An event-driven privacy protection method for cooperative formation of a UAV cluster system

Abstract

The application provides an event-driven privacy protection method for a UAV cluster system cooperative formation, and relates to the technical field of UAV cluster control. The method comprises the following steps: modeling the communication topology of the UAV cluster by using graph theory, and determining the formation center of the strongly connected balanced topology; establishing the motion model of the UAV cluster according to the topology and the formation center of the UAV cluster; introducing the control item based on the event-triggered strategy on the basis of the traditional privacy protection cooperative algorithm, and protecting the real communication data by using a kind of finite time mask function, and proposing a new type of privacy protection composite control law; and the design realizes the cooperative formation of the UAV cluster system and ensures the privacy of the initial position and speed of each UAV. The application solves the network topology limitation problem in the existing technology of realizing the UAV privacy protection formation by using the deterministic interference.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle cluster control, in particular to an event-driven privacy protection method for cooperative formation of unmanned aerial vehicle cluster system. BACKGROUND

[0002] Formation control of multi-agent systems has attracted increasing attention in many scientific fields, such as mobile robot formation systems, unmanned aerial vehicle and satellite formation systems. However, due to the open nature of wireless communication channels, when unmanned aerial vehicle cluster systems communicate with each other using real information, their information is easily eavesdropped, leading to individual privacy leakage. For example, when multiple unmanned aerial vehicles from different interest parties perform formation tasks, each unmanned aerial vehicle may carry its own initial sensitive information, which is not allowed to be accessed by other unmanned aerial vehicles or attackers. Therefore, it is of great significance to study privacy protection problems.

[0003] The current privacy protection methods can be mainly divided into three categories: cryptography methods, state decomposition methods and adding interference signal methods. The cryptography-based method mainly uses homomorphic encryption systems, such as the Paillier encryption system; however, due to the huge computational cost caused by the encryption process, it is not suitable for small multi-agent clusters like unmanned aerial vehicle clusters. The state decomposition method is to decompose individual information into two parts, only one part of which is used for system formation control. This method is currently often applied to discrete systems. The adding interference signal method is to introduce some interference into the real signal to obtain a false state value, and each agent exchanges the false information through the communication network and uses it for formation controller design. In current research, there are usually two kinds of interference signals, namely random interference and deterministic interference. The method using random interference signal is represented by the differential privacy method, which often cannot achieve accurate formation control, while the method based on deterministic interference needs to add some topological restrictions to achieve effective privacy protection, which greatly limits its application range. SUMMARY

[0004] In order to overcome the shortcomings of the prior art, the purpose of the present application is to provide an event-driven privacy protection method for cooperative formation of unmanned aerial vehicle cluster system.

[0005] To achieve the above purpose, the present application provides the following scheme:

[0006] An event-driven privacy protection method for cooperative formation of unmanned aerial vehicle cluster system, comprising:

[0007] modeling the communication topology of the unmanned aerial vehicle cluster using graph theory and determining the formation center of the strongly connected balanced topology thereof;

[0008] According to the directed strongly connected balanced topology of the UAV cluster and the formation center, a motion model of the UAV cluster formation is established;

[0009] According to the motion model, a finite time mask function is designed, and a privacy protection control law of the cooperative formation of the UAV cluster system is established based on false information;

[0010] According to the motion model and the above control law, a control law based on an event-triggered strategy is introduced, and a privacy protection composite control law of the system formation is obtained;

[0011] According to the designed privacy protection composite control law, the target UAV cluster is controlled in formation.

[0012] Preferably, the communication topology of the UAV cluster is modeled by graph theory, comprising:

[0013] Determine the set of UAV nodes;

[0014] According to the set of UAV nodes, determine the out-degree and in-degree of the UAV nodes;

[0015] Determine the adjacency matrix of the network topology structure;

[0016] According to the out-degree of each UAV node and the adjacency matrix, determine the Laplacian matrix of the network topology;

[0017] According to the Laplacian matrix and the out-degree and in-degree of each UAV node, construct the directed strongly connected balanced communication topology of the UAV cluster.

