A preset time containment control method for a UAV cluster based on a self-triggering mechanism

Abstract

The application discloses a preset time containment control method for a UAV cluster based on a self-triggering mechanism and belongs to the technical field of cooperative control of a UAV cluster. Firstly, a feedback linearization method is adopted to convert the kinematics and dynamics model of a fixed-wing UAV into a second-order integrator model, then a communication relationship among the UAVs is described based on a directed graph theory, and a desired convex hull formed by a leader is set; a preset time state observer is designed to realize estimation of acceleration, speed and position state of the convex hull within a preset time; a preset time containment controller based on a self-triggering mechanism is designed to realize preset time containment control based on the state observation value; and a self-triggering condition is defined to determine when to communicate and update the controller. Under the premise of realizing the preset time containment control, the self-triggering mechanism is designed to effectively avoid continuous communication and continuous updating of the controller, and asynchronous communication among the UAVs is realized.

Description

Technical Field

[0001] This invention relates to the field of collaborative control technology for unmanned aerial vehicle (UAV) swarms, specifically to a pre-set time-bound control method for UAV swarms based on a self-triggering mechanism. Background Technology

[0002] Unmanned Aerial Vehicles (UAVs) have been widely used in both military and civilian fields due to their mobility, ease of deployment, and low operating costs. However, with the increasing scale of missions and the growing complexity of operating environments, relying on a single UAV is no longer sufficient to meet the demands of complex tasks in terms of coverage, execution efficiency, and system reliability. In recent years, with the development of technologies such as unmanned systems, artificial intelligence, and communication networks, multiple UAVs have formed UAV swarms through information sharing and collaborative control, enabling them to collaboratively complete various complex tasks such as search and reconnaissance, target surveillance, power line inspection, and precision agriculture. Compared to single UAVs, UAV swarms offer higher mission execution efficiency, stronger environmental adaptability, better robustness, and scalability. Therefore, UAV swarm collaborative control has become an important research area in the field of unmanned systems. Among these, containment control is a key research hotspot in the field of UAV swarm collaborative control. It aims to drive all follower UAVs into the interior of a convex hull formed by multiple leader UAVs, which has significant application value in tasks such as collision avoidance, escort, and encirclement in complex environments.

[0003] However, most existing research results can only achieve asymptotic convergence, meaning that the UAV swarm system can only complete the containment control task when time approaches infinity, which is difficult to meet the application requirements of high timeliness. Although finite-time control and fixed-time control methods improve the convergence speed to some extent, their convergence time is affected by the initial state of the system or is difficult to set explicitly through control parameters, causing inconvenience in practical applications. In addition, traditional control methods usually require continuous communication between UAVs, and the controller also needs to continuously perform calculations and updates. This places extremely high demands on communication bandwidth, onboard computing resources, and actuator execution capabilities, greatly limiting the engineering application of related methods in the context of the trend of UAV miniaturization and micro-miniaturization. In recent years, preset time control methods have emerged, allowing users to set the convergence time themselves, enabling UAV swarms to complete convergence within any specified preset time, which has strong practicality. However, existing UAV swarm containment control methods based on preset time control still rely on continuous communication and continuous controller updates. A few studies that combine event triggering mechanisms still cannot simultaneously avoid the requirements of continuous communication, continuous controller updates, and synchronous communication, resulting in wasted system resources and difficulties in practical deployment.

[0004] To address the shortcomings of existing UAV swarm containment control methods, such as slow convergence speed, reliance on continuous communication, reliance on continuous controller updates, and the need for synchronous communication, this invention proposes a UAV swarm preset time containment control method based on a self-triggering mechanism. Summary of the Invention

[0005] To address the technical problems existing in the prior art, this application provides a pre-set time-coverage control method for UAV swarms based on a self-triggering mechanism, belonging to the field of UAV swarm cooperative control technology. First, a feedback linearization method is used to transform the kinematic and dynamic models of fixed-wing UAVs into second-order integrator models. Then, the communication relationships between UAVs are described based on directed graph theory, and the desired convex hull formed by the leader is defined. A pre-set time state observer is designed to estimate the acceleration, velocity, and position state of the convex hull within a pre-set time. Based on the state observations, a pre-set time-coverage controller based on a self-triggering mechanism is designed to implement pre-set time-coverage control. Self-triggering conditions are defined to determine when communication and controller updates occur. This invention, while achieving pre-set time-coverage control, effectively avoids continuous communication and continuous controller updates through the design of a self-triggering mechanism, while simultaneously realizing asynchronous communication between UAVs.

