A method for UAV swarm formation control based on distributed contention communication

Abstract

This invention discloses a method for UAV swarm formation control based on distributed contention communication. The method includes: establishing a UAV swarm communication network topology and a system dynamics model of the i-th UAV; pre-setting one or more virtual UAVs in the UAV swarm, setting the dynamics information of the l-th virtual UAV, and establishing a leader-follower model; calculating the state error information of the i-th UAV; and when the i-th UAV joins the communication contention, allocating a communication channel to the i-th UAV based on the communication network topology model and the state error information of the i-th UAV. The technical solution in this embodiment of the invention can achieve distributed channel scheduling with limited channels to ensure UAV swarm formation control and the execution of dynamic cooperative tasks.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) swarm formation control, and more particularly to a UAV swarm formation control method based on distributed competitive communication. Background Technology

[0002] The rapid advancement of science and technology has led to drones appearing more and more frequently in the public eye. With the booming development of the drone industry, drone technology is permeating many aspects of production and daily life.

[0003] Today, simply achieving efficient control of a single drone is no longer sufficient to meet people's growing needs. How to achieve good control of complex systems composed of multiple drones has become a key issue that many scholars are dedicated to researching. Among these, multi-drone cooperative control is one of the most crucial core technologies for drone swarms.

[0004] However, constrained by external environment and malicious interference, self-organizing wireless communication networks built by drone swarms often face the dilemma of scarce reliable communication channels and inability to meet the simultaneous communication needs of a large number of drones. In this context of limited effective channel resources, a new control method is urgently needed to meet the communication needs of drones while maintaining good control over the drone swarm. Summary of the Invention

[0005] This invention provides a method for controlling a swarm of unmanned aerial vehicles (UAVs) based on distributed competitive communication, so as to achieve cooperative control of multiple UAVs under limited communication channels.

[0006] The UAV swarm formation control method based on distributed contention communication provided in this embodiment of the invention includes:

[0007] S110. Establish a communication network topology diagram of the UAV cluster, and construct a system dynamics model of the i-th UAV using the dynamics information of any i-th UAV in the UAV cluster;

[0008] S120. In the drone cluster, one or more virtual drones are preset, the dynamics information of the l-th virtual drone is set, and a leader-follower model is established; the leader-follower model is designed to: input the dynamics information of the l-th virtual drone and the dynamics information of the i-th drone, and output the expected information of the i-th drone;

[0009] S130. Based on the system dynamics model of the i-th UAV and the expected information of the i-th UAV, obtain the state error information of the i-th UAV.

[0010] S140. When the i-th UAV joins the communication competition, a distributed competition communication strategy is constructed based on the communication network topology model and the state error information of the i-th UAV, and a communication channel is allocated to the i-th UAV that joins the communication competition.

[0011] Furthermore, in S110, the system dynamics model of the i-th UAV is expressed as:

[0012]

[0013] Among them, s i (t) represents the state variable information of the i-th UAV. For s i The derivative of (t), I3 is a 3x3 identity matrix, u i (t) represents the control input data of the i-th UAV, ζ i (t) represents interference data.

[0014] Furthermore, interference data ζ i The estimated value of (t) The interference observer model is calculated from the interference observer model and is constructed as follows:

[0015]

[0016] in, It is an auxiliary variable. It is the derivative of the auxiliary variable, and H is the number of harmonics. It is an auxiliary interference variable. It is an auxiliary disturbance variable. The estimated value, It is the gain matrix.

[0017] Furthermore, in S120, the process of obtaining the desired information of the i-th UAV includes:

[0018] S1201. Based on the dynamic information of the l-th virtual UAV, construct the flight position equations of the l-th virtual UAV;

[0019] S1202. Based on the flight position equations of the l-th virtual UAV and the dynamic information of the i-th UAV, obtain the relative expected position information of the i-th UAV.

