Multi-unmanned aerial vehicle event-triggered consensus control strategy and system under communication delay constraint

Abstract

This invention provides a multi-UAV event-triggered consistency control strategy and system under communication delay constraints, relating to the field of UAV guidance and control technology. The strategy includes: constructing a follower model and a leader model for a discrete-time multi-UAV control system; constructing a communication network topology matrix and a distributed adaptive event-triggered communication strategy based on the follower and leader models; constructing UAV state prediction dynamic equations for the multi-UAV control system based on the communication network topology matrix; solving the UAV state prediction dynamic equations using the distributed adaptive event-triggered communication strategy to obtain the UAV state prediction results within a preset time period, and achieving consistency control of the UAVs. This invention reduces resource consumption and improves communication efficiency by constructing a distributed adaptive event-triggered communication strategy, and predicts the UAV state by constructing UAV state prediction dynamic equations, enabling UAVs to maintain consistency control even under high latency conditions.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) guidance and control technology, and in particular to a multi-UAV event triggering consistency control strategy and system under communication delay constraints. Background Technology

[0002] With the development of technology, drone technology is also gradually advancing and has been widely applied in many fields. Consistency problems are a key issue in drone cooperative control, attracting increasing attention from scholars. Drone system consistency requires all drones, under the condition of exchanging information only with their neighbors, to gradually converge their states (position, velocity, attitude, etc.) to a consistent value or jointly track a reference state given by the leader. Researchers typically use a leader-follower framework to construct distributed consistency protocols: the leader generates the desired trajectory or task instructions, and followers achieve cooperative tracking of the leader through local communication and distributed control laws, thereby completing tasks such as formation, coverage, cooperative reconnaissance, and cooperative transportation.

[0003] However, in practical engineering applications, drone swarms typically rely on wireless communication networks for information exchange. Limited by the communication power and bandwidth of onboard equipment, as well as the complex electromagnetic environment, communication networks inevitably suffer from non-ideal characteristics such as time-varying communication delays and bandwidth limitations. First, existing methods assume that the UAV can obtain flight information from surrounding UAVs in real time through communication. Consistency control schemes still implicitly assume "real-time or low-delay communication" or passively accept the existence of delays. However, under time-varying delay, congestion, and interruption conditions, it is easy for the UAV to fail to obtain flight information from surrounding UAVs in real time, and surrounding UAVs to fail to obtain the UAV's flight information in real time. This reduces the accuracy of the planned flight path, affects the stability of the control system, and greatly increases the probability of UAV collisions.

[0004] Secondly, most existing multi-UAV consistency control schemes employ time-triggered or fixed-period sampling communication methods, typically assuming frequent or even continuous communication between UAVs. However, in real-world industrial scenarios, communication bandwidth is limited, and frequent communication can lead to bandwidth constraints, easily causing communication congestion, data loss, and even system crashes. Therefore, selecting a suitable event-triggered transmission strategy to achieve the desired consistency control performance with minimal resource consumption is a challenging issue. Summary of the Invention

[0005] To overcome the poor consistency control effect of multiple UAVs under conditions of poor communication signal and limited communication bandwidth resources, this invention provides a consistency control strategy and system for multiple UAVs triggered by events under communication delay constraints.

[0006] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: This invention provides a consensus control strategy for multi-UAV event triggering under communication delay constraints, including: Constructing a multi-UAV dynamics model for a discrete-time multi-UAV control system; Based on the multi-UAV dynamics model, a communication network topology matrix and a distributed adaptive event-triggered communication strategy with a positive definite matrix are constructed. Based on the multi-UAV dynamics model and the communication network topology matrix, a multi-UAV state prediction dynamic equation with a control gain matrix under communication constraints is constructed. Solving for the positive definite matrix and the control gain matrix yields their expressions. Substituting the positive definite matrix into the distributed adaptive event-triggered communication strategy, the trigger interval range is obtained; Substitute the control gain matrix into the multi-UAV state prediction dynamics equation to solve for the UAV state prediction results within the trigger interval. Consistent control of the UAV is achieved based on the state prediction results.

