A multi-unmanned aerial vehicle formation control method and device based on event-triggered communication

Abstract

The application discloses a multi-unmanned aerial vehicle formation control method and device based on event-triggered communication, and constructs a dynamic model and formation control logic of a multi-unmanned aerial vehicle system. When a follower unmanned aerial vehicle receives state quantities of neighbor unmanned aerial vehicles, the follower unmanned aerial vehicle updates local controller parameters and a trigger function according to the received state quantities and updates a flag bit. When the flag bit is in a first state, a control auxiliary variable of the follower unmanned aerial vehicle is calculated, a control trigger function is updated, and the flag bit is reset. When the control trigger function meets a trigger condition, an adaptive disturbance observer and a sliding mode control are combined to obtain a control quantity of the follower unmanned aerial vehicle. Meanwhile, when a local trigger function meets a communication trigger condition, the follower unmanned aerial vehicle broadcasts local state quantities to neighbor unmanned aerial vehicles, a closed loop of the trigger communication control is formed, an update time can be reasonably determined, a communication proportion and a control proportion of each unmanned aerial vehicle are constrained, and control and communication efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a method and apparatus for controlling multi-UAV formations based on event-triggered communication. Background Technology

[0002] When multiple unmanned aerial vehicle (UAV) systems perform formation missions, an effective and reliable communication network is needed to enable information exchange, mission planning, and collaborative control, fully leveraging the advantages of multi-UAV cooperation. The main idea of ​​a distributed communication architecture is to allow UAVs in a formation to communicate only with other UAVs within the formation, based on a pre-defined communication architecture. Its key characteristic is the absence of a central control unit; each UAV has equal status and a degree of autonomous control and decision-making capability. Therefore, the global formation control task can be decomposed into multiple sub-tasks. UAVs independently calculate control commands based on the status information of neighboring nodes and the corresponding sub-tasks, completing the formation mission through collaborative cooperation, significantly reducing computational and communication costs. The distributed structure frees the UAV swarm from dependence on a unified information control center. Each UAV can temporarily network with nearby UAVs, and the failure or damage of any single UAV will not affect the operation of the entire formation. Therefore, the entire system possesses a degree of fault tolerance, robustness, and flexibility.

[0003] However, in practical applications, it is difficult to meet the requirement of continuous communication between UAVs. Therefore, in digital implementation, a time-driven periodic communication strategy is often adopted. But when considering the limited communication channel capacity, efficient utilization of communication resources becomes essential and realistic. To address the redundant communication problem inherent in periodic communication strategies, some related research has proposed event-triggered communication strategies. In this strategy, communication between UAVs is not performed at predefined times, but rather when the system requires it. This means it is a resource-efficient solution that can effectively improve communication efficiency and alleviate network congestion. Thanks to this advantage, event-triggered communication has been widely used in various control problems. The core of this communication strategy is how to design communication triggering conditions to determine when the UAV's state information should be updated. The design of communication triggering conditions is often related to event triggering measurement errors and triggering thresholds.

[0004] Current communication triggering conditions are often defined as a constant trigger threshold. Communication is only triggered when the event-triggered measurement error exceeds a tolerable threshold. Therefore, the trigger threshold is closely related to the data rate transmitted over the network. For example, choosing a larger threshold will result in longer event intervals, thus reducing the number of data packets transmitted. However, in practical applications, simply using a constant trigger threshold may not be reasonable. For instance, in drone formation, frequent communication is required to quickly converge to the desired formation. However, once formation is complete, only intermittent communication is needed to maintain formation stability. If a constant trigger threshold is still used, it will be difficult to meet the communication requirements based on real-time changes in the system's state. Summary of the Invention

[0005] This application provides a multi-UAV formation control method and apparatus based on event-triggered communication, which solves the technical problem that the existing event-triggered communication triggering conditions for multi-UAV formation control are inflexible and cannot meet the current formation control communication requirements of multi-UAV systems.