[0006] A pre-set time tolerance control method for drone swarms based on a self-triggering mechanism, such as Figure 1 As shown, it includes the following steps:

[0007] Step 1: Establish a six-degree-of-freedom kinematic and dynamic model of a fixed-wing UAV, and use feedback linearization to transform the kinematic and dynamic model into a second-order integrator model;

[0008] Step 2: Establish the communication topology between UAVs based on directed graph theory and define the expected convex hull formed by the navigator;

[0009] Step 3: Based on the communication topology between UAVs, and according to the second-order integrator model, design a preset time state observer;

[0010] The preset time state observer generates a desired convex hull state estimate; the desired convex hull state estimate includes acceleration, velocity, and position state;

[0011] The preset time state observer includes a preset time acceleration state observer, a preset time velocity state observer, and a preset time position state observer;

[0012] Step 4: Based on the auxiliary tracking error and the expected convex hull state estimate in Step 3, construct a preset time containment controller for the follower UAV according to the second-order integrator model, so that the follower UAV enters the expected convex hull region formed by the leader UAV within a preset time, thereby achieving the UAV swarm containment control objective; the auxiliary tracking error includes position auxiliary tracking error and velocity auxiliary tracking error.

[0013] Step 5: Based on the preset time-inclusive controller designed in Step 4, design self-triggering conditions, calculate the communication time and the update time of the preset time-inclusive controller, and establish an asynchronous communication mechanism.

[0014] Furthermore, in step 1, the kinematic and dynamic model is constructed as follows:

[0015] Fixed-wing unmanned aerial vehicle (UAV) swarm systems include ( ) drones, of which A follower drone, A Navigator drone; The Navigator drone forms a three-dimensional convex hull. The follower drone enters the convex hull within a preset time according to the preset time containment controller; the drone The kinematic and dynamic models are as follows:

[0016] (1);

[0017] In the formula: positive superscript Indicates the derivative symbol; , among which, when The time indicates the follower drone, This indicates a group of follower drones, when The time indicates the Navigator drone, This indicates a collection of leading drones; , and They represent drones The three-dimensional position; , and These represent ground speed, pitch angle, and yaw angle, respectively. , and These represent the original control inputs for ground speed, pitch, and yaw direction, respectively. It represents the acceleration due to gravity.

[0018] Furthermore, in step 1, the second-order integrator model is as follows:

[0019] (2);

[0020] ;

[0021] ;

[0022] ;

[0023] in, , and They represent drones In three-dimensional space Virtual control inputs, position, and speed below the axis; They represent drones exist The coordinate position under the axis; , , They represent drones exist Control input below the axis; T represents transpose; virtual control input Respectively with the original control input , and The conversion relationships are as follows:

[0024] (3).

[0025] Furthermore, in step 2, the communication topology between UAVs is established based on directed graph theory, and the expected convex hull formed by the navigator is defined:

[0026] Step 2.1, the communication relationships between UAVs are represented by a directed graph. Description, in which Represents a set of drone nodes. Represent the set of edges; define the adjacency matrix. , It is the set of real numbers; Defined as an adjacency matrix The element represents the drone. and drones The communication topology; if ,but , indicating drone Can receive drones Communication information and drones Defined as a drone Neighboring drones, otherwise Define the degree matrix ,in Define the Laplace matrix. ; Represents the Laplace matrix The elements are derived from the degree matrix. and adjacency matrix Calculations show that the drone swarm consists of... Follower drones and Composed of a navigator drone, Laplace matrix Split into a matrix sum matrix Laplace matrix The breakdown is as follows:

[0027] ;

[0028] In the formula, The dimension is The zero matrix; The dimension is The zero matrix;

[0029] Step 2.2, define the desired convex hull formed by the navigator drone:

[0030] The drone swarm preset time containment controller based on the self-triggering mechanism enables follower drones to enter the convex hull formed by the leader drone within a preset time, and the speed is consistent with the speed of the convex hull.