[0007] Preferably, the construction of a multi-UAV dynamics model for a discrete-time multi-UAV control system includes: The discrete-time multi-UAV control system includes several follower UAVs and one leader UAV; The multi-drone dynamics model includes several follower drone dynamics models and one leader drone dynamics model; The dynamic equations for each of the aforementioned follower drones are:

[0008] The dynamic equations of the leader drone are as follows:

[0009] in, and Let these represent the state and control input of the i-th follower drone, respectively. The state of the leader drone is represented by A, the system matrix is ​​B, the input matrix is ​​N, and the number of follower drones is N. For discrete-time multi-UAV control systems, when At that time, the system ensures consistency in leadership.

[0010] Preferably, constructing a communication network topology matrix based on the multi-UAV dynamics model includes: The communication network between the leader drone and the follower drones is a directed connection, while the communication network between the follower drones is an undirected connection. Construct a communication network topology matrix among follower drones ,in , , This describes the communication status between follower drone i and follower drone j. When follower drone i and follower drone j are connected, =1, otherwise, =0; Construct a communication network topology matrix between leader drones and follower drones. When the leader drone connects with the follower drone, ,otherwise .

[0011] Preferably, a distributed adaptive event-triggered communication strategy with a positive definite matrix is ​​constructed based on the multi-UAV dynamics model, including: Each follower drone has an event trigger on its sensor side. The trigger time and trigger state of follower drone i are represented as follows: and ; The distributed adaptive event-triggered communication strategy is as follows:

[0012] Where q is a positive integer, representing the preset forced trigger interval. For trigger function The calculated trigger time interval, From the last trigger time Start time increment; Determine if at time [time] The trigger function that initiates communication. It is a positive definite matrix. , Let i be the state error between the follower drone i and the predicted value of the leader drone at time t. The adaptive event triggering parameters have the following adaptive rules:

[0013] in, .

[0014] Preferably, based on the multi-UAV dynamics model and the communication network topology matrix, a multi-UAV state prediction dynamic equation with a control gain matrix under communication constraints is constructed, including: make Indicates time Follower drone Its own internal time delay, Indicates at time From follower drones To follower drone Transmission delay, , It is a follower drone At any moment Received follower drone The timestamp of the latest sent / triggered packet, ; Define follower drone At any moment Available information set :

[0015] The information set Only at time Satisfying the delay inequality It is only effective at certain times, among which, For the follower drone i moment Control the input predicted value; At any time The state prediction dynamic equation for follower drone i is as follows:

[0016] in, These are the predicted state values ​​of follower drone i and the timestamps of follower drone j. status packet State prediction value, The number of steps that its information lags behind is subject to the physical constraint of communication delay: Similarly, the information lag steps from follower drone j to follower drone i are... , It is the control gain matrix.

[0017] Preferably, the positive definite matrix and the control gain matrix are solved to obtain their expressions, including: Solve for matrix P using the Riccati algebraic equation:

[0018] in, =0.5, For matrix ( The smallest eigenvalue of ). Solving for the positive definite matrix and control gain matrix using matrix P, we obtain: =

[0019] .

[0020] Preferably, the positive definite matrix is ​​substituted into the distributed adaptive event-triggered communication strategy to obtain the trigger interval interval, including: The trigger interval range includes the initial trigger interval range. and subsequent trigger interval ,in, Indicates time The follower drone's own internal time delay.

[0021] Preferably, the control gain matrix is ​​substituted into the multi-UAV state prediction dynamics equation to solve for the state prediction results of the UAV within the trigger interval, including solving for the state prediction results of the UAV within the initial trigger interval. Initially, all follower drones send a status update. , ; exist At that moment, all follower drones were in their initial state. The initial state of the leader drone Initial state of the neighboring follower drone Given, and with the assumptions ; Calculate the future discrete time points of the follower drone i common Step-by-step state prediction; exist At that time, due to ,have Based on state Calculate the follower drone i in Timing control input: , Subsequent iterations: At any given time, based on the multi-UAV state prediction dynamics equations, the updated [method / approach] is used. The state of follower drone i To make a prediction, the formula for predicting the state of the follower drone j is:

[0022] Based on the predicted state of follower drone j and the dynamic equation for drone state prediction, the state prediction result of follower drone i is obtained by solving the equation. Save a portion of the output data packets of the follower drone i , used for the next interval.