[0006] To address the aforementioned technical problems, the first aspect of this application provides a multi-UAV formation control method based on event-triggered communication, comprising:

[0007] Based on the drone formation and the communication link information between drones, construct the communication link topology of the multi-drone system;

[0008] Based on the communication link topology of the multi-UAV system and combined with the preset leader-follower UAV formation rules, a single-UAV dynamic model and a relative dynamic model are constructed in the multi-UAV system. Based on the single-UAV dynamic model and the relative dynamic model, the formation control logic of the multi-UAV system is obtained. The single-UAV dynamic model represents the dynamic model of any UAV, and the relative dynamic model represents the relative dynamic model between any follower UAV and the leader UAV.

[0009] During the formation cruise process of the various UAVs in the multi-UAV system based on the formation control logic, when a following UAV receives the state data of a neighboring UAV, the following UAV updates its local controller parameters and communication trigger function according to the received state data and sets the flag bit to the first state. If no state data is received from a neighboring UAV, the communication trigger function is updated using the previously saved state data. When the communication trigger function meets the trigger condition, the following UAV broadcasts its local state data to the neighboring UAV, resets its local communication trigger function, and sets the flag bit to the first state. When the flag bit is in the first state, the control auxiliary variable of the following UAV is calculated based on the controller parameters and a preset consensus algorithm, the control trigger function is updated, and the flag bit is set to the initial state. When the control trigger function meets the trigger condition, the control quantity of the following UAV is obtained based on the control auxiliary variable, combined with an adaptive disturbance observer and adaptive sliding mode control.

[0010] Preferably, the controller parameters specifically include: position measurement error, velocity measurement error, trigger frequency influence parameter, combined position influence parameter, and combined velocity influence parameter.

[0011] Preferably, the communication trigger function is specifically:

[0012]

[0013] 2aγ 2 -B>0

[0014]

[0015] In the formula, H is a symmetric positive definite matrix, a is the smallest non-zero eigenvalue of matrix H, and I... n It is an n-dimensional identity matrix, where η is the trigger frequency influence parameter, β is the combination position influence parameter, and β is the combination velocity influence parameter. To track the position measurement error of the drone i, To track the speed measurement error of drone i, This represents the unknown state of the combination following drone i. This represents the combined speed state of the drone i. To account for the position measurement error of the navigation drone, For the speed measurement error of the pilot drone, This represents the kth moment in the communication trigger sequence following drone i.

[0016] Preferably, the formula for calculating the control auxiliary variable is as follows:

[0017]

[0018] In the formula, q i (t) represents the control auxiliary variable for the following UAV i, β represents the combined position influence parameter, and γ represents the combined velocity influence parameter. This represents the unknown state of the combination following drone i. This represents the combined speed state of the drone i. This represents the kth moment in the communication trigger sequence following drone i.

[0019] Preferably, the formula for calculating the control quantity is as follows:

[0020]

[0021] In the formula, As a control auxiliary variable to follow the drone i, This represents the k-th moment in the communication trigger sequence following drone i. To follow the adaptive switching gain parameters of UAV i, To follow the sliding surface of drone i, To track the perturbation observations of UAV i, Λ is a custom gain matrix, which is a diagonal matrix and each element is a positive number.

[0022] A second aspect of this application provides a multi-UAV formation control device based on event-triggered communication, comprising:

[0023] The communication link topology construction unit is used to construct the communication link topology of a multi-UAV system based on the UAV formation and the communication link information between UAVs.

[0024] The formation control logic establishment unit is used to construct the single-drone dynamics model and relative dynamics model in the multi-drone system according to the communication link topology of the multi-drone system and in combination with the preset leader-follower drone formation rules, so as to obtain the formation control logic of the multi-drone system based on the single-drone dynamics model and the relative dynamics model. The single-drone dynamics model represents the dynamics model of any drone, and the relative dynamics model represents the relative dynamics model between any follower drone and the leader drone.

