Unmanned aerial vehicle formation mixed triggering cooperative control method and system
By constructing a hybrid triggering and collaborative control system model for UAV formations, the problems of DoS attacks and limited communication bandwidth for UAV formations were solved, achieving stable collaborative control in complex environments and enhancing the robustness and computational efficiency of the system.
Patent Information
- Application Number
- CN202511732500.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-24
- Publication Date
- 2026-02-13
AI Technical Summary
Drone formations are vulnerable to DoS attacks and limited communication bandwidth in open wireless network environments, leading to formation instability. Existing event triggering mechanisms fail under DoS interference, and traditional PID controllers cannot adapt to dynamic models with multimodal random switching.
A hybrid triggering cooperative control system model for UAV formations is constructed, including UAV dynamics, DoS attack, hybrid triggering mechanism, state detector and controller sub-model. The matrix inequality is optimized by Lyapunov function and preset tools, and the controller gain and triggering matrix are calculated to achieve stable cooperative control.
Stable cooperative control of UAV formations was achieved under DoS attacks and incomplete observation signals, enhancing the system's robustness and reducing computational burden.
Smart Images
Figure CN121523366A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of unmanned aerial vehicle (UAV) cooperative control technology, and in particular to a UAV formation hybrid trigger cooperative control method and system. Background Technology
[0002] Unmanned aerial vehicle (UAV) swarming has become a research hotspot in recent years, with broad application prospects in reconnaissance, surveying, logistics, and agriculture. The core of swarming flight is a networked control system, where multiple UAVs exchange information with the controller via a wireless network to achieve coordinated movement.
[0003] However, this open wireless network environment brings two fatal flaws: 1. Limited communication bandwidth: The more drones there are, the more exponentially the required communication bandwidth becomes. Traditional periodic control quickly fills the channel, leading to network latency and packet loss, causing formation instability. 2. Susceptibility to malicious interference: The wireless channel is open and highly vulnerable to electronic interference from adversaries, i.e., denial-of-service attacks. Attackers can periodically block the channel, preventing the controller from receiving drone status information, and the drones from receiving control commands, leading to formation loss of control or crashes.
[0004] Existing event-triggered mechanisms only trigger communication when the system state error is sufficiently large. This effectively saves bandwidth. However, a purely event-triggered mechanism may cause the controller to be completely unaware of the drone's survival when the system is in a quiet state for extended periods. Drone formations operate in multiple modes in real-world missions, each with completely different dynamic models, and the switching between these modes is random. Traditional PID controllers cannot adapt to this multimodal random switching, while random switching system theory is specifically designed for this purpose. It models the system modes as a random process, allowing the design of a controller that can robustly switch between all modes. Traditional event-triggered control relies on the assumption of unobstructed communication channels; under DoS interference, the triggering mechanism itself will fail, leading to formation instability. Summary of the Invention
[0005] To overcome the instability of UAV collaborative control systems caused by DoS attacks, lack of complete observation signals, and single triggering mechanisms, this invention provides a UAV formation hybrid triggering collaborative control method and system.
[0006] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: This invention provides a method for hybrid triggering and cooperative control of unmanned aerial vehicle (UAV) formations, comprising: A model for a collaborative control system for UAV formations is constructed, including: a UAV dynamics sub-model, a controlled output sub-model, a DoS attack sub-model, a hybrid triggering mechanism control sub-model, a state detector sub-model, and a controller sub-model. Based on the aforementioned UAV dynamics sub-model, DoS attack sub-model, hybrid triggering mechanism control sub-model, state detector sub-model, and controller sub-model, a closed-loop dynamics system model and its control objective of the UAV formation cooperative control system model are constructed. The control objective is transformed using a controlled output sub-model, a hybrid triggering mechanism, and a Lyapunov function to obtain a jointly solved matrix inequality. The matrix inequalities obtained by the collaborative solution are solved using a preset tool to obtain a first matrix and a second matrix. The controller gain and the trigger matrix of the hybrid triggering mechanism are then calculated based on the first matrix and the second matrix. The trigger matrix based on the controller gain and hybrid triggering mechanism enables coordinated control of UAV formations.
