Unmanned aerial vehicle cluster formation control method based on distributed competition communication

Abstract

The invention discloses an unmanned aerial vehicle cluster formation control method based on distributed competition communication. The method comprises the following steps: establishing an unmanned aerial vehicle cluster communication network topological graph and a system dynamics model of the ith unmanned aerial vehicle; presetting one or more virtual unmanned aerial vehicles in the unmanned aerial vehicle cluster, setting dynamic information of the lth virtual unmanned aerial vehicle, and establishing a leader-follower model; calculating state error information of the ith unmanned aerial vehicle; and when the ith unmanned aerial vehicle joins in the communication competition, allocating a communication channel to the ith unmanned aerial vehicle joining in the communication competition according to the communication network topology model and the state error information of the ith unmanned aerial vehicle. According to the technical scheme provided by the embodiment of the invention, the distributed channel scheduling can be realized when the channel is limited so as to ensure the formation control of the unmanned aerial vehicle cluster and the execution of the dynamic cooperative task.

Description

Technical Field

[0001] The present invention relates to the field of UAV swarm formation control, and particularly to a UAV swarm formation control method based on distributed competitive communication. Background Art

[0002] With the rapid development of science and technology, UAV products have increasingly frequently appeared in the public eye. With the booming rise of the UAV industry, UAV technology has been widely penetrated into many aspects of production and life.

[0003] Nowadays, merely achieving efficient control of a single UAV can no longer meet the growing needs of people. How to achieve good control of a complex system composed of multiple UAVs has become a key issue that many scholars have been devoting themselves to studying. Among them, multi-UAV cooperative control is one of the crucial core technologies of UAV swarms.

[0004] However, restricted by the external environment and malicious interference, the self-organizing wireless communication network constructed by UAV swarms often suffers from the scarcity of reliable communication channels and the dilemma of being unable to meet the simultaneous communication needs of a large number of UAVs. In this context of limited effective channel resources, there is an urgent need for a new control method to meet the communication needs of UAVs while maintaining good control of UAV swarms. Summary of the Invention

[0005] Embodiments of the present invention provide a UAV swarm formation control method based on distributed competitive communication to achieve multi-UAV cooperative control under limited communication channels.

[0006] The UAV swarm formation control method based on distributed competitive communication provided by embodiments of the present invention includes:

[0007] S110. Establish a communication network topology map of the UAV swarm, and construct a system dynamics model of the i-th UAV through the dynamic information of any i-th UAV in the UAV swarm;

[0008] S120. Preset one or more virtual UAVs in the UAV swarm, set the dynamic information of the l-th virtual UAV, and establish a leader-follower model; the leader-follower model is designed to: input the dynamic information of the l-th virtual UAV and the dynamic information of the i-th UAV, and output the expected information of the i-th UAV;

[0009] S130. Obtain the state error information of the i-th UAV according to the system dynamics model of the i-th UAV and the expected information of the i-th UAV;

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

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

[0012]

[0013] where, s i (t) represents the state variable information of the i-th drone, is the derivative of s i (t), I3 is a 3-order identity matrix, u i (t) represents the control input data of the i-th drone, and ζ i (t) is the interference data.

[0014] Further, the estimated value of the interference data ζ i (t) is calculated by the interference observer model, and the interference observer model is constructed as:

[0015]

[0016] where, is an auxiliary variable, is the derivative of the auxiliary variable, H is the number of harmonics, is an auxiliary interference variable, is an auxiliary interference variable of the estimated value, is the gain matrix.

[0017] Further, in S120, the process of obtaining the expected information of the i-th drone includes:

[0018] S1201. According to the dynamics information of the l-th virtual drone, construct the flight position equation set of the l-th virtual drone;

[0019] S1202. According to the flight position equation set of the l-th virtual drone and the dynamics information of the i-th drone, obtain the relative expected position information of the i-th drone;

[0020] S1203. According to the dynamics information of the i-th drone and the relative expected position information of the i-th drone, obtain the expected position information of the i-th drone and the expected state variable information of the i-th drone

[0021] Further, in S1202, the relative desired position information p li of the i-th drone at time t is represented in the form of a transposed matrix as follows:

[0022]

[0023] where p lx (t), p ly (t), and p lz (t) are the position coordinates of the l-th virtual drone in the three-axis directions, V l (t) is the speed data of the l-th virtual drone, α l (t) and β l (t) are the track inclination angle and track azimuth angle of the l-th virtual drone respectively, d li is the distance between the centroid of the l-th virtual drone and the centroid of the i-th drone, γ li is the angle between the straight line d li and d li projected onto the x-y plane of the i-th drone, and δ li is the angle between the projection of d li onto the x-y plane of the i-th drone and the projection of V i (t) onto the x-y plane of the i-th drone.