[0031] The expression for the expected convex hull formed by the Navigator UAV is:

[0032] (4);

[0033] In the formula, and Let represent the position and velocity of UAV j in three-dimensional space at time t, respectively; This indicates the total preset time; the follower drone will operate within the total preset time. It enters the convex hull formed by the Navigator drone. = + + + T1, T2, T3, and T4 are the preset times for the preset time acceleration state observer, preset time velocity state observer, preset time position state observer, and preset time containment controller, respectively. Representation matrix Elements of the matrix Defined as ;element Existence Relationship: , That is, satisfying the convex hull Mathematical definition of:

[0034] (5);

[0035] in, Indicates the formation of the convex hull A collection of elements; Represents a set Elements in; Represents element The weights; Represents a set The number of elements contained.

[0036] Furthermore, in step 3, the preset time acceleration state observer:

[0037] (6);

[0038] In the formula, and These represent follower drones. The estimated value of the acceleration state of the convex hull for the following UAV j; This represents the preset time acceleration state observer gain parameter; This represents the nonlinear term parameters of the preset time acceleration state observer; This represents the time-varying scaling function of the preset time acceleration state observer. Indicates the initial time; ;Specified manually according to user needs; Represents the time-varying scaling function The constant term; Represents the time-varying scaling function The power exponent; based on a preset time acceleration state observer, the follower drone will... The acceleration state of the convex hull is estimated internally.

[0039] Furthermore, in step 3, the preset time-velocity state observer:

[0040] (7);

[0041] In the formula, and These represent follower drones. The estimated values ​​of the velocity state of the convex hull for the follower drone j; The time-varying scaling function represents the preset time-velocity state observer: It is manually specified according to user needs; Represents the time-varying scaling function The constant term; Represents the time-varying scaling function The power exponent; based on a preset time-velocity state observer, the follower drone will... The velocity state of the convex hull is estimated internally.

[0042] Furthermore, in step 3, the preset time position state observer:

[0043] (8);

[0044] In the formula, and These represent follower drones. The estimated values ​​of the convex hull position state of the follower drone j; The time-varying scaling function represents the preset time location state observer: It is manually specified according to user needs; Represents the time-varying scaling function The constant term; Represents the time-varying scaling function The power exponent; based on a preset time, position, and state observer, the follower drone will... The position and state of the convex hull are estimated internally.

[0045] Based on the established preset time acceleration, velocity, and position state observers, the follower drone will sequentially... , and Within a given time period, the acceleration, velocity, and position state of the convex hull are estimated respectively.

[0046] Furthermore, in step 4, the preset time-coverage controller of the follower drone... as follows:

[0047] (9);

[0048] In the formula: and These represent follower drones. The latest trigger moment for the follower drone j; This indicates the preset time encompassing the position error feedback gain parameter of the controller; This indicates the speed error feedback gain of the preset time-inclusive controller; and These represent follower drones. Position and velocity assist status:

[0049] (10);

[0050] In the formula: The time-varying scaling function of the preset time-encompassing controller is defined as:

[0051] (11);

[0052] In the formula: It is manually specified according to user needs; Represents the time-varying scaling function The constant term; Represents the time-varying scaling function The power exponent, based on a preset time-bound controller, will cause the follower drone to... It enters the convex hull formed by the Navigator drone.