[0007] Preferably, the UAV dynamics sub-model and the controlled output sub-model include: The UAV dynamics sub-model is represented as follows: (1) in, It is the UAV state vector at time k. It is the actual control input executed after the attack at time k. Let k be the external disturbance at time k, and A, B, and D be known system matrices describing the characteristics of the system dynamics, control input, and disturbance effect, respectively. The system mode at time k; The controlled output sub-model is represented as follows: (2) in, Let be the performance output vector at time k, C be the relationship matrix between performance output and system state, and F be the relationship matrix between performance output and disturbance.
[0008] Preferably, the DoS attack sub-model is represented as follows: (3) in, This indicates that the DoS attack was successful; This indicates that the attack failed. Commands sent to the controller; These are the actual instructions received by the drone.
[0009] Preferably, the state detector sub-model, controller sub-model, and hybrid triggering mechanism control sub-model include: The state detector sub-model is represented as follows: (4) (5) The controller sub-model is represented as follows: (6) in, The controller gain to be calculated; The hybrid triggering mechanism control sub-model is represented as follows: (7) in, It is the measurement error between the current state and the last transmitted state. and These are the shape and threshold that control the triggering conditions, respectively; As a dynamic variable, its evolution is represented as: (8) in, >0, ensuring that when Even when the value is 0, a status message will still be sent.
[0010] Preferably, the closed-loop dynamic system model and its control objective for constructing the UAV formation cooperative control system model include: Substituting the DoS attack sub-model and the controller sub-model into the UAV dynamics sub-model, we obtain the closed-loop dynamics system model, as follows: (9) The control objective is expressed as follows: set up Performance represents the system's robustness to external disturbances. Performance metrics are defined as follows:
[0011] in, This represents the disturbance attenuation level. In order to achieve the expected Performance-wise, the control objectives for the closed-loop system dynamics are set as follows: (k)z(k)- [(k)w(k)]}<0 (10) in, It is an anti-disturbance performance indicator, and E is the control target.
[0012] Preferably, the control objective is transformed using a controlled output sub-model, a hybrid triggering mechanism, and a Lyapunov function to obtain a jointly solved matrix inequality, including: A Lyapunov function is constructed based on the control objective. The Lyapunov function is then differentially calculated to obtain a differential criterion function. The differential criterion function is scaled using a hybrid triggering mechanism to obtain an upper bound for the differential criterion function. By using the upper bound of the difference criterion function and the controlled output of the sensor to optimize the control objective, a matrix inequality that can be solved in a coordinated manner is obtained.
[0013] Preferably, a Lyapunov function is constructed based on the control objective, and the Lyapunov function is differentially calculated to obtain a difference criterion function. A hybrid triggering mechanism is then used to scale the difference criterion function to obtain its upper bound, including: (11) in, Let be the positive definite matrix to be solved; calculate The desired difference along the closed-loop system dynamics:
[0014] Transforming the above equation, we get: (12) Calculate separately and : ...(13) (14) Combining equations (13) and (14), we get: (15) By scaling the difference criterion function using a hybrid triggering mechanism, the upper bound of the difference criterion function is obtained: right Scaling the terms, we get the following formula: (16) Substituting equation (16) into equation (15), we get Upper bound: (17).
[0015] Preferably, the control objective is optimized using the upper bound of the difference criterion function and the controlled output of the sensor to obtain a jointly solved matrix inequality, including: When reached When performance is required, it must meet the following requirements. , in, ; Substitute equations (2) and (17) into Introducing matrix variable substitution ( And by applying Schul's complement lemma, we obtain the co-solution criterion; The collaborative solution criterion is stated as follows: For a given Performance indicators and HTM parameters If a positive definite matrix exists sum matrix This makes it possible for all The following matrix inequalities hold: <0(18) The closed-loop system is stable and satisfies Performance indicators .
[0016] Preferably, the matrix inequalities obtained through collaborative solving are solved using a preset tool to obtain a first matrix and a second matrix. Based on the first and second matrices, the controller gain and the trigger matrix of the hybrid triggering mechanism are calculated, including: The preset tools include MATLAB's matrix inequality toolbox; The first matrix is The second matrix is ; The controller gain is calculated as follows: ; The trigger matrix calculation method for the hybrid triggering mechanism is as follows: .