[0024] Further, in S1203,

[0025] the desired position information of the i-th drone is as follows:

[0026]

[0027] where p l (t) is the position coordinate data of the l-th virtual drone, p li (t) is the relative desired position information of the i-th drone, is the derivative of and is denoted by ;

[0028] The desired state variable information of the i-th drone is as follows:

[0029] Further, it also includes a preset collaborative control law for triggering drone dynamic events:

[0030] Define the next event trigger time as follows:

[0031]

[0032] where, is the last event trigger time of the i-th UAV, is the next event trigger time of the i-th UAV;

[0033] is the event trigger function:

[0034]

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

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

[0037] is the state error between the state variable information s i (t) of the i-th UAV and the desired state variable information ;

[0038] represents the difference between the state error at the last event trigger time of the i-th UAV and the state error at time t;

[0039] represents the average state error of the i-th UAV and its j-th neighbor UAVs, N i is the set of neighbor UAVs of the i-th UAV, n i is the number of neighbor UAVs of the i-th UAV, is the latest transmission data from the j-th neighbor UAV;

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

[0041] Furthermore, when the control input data of the i-th UAV is:

[0042]

[0043] wherein, is the last event trigger time of the i-th drone, is the next event trigger time of the i-th drone, is the latest transmission data from the j-th neighbor drone, and K1, K2 are controller gains, is The derivative of represents the desired acceleration of the i-th drone, a i0 , a ij are all elements in the adjacency matrix of the communication topology graph, is the interference data ζ i (t) is the estimated value.

[0044] Furthermore, when the i-th drone meets the event trigger condition, its channel request signal is calculated through the channel request function ω i (t), and the channel request function ω i (t) is:

[0045]

[0046] wherein, Ω i , Γ i , Θ i are weighting coefficients, e is the natural logarithm, d i is the degree of the i-th drone, ρ i (t) represents the difference between the state error at the last event trigger time of the i-th drone and the state error at time t, and q i (t) represents the average state error of the i-th drone and its j-th neighbor drone.

[0047] Furthermore, the working process of the i-th drone is specifically as follows:

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

[0049] S1402. Judge whether the event trigger function meets the event trigger condition, and the event trigger condition is: If it is satisfied, then execute S1403 and then S1404; if it is not satisfied, then directly execute S1404;

[0050] S1403. Enter the distributed competitive communication strategy, compete for the limited communication channel based on the state estimation error and topology information, and judge whether the competition is successful; if the competition is successful, then access the wireless communication network and send information; if the competition is not successful, then do not access the wireless communication network;

[0051] S1404. Calculate the control input data u based on the latest transmission data received from the j-th neighboring UAV and the state error information of itself. i Update its own state according to the control input data u i (t).

[0052] In the embodiment of the present invention, a leader-follower model is established to control the flight trajectory of the UAV. When the UAV needs to adjust its position, a UAV dynamic event-triggered cooperative control law is triggered, and a distributed competition communication strategy is used to allocate communication channels to the UAVs participating in the communication competition. The control of the UAV cluster formation is realized under the condition of limited communication resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0054] Figure 1 FIG. is a flowchart of a UAV cluster formation control method based on distributed competition communication provided by an embodiment of the present invention;

[0055] Figure 2 FIG. is a schematic diagram of the flight state of the i-th UAV provided by an embodiment of the present invention;

[0056] Figure 3 FIG. is a schematic diagram of the positional relationship between the i-th UAV and the l-th virtual UAV provided by an embodiment of the present invention;

[0057] Figure 4 FIG. is a schematic diagram of the working process of the i-th UAV provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] The present invention will be further described in detail below with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the present invention, rather than limiting the present invention. In addition, it should be noted that only parts related to the present invention are shown in the drawings for the sake of convenience of description, rather than all 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. By using state estimation information, an event triggering function is designed, which converts the UAV communication and control update problem into a problem of judging the value of the triggering function, and solves the problem of frequent information update among UAVs in the process of multi-UAV formation cooperative control.