[0053] Furthermore, in step 5, the process of establishing the asynchronous communication mechanism is as follows:

[0054] Step 5.1, define the auxiliary tracking error, which includes the position auxiliary tracking error. and position-assisted tracking error ;

[0055] (12);

[0056] Step 5.2, define the measurement error, which includes the position measurement error. Speed ​​measurement error and acceleration measurement error ;

[0057] (13);

[0058] Step 5.3: Design the follower drone based on the auxiliary tracking error and measurement error. The self-triggering condition is:

[0059] (14);

[0060] In the formula: Indicates the self-trigger adjustment coefficient;

[0061] Step 5.4, according to the self-triggering condition, when (14) is satisfied, the event is triggered: follower drone. Broadcast messages to neighboring drones and update the preset time-inclusive controller (9) based on the latest broadcast messages from neighboring drones.

[0062] Compared with the prior art, the technical effects of the present invention are as follows:

[0063] (1) The present invention designs a preset time containment controller, which enables the UAV swarm system to achieve containment control within a preset time specified by the user, significantly improving the convergence speed and the robustness of the control system, and is better suited to application scenarios with high task timeliness requirements.

[0064] (2) This invention designs a self-triggering mechanism that avoids continuous updates and communication of the controller while achieving preset time-bound control. The follower drone only communicates when an event is triggered and updates the controller based on the latest triggering state of the neighboring drone, saving system energy and avoiding wear and tear on the hardware caused by high-frequency controller updates;

[0065] (3) The present invention designs an asynchronous communication strategy based on a self-triggering mechanism, which adopts an asynchronous communication mechanism similar to "broadcast-receive": when an event is triggered, the follower UAV broadcasts a message and updates the controller according to the latest broadcast message of the neighboring UAV, thus realizing asynchronous communication and avoiding the requirements of clock height synchronization and high-frequency communication that exist in synchronous communication, and has stronger practicality. Attached Figure Description

[0066] Figure 1 This is a flowchart of the method of the present invention;

[0067] Figure 2 This is a communication topology diagram of an unmanned aerial vehicle (UAV) swarm system according to an embodiment of the present invention;

[0068] Figure 3 This is a diagram showing the error in the state observation of a follower drone according to an embodiment of the present invention.

[0069] Figure 4 This is a flight trajectory diagram of a drone swarm according to an embodiment of the present invention;

[0070] Figure 5 This is a tracking error diagram of a follower drone according to an embodiment of the present invention;

[0071] Figure 6 This is a communication timeline diagram of the follower drone in an embodiment of the present invention;

[0072] Figure 7 This is a timeline of controller updates for a follower drone according to an embodiment of the present invention. Detailed Implementation

[0073] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0074] This invention proposes a pre-set time-inclusive control method for UAV swarms based on a self-triggering mechanism. This method, while achieving pre-set time-inclusive control, effectively avoids continuous communication and controller updates through the design of a self-triggering mechanism, and simultaneously enables asynchronous communication between UAVs. The invention is further described below with reference to the accompanying drawings and embodiments.

[0075] A pre-set time tolerance control method for drone swarms based on a self-triggering mechanism, such as Figure 1 As shown, it includes the following steps:

[0076] Step 1: Establish a six-degree-of-freedom kinematic and dynamic model of a fixed-wing UAV, and use feedback linearization to transform the kinematic and dynamic model into a second-order integrator model, providing a unified mathematical model basis for the subsequent design of a UAV swarm preset time state observer and preset time containment controller;

[0077] Step 2: Establish the communication topology between UAVs based on directed graph theory, and set the expected convex hull formed by the leader to provide target constraints for the state estimation and containment control of the follower UAVs.

[0078] Step 3: Addressing the issue that some follower UAVs cannot directly obtain complete state information of the navigator UAV under communication topology constraints, based on the communication topology structure constructed in Step 2, and according to the second-order integrator model, a preset time state observer is designed to quickly estimate the expected convex hull state information formed by the navigator, providing state feedback information for the subsequent containment controller; the preset time state observer generates the expected convex hull state estimate formed by the navigator; the expected convex hull state estimate includes acceleration, velocity, and position state;

[0079] The preset time state observer includes a preset time acceleration state observer, a preset time velocity state observer, and a preset time position state observer;

[0080] Based on the preset time acceleration state observer, preset time velocity state observer, and preset time position state observer, the acceleration, velocity, and position state of the desired convex hull formed by the navigator are estimated sequentially.