[0023] Preferably, the control gain matrix is ​​substituted into the multi-UAV state prediction dynamics equation to solve for the state prediction results of the UAV within the trigger interval interval, and the solution also includes solving for the state prediction results of the UAV within subsequent trigger interval intervals. exist At any moment, for information sets renew:

[0024] Calculate the follower drone i at future discrete time points common Step prediction ( ); exist At any given time, based on the multi-UAV state prediction dynamics equations, the updated [dynamics] is used. Regarding one's own state To make a prediction, the formula for predicting the state of the follower drone j is:

[0025] Based on the predicted state of follower drone j and the dynamic equation for drone state prediction, the state prediction result of follower drone i is obtained by solving the equation. Save a portion of the output of the follower drone i in a new data packet. , used for the next interval.

[0026] This invention also provides a multi-UAV event-triggered consistency control system under communication delay constraints, comprising: The dynamics model building module is used to build multi-UAV dynamics models for discrete-time multi-UAV control systems. A communication network topology matrix and communication strategy construction module is used to construct a communication network topology matrix and a distributed adaptive event-triggered communication strategy with a positive definite matrix based on the multi-UAV dynamics model. The multi-UAV state prediction dynamic equation construction module is used to construct multi-UAV state prediction dynamic equations with control gain matrix under communication constraints based on the multi-UAV dynamic model and communication network topology matrix. The module for solving the positive definite matrix and the control gain matrix is ​​used to solve the positive definite matrix and the control gain matrix to obtain their expressions. The trigger interval acquisition module is used to substitute the positive definite matrix into the distributed adaptive event triggering communication strategy to obtain the trigger interval. The state prediction result solving module is used to substitute the control gain matrix into the multi-UAV state prediction dynamic equation to solve the state prediction result of the UAV within the trigger interval. The consistency control module is used to implement consistency control of the UAV based on the state prediction results.

[0027] Compared with the prior art, the beneficial effects of the technical solution of the present invention are: This invention first constructs a follower model and a leader model for a discrete-time multi-UAV control system; then, based on the follower and leader models, it constructs a communication network topology matrix and a distributed adaptive event-triggered communication strategy; next, based on the communication network topology matrix, it constructs the UAV state prediction dynamics equations for the multi-UAV control system; finally, it uses the distributed adaptive event-triggered communication strategy to solve the UAV state prediction dynamics equations, obtaining the UAV state prediction results within a preset time period, and achieving consistent control of the UAVs based on the state prediction results. This invention reduces resource consumption and improves communication efficiency by constructing a distributed adaptive event-triggered communication strategy, and enables consistent control of UAVs even under high latency conditions by predicting the UAV state through the constructed UAV state prediction dynamics equations. Attached Figure Description

[0028] Figure 1 This is a flowchart illustrating a multi-UAV event-triggered consistency control strategy under communication delay constraints in Example 1. Figure 2 This is a schematic diagram of a multi-UAV event-triggered consistency control system under communication delay constraints in Example 3. Detailed Implementation

[0029] 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.

[0030] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0031] Example 1 This embodiment provides a consensus control strategy for multi-UAV event triggering under communication latency constraints, such as... Figure 1 As shown, it includes: Constructing a multi-UAV dynamics model for a discrete-time multi-UAV control system; Based on the multi-UAV dynamics model, a communication network topology matrix and a distributed adaptive event-triggered communication strategy with a positive definite matrix are constructed. Based on the multi-UAV dynamics model and the communication network topology matrix, a multi-UAV state prediction dynamic equation with a control gain matrix under communication constraints is constructed. Solving for the positive definite matrix and the control gain matrix yields their expressions. Substituting the positive definite matrix into the distributed adaptive event-triggered communication strategy, the trigger interval range is obtained; Substitute the control gain matrix into the multi-UAV state prediction dynamics equation to solve for the UAV state prediction results within the trigger interval. Consistent control of the UAV is achieved based on the state prediction results.

