A multi-unmanned aerial vehicle adaptive event-triggered cooperative networking method under time-varying weak communication

Abstract

The application discloses a kind of multi-unmanned aerial vehicle adaptive event-triggered cooperative networking methods under time-varying weak communication, the method includes using undirected graph to describe the information interaction relationship between unmanned aerial vehicle in formation, the dynamic system state equation of unmanned aerial vehicle formation under variable topology is constructed to obtain formation reference information, by constructing adaptive extended state observer, the dynamic disturbance and reference information change in system that cannot be measured are estimated in real time, to provide accurate state prediction for formation;In order to reduce the communication consumption and resource limited under weak communication condition, the iteration update mechanism of controller trigger time based on event triggering is designed, i.e. only update the controller state when the trigger condition is met, otherwise keep the original state;Multi-unmanned aerial vehicle adaptive event-triggered cooperative networking is realized by the optimized event-triggered adaptive controller designed.The application solves the problem that multi-unmanned aerial vehicle cannot realize efficient and stable formation networking control under weak communication environment without global topology information.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) formation control technology, and in particular to a multi-UAV adaptive event-triggered cooperative networking method under time-varying weak communication. Background Technology

[0002] Cooperative formation networking of UAV swarms in weak communication environments is a high-order cooperative task, requiring the achievement and maintenance of stable formation patterns under conditions of limited communication bandwidth, time-varying topology, and external disturbances. However, practical applications face three core challenges: 1) Some nodes cannot continuously acquire formation reference information, causing traditional control strategies based on global information to fail; 2) Node communication and computing resources are limited, making continuous communication and control updates difficult to achieve; 3) Node maneuvers cause intermittent communication links and dynamic changes in formation topology, threatening system stability and networking efficiency. Therefore, developing a lightweight formation control framework that does not rely on global topology information and adapts to weak communication conditions has become crucial for promoting the practical application of UAV swarms.

[0003] In recent years, scholars both domestically and internationally have conducted extensive research on the formation and networking control of UAV swarms. Existing research mainly focuses on how to achieve and maintain stable formation configurations, and can be divided into two categories based on the communication and dynamic conditions: formation control under ideal communication conditions and formation control under constrained communication conditions. Under ideal or strong communication conditions, research is mostly based on continuous and reliable information interaction, emphasizing geometric configuration generation and accurate tracking. For example, existing distributed formation strategies based on relative positions have enabled cooperative motion and collision avoidance in multi-robot systems; others have introduced virtual reference point technology to design hybrid control strategies that can eliminate the influence of nonholonomic constraints, enabling formation and maintenance under static reference conditions. However, in practical applications, communication bandwidth, energy, and computing resources are often severely limited, i.e., in weak or time-varying communication environments. To address this, scholars have developed resource-saving control methods such as event triggering and quantized communication. For example, some studies model the uncertainty of information exchange as a perturbation of the graph Laplace matrix and design adaptive laws for compensation; other works design time-varying formation protocols based on directional communication topology, generating dynamic geometric constraints through a virtual leader. To further reduce the need for reference information, some methods have introduced adaptive mechanisms to estimate unmeasurable system states. Nevertheless, existing methods face significant limitations in time-varying weak communication environments: most strategies still rely on partially known information about reference dynamics (such as velocity and acceleration), making it difficult to achieve stable networking under conditions where reference information is completely unknown or non-cooperative; simultaneously, although mechanisms such as event triggering have been introduced, their designs are often based on fixed thresholds or rely on global topology information, resulting in insufficient adaptability to time-varying topologies with random on / off communication links, easily leading to decreased control accuracy or excessively frequent triggering. Therefore, developing a fully distributed formation networking method that does not rely on global information and accurate reference models, and can adapt to time-varying weak communication remains a critical problem that urgently needs to be solved.