[0025] The formation communication trigger control unit is used to, during the formation cruise process of the various UAVs in the multi-UAV system based on the formation control logic, update the local controller parameters and communication trigger function according to the received state value when a following UAV receives a state value from a neighboring UAV, and set a flag bit to the first state. If no state value from a neighboring UAV is received, the communication trigger function is updated using the previously saved state value. When the communication trigger function meets the trigger condition, the following UAV broadcasts its local state value to the neighboring UAV, resets the local communication trigger function, and sets the flag bit to the first state. When the flag bit is in the first state, the control auxiliary variable of the following UAV is calculated based on the controller parameters and a preset consensus algorithm, the control trigger function is updated, and the flag bit is set to the initial state. When the control trigger function meets the trigger condition, the control quantity of the following UAV is obtained based on the control auxiliary variable, combined with an adaptive disturbance observer and adaptive sliding mode control.

[0026] Preferably, the controller parameters specifically include: position measurement error, velocity measurement error, trigger frequency influence parameter, combined position influence parameter, and combined velocity influence parameter.

[0027] Preferably, the communication trigger function is specifically:

[0028]

[0029] 2aγ 2 -β>0

[0030]

[0031] In the formula, H is a symmetric positive definite matrix, a is the smallest non-zero eigenvalue of matrix H, and I... n It is an n-dimensional identity matrix, where η is the trigger frequency influence parameter, β is the combined position influence parameter, and γ is the combined velocity influence parameter. To track the position measurement error of the drone i, To track the speed measurement error of drone i, This represents the unknown state of the combination following drone i. This represents the combined speed state of the drone i. To account for the position measurement error of the navigation drone, For the speed measurement error of the pilot drone, This represents the kth moment in the communication trigger sequence following drone i.

[0032] Preferably, the formula for calculating the control auxiliary variable is as follows:

[0033]

[0034] In the formula, q i (t) represents the control auxiliary variable for the following UAV i, β represents the combined position influence parameter, and γ represents the combined velocity influence parameter. This represents the unknown state of the combination following drone i. This represents the combined speed state of the drone i. This represents the kth moment in the communication trigger sequence following drone i.

[0035] Preferably, the formula for calculating the control quantity is as follows:

[0036]

[0037] In the formula, As a control auxiliary variable to follow the drone i, This represents the k-th moment in the communication trigger sequence following drone i. To follow the adaptive switching gain parameters of UAV i, To follow the sliding surface of drone i, To track the perturbation observations of UAV i, Λ is a custom gain matrix, which is a diagonal matrix and each element is a positive number.

[0038] As can be seen from the above technical solutions, this application has the following advantages:

[0039] The technical solution provided in this application first determines the communication link topology of a multi-UAV system. Based on this topology, a dynamic model and formation control logic for the multi-UAV system are constructed. During the formation cruise process of the multi-UAV system, when a following UAV receives state data from a neighboring UAV, the following UAV updates its local controller parameters and communication trigger function according to the received state data and sets a flag to the first state. If no state data from a neighboring UAV is received, the communication trigger function is updated using the previously saved state data. When the flag is in the first state, the control auxiliary variables of the following UAV are calculated based on the controller parameters and a preset consensus algorithm, the control trigger function is updated, and the flag is set to the initial state. When the control trigger function satisfies... When the trigger condition is met, the control quantity of the following UAV is obtained based on the control auxiliary variable, combined with the adaptive disturbance observer and adaptive sliding mode control. At the same time, when the local communication trigger function meets the communication trigger condition, the following UAV broadcasts its local state quantity to the neighboring UAVs and resets its local communication trigger function. When these neighboring UAVs receive the state quantity, they will also repeat the aforementioned process of updating the local controller parameters and communication trigger function and setting the flag bit to the first state, forming a closed loop of trigger communication control within the UAV system. Through the event-triggered communication and control provided in this application, the update time can be reasonably determined, and each UAV can achieve excellent robust formation performance with a communication ratio of about 3% and a control ratio of about 1%, while significantly improving control and communication efficiency. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this application 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 only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a flowchart illustrating an embodiment of a multi-UAV formation control method based on event-triggered communication provided in this application.

[0042] Figure 2 This is a schematic diagram of the overall framework of an embodiment of a multi-UAV formation control method based on event-triggered communication provided in this application.

[0043] Figure 3 This is a pseudocode diagram illustrating an embodiment of a multi-UAV formation control method based on event-triggered communication provided in this application.