[0032] In the specific implementation process, firstly, a multi-UAV dynamic model of a discrete-time multi-UAV control system is constructed; then, based on the multi-UAV dynamic model, a communication network topology matrix and a distributed adaptive event-triggered communication strategy with a positive definite matrix are constructed; secondly, based on the multi-UAV dynamic model and the communication network topology matrix, a multi-UAV state prediction dynamic equation with a control gain matrix under communication constraints is constructed; then, the positive definite matrix and the control gain matrix are solved to obtain their expressions; the positive definite matrix is ​​substituted into the distributed adaptive event-triggered communication strategy to obtain the trigger interval; the control gain matrix is ​​substituted into the multi-UAV state prediction dynamic equation to solve for the UAV state prediction results within the trigger interval; finally, consistent control of the UAVs is achieved based on the state prediction results. This invention reduces resource consumption and improves communication efficiency by constructing a distributed adaptive event-triggered communication strategy, and by constructing a UAV state prediction dynamic equation to predict the UAV state, enabling the UAVs to maintain consistent control even under high latency conditions.

[0033] Example 2 This embodiment provides a consensus control strategy for multi-UAV event triggering under communication delay constraints, including: Constructing a multi-UAV dynamics model for a discrete-time multi-UAV control system; Based on the multi-UAV dynamics model, a communication network topology matrix and a distributed adaptive event-triggered communication strategy with a positive definite matrix are constructed. Based on the multi-UAV dynamics model and the communication network topology matrix, a multi-UAV state prediction dynamic equation with a control gain matrix under communication constraints is constructed. Solving for the positive definite matrix and the control gain matrix yields their expressions. Substituting the positive definite matrix into the distributed adaptive event-triggered communication strategy, the trigger interval range is obtained; Substitute the control gain matrix into the multi-UAV state prediction dynamics equation to solve for the UAV state prediction results within the trigger interval. Consistent control of the UAV is achieved based on the state prediction results.

[0034] It should be noted that, in this embodiment, the construction of the multi-UAV dynamics model for the discrete-time multi-UAV control system includes: The discrete-time multi-UAV control system includes several follower UAVs and one leader UAV; The multi-drone dynamics model includes several follower drone dynamics models and one leader drone dynamics model; The dynamic equations for each of the aforementioned follower drones are:

[0035] The dynamic equations of the leader drone are as follows:

[0036] in, and Let these represent the state and control input of the i-th follower drone, respectively. The state of the leader drone is represented by A, the system matrix is ​​B, the input matrix is ​​N, and the number of follower drones is N. For discrete-time multi-UAV control systems, when At that time, the system ensures consistency in leadership.

[0037] It should be noted that, in this embodiment, constructing the communication network topology matrix based on the multi-UAV dynamics model includes: The communication network between the leader drone and the follower drones is a directed connection, while the communication network between the follower drones is an undirected connection. Construct a communication network topology matrix among follower drones ,in , , This describes the communication status between follower drone i and follower drone j. When follower drone i and follower drone j are connected, =1, otherwise, =0; Construct a communication network topology matrix between leader drones and follower drones. When the leader drone connects with the follower drone, ,otherwise .

[0038] It should be noted that, in this embodiment, the distributed adaptive event-triggered communication strategy with a positive definite matrix is ​​constructed based on the multi-UAV dynamics model, including: Each follower drone has an event trigger on its sensor side. The trigger time and trigger state of follower drone i are represented as follows: and ; The distributed adaptive event-triggered communication strategy is as follows:

[0039] Where q is a positive integer, representing the preset forced trigger interval. For trigger function The calculated trigger time interval, From the last trigger time Start time increment; Determine if at time [time] The trigger function that initiates communication. It is a positive definite matrix. , Let i be the state error between the follower drone i and the predicted value of the leader drone at time t. The adaptive event triggering parameters have the following adaptive rules:

[0040] in, .