[0004] On the other hand, in actual operation, UAV swarms often operate in time-varying, weak communication environments, posing a severe challenge to formation networking control that relies on continuous information exchange. To alleviate the communication burden, event-triggered control, as an effective solution that only communicates and updates when necessary, has received widespread attention. However, existing research on event-triggered formation control still has significant limitations: for example, existing event-triggered strategies for multiple dynamic references reduce communication frequency through task decomposition but do not consider the dynamic switching of communication topology; furthermore, existing leader-based dynamic event-triggered formation controllers can maintain formation under time-varying disturbances, but their triggering conditions depend on global topology information (such as the Laplace matrix), making them difficult to implement in a fully distributed system. Overall, current research focuses primarily on single-dimensional optimization—either emphasizing reducing communication load or enhancing disturbance suppression—failing to systematically and collaboratively address the complex challenges of the coupling between "unmeasurable reference inputs," "time-varying communication topology," and "external disturbance suppression." Especially when the communication topology changes in real time due to the maneuvering of UAVs, existing event-triggered strategies, which mostly rely on fixed thresholds or global network information, often lead to a decrease in control accuracy and a reduction in the utilization rate of communication resources, making it difficult to achieve efficient and robust formation networking under real weak communication conditions.

[0005] In summary, the existing technology has the following problems: 1) In time-varying weak communication environments with unknown reference dynamics and environmental disturbances, traditional control methods, which use fixed gain, are difficult to adapt to topology switching, leading to instability of the formation system.

[0006] 2) Under the condition of discarding global topology information (such as the Laplace matrix) and dynamic changes in communication links, existing distributed coordination strategies are prone to failure due to the lack of adaptive mechanisms.

[0007] 3) Existing methods suffer from slow convergence speed, low tracking accuracy, and resource waste due to periodic communication in complex scenarios where reference information is unknown, topology is time-varying, and external interference coexists. Summary of the Invention

[0008] This invention provides a method for adaptive event-triggered cooperative networking of multiple UAVs under time-varying weak communication to overcome the above-mentioned technical problems.

[0009] To achieve the above objectives, the technical solution of the present invention is as follows: A method for adaptive event-triggered cooperative networking of multiple UAVs under time-varying weak communication includes the following steps: S1: An undirected graph is used to describe the information interaction relationship between UAVs in the formation, and the state equation of the dynamic system of UAV formation under variable topology is constructed. S2: Based on the state equations of the dynamic system and the dynamic model of the formation UAV, obtain the distributed error dynamic system; S3: Define a mixture of UAV formation speed tracking error and control inputs with respect to external disturbances and unknown targets to construct an adaptive extended state observer; S4: Construct a distributed adaptive formation controller based on a distributed error dynamics system, and obtain the UAV adaptive controller by combining an adaptive extended state observer; S5: Based on the iterative update mechanism of the controller trigger time of each UAV under time-varying weak communication, the UAV adaptive controller is rewritten, and based on the rewritten UAV adaptive controller, the event triggering formation controller is obtained by combining the distributed adaptive formation controller. S6: Based on the output of the event-triggered formation controller and the iterative update mechanism of the controller triggering time, obtain the measurement error of each UAV, and rewrite the event-triggered formation controller according to the measurement error to obtain an optimized event-triggered adaptive formation controller; and realize the control of multi-UAV adaptive event-triggered collaborative networking under time-varying weak communication by optimizing the event-triggered adaptive formation controller.

[0010] Furthermore, the state equations of the dynamic system of the UAV formation under the variable topology constructed in S1 are as follows:

[0011]

[0012] In the formula: Represents the dynamic system state variables of the designated node UAV; This represents the control input for the node drone; express The first derivative; The parameter matrix representing the node-based UAV; Indicates the first The dynamic system state variables of a formation of unmanned aerial vehicles; They represent the first The control inputs of the formation of drones and disturbances caused by the external environment; They represent the first Speed ​​and damping coefficient of a formation of drones; express The first derivative; This represents the parameter matrix of the formation of drones.

[0013] Furthermore, step S2 specifically includes the following steps: S21: The dynamic models of the node UAV and the formation UAV are obtained as follows:

[0014] In the formula: These represent the position and velocity of the node drone, respectively; Indicates the first The location of the formation of drones; S22: Based on the dynamic model and the state equations of the dynamic system, the formation error is defined as:

[0015]

[0016]

[0017] In the formula: Represents a continuously differentiable formation vector; Represents the dynamic system state variables of the node-based UAV; Indicates the first The relative positions of the formation of the drones; Indicates the formation position tracking error; Indicates formation speed tracking error; Indicates the first Formation error of individual drone formations; Indicates transpose; S23: The distributed error dynamics system obtained from the formation error is:

[0018] In the formula: express The first derivative.