[0060] However, the existing UAV cluster control systems default that UAV communication resources are rich and can simultaneously meet the communication needs of all UAVs in the system, or adopt a centralized channel scheduling strategy to allocate channels for each UAV. The technical solution in the embodiment of the present invention aims to realize distributed channel scheduling under limited channels to ensure UAV cluster formation control and execution of dynamic cooperative tasks.

[0061] Embodiment 1

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

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

[0064] S110. Establish a communication network topology diagram of the UAV cluster, and construct a system dynamics model of the i-th UAV through the dynamics information of any i-th UAV in the UAV cluster.

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

[0066] Assume that there are N UAVs in the UAV cluster, and information interaction between UAVs is realized through an ad hoc wireless communication network. The UAV cluster communication topology can be represented by G = {Z, E}, where Z = {z0, z1,..., z N} is the UAV point set, E is the edge set, z0 is the point of the virtual UAV in the topology, and {z1,..., z N} are the points of the UAVs in the topology. The set of neighbor UAVs of the i-th UAV is N i , and the number is n i . Further define the adjacency matrix A of the UAV cluster, A = [a ij (N+1)×(N+1) represents the communication topology between UAVs, where i ∈ 0, 1..., N and j ∈ 0, 1..., N. When j = 0, a i0 represents the communication between the i-th UAV and the virtual UAV, a ij ​Indicates that the i-th drone sends information to the j-th neighbor drone, a ij > 0 indicates that the j-th neighbor drone can receive the information from the i-th drone, a ij = 0 indicates that the i-th drone and the j-th neighbor drone cannot communicate with each other.

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

[0068] Before establishing the system dynamics model of the i-th drone, it should be noted that:

[0069] In the embodiments of the present invention, the dynamic information includes: position coordinate data, velocity data, pose data, input data, interference data, etc.

[0070] It should be noted that the t moment in the embodiments of the present invention is used to refer to the synchronization of each parameter in time. The derivatives in the embodiments of the present invention are all derivatives with respect to time.

[0071] T in the embodiments of the present invention represents that its parameter is in the form of a transpose matrix, that is, different representation forms of the same parameter. For example: s i (t) and both represent the state variables of the i-th drone, but only in different representation forms.

[0072] Among them, the position coordinate data can be represented in the form of a transpose matrix as: Among them, p ix (t) is the position coordinate of the i-th drone in the x direction, p iy (t) is the position coordinate of the i-th drone in the y direction, p iz (t) is the position coordinate of the i-th drone in the z direction.

[0073] The velocity data can be expressed as: V i (t).

[0074] The pose data includes: the track tilt angle α i (t) of the i-th drone and the track azimuth angle β i (t) of the i-th drone.

[0075] The input data includes: the actual input data of the i-th drone and the control input data u i (t), where a ix (t), a iy (t), aiz The roll angular acceleration \(a_{r_i}(t)\), pitch angular acceleration \(a_{p_i}(t)\), and yaw angular acceleration \(a_{y_i}(t)\) of the \(i\)-th UAV are respectively ix \(a_{r_i}(t)\), \(a_{p_i}(t)\), iy and \(a_{y_i}(t)\). iz (t).

[0076] The interference data can be expressed as: \(\zeta(t)\). i (t).

[0077] Figure 2 FIG. Figure 2 is a schematic diagram of the flight state of the \(i\)-th UAV provided by the embodiment of the present invention. Combining

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

[0079] The first way is to consider the \(i\)-th UAV as a particle motion with the centroid as the coordinate origin, and use the position coordinate data, velocity data, pose data, and actual input data to construct a system of equations to describe the flight state of the \(i\)-th UAV:

[0080]

[0081] Among them, is the derivative of \(p_{r_i}(t)\), ix (t), is the derivative of \(p_{p_i}(t)\), iy (t), is the derivative of \(p_{y_i}(t)\), iz (t), is the derivative of \(V(t)\), is the derivative of \(\beta_{i}(t)\), i (t), is the derivative of \(\alpha_{i}(t)\), and \(g\) is the acceleration due to gravity. i (t).