[0044] Figure 4This is a schematic diagram of an embodiment of a multi-UAV formation control device based on event-triggered communication provided in this application. Detailed Implementation

[0045] This application provides a method and apparatus for multi-UAV formation control based on event-triggered communication, which solves the technical problem that existing event-triggered communication-based multi-UAV formation control methods have low flexibility in triggering conditions and cannot meet the current formation control communication requirements of multi-UAV systems.

[0046] To make the inventive objectives, features, and advantages of this application more apparent and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0047] First, this application provides a detailed description of an embodiment of a multi-UAV formation control method based on event-triggered communication, as follows:

[0048] Please see Figure 1 , Figure 2 and Figure 3 This application provides an embodiment of a multi-UAV formation control method based on event-triggered communication, comprising:

[0049] Step 101: Based on the UAV formation and the communication link information between UAVs, construct the communication link topology of the multi-UAV system;

[0050] It should be noted that the method provided in this embodiment can first construct the communication link topology of the multi-UAV system and clarify the data relationships in the multi-UAV system. For example, an undirected graph G = (V, E, A) can be used to represent the communication topology of the multi-UAV system, where V represents the set of UAV nodes and E represents the UAV communication link. If there is a communication link between nodes i and j, then element a in matrix A... ij >0. Matrix D = diag{d1,d2,…,d N} is the in-degree matrix, where Matrix B = diag{b1,b2,…,b N} is the communication matrix for leader-follower groupings, where b is true if and only if follower i is connected to the leader. i >0. Matrix L = DA represents the Laplace matrix, then we define matrix H = L + B, and we know that matrix H is a symmetric positive definite matrix.

[0051] Step 102: Based on the communication link topology of the multi-UAV system and combined with the preset leader-follower UAV formation rules, construct the single-UAV dynamics model and relative dynamics model in the multi-UAV system, so as to obtain the formation control logic of the multi-UAV system based on the single-UAV dynamics model and relative dynamics model.

[0052] Among them, the single-machine dynamics model represents the dynamics model of any UAV, and the relative dynamics model represents the relative dynamics model of any follower UAV and the lead UAV.

[0053] It should be noted that, after determining the communication link topology of the multi-UAV system based on step 101, considering a multi-UAV system consisting of N UAVs following a leader-follower formation, the second-order dynamics model of the multi-UAV system, without considering external disturbances, can describe the position vector and velocity vector of UAV i in three-dimensional space as follows:

[0054]

[0055] Where u i If (t) is the input of UAV i, then the dynamic model of leader (UAV 0) is:

[0056]

[0057] p di Define the position of drone i relative to the leader in a specified formation. as well as Let represent the position, velocity, and deviation of the control input from the leader of the i-th follower, respectively. Then, the relative dynamic model of follower i relative to the leader is:

[0058]

[0059] Where d i (t) represents the unknown disturbance formed by the convergence of common external disturbances and model uncertainties faced by the system. Since the unknown disturbance is a bounded signal but its boundary is unknown, we consider that it satisfies the upper bound of state dependence.

[0060]

[0061] Where the unknown parameters Unknown disturbances are only considered in accordance with state-related upper bounds, without imposing prior boundaries on them.

[0062] In consistent formation control, for any initial position and velocity conditions, the UAV should be able to achieve leader-follower formation control. Therefore, the following formula holds true.

[0063]

[0064]

[0065] Step 103: During the formation cruise process of each UAV in the multi-UAV system based on formation control logic, when a following UAV receives the state value of a neighboring UAV, the following UAV updates its local controller parameters and communication trigger function according to the received state value and sets the flag bit to the first state. If no state value of a neighboring UAV is received, the communication trigger function is updated using the previously saved state value. When the communication trigger function meets the trigger condition, the following UAV broadcasts its local state value to the neighboring UAV, resets its local communication trigger function, and sets the flag bit to the first state. When the flag bit is in the first state, the control auxiliary variable of the following UAV is calculated based on the controller parameters and a preset consensus algorithm, the control trigger function is updated, and the flag bit is set to the initial state. When the control trigger function meets the trigger condition, the control quantity of the following UAV is obtained based on the control auxiliary variable, combined with the adaptive disturbance observer and adaptive sliding mode control.