[0041] It should be noted that, in this embodiment, based on the multi-UAV dynamics model and the communication network topology matrix, a multi-UAV state prediction dynamic equation with a control gain matrix under communication constraints is constructed, including: make Indicates time Follower drone Its own internal time delay, Indicates at time From follower drones To follower drone Transmission delay, , It is a follower drone At any moment Received follower drone The timestamp of the latest sent / triggered packet, ; Define follower drone At any moment Available information set :

[0042] The information set Only at time Satisfying the delay inequality It is only effective at certain times, among which, For the follower drone i moment Control the input predicted value; At any time The state prediction dynamic equation for follower drone i is as follows:

[0043] in, These are the predicted state values ​​of follower drone i and the timestamps of follower drone j. status packet State prediction value, The number of steps that its information lags behind is subject to the physical constraint of communication delay: Similarly, the information lag steps from follower drone j to follower drone i are... , It is the control gain matrix.

[0044] It should be noted that, in this embodiment, the positive definite matrix and the control gain matrix are solved to obtain their expressions, including: Solve for matrix P using the Riccati algebraic equation:

[0045] in, =0.5, For matrix ( The smallest eigenvalue of ). Solving for the positive definite matrix and control gain matrix using matrix P, we obtain: =

[0046] .

[0047] It should be noted that, in this embodiment, substituting the positive definite matrix into the distributed adaptive event-triggered communication strategy yields the trigger interval range, including: The trigger interval range includes the initial trigger interval range. and subsequent trigger interval ,in, Indicates time The follower drone's own internal time delay.

[0048] It should be noted that, in this embodiment, the control gain matrix is ​​substituted into the multi-UAV state prediction dynamics equation to solve the state prediction result of the UAV within the trigger interval, including solving the state prediction result of the UAV within the initial trigger interval. Initially, all follower drones send a status update. , ; exist At that moment, all follower drones were in their initial state. The initial state of the leader drone Initial state of the neighboring follower drone Given, and with the assumptions ; Calculate the future discrete time points of the follower drone i common Step-by-step state prediction; exist At that time, due to ,have Based on state Calculate the follower drone i in Timing control input: , Subsequent iterations: At any given time, based on the multi-UAV state prediction dynamics equations, the updated [method / approach] is used. The state of follower drone i To make a prediction, the formula for predicting the state of the follower drone j is:

[0049] Based on the predicted state of follower drone j and the dynamic equation for drone state prediction, the state prediction result of follower drone i is obtained by solving the equation. Save a portion of the output data packets of the follower drone i , used for the next interval.

[0050] It should be noted that, in this embodiment, the control gain matrix is ​​substituted into the multi-UAV state prediction dynamic equation to solve the state prediction result of the UAV within the trigger interval interval, and the solution also includes solving the state prediction result of the UAV within the subsequent trigger interval interval. exist At any moment, for information sets renew:

[0051] Calculate the follower drone i at future discrete time points common Step prediction ( ); exist At any given time, based on the multi-UAV state prediction dynamics equations, the updated [dynamics] is used. Regarding one's own state To make a prediction, the formula for predicting the state of the follower drone j is:

[0052] Based on the predicted state of follower drone j and the dynamic equation for drone state prediction, the state prediction result of follower drone i is obtained by solving the equation. Save a portion of the output of the follower drone i in a new data packet. , used for the next interval.

[0053] Example 3 This embodiment provides a multi-UAV event-triggered consistency control system under communication delay constraints, used to implement the multi-UAV event-triggered consistency control strategy under communication delay constraints described in Embodiment 1 or 2, such as... Figure 2 As shown, it includes: The dynamics model building module is used to build multi-UAV dynamics models for discrete-time multi-UAV control systems. A communication network topology matrix and communication strategy construction module is used to construct a communication network topology matrix and a distributed adaptive event-triggered communication strategy with a positive definite matrix based on the multi-UAV dynamics model. The multi-UAV state prediction dynamic equation construction module is used to construct multi-UAV state prediction dynamic equations with control gain matrix under communication constraints based on the multi-UAV dynamic model and communication network topology matrix. The module for solving the positive definite matrix and the control gain matrix is ​​used to solve the positive definite matrix and the control gain matrix to obtain their expressions. The trigger interval acquisition module is used to substitute the positive definite matrix into the distributed adaptive event triggering communication strategy to obtain the trigger interval. The state prediction result solving module is used to substitute the control gain matrix into the multi-UAV state prediction dynamic equation to solve the state prediction result of the UAV within the trigger interval. The consistency control module is used to implement consistency control of the UAV based on the state prediction results.