[0081] Step 4: Based on the expected convex hull state estimate generated by the preset time state observer in Step 3 and the designed auxiliary state variables, construct a preset time containment controller for the follower drones, so that the follower drones enter the expected convex hull region formed by the leader drones within a preset time, thereby achieving the drone swarm containment control objective.

[0082] Step 5: Based on the preset time-inclusive controller designed in Step 4, design self-triggering conditions, calculate the communication time and the update time of the preset time-inclusive controller, and establish an asynchronous communication mechanism.

[0083] In this embodiment, a fixed-wing UAV swarm system consisting of 4 leader UAVs and 8 follower UAVs is considered, and its initial state is shown in Table 1.

[0084] Table 1 Initial state parameters of the UAV swarm system

[0085]

[0086] The specific steps for containment control according to the method proposed in this invention are as follows:

[0087] Step 1: Establish a six-degree-of-freedom kinematic and dynamic model of the fixed-wing UAV, and perform feedback linearization on the model to obtain a second-order integrator model:

[0088] Step 1.1: The fixed-wing UAV swarm system comprises 12 UAVs, including 8 follower UAVs and 4 leader UAVs. The 4 leader UAVs form a three-dimensional convex hull, and the 8 follower UAVs enter the convex hull within a preset time according to the preset time control. (UAVs) The kinematic and dynamic models are as follows:

[0089] (17);

[0090] In the formula: algebraic table below Indicates the drone number, where Indicates a follower drone, This refers to the Navigator drone; , , They represent drones The three-dimensional position, in units of ; Ground speed is expressed in units of 1. ; Indicates pitch angle, unit is ; Indicates the yaw angle, in units of ; , , These represent the control inputs in the ground speed, pitch, and yaw directions, respectively, in units of... ; Represents gravitational acceleration, with units of . .

[0091] Step 1.2: Transform the fixed-wing UAV model into a second-order integrator model using feedback linearization:

[0092] (18);

[0093] in: ,

[0094] and They represent drones Velocity, position, and virtual control input in three-dimensional space, in units of respectively , and Among them, virtual control input With raw control input , , The conversion relationships are as follows:

[0095] (19);

[0096] Step 2: Establish the communication topology between UAVs based on directed graph theory, and define the expected convex hull formed by the navigator:

[0097] Step 2.1: The communication relationships between UAVs are represented by a directed graph. Description, such as Figure 2 As shown in the figure, Represents a set of drone nodes. Represent the set of edges. Define the adjacency matrix. :like ,but , indicating drone Can receive drones Communication information and drones Defined as a drone Neighboring drones; otherwise, Specifically, the adjacency matrix Set to:

[0098] (20);

[0099] Define degree matrix ,in :

[0100] (twenty one);

[0101] Define the Laplace matrix :

[0102] (twenty two);

[0103] Step 2.2: Define the desired convex hull formed by the Navigator UAV:

[0104] (twenty three);

[0105] According to With the set desired trajectory and the initial state shown in Table 1, the four navigator drones fly at the same speed, forming a tetrahedral convex hull.

[0106] Step 3: For follower drones in the drone swarm system where the status information of the leader drone cannot be directly obtained, a preset time state observer is designed to sequentially estimate the acceleration, velocity, and position state of the convex hull formed by the leader drone:

[0107] Step 3.1: Design a preset time acceleration state observer:

[0108] (twenty four);

[0109] In the formula: Indicates follower drone An estimate of the acceleration state of the convex hull, in units of ; Set to 2; Set to 3; Representing the time-varying scaling function: The initial time is represented by 0s; This indicates the preset time, set to 0.2s; Set to 2; Set to 2. Based on the preset time acceleration state observer, the follower drone will estimate the acceleration state of the convex hull within a preset time of 0.2s.