[0066] It should be noted that, in order to reduce the communication load on the system, an event-triggered mechanism is set up to reduce the number of communication triggers. Assume each drone follower has the following tasks:

[0067] 1) Listen to the communication port to receive the latest status of the neighbors, and update its own controller immediately based on the received status;

[0068] 2) Continuously monitor its own status and update the trigger function. Once the trigger condition is met, broadcast its own status at the time of triggering to its neighbors and reset the trigger function.

[0069] 3) If there is a communication link between the follower and the leader, a request to obtain the current state of the leader must be sent when the communication is triggered.

[0070] definition Let i represent the communication trigger time sequence of drone i. The consensus algorithm is described as follows:

[0071]

[0072] Both β and γ are greater than 0. The combined unknown states of drone i are represented by the following formula:

[0073]

[0074] as well as The combined velocity state of drone i is represented by the following formula:

[0075]

[0076] For each follower, the position and velocity measurement errors can be expressed as follows: and If b i If the value is greater than 0, then there is a communication link between follower i and leader. The position and velocity measurement errors of the leader are defined as follows: and

[0077] The next communication trigger time for drone i is represented as:

[0078]

[0079] Where h i (t) is the trigger function, defined as:

[0080]

[0081] Where η > 0. When the trigger function h i When (t) > 0, UAV i executes a predefined task. The β, γ, and η mentioned above are all scalars greater than 0, and must satisfy the following condition:

[0082] 2aγ 2 -β>0

[0083]

[0084] Here, parameter η is the trigger frequency influence parameter, which can be used to adjust the event trigger function. A larger value of η leads to a significant reduction in communication frequency, but may increase the system convergence time. In practical applications, the selection of η should be based on the actual requirements for system convergence time and communication frequency, while ensuring that it is within the range of system stability conditions.

[0085] Parameter β is the combined position influence parameter, and parameter γ is the combined velocity influence parameter. These two parameters are used to adjust the combined position and velocity influence between the i-th UAV and its neighbors, respectively. Parameter β provides a flexible adjustment range for parameter γ. When β remains constant, increasing γ will slow down the convergence speed. Therefore, γ can be set to a value that is not explicitly defined. On the other hand, while increasing β can accelerate system convergence, its value should not be chosen too large to avoid input saturation constraints. In practice, the parameter β can usually be set in the range of [0.5, 2.5].

[0086] When a follower drone receives state data from a neighboring drone, it updates its locally stored neighbor parameters and communication trigger function based on the received state data, and sets the flag to the first state (e.g., flag is set to 1). If no follower drone receives state data from a neighboring drone, it updates the communication trigger function using the original neighbor parameters. When the communication trigger function meets the triggering condition (e.g., h...),... i When (t)>0, the drone broadcasts the local state variables to neighboring drones and resets the local communication trigger function, setting the flag bit to the first state, such as setting flag to 1.

[0087] When the flag is in the first state (flag=1), the control auxiliary variables for following the UAV are calculated based on the controller parameters and a preset consensus algorithm. The control trigger function is then updated based on these variables, and the flag is set to the initial state. When the control trigger function meets the trigger condition, such as f... i When (t)>0, the control quantity of the UAV is obtained by combining the control auxiliary variable, the adaptive disturbance observer and the adaptive sliding mode control.

[0088] Specifically, ASMC (adaptive sliding mode control) is introduced to handle unknown disturbances. The sliding surface is defined as:

[0089]

[0090] Where S i (t)=[s1(t),s2(t),...,s n (t)] T To accelerate convergence, a fast convergence law is chosen:

[0091]

[0092] Where the gain matrix Λ = diag[Λ1,Λ2,...,Λ] n All are positive numbers. ASMC's gain adaptive rate ρ i (t) is set as:

[0093]

[0094] Where τ i K is a scalar that is greater than 0 and can be arbitrarily designed. j (t) is adjusted using the following formula

[0095]

[0096] Parameter Kj (0) and δ j (j=0,1,2) is also a scalar greater than 0 and can be arbitrarily designed.