[0019] Furthermore, step S3 specifically includes the following steps: S31: Define the drone formation speed tracking error Mixed terms with control inputs that are unknown about external disturbances and targets for:

[0020]

[0021] In the formula: Indicates the first The relative speed of the formation of drones; S32: Based on step S31, construct the adaptive extended state observer as follows:

[0022]

[0023]

[0024] In the formula: Represents positive numbers; This represents the observer's error signal; Indicates the first Auxiliary variables for the observers of the formation of UAVs; Indicates the first Formation of drones and the first The relative speed of the formation of drones; Indicates the first Formation of drones and the first Estimated speed tracking error of a formation of UAVs; express The first derivative; Indicates the first The decision variable is whether a formation of drones can detect the relative positions of neighboring formations of drones; if so, then... ,otherwise ; Indicates the first Formation of drones and the first The decision variable for whether the formation of drones has an adjacency relationship is: if so, then... ,otherwise ; Indicates the first The set of neighboring nodes of the formation of drones; express The renewal law; express The estimated value; express The first derivative.

[0025] Furthermore, step S4 specifically includes the following steps: S41: Obtaining intermediate parameter quantities based on a distributed error dynamics system for:

[0026] In the formula: Indicates the first Formation error of individual drone formations; S42: Define error variables By combining the intermediate parameter quantity The distributed adaptive formation controller is constructed as follows:

[0027]

[0028] In the formula: This represents the output of the distributed adaptive formation controller; Indicates the quantity of intermediate parameters and , ; , Represents positive numbers; Describe a positive definite matrix; Indicates the first The gain coefficient in the adaptive controller of the formation UAV is used to adjust the adaptive terms. Parameters; The virtual control law representing the distributed adaptive formation controller; express The first derivative; S43: Based on the distributed adaptive formation controller and combined with the adaptive extended state observer, the UAV adaptive controller is obtained as follows:

[0029] In the formula: Indicates the drone formation compensation item and meets the following conditions. , Represents the parameter matrix and , ; express The abbreviated form of .

[0030] Furthermore, S5 specifically includes the following steps: S51: The controller trigger time iteration update mechanism under time-varying weak communication is set as follows:

[0031] In the formula: Indicates the trigger time; Indicates the next trigger time; This represents the trigger function to be designed; S52: Based on the iterative update mechanism of the controller trigger time for each UAV under time-varying weak communication, the UAV adaptive controller is rewritten as follows:

[0032] In the formula: Indicates the trigger time Corresponding drone formation compensation items; Indicates the trigger time The corresponding estimated value of the mixed term; Indicates the trigger time The output of the corresponding distributed adaptive formation controller; S53: Based on the rewritten UAV adaptive controller, the event-triggered formation controller is obtained by combining a distributed adaptive formation controller: .

[0033] Furthermore, step S6 specifically includes the following steps: S61: Based on the output of the event-triggered formation controller and the controller's iterative update mechanism at the trigger time, obtain the measurement error of each UAV. for:

[0034] In the formula: They represent the trigger times respectively. Time corresponding to The value; They represent the current time. Time corresponding to The value; S62: Rewrite the event-triggered formation controller based on the measurement error to obtain the optimized event-triggered adaptive formation controller as follows:

[0035] By optimizing the event-triggered adaptive formation controller, we can achieve adaptive event-triggered collaborative networking control of multiple UAVs under time-varying weak communication conditions.

[0036] Furthermore, the design formula for the trigger function described in S51 is as follows:

[0037] In the formula: Indicate design parameters and .