[0017] The present invention also provides a hybrid triggering and cooperative control system for unmanned aerial vehicle (UAV) formations, comprising: The collaborative control system model building module is used to build a collaborative control system model for UAV formations, including: UAV dynamics sub-model, controlled output sub-model, DoS attack sub-model, hybrid triggering mechanism control sub-model, state detector sub-model, and controller sub-model. A closed-loop dynamic system model and its control target construction module is used to construct the closed-loop dynamic system model and its control target of the UAV formation cooperative control system model based on the UAV dynamic sub-model, DoS attack sub-model, hybrid triggering mechanism control sub-model, state detector sub-model and controller sub-model. The collaborative solution matrix inequality acquisition module is used to transform the control objective using the controlled output sub-model, hybrid triggering mechanism and Lyapunov function to obtain the collaborative solution matrix inequalities; The controller gain and trigger matrix acquisition module is used to solve the matrix inequalities of the collaborative solution using a preset tool to obtain a first matrix and a second matrix, and to calculate the controller gain and the trigger matrix of the hybrid triggering mechanism based on the first matrix and the second matrix. The collaborative control module is used to perform collaborative control of the UAV formation based on the controller gain and the trigger matrix of the hybrid triggering mechanism.
[0018] Compared with the prior art, the beneficial effects of the technical solution of the present invention are: This invention constructs a closed-loop dynamic system model and control objective with a hybrid triggering mechanism based on DoS attacks and incomplete observation signals. By transforming and solving the control objective, the controller gain and triggering matrix are obtained. Based on the controller gain and triggering matrix, the UAV is coordinated and controlled, achieving stable coordinated control of the UAV under DoS attacks and incomplete signals, with stronger robustness. Attached Figure Description
[0019] Figure 1 This is a flowchart illustrating a hybrid triggering and cooperative control method for UAV formations in Example 1. Figure 2 This is a schematic diagram of the structure of the UAV formation cooperative control system model in Example 2; Figure 3 This is a schematic diagram of the control method flow of a UAV formation hybrid triggering cooperative control method in Example 2; Figure 4 This is a schematic diagram of the structure of a UAV formation hybrid triggering and cooperative control system in Example 3. Detailed Implementation
[0020] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the scope of this patent. To better illustrate this embodiment, some parts in the accompanying drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions; It will be understood by those skilled in the art that certain well-known structures and their descriptions may be omitted in the accompanying drawings.
[0021] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.
[0022] Example 1 This embodiment provides a method for hybrid triggering and cooperative control of UAV formations, such as... Figure 1 As shown, it includes: A model for a collaborative control system for UAV formations is constructed, including: a UAV dynamics sub-model, a controlled output sub-model, a DoS attack sub-model, a hybrid triggering mechanism control sub-model, a state detector sub-model, and a controller sub-model. Based on the aforementioned UAV dynamics sub-model, DoS attack sub-model, hybrid triggering mechanism control sub-model, state detector sub-model, and controller sub-model, a closed-loop dynamics system model and its control objective of the UAV formation cooperative control system model are constructed. The control objective is transformed using a controlled output sub-model, a hybrid triggering mechanism, and a Lyapunov function to obtain a jointly solved matrix inequality. The matrix inequalities obtained by the collaborative solution are solved using a preset tool to obtain a first matrix and a second matrix. The controller gain and the trigger matrix of the hybrid triggering mechanism are then calculated based on the first matrix and the second matrix. The trigger matrix based on the controller gain and hybrid triggering mechanism enables coordinated control of UAV formations.
[0023] In its specific implementation, this invention first constructs a UAV formation cooperative control system model, including: a UAV dynamics sub-model, a controlled output sub-model, a DoS attack sub-model, a hybrid triggering mechanism control sub-model, a state detector sub-model, and a controller sub-model. Then, based on the UAV dynamics sub-model, DoS attack sub-model, hybrid triggering mechanism control sub-model, state detector sub-model, and controller sub-model, a closed-loop dynamic system model and its control objective of the UAV formation cooperative control system model are constructed. Next, the controlled output sub-model, hybrid triggering mechanism, and Lyapunov function are used to transform the control objective, resulting in a collaboratively solved matrix inequality. Then, a preset tool is used to solve the collaboratively solved matrix inequality, obtaining a first matrix and a second matrix. Based on the first and second matrices, the controller gain and the triggering matrix of the hybrid triggering mechanism are calculated. Finally, the UAV formation is collaboratively controlled based on the controller gain and the triggering matrix of the hybrid triggering mechanism.
