

[0045]

[0046] S14. Synchronization error of coordinated formation After performing an equivalent error transformation, the transformed error variables are as follows:

[0047]

[0048] in ;

[0049] S2. Based on the dynamic model of the trajectory subsystem corresponding to each quadcopter UAV, and based on the equivalent error... Command filtering backstepping control technology and neural network interference estimator were used to design the dynamic event triggering trajectory subsystem controller for each quadrotor UAV in the formation system. This is used to control the trajectory subsystems of each quadcopter drone separately, so that each quadcopter drone can form a stable formation configuration and track the expected trajectory of the preset virtual navigator with trajectory changes in the interference environment, and then proceed to step S3.

[0050] In the specific design and implementation, step S2 above is performed as follows:

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

[0052]

[0053] in, Indicates the first Virtual control signals for the position loop of a quadcopter UAV; variables , , ; ,in , ,and Represents positive numbers; This is the equivalent error vector; ; Indicates the first The speed of a quadcopter drone; For the first The speed of a quadcopter drone; express The first derivative, for The first derivative;

[0054] S22. Establish the dynamic event triggering position loop controllers for each quadrotor trajectory subsystem in the formation system as follows:

[0055]

[0056]

[0057]

[0058] in, This serves as the virtual control input for the trajectory subsystem. It is the first Euler angles of a quadcopter UAV in the body coordinate system Indicates control input; To account for measurement error, It is an intermediate continuous control signal; The moment the event is triggered; ; , and For design parameters, Represents absolute value; ,in , ,and For design parameters; These are the weights of a neural network. express The estimated value; For activation functions; It is the input vector of the neural network disturbance estimator; For speed tracking error, Indicates the first The speed of the quadcopter drone in the inertial coordinate system. express The output signal after passing through the command filter; Let be the norm of the matrix; and The construction is as follows:

[0059]

[0060] in, , , and Represents positive numbers; As the initial value, satisfying ; satisfy ; , For positive integers, The design formulas for the command filter and neural network interference estimator, representing the set of real numbers, are as follows:

[0061]

[0062]

[0063] in, and This represents the filter output, with the filter's initial value set to... , ; , For design parameters; express The estimated value, Indicates the first The speed of a quadcopter drone Indicates the estimation error. ; It is the weight matrix of a neural network. express The estimated value; It is the input vector of the neural network disturbance estimator; This is the gain matrix; It is an adaptive gain matrix. This represents the correction parameter matrix.

[0064] S3. Based on the dynamic model of the attitude subsystem corresponding to each quadcopter UAV, and using command filtering backstepping control technology and neural network interference estimator, design a robust attitude subsystem controller for each quadcopter UAV in the formation system. This is used to control the stable operation of the quadcopter UAV attitude subsystem in a jammed environment, and then proceeds to step S4;

[0065] In the specific design and implementation, step S3 above shall be performed as follows:

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

[0067]

[0068]

[0069] in, It is a strong coupling relationship between translational and rotational dynamics. ,in Represents gravitational acceleration. Indicates the first The mass of a quadcopter drone; Indicates control input, , , and For the thrust of the quadcopter drone's propeller; The yaw angle command is a freely adjustable variable that can be set by the ground station.

[0070] S32, Constructing the formation system Virtual controller for attitude angle loop of a quadcopter drone :

[0071]

[0072] Indicates the attitude angle tracking error. Indicates the first Euler angles for a quadcopter; ,in , , It is a positive number;

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

[0074]

[0075] in, This represents the actual control quantity, which is the control torque of the quadcopter drone. , , ,in and These represent the distance from each propeller to the center of mass of the quadcopter and the torque coefficient, respectively. It is a positive definite inertia matrix; , It is a diagonal matrix whose elements are all positive constants; for Filtered signal; These are the weights of a neural network. for The estimated value; For activation functions; It is the input vector of the neural network disturbance estimator; For angular velocity tracking error, Indicates the first The angular velocity of the quadcopter drone in the body coordinate system The output signal of the command filter; The Filippov solution to the following designed differential equation is given:

[0076]

[0077] in, For robust feedback gain;

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

[0079]

[0080]

[0081] in, , and It is a diagonal matrix whose elements are all positive constants; , For design parameters; yes The estimated value, Indicates the estimation error;

[0082] S4. Constructing Lyapunov functions Using Lyapunov stability theory, it is proved that the designed control method can achieve closed-loop system stability and achieve the expected formation effect.