[0020] S1203. Based on the dynamics information of the i-th UAV and the relative desired position information of the i-th UAV, obtain the desired position information of the i-th UAV. Information on the expected state variables of the i-th drone

[0021] Furthermore, in S1202, the relative desired position information p of the i-th UAV... li (t), expressed in transpose form, is:

[0022]

[0023] Where, p lx (t), p ly (t), p lz (t) represents the position coordinates of the l-th virtual drone along its three axes, V l (t) represents the velocity data of the l-th virtual drone, α l (t), β l (t) represents the trajectory tilt angle and trajectory azimuth angle of the l-th virtual UAV, respectively, and d li γ is the distance between the centroid of the l-th virtual drone and the centroid of the i-th drone. li For the line d li With d li The angle δ between the projections of the i-th UAV onto the xy-plane. li For d li The projection of the i-th UAV onto the xy-plane and V i (t) is the angle between the projections of the i-th UAV onto the xy-plane.

[0024] Furthermore, in S1203,

[0025] Desired location information of the i-th drone for:

[0026]

[0027] Where, p l (t) represents the position coordinates of the l-th virtual drone, p li (t) represents the relative desired position information of the i-th drone. for The derivative and express;

[0028] The expected state variable information for the i-th drone is:

[0029] Furthermore, it also includes a pre-defined collaborative control law triggered by dynamic events of the drone:

[0030] Define the time when the next event is triggered. for:

[0031]

[0032] in, Let be the time of the last event trigger for the i-th drone. Let i be the time when the next event is triggered for the i-th drone;

[0033] For the event triggering function:

[0034]

[0035] g2(ξ i (t),q i (t))=||ξ i (t)||2-π i (t)||q i (t)||2.

[0036] Among them, b i ,c i They represent the weighting coefficients, π i It is an adjustable constant, and π i =h2(s i h1(·) and h2(·) are given functions determined based on state variable information;

[0037] The state variable information s of the i-th UAV i (t) and expected state variable information State error;

[0038] Indicates the time of the last event trigger for the i-th drone. The difference between the state error at time t and the state error at time t;

[0039] N represents the average state error of the i-th drone and its j-th neighboring drones. i Let n be the set of neighboring drones of the i-th drone. i Let be the number of neighboring drones of the i-th drone. This is the latest transmission data from the j-th neighboring drone;

[0040] It is the time of the last event trigger for the i-th drone. The difference between the average state error at time t and the average state error at time t.

[0041] Furthermore, when At that time, the control input data for the i-th UAV is:

[0042]

[0043] in, Let i be the time when the last event was triggered for the i-th drone. Let i be the time when the next event is triggered for the i-th drone. This is the latest transmitted data from the j-th neighboring drone, where K1 and K2 are the controller gains. yes The derivative of a represents the expected acceleration of the i-th drone. i0 a ij All are elements in the adjacency matrix of the communication topology graph. For interference data ζ i The estimated value of (t).

[0044] Furthermore, when the i-th UAV meets the event triggering condition, its channel request signal is sent through the channel request function ω. i (t) is calculated to obtain the channel request function ω. i (t) is:

[0045]

[0046] Among them, Ω i ,Γ i Θ i These are the weighting coefficients, where e is the natural logarithm and d is the weighting factor. i Let ρ be the degree of the i-th drone. i (t) represents the difference between the state error of the i-th UAV at the time of the last event trigger and the state error at time t, q i (t) represents the average state error of the i-th UAV and its j-th neighboring UAV.

[0047] Furthermore, the workflow of the i-th drone is as follows:

[0048] S1401, The i-th UAV obtains its own state error information. Through state error information Calculate the event trigger function

[0049] S1402. Determine whether the event triggering function meets the event triggering condition. The event triggering condition is: If the conditions are met, then execute S1403 and then S1404; if the conditions are not met, then execute S1404 directly.

[0050] S1403. Enter the distributed contention communication strategy. Based on the state estimation error and topology information, compete for a limited communication channel and determine whether the competition is successful. If the competition is successful, access the wireless communication network and send information. If the competition is unsuccessful, do not access the wireless communication network.