[0110] Step 3.2: Design a preset time-velocity state observer:

[0111] (25);

[0112] In the formula: Indicates follower drone The estimated value of the convex hull velocity state, in units of ; Representing the time-varying scaling function: This indicates the preset time, set to 0.4s; Set to 2; Set to 2. Based on the preset time velocity state observer, the follower drone will estimate the velocity state of the convex hull within a preset time of 0.4s.

[0113] Step 3.3: Design a preset time, position, and state observer:

[0114] (26);

[0115] In the formula: Indicates follower drone The estimated value of the convex hull position state, in units of ; Representing the time-varying scaling function: Indicates the preset time, set to ; Set to 2; Set to 2. Based on the preset time and position state observer, the follower drone will estimate the position state of the convex hull within a preset time of 0.6s.

[0116] Based on the designed preset time state observer, the follower UAV estimates the acceleration, velocity, and position of the convex hull at 0.2s, 0.4s, and 0.6s, respectively. The state observation error is as follows: Figure 3 As shown.

[0117] Step 4: Based on the state observations and the defined auxiliary states, establish a preset time-bound controller:

[0118] (27);

[0119] In the formula: Indicates follower drone The control input, in units of ; Indicates follower drone The latest trigger time; Set to 20; It is 10; , Indicates follower drone Auxiliary status:

[0120] (28);

[0121] In the formula: Indicates the positional assistance status; Indicates speed assist status; The time-varying scaling function is defined as:

[0122] (29);

[0123] In the formula: This indicates the preset time, set to 10 seconds; Set to 2. Based on the preset time containment controller, the follower drone will enter the convex hull formed by the navigator drone within a preset time of 10 seconds.

[0124] Specifically, the flight trajectory of the drone swarm is as follows: Figure 4 As shown, the tracking error of the follower drone is as follows: Figure 5As shown, the follower drone successfully entered the convex hull formed by the navigator drone within the preset time of 10 seconds, and the tracking error converged to 0.

[0125] Step 5: Design self-triggering conditions, calculate communication time and controller update time, and establish an asynchronous communication mechanism.

[0126] Define the auxiliary tracking error:

[0127] (30);

[0128] In the formula: This indicates the position-aided tracking error; This indicates the speed-assisted tracking error.

[0129] Define measurement error:

[0130] (31);

[0131] In the formula: Indicates position measurement error; Indicates speed measurement error; This indicates the error in acceleration measurement.

[0132] Based on the error defined above, design a follower drone. The self-triggering condition is:

[0133]

[0134] In the formula: This represents the self-triggering adjustment coefficient, set to 0.4; By Laplace matrix Decomposed to obtain:

[0135] (32);

[0136] According to the self-triggering condition, when (31) is met, the event is triggered: Follower drone Broadcast messages to neighboring drones and adjust the controller based on the latest broadcast messages from neighboring drones. renew.

[0137] Specifically, the communication timing of the follower drone is as follows: Figure 6 As shown, the controller update time is as follows Figure 7As shown in the table, the designed self-triggering mechanism avoids continuous communication and controller updates for the follower drones. To more intuitively demonstrate the effectiveness of the designed self-triggering mechanism, Table 2 shows the number of communications and controller updates for each follower drone within 10 seconds. It can be seen that compared to the traditional containment control algorithm (requiring continuous communication and controller updates, requiring 10e3 communications and 10e3 controller updates within 10 seconds), the self-triggering mechanism designed in this invention significantly reduces the communication frequency and controller update frequency of the follower drones. Specifically, the communication frequency is reduced by 94.86%, and the controller update frequency is reduced by 92.65%.

[0138] Table 2. Number of follower drone communication attempts and controller update attempts within 10 seconds.

[0139]

[0140] In summary, this invention proposes a novel pre-set time-bound control method for fixed-wing UAV swarms based on a self-triggering mechanism. By introducing a self-triggering mechanism, pre-set time-bound control is achieved while avoiding continuous communication and controller updates, thus realizing asynchronous communication. This invention can provide a reference for the bounded control of other unmanned system swarms (unmanned vehicles, unmanned ships, etc.).