[0024] Example 2 This embodiment provides a method for hybrid triggering and cooperative control of UAV formations, including: Construct a model for a collaborative control system for drone formations, such as Figure 2 As shown, it includes: UAV dynamics sub-model, controlled output sub-model, DoS attack sub-model, hybrid triggering mechanism control sub-model, state detector sub-model, and controller sub-model; Based on the aforementioned UAV dynamics sub-model, DoS attack sub-model, hybrid triggering mechanism control sub-model, state detector sub-model, and controller sub-model, a closed-loop dynamics system model and its control objective of the UAV formation cooperative control system model are constructed. The control objective is transformed using a controlled output sub-model, a hybrid triggering mechanism, and a Lyapunov function to obtain a jointly solved matrix inequality. The matrix inequalities obtained by the collaborative solution are solved using a preset tool to obtain a first matrix and a second matrix. The controller gain and the trigger matrix of the hybrid triggering mechanism are then calculated based on the first matrix and the second matrix. The trigger matrix based on the controller gain and hybrid triggering mechanism enables coordinated control of UAV formations.
[0025] The UAV dynamics sub-model and controlled output sub-model include: The UAV dynamics sub-model is represented as follows: (1) in, It is the UAV state vector at time k. It is the actual control input executed after the attack at time k. Let k be the external disturbance at time k, and A, B, and D be known system matrices describing the characteristics of the system dynamics, control input, and disturbance effect, respectively. For the system mode at time k, in a finite set The value is taken from the middle, and its transition probability is , indicating from the pattern Switch to mode The probability of; The UAV state vector includes state variables such as position and velocity; The external disturbances include gusts of wind, measurement noise, etc. The controlled output sub-model is represented as follows: (2) in, Let be the performance output vector at time k, including tracking error, control cost, etc., used to evaluate the control performance of the system. C is the relationship matrix between performance output and system state, and F is the relationship matrix between performance output and disturbance.
[0026] The DoS attack sub-model is represented as follows: (3) in, This indicates that the DoS attack was successful; This indicates that the attack failed. Commands sent to the controller; This refers to the actual commands received by the drone. The attack model associated with this pattern better reflects the varying attack probabilities under different mission modes in real-world scenarios.
[0027] The state detector sub-model, controller sub-model, and hybrid triggering mechanism control sub-model include: The state detector sub-model is represented as follows: (4) (5) These observation probability matrices describe the uncertainty relationship between the real mode and the observed mode, making the controller design more in line with real-world application scenarios.
[0028] The controller sub-model is represented as follows: (6) in, The controller gain to be calculated; The hybrid triggering mechanism control sub-model is represented as follows: (7) in, It is the measurement error between the current state and the last transmitted state. and These are the shape and threshold that control the triggering conditions, respectively; As a dynamic variable, its evolution is represented as: (8) Among them, ensuring It exhibits a natural growth trend. When When the value grows to the point that the right side of formula (4) becomes 0 (i.e., timeout), a transmission will also be triggered, ensuring that communication can be maintained regularly even when the system state changes slowly, preventing the controller from losing contact with the actuator.
[0029] The closed-loop dynamic system model and its control objective for constructing the aforementioned UAV formation cooperative control system model include: Substituting the DoS attack sub-model and the controller sub-model into the UAV dynamics sub-model, we obtain the closed-loop dynamics system model, as follows: (9) This closed-loop system equation fully describes the dynamic behavior of the system under the combined effects of DoS attacks and hybrid triggering mechanisms.
[0030] Collaborative design of controller gain Trigger parameters And constants λ and τ, such that the closed-loop system (9) achieves the following under the conditions of HTM (4) and (5): Random stability: when At this point, the system is statistically stable. This means that even with mode switching and DoS attacks, the system's state will not diverge.