[0051] S1404, Based on the latest transmission data received from the j-th neighboring drone... Error information with itself Calculate control input data u i (t), based on control input data u i (t) Update its own state.

[0052] This invention controls the trajectory of drones by establishing a leader-follower model. When a drone needs to adjust its position, a dynamic event triggers a cooperative control law. A distributed competitive communication strategy allocates communication channels to drones participating in the communication competition. This enables control of drone swarm formations even with limited communication resources. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is a flowchart of the UAV swarm formation control method based on distributed contention communication provided in an embodiment of the present invention;

[0055] Figure 2 This is a schematic diagram of the flight status of the i-th UAV provided in an embodiment of the present invention;

[0056] Figure 3 This is a schematic diagram illustrating the positional relationship between the i-th drone and the l-th virtual drone provided in an embodiment of the present invention;

[0057] Figure 4 This is a schematic diagram of the workflow of the i-th UAV provided in an embodiment of the present invention. Detailed Implementation

[0058] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0059] It should be noted that the invention patent CN112445236B discloses a communication control method in multi-UAV cooperative control based on event triggering. It uses state estimation information to design an event triggering function, which transforms the communication and control update problem between UAVs into a problem of determining the value of the triggering function, thus solving the problem of frequent information updates between UAVs in the process of multi-UAV formation cooperative control.

[0060] However, existing UAV swarm control systems assume abundant UAV communication resources, capable of simultaneously meeting the communication needs of all UAVs in the system, or employ a centralized channel scheduling strategy to allocate channels to each UAV. The technical solution in this invention aims to achieve distributed channel scheduling with limited channels to ensure UAV swarm formation control and the execution of dynamic collaborative tasks.

[0061] Example 1

[0062] Figure 1 This is a flowchart of a UAV swarm formation control method based on distributed competitive communication provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation of collaborative control of UAV swarm formation under limited communication channel resources.

[0063] like Figure 1 As shown, the method specifically includes the following steps:

[0064] S110. Establish a communication network topology diagram for the UAV swarm. Construct a system dynamics model for the i-th UAV using the dynamics information of any i-th UAV in the UAV swarm.

[0065] In this embodiment of the invention, the process of establishing the topology model of the UAV swarm communication network is as follows:

[0066] Assume there are N drones in a drone swarm, and the drones communicate with each other through a self-organizing wireless communication network. The drone swarm communication topology can be represented by G = {Z, E}, where Z = {z0, z1, ..., z...}. N Let {z0} be the set of points of the virtual drone, E be the set of edges, and z0 be the points of the virtual drone in the topology. N Let} be a point in the topology where the i-th drone is located. The set of neighboring drones of the i-th drone is N. i The number is n i Further define the adjacency matrix A of the drone swarm, A = [a ij ] (N+1)×(N+1) Let represent the communication topology between UAVs, where i∈0,1,...,N and j∈0,1,...,N. When j=0, a i0 This indicates that the i-th drone communicates with the virtual drone, a ijThis indicates that the i-th drone sends a message to the j-th neighboring drone, a ij >0 indicates that the j-th neighboring drone can receive information from the i-th drone, a ij =0 indicates that the i-th drone cannot communicate with the j-th neighboring drone.

[0067] Define the degree of the i-th drone. It should be noted that when the i-th drone and the j-th neighboring drone are the same drone, i.e., when i = j, a ij =0 indicates that the drone does not communicate with itself.

[0068] Before establishing the system dynamics model of the i-th UAV, it is necessary to explain that:

[0069] In this embodiment of the invention, the dynamic information includes: position coordinate data, velocity data, pose data, input data, disturbance data, etc.

[0070] It should be noted that time t in the embodiments of the present invention is used to indicate that the parameters are synchronized in time. The derivatives in the embodiments of the present invention are all derivatives with respect to time.

[0071] In this embodiment of the invention, T indicates that the parameter is in transpose matrix form, that is, different representations of the same parameter. For example: s i (t) and Both represent the state variables of the i-th drone, only their representations differ.