[0038] Beneficial Effects: This invention provides a multi-UAV adaptive event-triggered cooperative networking method under time-varying weak communication conditions. It includes using an undirected graph to describe the information interaction relationships between UAVs in the formation, constructing the dynamic system state equation of the UAV formation under a variable topology to obtain formation reference information, and constructing an adaptive extended state observer to estimate unmeasurable dynamic disturbances and changes in reference information in real time, providing accurate state prediction for the formation. To reduce communication consumption and resource constraints under weak communication conditions, an event-triggered controller triggering time iterative update mechanism is designed, i.e., the controller state is updated only when the triggering condition is met, otherwise the original state is maintained. By obtaining an optimized event-triggered adaptive formation controller through the designed event-triggered adaptive controller, efficient and stable formation networking control of multiple UAVs in weak communication environments can be achieved without global topology information. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart of the multi-UAV adaptive event-triggered cooperative networking method under time-varying weak communication according to the present invention; Figure 2 This is a schematic diagram of the UAV communication topology in this embodiment; Figure 3 This is a trajectory diagram of the UAV network in this embodiment; Figure 4 This is a diagram showing the three-axis position error curves of the UAV in the network configuration in this embodiment; Figure 5 This is a diagram showing the drone trigger interval in this embodiment; Figure 6 This is a distribution diagram of the drone trigger times in this embodiment; Figure 7 This is a chart showing the number of times the drone was triggered in this embodiment; Figure 8 This is a graph showing the measurement error curve of the UAV in this embodiment; Figure 9 This is a graph showing the change of UAV control input over time in this embodiment; Figure 10 This is a graph showing the three-axis position error curves of the UAV using the comparison method in this embodiment; Figure 11 The following are simulation diagrams of the trigger intervals of various UAVs in the comparison method in this embodiment; Figure 12 This is a distribution diagram of the trigger times of the comparison method in this embodiment. Figure 13 This is a comparison chart of the number of triggers between the comparison method in this embodiment and the method described in this embodiment. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] This embodiment provides a multi-UAV adaptive event-triggered cooperative networking method under time-varying weak communication, such as... Figure 1 As shown, the steps include: S1: An undirected graph is used to describe the information interaction relationship between UAVs in the formation, and the state equation of the dynamic system of UAV formation under variable topology is constructed. Specifically, such as Figure 2 The embodiment shown uses an undirected graph. Describes the information interaction between drones in the formation, where This represents the set of nodes for drones. Representation diagram The set of edges in the, if the drone i and j If there is information exchange ,otherwise Since this invention uses an undirected graph, Equivalent to drones i and j They can share information with each other; drones i The neighbor node can be defined as .picture adjacency matrix Defined as ,if ,but ,otherwise ,and In a drone swarm, drones equipped with sensors can obtain the swarm's reference position and velocity information, while the remaining drones obtain neighbor information through local communication. The degree matrix of the defined graph is... If the drone can detect the relative position of the target, then ,otherwise Furthermore, the Laplace matrix of an undirected graph is defined as follows: ; The state equation of the dynamic system of the UAV formation under the variable topology constructed in this embodiment is:

[0043]

[0044] In the formula: Represents the dynamic system state variables of the designated node UAV; This represents the control input for the node drone; express The first derivative; The parameter matrix representing the node-based UAV; Indicates the first The dynamic system state variables of a formation of unmanned aerial vehicles; They represent the first The control inputs of the formation of drones and disturbances caused by the external environment; They represent the first Speed ​​and damping coefficient of a formation of drones; express The first derivative; The parameter matrix representing the formation of drones; , , , ; S2: Based on the state equations of the dynamic system and the dynamic model of the formation UAVs, obtain the distributed error dynamic system, specifically including the following steps: S21: The dynamic models of the node UAV and the formation UAV are obtained as follows:

[0045] In the formula: These represent the position and velocity of the node drone, respectively; Indicates the first The location of the formation of drones; S22: Based on the dynamic model and the state equations of the dynamic system, the formation error is defined as:

[0046]

[0047]

[0048] In the formula: Represents a continuously differentiable formation vector; Represents the dynamic system state variables of the node-based UAV; Indicates the first The relative positions of the formation of the drones; Indicates the formation position tracking error; Indicates the formation speed tracking error; Indicates the first Formation error of individual drone formations; Indicates transpose; S23: The distributed error dynamics system obtained from the formation error is:

[0049] In the formula: express The first derivative; S3: Define a mixture of UAV formation speed tracking error and control inputs with respect to external disturbances and unknown targets to construct an adaptive extended state observer, specifically including the following steps: S31: Define the drone formation speed tracking error Mixed terms with control inputs that are unknown about external disturbances and targets for:

[0050]

[0051] In the formula: Indicates the first The relative speed of the formation of drones; S32: Based on step S31, construct the adaptive extended state observer as follows:

[0052]

[0053]

[0054] In the formula: , Represents positive numbers; This represents the observer's error signal, used to assess reaction speed tracking error and differences between neighboring nodes; Indicates the first Auxiliary variables for the observers of the formation UAVs are used for adaptive adjustment of the observers; Indicates the first Formation of drones and the first The relative speed of the formation of drones; Indicates the first Formation of drones and the first Estimated speed tracking error of a formation of UAVs; express The first derivative; Indicates the first The decision variable is whether a formation of drones can detect the relative positions of neighboring formations of drones; if so, then... ,otherwise ; Indicates the first Formation of drones and the first The decision variable for whether the formation of drones has an adjacency relationship is: if so, then... ,otherwise ; Indicates the first The set of neighboring nodes of the formation of drones; express The renewal law; express The estimated value; express The first derivative; S4: Construct a distributed adaptive formation controller based on a distributed error dynamics system, and obtain the UAV adaptive controller by combining an adaptive extended state observer. Specific steps include: S41: Define the neighbor formation error for each drone as:

[0055] For all formation drone definitions Then, the intermediate parameter quantity is obtained based on the distributed error dynamics system. for:

[0056] In the formula: Indicates the first Formation error of individual drone formations; S42: Define error variables By combining the intermediate parameter quantity Constructing a distributed adaptive formation controller, that is, a distributed adaptive formation controller that achieves the specified performance without requiring any global topology information, is as follows:

[0057]

[0058] In the formula: This represents the output of a distributed adaptive formation controller, which is the output of a distributed adaptive formation controller that achieves the specified performance without requiring any global topology information. Indicates the quantity of intermediate parameters and , ; , Represents positive numbers; Describe a positive definite matrix; Indicates the first The gain coefficient in the adaptive controller of the formation UAV is used to adjust the adaptive terms. Parameters; The virtual control law representing the distributed adaptive formation controller; express The first derivative; S43: Based on the distributed adaptive formation controller and combined with the adaptive extended state observer, the UAV adaptive controller is obtained as follows:

[0059] In the formula: Indicates the drone formation compensation item and meets the following conditions. , Represents the parameter matrix and , ; express The abbreviated form of .

[0060] S5: Based on the iterative update mechanism of the controller triggering time of each UAV under time-varying weak communication, the UAV adaptive controller is rewritten. Based on the rewritten UAV adaptive controller, the event-triggered formation controller is obtained by combining it with the distributed adaptive formation controller. The specific steps include: S51: During dynamic networking, the positions of the formation drones continuously change, and the communication link is dynamically updated or interrupted accordingly. Simultaneously, communication bandwidth and node energy are limited, making continuous and stable communication impossible. Therefore, the variability of the communication topology needs to be considered to better suit the requirements of real-world scenarios. This embodiment will build upon this, extending existing results to variable topology scenarios: First, a switching signal is given. ,in It is a finite set; based on this Represents an undirected graph set. Representing an undirected graph index, graph for The graph corresponding to each moment, switch the time series to , , ...; therefore, using To describe variable topologies and The network is connected at any time interval; in this embodiment, to address the errors caused by variable topology and the problem of limited node resources, each UAV... Controller trigger time Iterative updates will be performed in the following manner: The controller trigger time iteration update mechanism under time-varying weak communication is set as follows:

[0061] In the formula: Indicates the trigger time; Indicates the next trigger time; This represents the trigger function to be designed; S52: Based on the iterative update mechanism of the controller trigger time for each UAV under time-varying weak communication, the UAV adaptive controller is rewritten as follows:

[0062] In the formula: Indicates the trigger time Corresponding drone formation compensation items; Indicates the trigger time The corresponding estimated value of the mixed term; Indicates the trigger time The output of the corresponding distributed adaptive formation controller; This embodiment also includes rewriting the distributed error dynamics system based on the rewritten UAV adaptive controller as follows: ; In this embodiment, through the synergistic effect of the adaptive extended state observer, the rewritten UAV adaptive controller, and the event-triggered mechanism, the pursuer formation can ensure the completion of the encirclement and capture mission of the dynamic target under the constraint of limited communication resources. If there exists a normal number that satisfies the following conditions, the actual pursuer formation can complete the target encirclement and capture:

[0063] To achieve the above control objectives, the following lemma is required in this embodiment: It is a positive definite matrix, if eigenvalues Then there is Furthermore, there exists a column vector. , making ; S53: Based on the rewritten UAV adaptive controller, the event-triggered formation controller is obtained by combining a distributed adaptive formation controller:

[0064] Triggering time in this embodiment That is, drones The latest trigger time, This indicates the exact moment when the controller will trigger its next update, and for The drone's controller remains unchanged; S6: Based on the output of the event-triggered formation controller and the iterative update mechanism of the controller triggering time, obtain the measurement error of each UAV, and rewrite the event-triggered formation controller according to the measurement error to obtain an optimized event-triggered adaptive formation controller; by optimizing the event-triggered adaptive formation controller, realize the control of multi-UAV adaptive event-triggered cooperative networking under time-varying weak communication, specifically including the following steps: S61: Based on the output of the event-triggered formation controller and the controller's iterative update mechanism at the trigger time, obtain the measurement error of each UAV. for:

[0065] In the formula: They represent the trigger times respectively. Time corresponding The value; They represent the current time. Time corresponding The value; The design formula for the trigger function in this embodiment is:

[0066] In the formula: Indicate design parameters and ; S62: Rewrite the event-triggered formation controller based on the measurement error to obtain the optimized event-triggered adaptive formation controller as follows:

[0067] By optimizing the event-triggered adaptive formation controller, control of multi-UAV adaptive event-triggered cooperative networking under time-varying weak communication conditions is achieved. In this embodiment, when... Events are triggered and drones are updated simultaneously. The controller, and at the trigger time , It will be set to 0; when If the controller input remains unchanged from the previous moment, then the controller input will be retained.

[0068] The method described in this embodiment uses an undirected graph to describe the information interaction relationships between UAVs in a formation. Some nodes (referred to as "information nodes") can obtain formation reference information, while the remaining nodes rely on neighbor information for coordination. To address the problem of time-varying topology under weak communication conditions, a switching signal is introduced to describe the topology switching process and ensure that the communication topology remains connected within any time interval. In order to reduce communication and computational overhead, an event-triggered control update mechanism is designed, that is, the controller state is updated only when the trigger condition is met, otherwise the original state is maintained. The method described in this embodiment constructs an adaptive extended state observer to estimate the unmeasurable dynamic disturbances and changes in reference information in the system in real time, providing accurate state prediction for the formation. The optimized event-triggered adaptive controller can achieve stable formation networking of multiple UAVs in a weak communication environment without the need for global topology information.

[0069] This embodiment also includes the following simulation verification results: The drone formation considered in this invention consists of 4 drones and 1 target drone. First, a fixed communication topology is selected as follows: Figure 1 As shown; the observer parameters are selected as follows: , ; Controller parameters , The relevant parameters for the trigger function are as follows: , Select parameters The initial positions of the formation drones and the target drone are respectively... , , , , The initial velocities of both the formation drones and the target drone are... The desired formation information for the formation of drones is as follows: , , , The three axes affected by external disturbances are: , , .based on Figure 1 The corresponding Laplace matrix can be obtained as follows:

[0070] Simulation results of event-triggered formation networking control in a fixed communication topology are as follows: Figures 3 to 9 As shown. Figure 3 The demonstration showcased the movement trajectories of four drones achieving and maintaining a stable formation to complete collaborative networking. It is evident that, under the action of the event-triggered formation controller, the drones quickly form up to network with the target and achieve the required formation after departing from their initial positions. Figure 4 For the three-axis error curve of the UAV (specifically, Figure 4 (a) To achieve the x-axis position error curve of UAVs in a network configuration, Figure 4 (b) To achieve the y-axis position error curve of UAVs in a network configuration, Figure 4 (c) The z-axis position error curve of the UAV under the network configuration. As can be seen from the figure, the position error of the UAV can be converged to a certain range in about 4 seconds. Figure 5 The trigger intervals for each drone are given (specifically, Figure 5 (a) is the trigger interval for UAV 1; Figure 5 (b) is the trigger interval for UAV 2; Figure 5 (c) is the trigger interval for the UAV 3; Figure 5 (d) is the trigger interval for the UAV 4. It is clear that when the error is large in the initial stage, it is not necessary to update the controller frequently. Then, when the error is about to converge, the controller is frequently adjusted to achieve control accuracy. When the error converges to a certain range, the controller is no longer updated.