[0031] The control objective is expressed as follows: set up Performance represents the system's robustness to external disturbances. Performance metrics are defined as follows:
[0032] in, The preset disturbance attenuation level is used to quantify the system's response to external disturbances. The ability to inhibit.
[0033] In order to achieve the expected Performance-wise, the control objectives for the closed-loop system dynamics are set as follows: (k)z(k)- [(k)w(k)]}<0 (10) in, It is an anti-disturbance performance indicator, and E is the control target.
[0034] A Lyapunov function is constructed based on the control objective. The Lyapunov function is then differentially calculated to obtain a difference criterion function. This difference criterion function is then scaled using a hybrid triggering mechanism to obtain its upper bound, which includes: Constructing Lyapunov functions: (11) in, The Lyapunov function, which is a positive definite matrix to be solved, considers both the energy of the system state and the dynamic variables in the triggering mechanism, providing a mathematical basis for stability analysis.
[0035] calculate The desired difference along the closed-loop system dynamics:
[0036] Transforming the above equation, we get: (12) Calculate separately and : ...(13) (14) Combining equations (13) and (14), we get: (15) By scaling the difference criterion function using a hybrid triggering mechanism, the upper bound of the difference criterion function is obtained: right Scaling the terms, we get the following formula: (16) This step is crucial for achieving co-design of the controller and triggering mechanism. By utilizing the inverse of the triggering conditions, difficult-to-handle error terms are transformed into controllable state terms and dynamic variable terms.
[0037] Substituting equation (16) into equation (15), we get Upper bound: (17).
[0038] By optimizing the control objective using the upper bound of the difference criterion function and the controlled output of the sensor, we obtain the jointly solved matrix inequalities, including: When reached When performance is required, it must meet the following requirements. , in, ; Substitute equations (2) and (17) into Introducing matrix variable substitution ( And by applying Schul's complement lemma, we obtain the co-solution criterion; The collaborative solution criterion is stated as follows: For a given Performance indicators and HTM parameters If a positive definite matrix exists sum matrix This makes it possible for all The following matrix inequalities hold: <0(18) The closed-loop system is stable and satisfies Performance indicators .
[0039] Among them, each sub-block It is about and the matrix to be found The linear combinations are shown in Table 1 below.
[0040]
[0041] Table 1 in, .
[0042] The matrix inequalities obtained through collaborative solving are solved using a preset tool to obtain a first matrix and a second matrix. Based on the first and second matrices, the controller gain and the trigger matrix of the hybrid triggering mechanism are calculated, including: The preset tools include MATLAB's matrix inequality toolbox; The first matrix is The second matrix is ; The controller gain is calculated as follows: ; The trigger matrix calculation method for the hybrid triggering mechanism is as follows: .
[0043] This solution process is performed entirely offline, without the need for online parameter adjustments, which greatly reduces the computational burden for practical applications.
[0044] This embodiment also provides a process for coordinated control of UAV formation based on the trigger matrix of the controller gain and hybrid triggering mechanism, as follows: Figure 3 As shown, the process is described below: First, calculate the controller gain and trigger matrix offline; Next, the controlled output sub-model is used to detect the current state of the drone; Hybrid triggering mechanism controls the calculation error of the sub-model and dynamic variables ; Determine if the mixed triggering mechanism is satisfied: If the hybrid triggering mechanism is not satisfied, then bandwidth is saved and the drone dynamics are evolved to the next state; If the hybrid triggering mechanism is met, the current state of the drone is sent to the controller via a wireless channel, where it may be vulnerable to DoS attack sub-model attacks. The state detector sub-model assesses the observation pattern of the UAV's current state and uses the controller sub-model to calculate control commands; The controller sends control commands to the drone via a wireless channel, which may be vulnerable to DoS attack sub-model attacks. The drone receives and executes the instructions.