[0054] 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 consensus control strategy for multiple UAV events triggered under communication delay constraints, characterized in that, include: Constructing a multi-UAV dynamics model for a discrete-time multi-UAV control system; Based on the multi-UAV dynamics model, a communication network topology matrix and a distributed adaptive event-triggered communication strategy with a positive definite matrix are constructed. Based on the multi-UAV dynamics model and the communication network topology matrix, a multi-UAV state prediction dynamic equation with a control gain matrix under communication constraints is constructed. Solving for the positive definite matrix and the control gain matrix yields their expressions. Substituting the positive definite matrix into the distributed adaptive event-triggered communication strategy, the trigger interval range is obtained; Substitute the control gain matrix into the multi-UAV state prediction dynamics equation to solve for the UAV state prediction results within the trigger interval. Consistent control of the UAV is achieved based on the state prediction results.

2. The multi-UAV event-triggered consistency control strategy under communication delay constraints according to claim 1, characterized in that, Constructing a multi-UAV dynamics model for a discrete-time multi-UAV control system, including: The discrete-time multi-UAV control system includes several follower UAVs and one leader UAV; The multi-drone dynamics model includes several follower drone dynamics models and one leader drone dynamics model; The dynamic equations for each of the aforementioned follower drones are: The dynamic equations of the leader drone are as follows: in, and These represent the state and control input of the i-th follower drone, respectively. The state of the leader drone is represented by A, the system matrix is ​​B, the input matrix is ​​N, and the number of follower drones is N. For discrete-time multi-UAV control systems, when At that time, the system ensures consistency in leadership.

3. The multi-UAV event-triggered consistency control strategy under communication delay constraints according to claim 2, characterized in that, The communication network topology matrix is ​​constructed based on the multi-UAV dynamics model, including: The communication network between the leader drone and the follower drones is a directed connection, while the communication network between the follower drones is an undirected connection. Construct a communication network topology matrix among follower drones ,in , , This describes the communication status between follower drone i and follower drone j. When follower drone i and follower drone j are connected, =1, otherwise, =0; Construct a communication network topology matrix between leader drones and follower drones. When the leader drone connects with the follower drone, ,otherwise .

4. The multi-UAV event-triggered consistency control strategy under communication delay constraints according to claim 3, characterized in that, Based on the aforementioned multi-UAV dynamics model, a distributed adaptive event-triggered communication strategy with a positive definite matrix is ​​constructed, including: Each follower drone has an event trigger on its sensor side. The trigger time and trigger state of follower drone i are represented as follows: and ; The distributed adaptive event-triggered communication strategy is as follows: Where q is a positive integer, representing the preset forced trigger interval. For trigger function The calculated trigger time interval, From the last trigger time Start time increment; Determine if at time [time] The trigger function that initiates communication. It is a positive definite matrix. , Let i be the state error between the follower drone i and the predicted value of the leader drone at time t. The adaptive event triggering parameters have the following adaptive rules: in, .

5. A multi-UAV event-triggered consistency control strategy under communication delay constraints as described in claim 4, characterized in that, Based on the aforementioned multi-UAV dynamics model and communication network topology matrix, a multi-UAV state prediction dynamic equation with a control gain matrix under communication constraints is constructed, including: make Indicates time Follower drone Its own internal time delay, Indicates at time From follower drones To follower drone Transmission delay, , It is a follower drone At any moment Received follower drone The timestamp of the latest sent / triggered packet, ; Define follower drone At any moment Available information set : The information set Only at time Satisfying the delay inequality It is only effective at certain times, among which, For the follower drone i moment Control the input predicted value; At any time The state prediction dynamic equation for follower drone i is as follows: in, These are the predicted state values ​​of follower drone i and the timestamps of follower drone j. status packet State prediction value, The number of steps that its information lags behind is subject to the physical constraint of communication delay: Similarly, the information lag steps from follower drone j to follower drone i are... Delay constraint is , It is the control gain matrix.