[0083] In the specific design and implementation, step S3 above shall be performed as follows:

[0084] S41. Constructing Lyapunov functions :

[0085]

[0086] in, This is the equivalent error; Indicates speed tracking error; This represents the velocity state estimation error; and This indicates the error in weight estimation. and The ideal weight matrix; and It is an adaptive gain matrix; Indicates the attitude angle tracking error. This refers to the angular velocity tracking error. This represents the attitude angular velocity estimation error. and For auxiliary function variables, yes Filippov's solution, It is the neural network reconstruction error;

[0087] S42, will Differentiating and simplifying with respect to time, we get:

[0088]

[0089] That is, the system is closed-loop stable, where, , Corresponding to the variables in the above formula The coefficient before, Represents the smallest eigenvalue. Represents the largest eigenvalue; , and This is the upper bound of the filter's filtering error. and It is the upper bound of the neural network reconstruction error. and For the ideal weight matrix, For design parameters, and It is the correction parameter matrix;

[0090] Furthermore, we can obtain:

[0091]

[0092] That is, the system can achieve the expected formation effect, among which, , Represents the set of real numbers. for The set of real numbers; , Indicates the first The expected position deviation between the quadcopter and the virtual navigator for The set of real numbers, It is a 3-dimensional set of real numbers; , and These are design parameters for improving the preset performance function; for The upper bound; express The smallest eigenvalue, The Laplace matrix of the communication topology. , , , for The set of real numbers of dimension .

[0093] like Figure 8 As shown, the structural framework of an event-triggered cooperative control system for multi-quadrotor formation maneuvering flight, provided in an embodiment of the present invention, includes:

[0094] Model building unit 100 is used to establish the dynamic model of a quadcopter UAV in an inertial coordinate system in a distributed formation system, and to design the synchronization error of multi-quadcopter UAV cooperative formation. Establish a preset performance control function with the ability to cope with sudden changes in the reference trajectory to constrain the formation synchronization error, and perform corresponding equivalent error transformation;

[0095] The trajectory controller design unit 200 is used to design the trajectory controller based on the dynamic model of the trajectory subsystem corresponding to each quadcopter UAV and the equivalent error. Command filtering backstepping control technology and neural network interference estimator were used to design the dynamic event triggering trajectory subsystem controller for each quadrotor UAV in the formation system. ;

[0096] The attitude controller design unit 300 is used to design robust attitude subsystem controllers for each quadrotor UAV in the formation system based on the dynamic model of the attitude subsystem corresponding to each quadrotor UAV, and based on command filtering backstepping control technology and neural network disturbance estimator. ;

[0097] Verification unit 400 is used to construct Lyapunov functions. This verifies whether the closed-loop system is stable and whether the expected formation effect can be achieved.

[0098] Applying the above control method in practice, consider a distributed formation system consisting of a virtual navigator and three follower quadcopter drones:

[0099] The virtual navigator's desired trajectory is set as follows: This allows the virtual navigator to navigate along the desired trajectory, guiding a triangular formation of three quadcopter drones to follow the virtual navigator's path.

[0100] The desired positional deviation between each quadcopter drone and the virtual navigator is set to , , The interference settings for each quadcopter drone are as follows: ,as well as The starting positions of each quadcopter drone are set as follows: , , ;

[0101] Quadrone in formation system The design parameters are set as shown in Table 1:

[0102] Table 1

[0103]

[0104] The distributed formation motion trajectory of multiple quadrotor UAVs in the inertial coordinate system was obtained through experiments, as follows: Figure 2 As shown, the position status output curves of each UAV are as follows: Figure 3 As shown, the performance control effect of the preset formation synchronization error is as follows: Figure 4 As shown, the quadcopter drone in the formation system The control inputs and trigger time intervals are respectively as follows: Figure 5 , 6 As shown in Figure 7, it can be seen that the formation synchronization error always remains within the expected performance range and eventually stabilizes within the predetermined steady-state error range. The preset performance control method proposed in this embodiment of the invention has adaptive capability and can reduce the overshoot and oscillation of the control input to a certain extent, especially when the trajectory of the virtual navigator changes abruptly during the steady state.