[0045] Example 3 This embodiment provides a UAV formation hybrid triggering cooperative control system for implementing the UAV formation hybrid triggering cooperative control method described in Embodiment 1 or 2, such as... Figure 4 As shown, it includes: The collaborative control system model building module is used to build a collaborative control system model for UAV formations, including: UAV dynamics sub-model, controlled output sub-model, DoS attack sub-model, hybrid triggering mechanism control sub-model, state detector sub-model, and controller sub-model. A closed-loop dynamic system model and its control target construction module is used to construct the closed-loop dynamic system model and its control target of the UAV formation cooperative control system model based on the UAV dynamic sub-model, DoS attack sub-model, hybrid triggering mechanism control sub-model, state detector sub-model and controller sub-model. The collaborative solution matrix inequality acquisition module is used to transform the control objective using the controlled output sub-model, hybrid triggering mechanism and Lyapunov function to obtain the collaborative solution matrix inequalities; The controller gain and trigger matrix acquisition module is used to solve the matrix inequalities of the collaborative solution using a preset tool to obtain a first matrix and a second matrix, and to calculate the controller gain and the trigger matrix of the hybrid triggering mechanism based on the first matrix and the second matrix. The collaborative control module is used to perform collaborative control of the UAV formation based on the controller gain and the trigger matrix of the hybrid triggering mechanism.
[0046] The same or similar labels correspond to the same or similar parts; The terms used to describe positional relationships in the accompanying drawings are for illustrative purposes only and should not be construed as limiting this patent. Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.
Claims
1. A method for hybrid triggering and cooperative control of unmanned aerial vehicle (UAV) formations, characterized in that, include: A model for a collaborative control system for UAV formations is constructed, including: a UAV dynamics sub-model, a controlled output sub-model, a DoS attack sub-model, a hybrid triggering mechanism control sub-model, a state detector sub-model, and a controller sub-model. Based on the aforementioned UAV dynamics sub-model, DoS attack sub-model, hybrid triggering mechanism control sub-model, state detector sub-model, and controller sub-model, a closed-loop dynamics system model and its control objective of the UAV formation cooperative control system model are constructed. The control objective is transformed using a controlled output sub-model, a hybrid triggering mechanism, and a Lyapunov function to obtain a jointly solved matrix inequality. The matrix inequalities obtained by the collaborative solution are solved using a preset tool to obtain a first matrix and a second matrix. The controller gain and the trigger matrix of the hybrid triggering mechanism are then calculated based on the first matrix and the second matrix. The trigger matrix based on the controller gain and hybrid triggering mechanism enables coordinated control of UAV formations.
2. The method for hybrid triggering and cooperative control of UAV formations according to claim 1, characterized in that, The UAV dynamics sub-model and controlled output sub-model include: The UAV dynamics sub-model is represented as follows: (1) in, It is the UAV state vector at time k. It is the actual control input executed after the attack at time k. Let k be the external disturbance at time k, and A, B, and D be known system matrices describing the characteristics of the system dynamics, control input, and disturbance effect, respectively. The system mode at time k; The controlled output sub-model is represented as follows: (2) in, Let be the performance output vector at time k, C be the relationship matrix between performance output and system state, and F be the relationship matrix between performance output and disturbance.
3. The method for hybrid triggering and cooperative control of UAV formations according to claim 2, characterized in that, The DoS attack sub-model is represented as follows: (3) in, This indicates that the DoS attack was successful; This indicates that the attack failed. Commands sent to the controller; These are the actual instructions received by the drone.
4. The method for hybrid triggering and cooperative control of UAV formations according to claim 3, characterized in that, The state detector sub-model, controller sub-model, and hybrid triggering mechanism control sub-model include: The state detector sub-model is represented as follows: (4) (5) The controller sub-model is represented as follows: (6) in, The controller gain to be calculated; The hybrid triggering mechanism control sub-model is represented as follows: (7) in, It is the measurement error between the current state and the last transmitted state. and These are the shape and threshold that control the triggering conditions, respectively; As a dynamic variable, its evolution is represented as: (8) in, >0, ensuring that when Even when the value is 0, a status message will still be sent.
5. The UAV formation hybrid triggering cooperative control method according to claim 4, characterized in that, The closed-loop dynamic system model and its control objective for constructing the aforementioned UAV formation cooperative control system model include: Substituting the DoS attack sub-model and the controller sub-model into the UAV dynamics sub-model, we obtain the closed-loop dynamics system model, as follows: (9) The control objective is expressed as follows: set up Performance represents the system's robustness to external disturbances. Performance metrics are defined as follows: in, This represents the disturbance attenuation level. In order to achieve the expected Performance-wise, the control objectives for the closed-loop system dynamics are set as follows: (k)z(k)- (k)w(k)]}<0 (10) in, It is the disturbance rejection performance index, and E is the control target.