[0072] The position coordinate data can be represented in transpose matrix form as follows: Where, p ix (t) represents the position coordinates of the i-th UAV in the x-direction, p iy (t) represents the position coordinates of the i-th UAV in the y-direction, p iz (t) represents the position coordinates of the i-th UAV in the z-direction.

[0073] Speed ​​data can be represented as: V i (t).

[0074] Pose data includes: the trajectory tilt angle α of the i-th UAV. i (t) and the azimuth angle β of the i-th UAV's trajectory i (t).

[0075] The input data includes: the actual input data of the i-th drone. With control input data u i (t), where a ix (t),a iy (t),aiz (t) represents the roll acceleration a of the i-th UAV. ix (t), pitch acceleration a iy (t) and yaw acceleration a iz (t).

[0076] Interference data can be represented as: ζ i (t).

[0077] Figure 2 This is a schematic diagram of the flight status of the i-th UAV provided in an embodiment of the present invention, combined with... Figure 2 The various information of the i-th UAV is converted into a mathematical model to complete the construction of the system dynamics model.

[0078] In this embodiment of the invention, there are multiple ways to construct the system dynamics model of the i-th UAV:

[0079] The first approach treats the i-th drone as a point mass moving, with its center of mass as the origin. Using position coordinate data, velocity data, pose data, and actual input data, a system of equations is constructed to describe the flight state of the i-th drone:

[0080]

[0081] in, For p ix The derivative of (t), For p iy The derivative of (t), For p iz The derivative of (t), The derivative of V(t), For β i The derivative of (t), For α i The derivative of (t), where g is the acceleration due to gravity.

[0082] The second method, based on the first method, uses position coordinate data, pose data, and input data to transform the equation system into a second-order linear system dynamics model:

[0083]

[0084] in, For p i The derivative of (t), For v i The derivative of (t).

[0085] The construction process of the above second-order linear system dynamics model is as follows:

[0086] S1101: Definition Let represent the transpose of the actual input data of the i-th UAV.

[0087] S1102: Definition This represents the transpose of the position coordinate data of the i-th UAV.

[0088] S1103: From the flight state equations of the i-th UAV, we can obtain:

[0089]

[0090] in, for The derivative of u is the control input data. i (t), Δ i (t) is an invertible matrix.

[0091] S1104: Order The dynamic model of the second-order linear system described above can then be obtained.

[0092] The third approach, based on the second approach, utilizes position coordinate data, pose data, input data, and disturbance data to first optimize the dynamic model of the second-order linear system.

[0093]

[0094] Redefining This represents the state variable information of the i-th drone.

[0095] The third dynamic model is expressed as follows:

[0096]

[0097] in, For s i The derivative of (t), I3 is a 3-order identity matrix.

[0098] Furthermore, the interference data ζ i (t) is represented as:

[0099]

[0100] Where, ζ i (t) represents the interference data, and H represents the number of harmonics. It is an auxiliary interference variable. It is the derivative of the auxiliary disturbance variable. It is the gain matrix.

[0101] Furthermore, during flight, UAVs inevitably encounter interference. To eliminate the impact of interference on the control of the UAV system, an interference observer model is designed.

[0102] Interference data ζ i The estimated value of (t) The interference observer model is calculated from the interference observer model and is constructed as follows:

[0103]

[0104] in, It is an auxiliary variable. It is the derivative of the auxiliary variable. It is an auxiliary disturbance variable. The estimated value, It is the gain matrix.

[0105] S120. In the drone swarm, a virtual drone of type l is preset, the dynamic information of the virtual drone of type l is set, and a leader-follower model is established.

[0106] The leader-follower model is designed to obtain the desired information for the i-th drone by taking the dynamics information of the l-th virtual drone and the dynamics information of the i-th drone as input.

[0107] The expected information for the i-th drone includes: the expected location information of the i-th drone, the expected state variable information of the i-th drone, and so on.

[0108] It should be noted that, in order to achieve drone swarm formation control and maintain the desired formation, a virtual drone of type l is introduced as a virtual leader to provide the desired flight trajectory for the formation. Throughout the entire flight mission, all state information of the virtual drone of type l can be pre-set, meaning that at any given time, all following drones are aware of the dynamic information of the virtual leader drone.