[0105] In summary, the event-triggered cooperative control method for multi-quadrotor formation maneuvering provided by this invention first establishes a quadrotor UAV dynamic model incorporating unknown external disturbances to accurately describe the dynamic behavior of the UAV in complex environments. Based on this, an improved preset performance function capable of handling sudden changes in the reference trajectory is designed to constrain formation synchronization errors, ensuring the stability and accuracy of formation flight. Furthermore, the quadrotor UAV dynamic model is decomposed into two subsystems: trajectory and attitude. A dynamic event-triggered trajectory subsystem controller and a robust attitude subsystem controller based on command filtering backstepping technology are designed respectively to achieve precise control of the quadrotor UAV's position and attitude. The construction of the dynamic event-triggered position subsystem controller optimizes data transmission strategies, reduces unnecessary communication, and thus saves network resources. In addition, a neural network disturbance estimator is introduced to estimate and compensate for unknown disturbances in the system in real time, improving the system's anti-disturbance capability.

[0106] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An event-triggered cooperative control method for multi-quadrotor formation maneuvering flight, characterized in that, Includes the following steps: Establish a dynamic model of a quadrotor UAV in an inertial coordinate system within 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 cope with sudden changes in the reference trajectory to constrain the formation synchronization error, and perform corresponding equivalent error transformation; Based on the dynamic model of the trajectory subsystem corresponding to each quadcopter UAV, and based on the equivalent error Command filtering backstepping control technology and neural network interference estimator were used to design the dynamic event triggering trajectory subsystem controller for each quadrotor UAV in the formation system. ; Based on the dynamic model of the attitude subsystem corresponding to each quadrotor UAV, and using command filtering backstepping control technology and neural network disturbance estimator, a robust attitude subsystem controller for each quadrotor UAV in the formation system is designed. ; Constructing Lyapunov functions This verifies whether the closed-loop system is stable and whether the expected formation effect can be achieved.

2. The event-triggered cooperative control method for multi-quadrotor formation maneuvering flight according to claim 1, characterized in that, The dynamic model of the quadrotor UAV in the inertial coordinate system is established in the distributed formation system, and the synchronization error of the multi-quadrotor UAV cooperative formation is designed. The steps include establishing a preset performance control function capable of handling sudden changes in the reference trajectory to constrain formation synchronization errors, and performing corresponding equivalent error transformations. Based on the number of quadcopter drones in the distributed formation , , For each quadrotor UAV, establish a dynamic model of the follower quadrotor UAV in the inertial coordinate system; Design No. Synchronization error in coordinated formation of quadcopter drones: in, For the first Synchronization error in the coordinated formation of quadcopter drones Represents the set of real numbers. It is a 3-dimensional set of real numbers; Indicates the first Quadcopter and the first Communication weights between quadcopters; For the first The weight matrix between the quadcopter drone and the virtual navigator; Indicates the first The position of the quadcopter in the inertial coordinate system; Indicates the first The expected position deviation between the quadcopter drone and the virtual navigator ; To pre-determine the desired trajectory of the virtual navigator; Establish the following improved preset performance control function to reduce the synchronization error of the cooperative formation. satisfy : in, , , , , ,and For design parameters, ; Constructed by the following auxiliary function: in, and Indicates the system status. , This is the initial state; , and For design parameters; Represents the number of samples; Indicates the sampling interval; It is the acceleration factor; The following represents the fastest control synthesis function: Synchronization error of coordinated formation After performing an equivalent error transformation, the transformed error variables are as follows: in .

3. The event-triggered cooperative 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 quadcopter UAV is based on the equivalent error. Command filtering backstepping control technology and neural network interference estimator were used to design the dynamic event triggering trajectory subsystem controller for each quadrotor UAV in the formation system. The steps specifically include: Design a virtual controller for the position loop in the trajectory subsystem: in, Indicates the first Virtual control signals for the position loop of a quadcopter UAV; variables , , ; ,in , ,and Represents positive numbers; This is the equivalent error vector; ; Indicates the first The speed of a quadcopter drone; For the first The speed of a quadcopter drone; express The first derivative, for The first derivative; The dynamic event triggering position loop controllers for each quadrotor trajectory subsystem in the formation system are established as follows: in, This serves as the virtual control input for the trajectory subsystem. It is the first Euler angles of a quadcopter UAV in the body coordinate system Indicates control input; To account for measurement error, It is an intermediate continuous control signal; The moment the event is triggered; ; , and For design parameters, Represents absolute value; ,in , ,and For design parameters; These are the weights of a neural network. express The estimated value; For activation functions; It is the input vector of the neural network disturbance estimator; For speed tracking error, Indicates the first The speed of the quadcopter drone in the inertial coordinate system. express The output signal after passing through the command filter; Let be the norm of the matrix; and The construction is as follows: in, , , and Represents positive numbers; As the initial value, satisfying ; satisfy ; , For positive integers, The set of real numbers is represented; the design formulas for the command filter and neural network interference estimator are as follows: in, and This represents the filter output, with the filter's initial value set to... , ; , For design parameters; express The estimated value, Indicates the first The speed of a quadcopter drone Indicates the estimation error. ; It is the weight matrix of a neural network. express The estimated value; It is the input vector of the neural network disturbance estimator; This is the gain matrix; It is an adaptive gain matrix. This represents the correction parameter matrix.