6. The method for hybrid triggering and cooperative control of UAV formations according to claim 5, characterized in that, The control objective is transformed using a controlled output sub-model, a hybrid triggering mechanism, and a Lyapunov function, resulting in jointly solved matrix inequalities, including: A Lyapunov function is constructed based on the control objective. The Lyapunov function is then differentially calculated to obtain a differential criterion function. The differential criterion function is then scaled using a hybrid triggering mechanism to obtain an upper bound for the differential criterion function. By using the upper bound of the difference criterion function and the controlled output of the sensor to optimize the control objective, a matrix inequality that can be solved in a coordinated manner is obtained.
7. The method for hybrid triggering and cooperative control of UAV formations according to claim 6, characterized in that, A Lyapunov function is constructed based on the control objective. The Lyapunov function is then differentially calculated to obtain a difference criterion function. This difference criterion function is then scaled using a hybrid triggering mechanism to obtain its upper bound, which includes: (11) in, Let be the positive definite matrix to be solved; calculate The desired difference along the closed-loop system dynamics: Transforming the above equation, we get: (12) Calculate separately and : ...(13) (14) Combining equations (13) and (14), we get: (15) By scaling the difference criterion function using a hybrid triggering mechanism, the upper bound of the difference criterion function is obtained: right Scaling the terms, we get the following formula: (16) Substituting equation (16) into equation (15), we get Upper bound: (17)。 8. The method for hybrid triggering and cooperative control of UAV formations according to claim 7, characterized in that, By optimizing the control objective using the upper bound of the difference criterion function and the controlled output of the sensor, we obtain the jointly solved matrix inequalities, including: When reached When performance is required, it must meet the following requirements. , in, ; Substitute equations (2) and (17) into Introducing matrix variable substitution ( And by applying Schul's complement lemma, we obtain the co-solution criterion; The collaborative solution criterion is stated as follows: For a given Performance indicators and HTM parameters If a positive definite matrix exists sum matrix This makes it possible for all The following matrix inequalities hold: <0(18) The closed-loop system is stable and satisfies Performance indicators .
9. The method for hybrid triggering and cooperative control of UAV formations according to claim 8, characterized in that, The matrix inequalities obtained through collaborative solving are solved using a preset tool to obtain a first matrix and a second matrix. Based on the first and second matrices, the controller gain and the trigger matrix of the hybrid triggering mechanism are calculated, including: The preset tools include MATLAB's matrix inequality toolbox; The first matrix is The second matrix is ; The controller gain is calculated as follows: ; The trigger matrix calculation method for the hybrid triggering mechanism is as follows: .
10. A hybrid triggering and cooperative control system for UAV formations, used to implement the hybrid triggering and cooperative control method for UAV formations as described in claims 1-9, characterized in that, include: The collaborative control system model building module is used to build a collaborative control system model for UAV formations, including: UAV dynamics sub-model, controlled output sub-model, DoS attack sub-model, hybrid triggering mechanism control sub-model, state detector sub-model, and controller sub-model. A closed-loop dynamic system model and its control target construction module is used to construct the closed-loop dynamic system model and its control target of the UAV formation cooperative control system model based on the UAV dynamic sub-model, DoS attack sub-model, hybrid triggering mechanism control sub-model, state detector sub-model and controller sub-model. The collaborative solution matrix inequality acquisition module is used to transform the control objective using the controlled output sub-model, hybrid triggering mechanism and Lyapunov function to obtain the collaborative solution matrix inequalities; The controller gain and trigger matrix acquisition module is used to solve the matrix inequalities of the collaborative solution using a preset tool to obtain a first matrix and a second matrix, and to calculate the controller gain and the trigger matrix of the hybrid triggering mechanism based on the first matrix and the second matrix. The collaborative control module is used to perform collaborative control of the UAV formation based on the controller gain and the trigger matrix of the hybrid triggering mechanism.