[0109] In this embodiment of the invention, obtaining the desired information for the i-th drone specifically includes the following steps:

[0110] Step S1201: Based on the dynamic information of the l-th virtual UAV, construct the flight position equations of the l-th virtual UAV.

[0111] The flight position equations for the l-th virtual drone are:

[0112]

[0113] in, p is the transpose matrix of the position coordinate data of the l-th virtual drone. lx (t), ply (t), p lz (t) represents the position coordinates of the l-th virtual drone along its three axes, V l (t) represents the velocity data of the l-th virtual drone, α l (t), β l (t) represents the track tilt angle and track azimuth angle of the l-th virtual UAV, respectively.

[0114] Figure 3 This is a schematic diagram illustrating the positional relationship between the i-th drone and the virtual j-th neighboring drone provided in an embodiment of the present invention, combined with... Figure 3 The l-th virtual drone and the i-th drone are located in different xy planes. Figure 3 The image above shows the l-th virtual drone and its dynamics information. Figure 3 The i-th drone and its dynamics information are shown below.

[0115] In the formation control of drone swarms, at each moment, the i-th drone has a desired position, which is determined by the relative desired position of the l-th virtual drone and the i-th drone.

[0116] Step S1202: Based on the flight position equations of the l-th virtual UAV and the dynamic information of the i-th UAV, obtain the relative expected position information of the i-th UAV.

[0117] The relative desired position information p of the i-th drone li (t), expressed in transpose form, is:

[0118]

[0119] Where, d li Let d be the distance between the centroid of the l-th virtual drone and the centroid of the i-th drone. li 'for d li The projection of γ onto the xy plane of the i-th UAV. li For the line d li With d li The angle between ' and V i (t)' is V i (t) is the projection of the i-th UAV onto the xy-plane, δ li For d li 'with V i The angle between (t)'.

[0120] Step S1203: Based on the dynamics information of the i-th UAV and the relative expected position information of the i-th UAV, obtain the expected position information and expected state variable information of the i-th UAV.

[0121] Desired location information of the i-th drone for:

[0122]

[0123] The expected state variable information for the i-th drone is:

[0124] S130. Based on the system dynamics model of the i-th UAV and the expected information of the i-th UAV, obtain the state error information of the i-th UAV.

[0125] State error information The state variable information s of the i-th UAV is represented as follows. i (t) and expected state variable information The error.

[0126] S140. When the i-th UAV joins the communication competition, a distributed competition communication strategy is constructed based on the communication network topology model and the state error information of the i-th UAV, and a communication channel is allocated to the i-th UAV that joins the communication competition.

[0127] To achieve good formation control of UAVs, a control algorithm was designed. Furthermore, to further reduce the occupation of communication resources and lower the communication frequency between UAVs, a dynamic event-triggered cooperative control law was designed based on the event triggering mechanism.

[0128] Without considering event triggering, design the i-th UAV control input data u. i (t) is:

[0129]

[0130] Where K1 and K2 are the controller gains. yes The derivative of .

[0131] To reduce the frequency of communication between UAVs, a dynamic event triggering strategy is proposed to reduce network resource consumption. Based on the event triggering mechanism, for the i-th UAV, the time of the next event trigger is defined. for:

[0132]

[0133] in, Let be the time of the last event trigger for the i-th drone. Let be the time when the next event is triggered for the i-th drone.

[0134] For the event triggering function:

[0135]

[0136] g2(ξ i (t),q i (t))=||ξ i (t)||2-π i (t)||q i (t)||2.

[0137] Among them, b i ,c i They represent the weighting coefficients, The state variable information s of the i-th UAV is represented as follows. i (t) and expected state variable information The state error, Indicates the time of the last event trigger for the i-th drone. The difference between the state error at time t and the state error at time t.