[0097] Therefore, the control input deviation of UAV i in the ASMC-based consensus algorithm is:

[0098]

[0099] To further improve the robustness of multi-UAV systems against unknown disturbances, an adaptive disturbance observer is designed as follows:

[0100]

[0101] in and They represent v respectively i (t) and d i The estimated value of (t), Represents the velocity estimation error, with gain k w (t)(w=1,2,3,4) are designed as follows:

[0102]

[0103] Where α g G(t) is a scalar that is greater than 0 and can be arbitrarily designed. It is an adaptive variable with an initial value of G(0) > 0 and a parameter c. w (t)(w=1,2,3,4) is a constant that satisfies the following relationship.

[0104]

[0105] Therefore, after introducing the adaptive disturbance observer, the control input deviation of UAV i in the consensus algorithm is:

[0106]

[0107] To reduce the control load in the system, an event-triggered mechanism is set up to reduce the number of control triggers. Definition This represents the sequence of control trigger times for drone i. Note that the timing of control triggers and communication triggers are different. Control inputs are only updated at the control trigger time; therefore, when... At that time, the control input is:

[0108]

[0109] Define control measurement error as

[0110]

[0111] Therefore, the trigger function can be defined as follows:

[0112]

[0113] Control trigger time is

[0114]

[0115] Under the aforementioned control input and triggering conditions, the solution of the closed-loop system is globally uniformly bounded, and the final bound can be determined as follows:

[0116]

[0117] Where the scalar σ = 2min j {θ min (Λ),δ j / 2}, 0<ξ<σ.

[0118] It can be seen that the magnitude of the final boundary ω mainly depends on Effective compensation by using interference observers This can be significantly reduced. Therefore, by selecting an appropriate disturbance observer, the final boundary ω can be very small. However, in practice, it is difficult to completely observe the true magnitude of the disturbance using a disturbance observer. Therefore, the event-triggered and ASMC-based control algorithm can be effectively combined with an adaptive disturbance observer. By compensating for part of the disturbance through the adaptive disturbance observer, ASMC can still effectively handle the remaining disturbance, ensuring the robustness of the algorithm.

[0119] Furthermore, in the experiment, the function tanh was used instead of the function sign in order to obtain its derivative, i.e., sign(S i (t))≈tanh(ωS i (t)),∈>>1, the function tanh also helps to reduce the jitter caused by the sign function in ASMC.

[0120] The above is a detailed description of an embodiment of a multi-UAV formation control method based on event-triggered communication provided in this application. The following is a detailed description of an embodiment of a multi-UAV formation control device based on event-triggered communication.

[0121] Please see Figure 4 This application provides an embodiment of a multi-UAV formation control device based on event-triggered communication, including:

[0122] The communication link topology construction unit 201 is used to construct the communication link topology of a multi-UAV system based on the UAV formation and the communication link information between UAVs.

[0123] The formation control logic establishment unit 202 is used to construct the single-drone dynamics model and relative dynamics model in the multi-drone system based on the communication link topology of the multi-drone system and the preset leader-follower drone formation rules, so as to obtain the formation control logic of the multi-drone system based on the single-drone dynamics model and the relative dynamics model. The single-drone dynamics model represents the dynamics model of any drone, and the relative dynamics model represents the relative dynamics model between any follower drone and the leader drone.

[0124] The formation communication trigger control unit 203 is used in the process of formation cruise of each UAV in a multi-UAV system based on formation control logic. When a following UAV receives the state value of a neighboring UAV, the following UAV updates its local controller parameters and communication trigger function according to the received state value and sets the flag bit to the first state. If no state value of a neighboring UAV is received, the communication trigger function is updated using the previously saved state value. When the communication trigger function meets the trigger condition, the following UAV broadcasts its local state value to the neighboring UAV, resets its local communication trigger function, and sets the flag bit to the first state. When the flag bit is in the first state, the control auxiliary variable of the following UAV is calculated based on the controller parameters and a preset consensus algorithm, the control trigger function is updated, and the flag bit is set to the initial state. When the control trigger function meets the trigger condition, the control quantity of the following UAV is obtained based on the control auxiliary variable, combined with an adaptive disturbance observer and adaptive sliding mode control.