[0082] The second way is to convert the system of equations into a second-order linear system dynamics model based on the first construction, using the position coordinate data, pose data, and input data:

[0083]

[0084] Among them, is the derivative of \(p_{r_i}(t)\), i (t), is the derivative of \(v_{i}(t)\). i (t).

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

[0086] S1101: Define It represents the transposed matrix form of the actual input data of the i-th drone.

[0087] S1102: Define It represents the transposed matrix form of the position coordinate data of the i-th drone.

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

[0089]

[0090] Among them, is the derivative of , that is, the control input data u i (t), and Δ i (t) is an invertible matrix.

[0091] S1104: Let Then we can obtain the above second-order linear system dynamics model.

[0092] The third one is based on the second construction. Using the position coordinate data, pose data, input data, and interference data, first optimize the second-order linear system dynamics model:

[0093]

[0094] Then define It represents the state variable information of the i-th drone.

[0095] The third dynamics model is expressed as:

[0096]

[0097] Among them, is the derivative of s i (t), I3 is a 3-order identity matrix.

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

[0099]

[0100] Among them, ζ i (t) is the interference data, H is the number of harmonics, is the auxiliary interference variable, is the derivative of the auxiliary interference variable, is the gain matrix.

[0101] Furthermore, during the flight of the drone, it will inevitably be disturbed. To eliminate the influence of the disturbance on the control of the drone system, a disturbance observer model is designed.

[0102] Estimated value of the disturbance data ζ i (t) It is calculated by the disturbance observer model, and the disturbance observer model is constructed as follows:

[0103]

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

[0105] S120. Preset a l-th virtual drone in the drone swarm, set the dynamic information of the l-th virtual drone, and establish a leader-follower model.

[0106] The leader-follower model is designed as follows: Input the dynamic information of the l-th virtual drone and the dynamic information of the i-th drone to obtain the desired information of the i-th drone.

[0107] Among them, the desired information of the i-th drone includes: the desired position information of the i-th drone, the desired state variable information of the i-th drone, and so on.

[0108] It should be noted that in order to achieve the formation control of the drone swarm and maintain the desired formation, a l-th virtual drone is introduced as a virtual leader to provide the desired flight trajectory of the formation. During the entire flight mission time, all the state information of the l-th virtual drone can be preset, that is, all the following drones know the dynamic information of the virtual leader drone at any moment.

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

[0110] Step S1201. According to the dynamic information of the l-th virtual drone, construct the flight position equation set of the l-th virtual drone.

[0111] The flight position equation set of the l-th virtual drone is as follows:

[0112]

[0113] where is the transposed matrix form of the position coordinate data of the l-th virtual drone, p lx (t), ply (t), p lz (t) is the position coordinates of the l-th virtual UAV in the three-axis directions, V l (t) is the speed data of the l-th virtual UAV, α l (t), β l (t) are respectively the track inclination angle and the track azimuth angle of the l-th virtual UAV.

[0114] Figure 3 is a schematic diagram of the position relationship between the i-th UAV and the j-th virtual neighbor UAV provided by the embodiment of the present invention. Combining Figure 3 , the l-th virtual UAV and the i-th UAV are in different x-y planes. Figure 3 The l-th virtual UAV and its dynamic information are shown above. Figure 3 The i-th UAV and its dynamic information are shown below.

[0115] In the UAV swarm formation control, at each moment, the i-th UAV has an expected position, and this expected position is determined by the relative expected position between the l-th virtual UAV and the i-th UAV.

[0116] Step S1202: Obtain the relative expected position information of the i-th UAV according to the flight position equation set of the l-th virtual UAV and the dynamic information of the i-th UAV.

[0117] The relative expected position information p of the i-th UAV li (t), is represented in the form of a transposed matrix as:

[0118]

[0119] where, d li is the distance between the centroid of the l-th virtual UAV and the centroid of the i-th UAV, d li ' is the projection of d li on the x-y plane of the i-th UAV, γ li is the angle between the straight line d li and d li ', V i (t)' is the projection of V i (t) on the x-y plane of the i-th UAV, δ li is the angle between d li ' and V i (t)'.