[0138] This represents the average state error of the i-th drone and its j-th neighboring drone. This is the latest transmitted data from the j-th neighboring drone. It is the time of the last event trigger for the i-th drone. The difference between the average state error at time t and the average state error at time t. π i It is an adjustable constant, and π i =h2(s i h1(·) and h2(·) are given functions determined based on state variable information.

[0139] At the moment the event is triggered, the i-th UAV will compete for the limited communication channel based on the distributed competitive communication strategy of state estimation error and topology information, thereby improving the utilization of communication resources and ensuring good formation control of the UAV swarm.

[0140] When the event is triggered When this condition is met, the signal currently sampled by the i-th UAV will be triggered, and ρ i (t) and ξ i (t) will be simultaneously reset to zero. In addition, thanks to the zero-order hold provided in the controller, the controller input will remain unchanged until the next trigger signal is delivered.

[0141] Based on the dynamic event triggering mechanism, considering event triggering, when At that time, the control input data for the i-th UAV is:

[0142]

[0143] in, Let i be the time when the last event was triggered for the i-th drone. Let i be the time when the next event is triggered for the i-th drone. This is the latest transmitted data from the j-th neighboring UAV, where K1 and K2 are the controller gains. yes The derivative of a represents the expected acceleration of the i-th drone. i0 This indicates that the i-th drone is sending information to the l-th drone, a ij This indicates that the i-th drone sends a message to the j-th neighboring drone. For interference data ζ i The estimated value of (t).

[0144] Figure 4 This is a schematic diagram of the workflow of the i-th UAV provided in an embodiment of the present invention, combined with... Figure 4 The specific workflow of the i-th drone is as follows:

[0145] S1401, The i-th UAV obtains its own state error information. Through state error information Calculate the event trigger function

[0146] S1402. Determine whether the event triggering function meets the event triggering condition. The event triggering condition is: If the conditions are met, then execute S1403 and then S1404; if the conditions are not met, then execute S1404 directly.

[0147] S1403. Enter the distributed contention communication strategy. Based on the state estimation error and topology information, compete for a limited communication channel and determine whether the contention is successful. If the contention is successful, access the wireless communication network and send information; if the contention is unsuccessful, do not access the wireless communication network.

[0148] S1404, Based on the latest transmission data received from the j-th neighboring drone... Error information with itself Calculate the control input data, based on the control input data u i (t) Update its own state.

[0149] When the event triggering function does not meet the event triggering condition hour,

[0150] In drone swarms, the reliable communication channels of the wireless communication network are limited. At any given time, the wireless communication network can only allow a finite number of follower drones to access the network and transmit information. If the total number of follower drones in the swarm exceeds the communication resource limit of the wireless communication network, the network may not be able to meet the communication needs of all follower drones at any given time. Therefore, a distributed contention communication strategy based on state estimation error and topology information is designed.

[0151] According to the collaborative control law triggered by the dynamic events of the UAV, when the event triggering condition is met, the control input data u of the i-th UAV is... i (t) Update, and needs to send its own status information to neighboring drones. However, due to the limited number of reliable channels in the communication network, at the time the event is triggered, there may be more than just the i-th drone that meets the event triggering conditions. When the number of drones that meet the event triggering conditions exceeds the limited number of communication channels, the i-th drone may not be able to access the communication network and send information. Define the time when the i-th drone sends its status information as...

[0152] During a drone swarm's flight mission, the limited number of communication channels in the wireless communication network is set to η. The entire flight mission duration is divided into time periods, each containing η independent channel contention slots. Each channel contention slot consists of a channel request broadcast period and an information transmission period. Within a specific time period, drones meeting the event triggering conditions will broadcast their own channel request signal during the channel request broadcast period and acquire the right to use a channel contention slot through a contention mechanism. Drones that successfully acquire the right will then send their status information during the information transmission period.

[0153] At the event trigger time of the i-th UAV, its channel request signal can be transmitted through the channel request function ω. i (t) is calculated, and the channel request function ω is designed. i (t) is:

[0154]

[0155] Among them, Ω i ,Γ i Θ i These are the weighting coefficients, where e is the natural logarithm and d is the weighting factor. i Let be the degree of the i-th drone.