[0125] Furthermore, the controller parameters specifically include: position measurement error, velocity measurement error, trigger frequency influence parameters, combined position influence parameters, and combined velocity influence parameters.

[0126] Furthermore, the trigger function is specifically as follows:

[0127]

[0128] 2aγ 2 -β>0

[0129]

[0130] In the formula, H is a symmetric positive definite matrix, a is the smallest non-zero eigenvalue of matrix H, and I n It is an n-dimensional identity matrix. The UAV moves in 3-dimensional space, where n is typically 3. η is the trigger frequency influence parameter, β is the combined position influence parameter, and γ is the combined velocity influence parameter. To track the position measurement error of the drone i, To track the speed measurement error of drone i, This represents the unknown state of the combination following drone i. This represents the combined speed state of the drone i. To account for the position measurement error of the navigation drone, For the speed measurement error of the pilot drone, This represents the kth moment in the communication trigger sequence following drone i.

[0131] Furthermore, the specific formula for calculating the control auxiliary variable is as follows:

[0132]

[0133] In the formula, q i (t) represents the control auxiliary variable for the following UAV i, β represents the combined position influence parameter, and γ represents the combined velocity influence parameter. This represents the unknown state of the combination following drone i. This represents the combined speed state of the drone i. This represents the kth moment in the communication trigger sequence following drone i.

[0134] Furthermore, the specific formula for calculating the control quantity is as follows:

[0135]

[0136] In the formula, As a control auxiliary variable to follow the drone i, This represents the k-th moment in the communication trigger sequence following drone i. To follow the adaptive switching gain parameters of UAV i, To follow the sliding surface of drone i, To track the perturbation observations of UAV i, Λ is a custom gain matrix, which is a diagonal matrix and each element is a positive number.

[0137] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described apparatus and unit can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0138] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0139] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0140] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0141] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0142] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0143] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0144] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application 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 of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A multi-UAV formation control method based on event-triggered communication, characterized in that, include: Based on the drone formation and the communication link information between drones, construct the communication link topology of the multi-drone system; Based on the communication link topology of the multi-UAV system and combined with the preset leader-follower UAV formation rules, a single-UAV dynamic model and a relative dynamic model are constructed in the multi-UAV system. Based on the single-UAV dynamic model and the relative dynamic model, the formation control logic of the multi-UAV system is obtained. The single-UAV dynamic model represents the dynamic model of any UAV, and the relative dynamic model represents the relative dynamic model between any follower UAV and the leader UAV. During the formation cruise process of the various UAVs in the multi-UAV system based on the formation control logic, when a following UAV receives the state data of a neighboring UAV, the following UAV updates its local controller parameters and communication trigger function according to the received state data and sets the flag bit to the first state. If no state data is received from a neighboring UAV, the communication trigger function is updated using the previously saved state data. When the communication trigger function meets the trigger condition, the following UAV broadcasts its local state data to the neighboring UAV, resets its local communication trigger function, and sets the flag bit to the first state. When the flag bit is in the first state, the control auxiliary variable of the following UAV is calculated based on the controller parameters and a preset consensus algorithm, the control trigger function is updated, and the flag bit is set to the initial state. When the control trigger function meets the trigger condition, the control quantity of the following UAV is obtained based on the control auxiliary variable, combined with an adaptive disturbance observer and adaptive sliding mode control. The controller parameters specifically include: position measurement error, velocity measurement error, trigger frequency influence parameter, combined position influence parameter, and combined velocity influence parameter; The communication trigger function is specifically as follows: ； ； In the formula, H is a symmetric positive definite matrix. I is the smallest non-zero eigenvalue of matrix H. n It is an n-dimensional identity matrix. To trigger frequency affect parameters, To combine the positional influence parameters, For parameters affecting the combined speed, To track the position measurement error of the drone i, To track the speed measurement error of drone i, This represents the combined positional state of the drone i. This represents the combined speed state of the drone i. To account for the position measurement error of the navigation drone, For the speed measurement error of the pilot drone, This represents the kth moment in the communication trigger sequence following drone i.