Claims

1. A pre-set time tolerance control method for unmanned aerial vehicle (UAV) swarms based on a self-triggering mechanism, characterized in that, Includes the following steps: Step 1: Establish a six-degree-of-freedom kinematic and dynamic model of a fixed-wing UAV, and use feedback linearization to transform the kinematic and dynamic model into a second-order integrator model; Step 2: Establish the communication topology between UAVs based on directed graph theory and define the expected convex hull formed by the navigator; Step 3: Based on the communication topology between UAVs, and according to the second-order integrator model, design a preset time state observer; The preset time state observer generates a desired convex hull state estimate; the desired convex hull state estimate includes acceleration, velocity, and position state; The preset time state observer includes a preset time acceleration state observer, a preset time velocity state observer, and a preset time position state observer; Step 4: Based on the auxiliary tracking error and the expected convex hull state estimate in Step 3, construct a preset time containment controller for the follower UAV according to the second-order integrator model, so that the follower UAV enters the expected convex hull region formed by the leader UAV within a preset time, thereby achieving the UAV swarm containment control objective. Step 5: Based on the preset time-inclusive controller designed in Step 4, design self-triggering conditions, calculate the communication time and the update time of the preset time-inclusive controller, and establish an asynchronous communication mechanism.

2. The method for preset time tolerance control of UAV swarm based on self-triggering mechanism according to claim 1, characterized in that, In step 1, the kinematic and dynamic model is constructed as follows: Fixed-wing unmanned aerial vehicle (UAV) swarm systems include ( ) drones, of which A follower drone, A Navigator drone; The Navigator drone forms a three-dimensional convex hull. The follower drone enters the convex hull within a preset time according to the preset time containment controller; the drone The kinematic and dynamic models are as follows: (1)； In the formula, , among which, when The time indicates the follower drone, This indicates a group of follower drones, when The time indicates the Navigator drone, This indicates a collection of leading drones; , and They represent drones The three-dimensional position; , and These represent ground speed, pitch angle, and yaw angle, respectively. , and These represent the original control inputs for ground speed, pitch, and yaw, respectively. It represents the acceleration due to gravity.

3. The method for preset time tolerance control of UAV swarm based on self-triggering mechanism according to claim 1, characterized in that, In step 1, the second-order integrator model is as follows: (2)； ； ； ； in, , and They represent drones In three-dimensional space Virtual control inputs, position, and speed below the axis; They represent drones exist The coordinate position under the axis; , , They represent drones exist Control input below the axis; T represents transpose; virtual control input Respectively with the original control input , and The conversion relationship is as follows: (3)。 4. The method for preset time tolerance control of UAV swarm based on self-triggering mechanism according to claim 1, characterized in that, In step 2, the communication topology between UAVs is established based on directed graph theory, and the expected convex hull formed by the navigator is defined: Step 2.1, the communication relationships between UAVs are represented by a directed graph. Description, in which Represents a set of drone nodes. Represent the set of edges; define the adjacency matrix. , It is the set of real numbers; Defined as an adjacency matrix The element represents the drone. and drones The communication topology; if ,but , indicating drone Can receive drones Communication information and drones Defined as a drone Neighboring drones, otherwise Define the degree matrix ,in Define the Laplace matrix. ; Represents the Laplace matrix The elements are derived from the degree matrix. and adjacency matrix Calculated; Laplace matrix Split into a matrix sum matrix Laplace matrix The breakdown is as follows: ； In the formula, The dimension is The zero matrix; The dimension is The zero matrix; Step 2.2, define the desired convex hull formed by the navigator drone: The drone swarm preset time containment controller based on the self-triggering mechanism enables follower drones to enter the convex hull formed by the leader drone within a preset time, and the speed is consistent with the speed of the convex hull. The expression for the expected convex hull formed by the Navigator UAV is: (4)； In the formula, and Let represent the position and velocity of UAV j in three-dimensional space at time t, respectively; This indicates the total preset time, during which the follower drone will operate within the preset time. It enters the convex hull formed by the Navigator drone. = + + + T1, T2, T3, and T4 are the preset times for the preset time acceleration state observer, preset time velocity state observer, preset time position state observer, and preset time containment controller, respectively. Representation matrix Elements of the matrix Defined as ;element Existence Relationship: , That is, satisfying the convex hull Mathematical definition of: (5)； in, Indicates the formation of the convex hull A collection of elements; Represents a set Elements in; Represents element The weights; Represents a set The number of elements contained.