[0156] By the channel request function ω i (t) shows that the channel request signal of the i-th UAV at the event trigger time is determined by the state estimation error ρ of the i-th UAV at that time. i (t), ξ i(t) and d i A joint decision.

[0157] Within the time period of the event trigger moment, the i-th drone will broadcast a channel request signal to other drones that meet the event trigger conditions during the channel request broadcast period. Simultaneously, the i-th drone will listen for channel request signals sent by other drones that meet the event trigger conditions within the communication network. When it receives a channel request signal from another drone that meets the event trigger conditions, the i-th drone will compare its own channel request signal with the channel request signals of other drones and determine whether it should withdraw from the competition for this channel contention slot.

[0158] If the i-th UAV withdraws from the competition, it will continue to participate in the competition for the remaining channel competition slots in that time period until all η independent channel competition slots have been completed.

[0159] If the i-th drone does not withdraw from the contention, and continues to send channel request signals during the channel request broadcast period, it indicates that the i-th drone has successfully acquired the channel contention slot and can send its own status information to neighboring drones during the information transmission period of this channel contention slot. The time t is when the i-th drone sends its status information. k i It will be equal to the time when the event was triggered.

[0160] This distributed competition strategy effectively distinguishes the priorities of drones in the drone swarm that need to send information at a given moment by using the state estimation error information and topology information of the drones that meet the event triggering conditions. This ensures that high-priority drones can send information, thereby improving the utilization rate of communication channel resources while achieving good formation control of the drone swarm.

[0161] The distributed competitive communication strategy based on state estimation error and topology information proposed in this invention fully considers the limited channel resources of the communication network. It proposes that when the UAV triggers its own event, it combines state estimation error and topology information to distinguish the priority of sending information, and then competes to access the wireless communication network and send information, which effectively solves the dilemma of scarce communication resources and ensures good control of the UAV swarm.

[0162] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0163] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for controlling unmanned aerial vehicle (UAV) swarm formations based on distributed contention communication, characterized in that, include: S110. Establish a communication network topology diagram of the UAV cluster, and construct a system dynamics model of the i-th UAV using the dynamics information of any i-th UAV in the UAV cluster; S120. In the drone cluster, one or more virtual drones are preset, the dynamics information of the l-th virtual drone is set, and a leader-follower model is established; the leader-follower model is designed to: input the dynamics information of the l-th virtual drone and the dynamics information of the i-th drone, and output the expected information of the i-th drone; S130. Based on the system dynamics model of the i-th UAV and the expected information of the i-th UAV, obtain the state error information of the i-th UAV; S140. When the i-th UAV joins the communication competition, a distributed competition communication strategy is constructed based on the communication network topology model and the state error information of the i-th UAV, and a communication channel is allocated to the i-th UAV that joins the communication competition.

2. The method according to claim 1, characterized in that, In S110, the system dynamics model of the i-th UAV is expressed as: Among them, s i (t) represents the state variable information of the i-th UAV. For s i The derivative of (t), I3 is a 3x3 identity matrix, u i (t) represents the control input data of the i-th UAV, ζ i (t) represents interference data.

3. The method according to claim 2, characterized in that, Interference data ζ i The estimated value of (t) The interference observer model is calculated from the interference observer model and is constructed as follows: in, It is an auxiliary variable. It is the derivative of the auxiliary variable, and H is the number of harmonics. It is an auxiliary interference variable. It is an auxiliary disturbance variable. The estimated value, It is the gain matrix.