2. The multi-UAV formation control method based on event-triggered communication according to claim 1, characterized in that, The specific formula for calculating the control auxiliary variable is as follows: ； In the formula, As a control auxiliary variable to follow the drone i, To combine the positional influence parameters, For parameters affecting the combined speed, This represents the combined positional state of the drone i. This represents the combined speed state of the drone i. This represents the kth moment in the communication trigger sequence following drone i.

3. The multi-UAV formation control method based on event-triggered communication according to claim 1, characterized in that, The specific formula for calculating the control quantity is as follows: ； In the formula, As a control auxiliary variable to follow the drone i, This represents the k-th moment in the communication trigger sequence following drone i. To follow the adaptive switching gain parameters of UAV i, To follow the sliding surface of drone i, To follow the perturbation observations of UAV i, The custom gain matrix is ​​a diagonal matrix in which each element is a positive number.

4. A multi-UAV formation control device based on event-triggered communication, characterized in that, include: The communication link topology construction unit is used to construct the communication link topology of a multi-UAV system based on the UAV formation and the communication link information between UAVs. The formation control logic establishment unit is used to construct the single-drone dynamics model and relative dynamics model in the multi-drone system according to the communication link topology of the multi-drone system and in combination with the preset leader-follower drone formation rules, so as to obtain the formation control logic of the multi-drone system based on the single-drone dynamics model and the relative dynamics model. The single-drone dynamics model represents the dynamics model of any drone, and the relative dynamics model represents the relative dynamics model between any follower drone and the leader drone. The formation communication trigger control unit is used to, during the formation cruise process of the various UAVs in the multi-UAV system based on the formation control logic, update the local controller parameters and communication trigger function according to the received state value when a following UAV receives a state value from a neighboring UAV, and set a flag bit to the first state. If no state value from a neighboring UAV is received, the communication trigger function is updated using the previously saved state value. When the communication trigger function meets the trigger condition, the following UAV broadcasts its local state value to the neighboring UAV, resets the local communication trigger function, and sets the flag bit to the first state. When the flag bit is in the first state, the control auxiliary variable of the following UAV is calculated based on the controller parameters and a preset consensus algorithm, the control trigger function is updated, and the flag bit is set to the initial state. When the control trigger function meets the trigger condition, the control quantity of the following UAV is obtained based on the control auxiliary variable, combined with an adaptive disturbance observer and adaptive sliding mode control. The controller parameters specifically include: position measurement error, velocity measurement error, trigger frequency influence parameters, combined position influence parameters, and combined velocity influence parameters; The communication trigger function is specifically as follows: ； ； In the formula, H is a symmetric positive definite matrix. I is the smallest non-zero eigenvalue of matrix H. n It is an n-dimensional identity matrix. To trigger frequency affect parameters, To combine the positional influence parameters, For parameters affecting the combined speed, To track the position measurement error of the drone i, To track the speed measurement error of drone i, This represents the combined positional state of the drone i. This represents the combined speed state of the drone i. To account for the position measurement error of the navigation drone, For the speed measurement error of the pilot drone, This represents the kth moment in the communication trigger sequence following drone i.

5. A multi-UAV formation control device based on event-triggered communication according to claim 4, characterized in that, The specific formula for calculating the control auxiliary variable is as follows: ； In the formula, As a control auxiliary variable to follow the drone i, To combine the positional influence parameters, For parameters affecting the combined speed, This represents the combined positional state of the drone i. This represents the combined speed state of the drone i. This represents the kth moment in the communication trigger sequence following drone i.

6. A multi-UAV formation control device based on event-triggered communication according to claim 4, characterized in that, The specific formula for calculating the control quantity is as follows: ； In the formula, As a control auxiliary variable to follow the drone i, This represents the k-th moment in the communication trigger sequence following drone i. To follow the adaptive switching gain parameters of UAV i, To follow the sliding surface of drone i, To follow the perturbation observations of UAV i, The custom gain matrix is ​​a diagonal matrix in which each element is a positive number.

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