[0071] Figure 6 The graph shows the distribution of trigger times. It is clear from the graph that the controller updates frequently during the precision adjustment process, and there are no triggers after 4 seconds. Figure 7 It is a statistical chart of the number of triggers for each drone, recording the total number of triggers during the process of forming and maintaining a stable formation to complete collaborative networking. Figure 8 The curve representing the change in measurement error (specifically, Figure 8 (a) Curve showing the variation of measurement error of UAV 1; Figure 8 (b) The variation curve of the measurement error of UAV 2; Figure 8 (c) Variation curve of measurement error of UAV 3; Figure 8 (d) The curve showing the variation of the measurement error of UAV 4 can be seen. When the maximum threshold is reached The value will be set to 0 at the specified time, and the controller will be triggered. Figure 9 The curves showing the change of UAV control input over time are presented. These results verify that the method described in this embodiment can achieve and maintain stable formation under fixed topology conditions to complete cooperative networking. Simulation results for other controller event-triggered formation control methods under fixed communication topology, i.e., the comparative method (existing leader-based dynamic event-triggered formation controller: Wei L, et al. Dynamic event-triggered formation control [J]. IET Control Theory Appl., 2020, 14(17): 2514-2525.), are as follows: Figures 10 to 13 As shown, Figure 10This represents the formation control method triggered by other controller events under the condition of obtaining target control input, and the UAV three-axis error curve obtained by the controller (specifically, Figure 10 (a) shows the curve of the measurement error in the x-axis direction of the UAV; Figure 10 (b) shows the variation curve of the measurement error in the y-axis direction of the UAV; Figure 10 (c) shows the variation curve of the measurement error in the z-axis direction of the UAV. It can be seen from the figure that the error oscillates within a large range, making it difficult to achieve and maintain a stable formation to complete the collaborative networking. Figure 11 The trigger interval diagram for each drone is given (specifically, Figure 11 (a) Trigger interval curve of UAV 1; Figure 11 (b) Trigger interval curve of UAV 2; Figure 11 (c) Trigger interval curve of UAV 3; Figure 11 (d) Trigger interval curve of UAV 4), from the start of formation networking to the end, the trigger interval is relatively small; Figure 12 The diagram shows the distribution of trigger times. As can be seen from the diagram, due to the large error of the drone, the event is constantly being triggered, wasting communication resources. Figure 13 The comparison method was compared with the method described in this embodiment. The bar chart of the number of triggers under the observer and trigger conditions of the method described in this embodiment and the number of triggers under the comparison method clearly shows that the number of triggers of the method described in this embodiment is much smaller than the number of triggers in the comparison literature. Since the design greatly reduces the number of controller updates, it shows the superiority of the design of the method described in this embodiment.

[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for adaptive event-triggered cooperative networking of multiple UAVs under time-varying weak communication, characterized in that, The specific steps include: S1: An undirected graph is used to describe the information interaction relationship between UAVs in the formation, and the state equation of the dynamic system of UAV formation under variable topology is constructed. S2: Based on the state equations of the dynamic system and the dynamic model of the formation UAV, obtain the distributed error dynamic system; S3: Define a mixture of UAV formation speed tracking error and control inputs with respect to external disturbances and unknown targets to construct an adaptive extended state observer; S4: Construct a distributed adaptive formation controller based on a distributed error dynamics system, and obtain the UAV adaptive controller by combining an adaptive extended state observer; S5: Based on the iterative update mechanism of the controller triggering time of each UAV under time-varying weak communication, the UAV adaptive controller is rewritten, and based on the rewritten UAV adaptive controller, the event triggering formation controller is obtained by combining the distributed adaptive formation controller. S6: Based on the output of the event-triggered formation controller and the iterative update mechanism of the controller triggering time, obtain the measurement error of each UAV, and rewrite the event-triggered formation controller according to the measurement error to obtain an optimized event-triggered adaptive formation controller; Furthermore, by optimizing the event-triggered adaptive formation controller, we can achieve adaptive event-triggered collaborative networking control of multiple UAVs under time-varying weak communication conditions.

2. The method for adaptive event-triggered cooperative networking of multiple UAVs under time-varying weak communication as described in claim 1, characterized in that, The state equations of the dynamic system of UAV formation under variable topology constructed in S1 are as follows: In the formula: Represents the dynamic system state variables of the designated node UAV; This represents the control input for the node drone; express The first derivative; The parameter matrix representing the node-based UAV; Indicates the first The dynamic system state variables of a formation of unmanned aerial vehicles; They represent the first The control inputs of the formation of drones and disturbances caused by the external environment; They represent the first Speed ​​and damping coefficient of a formation of drones; express The first derivative; This represents the parameter matrix of the formation of drones.