4. The event-triggered cooperative control method for multi-quadrotor formation maneuvering flight according to claim 3, characterized in that, Based on the dynamic model of the attitude subsystem corresponding to each quadcopter UAV, and using command filtering backstepping control technology and a neural network disturbance estimator, a robust attitude subsystem controller for each quadcopter UAV in the formation system is designed. The steps specifically include: The coupling relationship between the trajectory subsystem and attitude subsystem of the quadcopter UAV is inversely transformed to obtain the desired attitude loop command. : in, It is a strong coupling relationship between translational and rotational dynamics. ,in Represents gravitational acceleration. Indicates the first The mass of a quadcopter drone; Indicates control input, , , and For the thrust of the quadcopter drone's propeller; This is the yaw angle command; Building the first in the formation system Virtual controller for attitude angle loop of a quadcopter drone : Indicates the attitude angle tracking error. Indicates the first Euler angles for a quadcopter; ,in , , It is a positive number; Design a robust angular velocity loop controller, i.e., the actual control quantity. as follows: in, This represents the actual control quantity, which is the control torque of the quadcopter drone. , , ,in and These represent the distance from each propeller to the center of mass of the quadcopter and the torque coefficient, respectively. It is a positive definite inertia matrix; , It is a diagonal matrix whose elements are all positive constants; for Filtered signal; These are the weights of a neural network. for The estimated value; For activation functions; It is the input vector of the neural network disturbance estimator; For angular velocity tracking error, Indicates the first The angular velocity of the quadcopter drone in the body coordinate system The output signal of the command filter; The Filippov solution to the following designed differential equation is given: in, For robust feedback gain; The design formulas for the command filter and the neural network interference estimator are as follows: in, , and It is a diagonal matrix whose elements are all positive constants; , For design parameters; yes The estimated value, This indicates the estimation error.

5. The event-triggered cooperative control method for multi-quadrotor formation maneuvering flight according to claim 4, characterized in that, The construction of Lyapunov functions 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, This is the equivalent error; Indicates speed tracking error; This represents the velocity state estimation error; and This indicates the error in weight estimation. and The ideal weight matrix; and It is an adaptive gain matrix; Indicates the attitude angle tracking error. This refers to the angular velocity tracking error. This represents the attitude angular velocity estimation error. and For auxiliary function variables, yes Filippov's solution, It is the neural network reconstruction error; Will Differentiating and simplifying with respect to time, we get: That is, the system is closed-loop stable, where, , Corresponding to the variables in the above formula The coefficient before, Represents the smallest eigenvalue. Represents the largest eigenvalue; , and This is the upper bound of the filter's filtering error. and It is the upper bound of the neural network reconstruction error. and For the ideal weight matrix, For design parameters, and It 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 The set of real numbers; , Indicates the first The expected position deviation between the quadcopter and the virtual navigator for The set of real numbers, It is a 3-dimensional set of real numbers; , and These are design parameters for improving the preset performance function; for The upper bound; express The smallest eigenvalue, The Laplace matrix of the communication topology. , , , for The set of real numbers of dimension .

6. An event-triggered cooperative control system for multi-quadrotor formation maneuvering, used to implement the event-triggered cooperative control method for multi-quadrotor formation maneuvering as described in any one of claims 1-5, characterized in that, include: The model building unit is used to establish the dynamic model of quadrotor UAVs in the inertial coordinate system in a distributed formation system, and to design the synchronization error of multi-quadrotor UAV cooperative formation. Establish a preset performance control function with the ability to cope 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 controller based on the dynamic model of the trajectory subsystem corresponding to each quadcopter UAV and the equivalent error. Command filtering backstepping control technology and neural network interference estimator were used to design the dynamic event triggering trajectory subsystem controller for each quadrotor UAV in the formation system. ; The attitude controller design unit is used to design robust attitude subsystem controllers for each quadrotor UAV in the formation system, based on the dynamic model of the attitude subsystem corresponding to each quadrotor UAV and using command filtering backstepping control technology and neural network disturbance estimator. ; Verification unit, used to construct Lyapunov functions This verifies whether the closed-loop system is stable and whether the expected formation effect can be achieved.

Images