[0120] Step S1203: Obtain the expected position information of the i-th UAV and the expected state variable information of the i-th UAV according to the dynamic information of the i-th UAV and the relative expected position information of the i-th UAV.

[0121] The expected position information of the i-th drone is as follows:

[0122]

[0123] The expected state variable information of the i-th drone is as follows:

[0124] S130. According to the system dynamics model of the i-th drone and the expected information of the i-th drone, obtain the state error information of the i-th drone.

[0125] The state error information is expressed as the error between the state variable information s i (t) of the i-th drone and the expected state variable information .

[0126] S140. When the i-th drone joins the communication competition, construct a distributed competition communication strategy according to the communication network topology model and the state error information of the i-th drone, and allocate a communication channel for the i-th drone joining the communication competition.

[0127] To achieve good formation control of the drones, a control algorithm is designed, and in order to further reduce the occupancy of communication resources and lower the communication frequency between the drones, a dynamic event-triggered cooperative control law is designed based on the event-triggering mechanism.

[0128] When not considering event triggering, design the control input data u i (t) of the i-th drone as follows:

[0129]

[0130] where K1 and K2 are controller gains, is the derivative of.

[0131] To reduce the communication frequency between the drones, a dynamic event-triggering strategy is further proposed to reduce network resource occupancy. According to the event-triggering mechanism, for the i-th drone, define the next event-triggering moment as follows:

[0132]

[0133] where, is the previous event-triggering moment of the i-th drone, is the next event-triggering moment of the i-th drone.

[0134] Is an event-triggered function:

[0135]

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

[0137] Where b i , c i Represent the weighting coefficients respectively, Represents the state variable information s i (t) of the i-th UAV and the desired state variable information The state error, Represents the difference between the state error at the previous event-triggered moment of the i-th UAV And the state error at time t.

[0138] Represents the average state error of the i-th UAV and its j-th neighbor UAVs, Is the latest transmission data from the j-th neighbor UAV, Is the difference between the average state error at the previous event-triggered moment of the i-th UAV And the average state error at time t, π i Is an adjustable constant, and π i = h2(s i (t)), h1(·), h2(·) are given functions determined based on the state variable information.

[0139] At the event-triggered moment, the i-th UAV will adopt a distributed competitive communication strategy based on the state estimation error and topological information, competing for limited communication channels, improving the utilization rate of communication resources while ensuring good formation control of the UAV swarm.

[0140] When the event-triggering condition Is satisfied, the signal sampled by the i-th UAV currently will be triggered, and ρ i (t) and ξ i (t) will be reset to zero simultaneously. In addition, with the help of the zero-order hold device equipped in the controller, the input of the controller will remain unchanged until the next trigger signal is transmitted.

[0141] Based on the dynamic event-triggering mechanism, it can be obtained that when considering the event-triggering situation, when The control input data of the i-th UAV is:

[0142]

[0143] wherein, is the last event trigger time of the i-th drone, is the next event trigger time of the i-th drone, is the latest transmission data from the j-th neighbor drone, and K1, K2 are controller gains, is The derivative of represents the expected acceleration of the i-th drone, a i0 represents that the i-th drone sends information to the l-th drone, a ij represents that the i-th drone sends information to the j-th neighbor drone, is the interference data ζ i (t) estimated value.

[0144] Figure 4 is the schematic diagram of the working process of the i-th drone provided by the embodiment of the present invention. Combining Figure 4 , the working process of the i-th drone is specifically as follows:

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

[0146] S1402. Determine whether the event trigger function meets the event trigger condition. The event trigger condition is: If it is satisfied, execute S1403 and then execute S1404; if it is not satisfied, directly execute S1404.

[0147] S1403. Enter the distributed competitive communication strategy. According to the state estimation error and topology information, compete for the 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 not successful, do not access the wireless communication network.

[0148] S1404. Calculate the control input data according to the latest transmission data received from the j-th neighbor drone and its own state error information Calculate the control input data, and update its own state according to the control input data u i (t).

[0149] When the event trigger function does not meet the event trigger condition at this time,

[0150] In a UAV formation, the reliable communication channels of the wireless communication network are limited. At each moment, the wireless communication network allows at most a finite number of follower UAVs to access the communication network and send information. If the total number of follower UAVs in the UAV cluster is greater than the communication resource limit of the wireless communication network, then at any moment, the wireless communication network may not be able to meet the communication requirements of all follower UAVs at that moment. Therefore, a distributed competitive communication strategy based on state estimation error and topological information is designed.