[0018] Preferably, the determination method of the formation center is:

[0019] Determine the position of the initial formation center;

[0020] Based on the directed strongly connected balanced topology, determine the coordinate value of the UAV node in the inertial coordinate system and the coordinate value of the out-neighbor UAV node of the current UAV node in the inertial coordinate system;

[0021] According to the coordinate value of the UAV node in the inertial coordinate system and the coordinate value of the out-neighbor UAV node of the current UAV node in the inertial coordinate system, determine the position vector coordinates of the UAV node and the out-neighbor UAV node of the current UAV node;

[0022] The first position deviation and the second position deviation are determined based on the position vector coordinates of the UAV node, the position vector coordinates of the current UAV node's outgoing neighbor UAV node, and the position of the initial formation center. The first position deviation is the vector coordinate of the deviation between the UAV node's position and the initial formation center position; the second position deviation is the vector coordinate of the deviation between the current UAV node's outgoing neighbor UAV node's position and the initial formation center position.

[0023] The position of the initial formation center is adjusted using the first position deviation and the second position deviation to determine the final position of the formation center.

[0024] Preferably, the expression for the motion model of the drone swarm formation is:

[0025]

[0026] Where, m i For drone node d i quality; p i (t) represents the drone node d i During flight, at time t, the position vector in the inertial coordinate system satisfies the 3*1 position vector; v i (t) represents the drone node d i During flight, at time t, the velocity vector in the inertial coordinate system satisfies a 3*1 velocity vector. Indicates the drone node d i The 3*1 acceleration vector at time t during flight; u i1 (t) and u i2 (t) represents the drone node d. i Control laws based on local false information exchange and control laws based on event-triggered strategies; d i This is the current drone node.

[0027] Preferably, the UAV node d i The expression for the control law based on local false information exchange is:

[0028]

[0029] in, and Representing the drone node d respectively i The false position and velocity information used for information exchange can be obtained by transforming the true position and velocity information in the inertial coordinate system through a finite time-varying mask function; i Represents drone node d i The vector coordinates representing the deviation between the position in the ground coordinate system and the position of the formation center. Let d jFor the current UAV node, the out-neighbor UAV node, similarly, and respectively represent the out-neighbor UAV node d j The false position information and speed information after the transformation of the limited time-varying mask function;o j represent the UAV node d j The vector coordinates of the position deviation from the formation center position in the ground coordinate system.a ij For the UAV node d i The adjacency matrix coefficient of the out-neighbor UAV node d j ; alpha is the position control coefficient, and beta is the speed control coefficient.

[0030] Preferably, all UAVs are triggered at the initial moment, that is, t1=0, and the expression of the event-triggered strategy control law is:

[0031]

[0032] Where, v i (t) and v i (t k ) are the real speed vectors of the UAV node d i at t and t k , respectively, t k is a series of trigger time points satisfying the event-triggered control condition, which is determined by the data center according to the event-triggered control condition; t k+1 represents the latest trigger time point after the trigger time t k ; T is a specific time point selected by the designer, reflecting the action interval of the event-triggered protection control law;

[0033] Preferably, the expression of the composite control law is:

[0034]

[0035] The present application discloses the following technical effects:

[0036] The application provides an event-driven privacy protection method for a UAV cluster system cooperative formation, comprising the following steps: establishing a directed strongly connected balanced communication topology of a UAV cluster by using graph theory and determining a formation center; establishing a motion model of a UAV cluster formation according to the directed strongly connected balanced communication topology of the UAV cluster and the formation center; respectively establishing a control law of a UAV node based on local false information exchange and a control law of a UAV node based on an event-triggered strategy according to the motion model; determining a composite control law according to the control law of the UAV node based on local false information exchange and the control law of the UAV node based on the event-triggered strategy; and controlling a target UAV cluster according to the composite control law. The method of the application establishes a UAV formation motion model and a directed strongly connected balanced communication topology of a formation system by using graph theory, constructs a privacy protection cooperative formation control law, solves the problem of privacy leakage caused by direct communication in a traditional control method, and realizes an expected formation flight target; the method of the application uses false information transformed by a limited time-varying mask function for local communication exchange control in a period of time, prevents the leakage of real initial position and speed information of a UAV in a communication process, and realizes an expected cooperative formation flight target; the method of the application uses an event-triggered strategy, each UAV node transmits real speed to a data center and determines a triggering time, thereby constructing a private non-trivial dynamic behavior of a UAV node, solving the network topology condition limitation problem generated in existing research, preventing other UAVs or attackers from inferring real initial position information of a UAV based on an observer, and having good feasibility and applicability; the method of the application has low complexity and is easy to implement. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained based on these drawings without creative labor.

[0038] Figure 1 A flow chart of an event-driven privacy protection method for a UAV cluster system cooperative formation provided by the embodiment of the present application;

[0039] Figure 2 A strategy schematic diagram of an event-driven privacy protection method for a UAV cluster system cooperative formation provided by the embodiment of the present application;

[0040] Figure 3 A communication topology graph of a 5-UAV cluster provided by the embodiment of the present application;

[0041] Figure 4A 3D schematic view of real initial positions and false initial positions after transformation of a limited time-varying mask function of 5 UAVs (UAV) take-off provided by the embodiment of the present application;

[0042] Figure 5 A trajectory diagram of 5 UAVs (UAV) provided by the embodiment of the present application, wherein, Figure 5 (a) x-axis, y-axis, z-axis position trajectory diagrams of real positions and false positions after transformation of a limited time-varying mask function, respectively, minus bias, Figure 5 (b) real speed and false speed trajectory diagrams of 5 UAVs (UAV) x-axis, y-axis, z-axis, respectively, after transformation of a limited time-varying mask function;

[0043] Figure 6 A trigger time interval diagram of 5 UAVs (UAV) based on an event-triggered controller provided by the embodiment of the present application,

[0044] Figure 7 A real speed error diagram of 5 UAVs provided by the embodiment of the present application Figure;

[0045] Figure 8 An x-axis privacy trigger error diagram of 5 UAVs (UAV) provided by the embodiment of the present application;

[0046] Figure 9 A 3D trajectory simulation diagram of 5 UAVs (UAV) flying at a time provided by the embodiment of the present application. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0048] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0049] As Figure 1 shown, the present application provides an event-driven privacy protection method for a UAV cluster system cooperative formation, comprising:

[0050] Step 100: model the communication topology of the UAV cluster by using graph theory, and determine the formation center of the strongly connected balanced topology thereof;

[0051] Step 200: establishing a motion model of the UAV cluster formation according to the strongly connected balanced topology of the UAV cluster and the formation center;

[0052] Step 300: designing a finite time mask function according to the motion model, and establishing a privacy protection control law of the cooperative formation of the UAV cluster system based on false information;

[0053] Step 400: introducing a control law based on an event-triggering strategy according to the motion model and the control law, to obtain a privacy protection composite control law of the system formation;

[0054] Step 500: performing formation control on the target UAV cluster according to the designed privacy protection composite control law.

[0055] Further, the strongly connected balanced communication topology of the UAV cluster is established by using graph theory, including:

[0056] determining a set of UAV nodes;

[0057] determining the out-degree and in-degree of the UAV nodes according to the set of UAV nodes;

[0058] determining the adjacency matrix of the network topology structure;

[0059] determining the Laplace matrix of the network topology structure according to the out-degree of each UAV node and the adjacency matrix;

[0060] constructing the strongly connected balanced communication topology structure of the UAV cluster according to the Laplace matrix and the out-degree and in-degree of each UAV node.