6. The multi-UAV event-triggered consistency control strategy under communication delay constraints according to claim 5, characterized in that, Solving for the positive definite matrix and the control gain matrix yields their expressions, including: Solve for matrix P using the Riccati algebraic equation: in, =0.5, For matrix ( The smallest eigenvalue of ). Solving for the positive definite matrix and control gain matrix using matrix P, we obtain: = 。 7. A multi-UAV event-triggered consistency control strategy under communication delay constraints as described in claim 6, characterized in that, Substituting the positive definite matrix into the distributed adaptive event-triggered communication strategy yields the trigger interval range, including: The trigger interval range includes the initial trigger interval range. and subsequent trigger interval ,in, Indicates time The follower drone's own internal time delay.

8. A multi-UAV event-triggered consistency control strategy under communication delay constraints as described in claim 7, characterized in that, Substitute the control gain matrix into the multi-UAV state prediction dynamics equation to solve the state prediction results of the UAV within the trigger interval, including solving the state prediction results of the UAV within the initial trigger interval. Initially, all follower drones send a status update. , ; exist At that moment, all follower drones were in their initial state. The initial state of the leader drone Initial state of the neighboring follower drone Given, and with the assumptions ; Calculate the future discrete time points of the follower drone i common Step-by-step state prediction; exist At that time, due to ,have Based on state Calculate the follower drone i in Timing control input: , Subsequent iterations: At any given time, based on the multi-UAV state prediction dynamics equations, the updated [method / approach] is used. The state of follower drone i To make a prediction, the formula for predicting the state of the follower drone j is: Based on the predicted state of follower drone j and the dynamic equation for drone state prediction, the state prediction result of follower drone i is obtained by solving the equation. Save a portion of the output data packets of the follower drone i , used for the next interval.

9. A multi-UAV event-triggered consistency control strategy under communication delay constraints as described in claim 7, characterized in that, Substituting the control gain matrix into the multi-UAV state prediction dynamics equation, the state prediction results of the UAV within the trigger interval are solved, and the state prediction results of the UAV within the subsequent trigger interval are also solved. exist At any moment, for information sets renew: Calculate the follower drone i at future discrete time points common Step prediction ( ); exist At any given time, based on the multi-UAV state prediction dynamics equations, the updated [dynamics] is used. Regarding one's own state To make a prediction, the formula for predicting the state of the follower drone j is: Based on the predicted state of follower drone j and the dynamic equation for drone state prediction, the state prediction result of follower drone i is obtained by solving the equation. Save a portion of the output of the follower drone i in a new data packet. , used for the next interval.

10. A multi-UAV event-triggered consistency control system under communication delay constraints, used to implement the multi-UAV event-triggered consistency control strategy under communication delay constraints as described in claims 1-9, characterized in that, include: The dynamics model building module is used to build multi-UAV dynamics models for discrete-time multi-UAV control systems. A communication network topology matrix and communication strategy construction module is used to construct a communication network topology matrix and a distributed adaptive event-triggered communication strategy with a positive definite matrix based on the multi-UAV dynamics model. The multi-UAV state prediction dynamic equation construction module is used to construct multi-UAV state prediction dynamic equations with control gain matrix under communication constraints based on the multi-UAV dynamic model and communication network topology matrix. The module for solving the positive definite matrix and the control gain matrix is ​​used to solve the positive definite matrix and the control gain matrix to obtain their expressions. The trigger interval acquisition module is used to substitute the positive definite matrix into the distributed adaptive event triggering communication strategy to obtain the trigger interval. The state prediction result solving module is used to substitute the control gain matrix into the multi-UAV state prediction dynamic equation to solve the state prediction result of the UAV within the trigger interval. The consistency control module is used to implement consistency control of the UAV based on the state prediction results.

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