3. The method for adaptive event-triggered cooperative networking of multiple UAVs under time-varying weak communication as described in claim 2, characterized in that, S2 specifically includes the following steps: S21: The dynamic models of the node UAV and the formation UAV are obtained as follows: In the formula: These represent the position and velocity of the node drone, respectively; Indicates the first The location of the formation of drones; S22: Based on the dynamic model and the state equations of the dynamic system, the formation error is defined as: In the formula: Represents a continuously differentiable formation vector; Represents the dynamic system state variables of the node-based UAV; Indicates the first The relative positions of the formation of the drones; Indicates formation position tracking error; Indicates formation speed tracking error; Indicates the first Formation error of individual drone formations; Indicates transpose; S23: The distributed error dynamics system obtained from the formation error is: In the formula: express The first derivative.

4. The method for adaptive event-triggered cooperative networking of multiple UAVs under time-varying weak communication as described in claim 3, characterized in that, S3 specifically includes the following steps: S31: Define the drone formation speed tracking error Mixed terms with control inputs that are unknown about external disturbances and targets for: In the formula: Indicates the first The relative speed of the formation of drones; S32: Based on step S31, construct the adaptive extended state observer as follows: In the formula: Represents positive numbers; This represents the observer's error signal; Indicates the first Auxiliary variables for the observers of the formation of UAVs; Indicates the first Formation of drones and the first The relative speed of the formation of drones; Indicates the first Formation of drones and the first Estimated speed tracking error of a formation of UAVs; express The first derivative; Indicates the first The decision variable is whether a formation of drones can detect the relative positions of neighboring formations of drones; if so, then... ,otherwise ; Indicates the first Formation of drones and the first The decision variable for whether the formation of drones has an adjacency relationship is: if so, then... ,otherwise ; Indicates the first The set of neighboring nodes of the formation of drones; express The renewal law; express The estimated value; express The first derivative.

5. A method for adaptive event-triggered cooperative networking of multiple UAVs under time-varying weak communication as described in claim 4, characterized in that, S4 specifically includes the following steps: S41: Obtaining intermediate parameter quantities based on a distributed error dynamics system for: In the formula: Indicates the first Formation error of individual drone formations; S42: Define error variables By combining the intermediate parameter quantity The distributed adaptive formation controller is constructed as follows: In the formula: This represents the output of the distributed adaptive formation controller; Indicates the quantity of intermediate parameters and , ; , Represents positive numbers; Describe a positive definite matrix; Indicates the first The gain coefficient in the adaptive controller of the formation UAV is used to adjust the adaptive terms. Parameters; The virtual control law representing the distributed adaptive formation controller; express The first derivative; S43: Based on the distributed adaptive formation controller and combined with the adaptive extended state observer, the UAV adaptive controller is obtained as follows: In the formula: Indicates the drone formation compensation item and meets the following conditions. , Represents the parameter matrix and , ; express The abbreviated form of .

6. The method for adaptive event-triggered cooperative networking of multiple UAVs under time-varying weak communication as described in claim 5, characterized in that, S5 specifically includes the following steps: S51: The controller trigger time iteration update mechanism under time-varying weak communication is set as follows: In the formula: Indicates the trigger time; Indicates the next trigger time; This represents the trigger function to be designed; S52: Based on the iterative update mechanism of the controller trigger time for each UAV under time-varying weak communication, the UAV adaptive controller is rewritten as follows: In the formula: Indicates the trigger time Corresponding drone formation compensation items; Indicates the trigger time The corresponding estimated value of the mixed term; Indicates the trigger time The output of the corresponding distributed adaptive formation controller; S53: Based on the rewritten UAV adaptive controller, the event-triggered formation controller is obtained by combining a distributed adaptive formation controller: 。 7. A method for adaptive event-triggered cooperative networking of multiple UAVs under time-varying weak communication as described in claim 6, characterized in that, S6 specifically includes the following steps: S61: Based on the output of the event-triggered formation controller and the controller's iterative update mechanism at the trigger time, obtain the measurement error of each UAV. for: In the formula: They represent the trigger times respectively. Time corresponding The value; They represent the current time. Time corresponding The value; S62: Rewrite the event-triggered formation controller based on the measurement error to obtain the optimized event-triggered adaptive formation controller: By optimizing the event-triggered adaptive formation controller, we can achieve adaptive event-triggered collaborative networking control of multiple UAVs under time-varying weak communication conditions.

8. A method for adaptive event-triggered cooperative networking of multiple UAVs under time-varying weak communication as described in claim 7, characterized in that, The design formula for the trigger function described in S51 is: In the formula: Indicate design parameters and .

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