[0151] According to the cooperative control law triggered by UAV dynamic events, when the i-th UAV meets the event-triggering condition, the control input data u i (t) is updated, and it is necessary to send its own state information to neighboring UAVs. However, due to the limited reliable channels of the communication network, at the event-triggering moment, there may be more than just the i-th UAV that meets the event-triggering condition. When the number of UAVs that meet the event-triggering condition is more than the number of limited communication channels, the i-th UAV may not be able to access the communication network and send information. Define the state information sending moment of the i-th UAV as

[0152] During the flight mission of the UAV cluster, it is assumed that the number of limited communication channels of the wireless communication network is η. The entire flight mission duration is divided according to time periods, and each time period contains η mutually independent channel competition time slots. Among them, the channel competition time slot consists of a channel request broadcast period and an information transmission period. Within a specific time period, UAVs that meet the event-triggering condition will broadcast their own channel request signals during the channel request broadcast period and obtain the usage right of the channel competition time slot through a competition mechanism. The UAVs that successfully obtain the permission will then send their own state information during the information transmission period.

[0153] At the event-triggering moment of the i-th UAV, its channel request signal can be calculated through the channel request function ω i (t). Design the channel request function ω i (t) as:

[0154]

[0155] Among them, Ω i , Γ i , Θ i are weighting coefficients, e is the natural logarithm, and d i is the degree of the i-th UAV.

[0156] From the channel request function ω i (t), it can be seen that the channel request signal of the i-th UAV at the event-triggering moment is composed of the state estimation error ρ i (t), ξ i(t) and d i jointly determine.

[0157] Within the time period when the event trigger moment occurs, 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. At the same time, the i-th drone will listen to the channel request signals sent by other drones that meet the event trigger conditions within the communication network. When receiving the channel request signal of other drones that meet the event trigger conditions, the i-th drone will compare its own channel request signal with that of other drones and determine whether to withdraw from the competition of this channel contention time slot.

[0158] If the i-th drone withdraws from the competition, it will continue to participate in the competition of the remaining channel contention time slots in this time period until all η independent channel contention time slots have been participated in.

[0159] For the case where the i-th drone does not withdraw from the competition, if the i-th drone does not withdraw and continuously sends a channel request signal during the channel request broadcast period, it indicates that the i-th drone has successfully competed for this channel contention time slot and can send its own status information to neighboring drones during the information transmission period of this channel contention time slot. At this time, the status information sending moment t of the i-th drone k i will be equal to this event trigger moment.

[0160] This distributed competition strategy effectively differentiates the priorities of the drones that need to send information in the drone cluster at this moment through the state estimation error information and topology information of the drones that meet the event trigger conditions, ensuring that the drones with high priorities can definitely send information. While achieving good formation control of the drone cluster, it improves the utilization rate of communication channel resources.

[0161] The distributed competition communication strategy based on state estimation error and topology information proposed in the embodiments of the present invention fully considers the limited communication network channel resources. It is proposed that the follower drones combine state estimation error and topology information at their own event trigger moments to distinguish the priorities of sending information, compete to access the wireless communication network and send information, effectively solving the dilemma of scarce communication resources while ensuring good control of the drone cluster.

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

[0163] The above specific embodiments do not constitute a limitation on the protection scope of the present 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 principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A UAV swarm formation control method based on distributed competitive communication, characterized in that: include: S110, establishing a drone cluster communication network topology diagram, and constructing a system dynamics model of the i-th drone through the dynamics information of any i-th drone in the drone cluster; S120, presetting one or more virtual drones in the drone cluster, setting the dynamic information of the lth virtual drone, and establishing a leader-follower model; the leader-follower model is designed to: input the dynamic information of the lth virtual drone and the dynamic information of the i-th drone, and output the expected information of the i-th drone; S130, obtaining state error information of the ith UAV according to the system dynamics model of the ith UAV and the expected information of the ith UAV; S140. When the i-th UAV joins the communication competition, a distributed competitive communication strategy is constructed according to the communication network topology model and the state error information of the i-th UAV to allocate a communication channel to the i-th UAV joining 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 i The derivative of (t), I3 is the 3rd-order identity matrix, u i (t) represents the control input data of the i-th UAV, ζ i (t) is the interference data.