5. The method for preset time tolerance control of UAV swarm based on self-triggering mechanism according to claim 1, characterized in that, In step 3, the preset time acceleration state observer: (6)； In the formula, and They represent drones The estimated values ​​of the acceleration state of the convex hull for UAV j; This represents the preset time acceleration state observer gain parameter; This represents the nonlinear term parameters of the preset time acceleration state observer; This represents the time-varying scaling function of the preset time acceleration state observer. Indicates the initial time; ;Specified manually according to user needs; Represents the time-varying scaling function The constant term; Represents the time-varying scaling function The power exponent; Based on a preset time acceleration state observer, the follower drone will... The acceleration state of the convex hull is estimated internally.

6. The method for preset time tolerance control of UAV swarm based on self-triggering mechanism according to claim 1, characterized in that, In step 3, the preset time-velocity state observer: (7)； In the formula, and These represent follower drones. The estimated values ​​of the velocity state of the convex hull for the follower drone j; The time-varying scaling function represents the preset time-velocity state observer: It is manually specified according to user needs; Represents the time-varying scaling function The constant term; Represents the time-varying scaling function The power exponent; Based on the preset time-velocity state observer, the follower drone will The velocity state of the convex hull is estimated internally.

7. The method for preset time tolerance control of UAV swarm based on self-triggering mechanism according to claim 1, characterized in that, In step 3, the preset time position state observer: (8)； In the formula, and These represent follower drones. The estimated values ​​of the convex hull position state of the follower drone j; The time-varying scaling function represents the preset time location state observer: It is manually specified according to user needs; Represents the time-varying scaling function The constant term; Represents the time-varying scaling function The power exponent; based on a preset time, position, and state observer, the follower drone will... The position and state of the convex hull are estimated internally.

8. The method for preset time tolerance control of UAV swarm based on self-triggering mechanism according to claim 1, characterized in that, In step 4, the preset time tolerance controller of the follower drone as follows: (9)； In the formula: and These represent follower drones. The latest trigger moment for the follower drone j; This indicates the preset time encompassing the position error feedback gain parameter of the controller; This indicates the speed error feedback gain of the preset time-inclusive controller; and These represent follower drones. Position and velocity assist status: (10)； In the formula: The time-varying scaling function of the preset time-encompassing controller is defined as: (11)； In the formula: It is manually specified according to user needs; Represents the time-varying scaling function The constant term; Represents the time-varying scaling function The power exponent, based on a preset time-bound controller, will cause the follower drone to... It enters the convex hull formed by the Navigator drone.

9. A pre-set time tolerance control method for UAV swarms based on a self-triggering mechanism according to claim 1, characterized in that, In step 5, the process of establishing the asynchronous communication mechanism is as follows: Step 5.1, define the auxiliary tracking error, which includes the position auxiliary tracking error. and position-assisted tracking error ; (12)； Step 5.2, define the measurement error, which includes the position measurement error. Speed ​​measurement error and acceleration measurement error ; (13)； Step 5.3: Design the follower drone based on the auxiliary tracking error and measurement error. The self-triggering condition is: (14)； In the formula: Indicates the self-trigger adjustment coefficient; Step 5.4, according to the self-triggering condition, when (14) is satisfied, the event is triggered: follower drone. Broadcast messages to neighboring drones and update the preset time-inclusive controller (9) based on the latest broadcast messages from neighboring drones.

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