4. The method according to claim 1, characterized in that, In S120, the process of obtaining the desired information for the i-th UAV includes: S1201. Based on the dynamic information of the l-th virtual UAV, construct the flight position equations of the l-th virtual UAV; S1202. Based on the flight position equations of the l-th virtual UAV and the dynamic information of the i-th UAV, obtain the relative expected position information of the i-th UAV. S1203. Based on the dynamics information of the i-th UAV and the relative desired position information of the i-th UAV, obtain the desired position information of the i-th UAV. Information on the expected state variables of the i-th drone 5. The method according to claim 4, characterized in that, In S1202, the relative desired position information p of the i-th UAV li (t), expressed in transpose form, is: Where, p lx (t), p ly (t), p lz (t) represents the position coordinates of the l-th virtual drone along its three axes, V l (t) represents the velocity data of the l-th virtual drone, α l (t), β l (t) represents the trajectory tilt angle and trajectory azimuth angle of the l-th virtual UAV, respectively, and d li γ is the distance between the centroid of the l-th virtual drone and the centroid of the i-th drone. li For the line d li With d li The angle δ between the projections of the i-th UAV onto the xy-plane. li For d li The projection of the i-th UAV onto the xy-plane and V i (t) is the angle between the projections of the i-th UAV onto the xy-plane.

6. The method according to claim 4, characterized in that, In S1203, Desired location information of the i-th drone Represented as: Where, p l (t) represents the position coordinates of the l-th virtual drone, p li (t) represents the relative desired position information of the i-th drone. yes The derivative and express; The desired state variable information for the i-th drone is represented as follows:

7. The method according to claim 1, characterized in that, It also includes a pre-defined collaborative control law triggered by dynamic events of the drone: Define the time of the next event trigger. for: in, Let be the time of the last event trigger for the i-th drone. Let i be the time when the next event is triggered for the i-th drone; For the event triggering function: g2(ξ i (t),q i (t))=||ξ i (t)||2-π i (t)||q i (t)|| 2； Among them, b i ,c i They represent the weighting coefficients, π i It is an adjustable constant, and π i =h2(s i h1(·) and h2(·) are given functions determined based on state variable information; The state variable information s of the i-th UAV i (t) and expected state variable information State error; Indicates the time of the last event trigger for the i-th drone. The difference between the state error at time t and the state error at time t; N represents the average state error of the i-th drone and its j-th neighboring drones. i Let n be the set of neighboring drones of the i-th drone. i Let be the number of neighboring drones of the i-th drone. This is the latest transmission data from the j-th neighboring drone; It is the time of the last event trigger for the i-th drone. The difference between the average state error at time t and the average state error at time t.

8. The method according to claim 7, characterized in that, when At that time, the control input data for the i-th UAV is: in, Let i be the time when the last event was triggered for the i-th drone. Let i be the time when the next event is triggered for the i-th drone. This is the latest transmitted data from the j-th neighboring drone, where K1 and K2 are the controller gains. yes The derivative of a represents the expected acceleration of the i-th drone. i0 a ij All are elements in the adjacency matrix of the communication topology graph. For interference data ζ i The estimated value of (t).

9. The method according to claim 1, characterized in that, When the i-th UAV meets the event triggering condition, its channel request signal is transmitted through the channel request function ω. i (t) is calculated to obtain the channel request function ω. i (t) is: Among them, Ω i ,Γ i Θ i These are the weighting coefficients, where e is the natural logarithm and d is the weighting factor. i Let ρ be the degree of the i-th drone. i (t) represents the difference between the state error of the i-th UAV at the time of the last event trigger and the state error at time t, q i (t) represents the average state error of the i-th UAV and its j-th neighboring UAV.

10. The method according to any one of claims 1-9, characterized in that, The specific workflow of the i-th drone is as follows: S1401, The i-th UAV obtains its own state error information. Through state error information Calculate the event trigger function S1402. Determine whether the event triggering function meets the event triggering condition. The event triggering condition is: If the conditions are met, then execute S1403 and then S1404; if the conditions are not met, then execute S1404 directly. S1403. Enter the distributed contention communication strategy, compete for a limited communication channel based on the state estimation error and topology information, and determine whether the contention is successful; if the contention is successful, access the wireless communication network and send information. If the competition is unsuccessful, access to the wireless communication network will not be granted; S1404, Based on the latest transmission data received from the j-th neighboring drone... Error information with itself Calculate control input data u i (t), based on control input data u i (t) Update its own state.

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