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

4. The method according to claim 1, characterized in that: In S120, the process of obtaining the expected information of the i-th drone includes: S1201, constructing a flight position equation group of the lth virtual drone according to the dynamic information of the lth virtual drone; S1202, obtaining the relative expected position information of the ith UAV according to the flight position equation group of the lth virtual UAV and the dynamic information of the ith UAV; S1203: Obtain the expected position information of the ith UAV based on the dynamic information of the ith UAV and the relative expected position information of the ith UAV. and the expected state variable information of the i-th UAV 5. The method according to claim 4, characterized in that In S1202, the relative expected position information p of the i-th UAV is li (t), expressed in transposed matrix form as: Among them, p lx (t), p ly (t), p lz (t) is the position coordinate of the lth virtual drone in the three-axis direction, V l (t) is the speed data of the lth virtual drone, α l (t), β l (t) are the track inclination angle and track azimuth of the lth virtual UAV, d li is the distance between the mass center of the lth virtual UAV and the mass center of the ith UAV, γ li is the straight line d li With d li The angle between the projections of the i-th drone on the xy plane, δ li is d li The projection of the i-th drone on the xy plane and V i (t) The angle between the projections of the ith UAV on the xy plane.

6. The method according to claim 4, characterized in that In S1203, The expected position information of the i-th UAV It is expressed as: Among them, p l (t) is the position coordinate data of the lth virtual drone, p li (t) is the relative expected position information of the i-th UAV, yes The derivative and express; The expected state variable information of the i-th UAV is expressed as:

7. The method according to claim 1, characterized in that It also includes preset UAV dynamic events to trigger collaborative control laws: Define the next event trigger time for: in, is the last event triggering time of the i-th drone, is the next event triggering time of the i-th drone; Trigger function for event: 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 weighting coefficients, π i is an adjustable constant, and π i =h2(s i (t)), h1(·), h2(·) are given functions determined based on the state variable information; is the state variable information s of the i-th UAV i (t) and expected state variable information The state error of Indicates the last event triggering time of the i-th drone The difference between the state error at time t and the state error at time t; represents the average state error between the ith UAV and its jth neighbor UAV, N i is the set of neighboring drones of the i-th drone, n i is the number of neighboring drones of the ith drone, is the latest transmission data from the jth neighboring drone; is the last event triggering time of 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 When , the control input data of the i-th UAV is: in, is the last event triggering time of the i-th drone, is the next event triggering time of the i-th drone, is the latest transmission data from the jth neighboring drone, K1, K2 are controller gains, yes The derivative of represents the expected acceleration of the i-th UAV, a i0 , a ij are all elements in the adjacency matrix of the communication topology graph. is the interference data i (t) is an estimated value.

9. The method according to claim 1, characterized in that: When the i-th UAV meets the event triggering condition, its channel request signal passes through the channel request function ω i (t) is calculated, the channel request function ω i (t) is: Among them, Ω i , Γ i , Θ i is the weighting coefficient, e is the natural logarithm, d i is the degree of the i-th UAV, ρ i (t) represents the difference between the state error of the i-th UAV at the last event triggering moment and the state error at moment t, q i (t) represents the average state error between the ith UAV and its jth neighbor UAV.

10. The method according to any one of claims 1 to 9, characterized in that: The specific workflow of the i-th drone is: S1401, the i-th UAV obtains its own state error information Through the state error information Calculate event trigger function S1402: Determine whether the event trigger function meets the event trigger condition. The event trigger condition is: If satisfied, execute S1403 and then S1404; if not satisfied, execute S1404 directly; S1403, entering the distributed contention communication strategy, competing for the limited communication channel based on the state estimation error and the topology information, and determining whether the competition is successful; if the competition is successful, accessing the wireless communication network and sending information; If the competition is unsuccessful, the wireless communication network will not be accessed; S1404: Based on the latest transmission data received from the jth neighboring drone Error information with its own state Calculate control input data u i (t), according to the control input data u i (t) Update its own status.

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