[0061] The network communication topology structure assumed in the application is a strongly connected balanced graph, which belongs to a widely used graph in graph theory.

[0062] Specifically, as shown in the figure, Figure 2 in order to realize the state representation of the UAV node, an inertial coordinate system E 地 -OXYZ is used.

[0063] Among them, the inertial coordinate system E 地 -OXYZ is a coordinate system fixed to the earth's surface, and the coordinate system origin O is selected at a point on the ground surface, the OX axis points to the target direction, the OY axis is perpendicular to the OX axis, and the OZ axis is perpendicular to the other two axes and forms a right-handed orthogonal coordinate system; the strongly connected balanced communication topology of the UAV cluster is:

[0064] G=(V,E);

[0065] Among them, V is a set of UAV nodes, V={d1,d2,…,d n}, where n is the total number of drone cluster nodes; E = V × V represents the set of edges between drone nodes; if drone node d j To drone node d i The drone node d is responsible for transmitting information. j It is drone node d i The outgoing neighbor node, the drone node d i It is drone node d j The incoming neighbor nodes, i∈n, j∈n, i≠j. The drone node d i The neighboring drone node d j The set is denoted as N i out N i out ={(d j |j=1,2,…,s,(i,j)∈E}, where s is the drone node d i The neighboring drone node d j The total number is called drone nodes d. i Out-degree; drone node d i The neighboring drone node d j The set is denoted as N i in N i in ={(d j |j=1,2,…,k,(j,i)∈E}, where k is the drone node d i The neighboring drone node d j The total number is called drone nodes d. i The in-degree of the adjacency matrix. If (i,j)∈E, then the coefficient a of the corresponding adjacency matrix is ​​called the in-degree. ij =1∈A, where A is the adjacency matrix of the network topology. Define L = DA as the Laplacian matrix of the network topology, where D = diag{s1,…,s...} n};L T Let L be the transpose of L; And λ2(L s ) for L s The second smallest eigenvalue excluding zero. If the out-degree of any UAV node equals its in-degree and there is a communication channel between any two UAV nodes, then the corresponding communication topology is called a directed strongly connected balanced communication topology. This study assumes that the communication topology is a directed strongly connected balanced communication topology;

[0066] Furthermore, the method for determining the formation center is as follows:

[0067] Determine the initial formation center position;

[0068] Based on the directed strongly connected balanced topology, the coordinates of the UAV node in the inertial coordinate system and the coordinates of the current UAV node's outgoing neighbor UAV nodes in the inertial coordinate system are determined.

[0069] The position vector coordinates of the drone node and its current neighboring drone node are determined based on the coordinates of the drone node in the inertial coordinate system and the coordinates of the current drone node in the inertial coordinate system.

[0070] The first position deviation and the second position deviation are determined based on the position vector coordinates of the UAV node, the position vector coordinates of the current UAV node's outgoing neighbor UAV node, and the position of the initial formation center. The first position deviation is the vector coordinate of the deviation between the UAV node's position and the initial formation center position; the second position deviation is the vector coordinate of the deviation between the current UAV node's outgoing neighbor UAV node's position and the initial formation center position.

[0071] The position of the initial formation center is adjusted using the first position deviation and the second position deviation to determine the final position of the formation center.

[0072] Specifically, such as Figure 3 As shown, drone node d i In inertial coordinate system E 地 The position within OXYZ and its deviation from the formation center are defined as follows:

[0073] p i Represents drone node d i In the ground coordinate system E 地 The position vector coordinates in -OXYZ, p i =[x i ,y i ,z i ].

[0074] x i Represents drone node d i In the ground coordinate system E 地 The coordinates in the X direction of -OXYZ.

[0075] y i Indicates the drone node d i In the ground coordinate system E 地 - The coordinates in the Y direction of OXYZ.

[0076] z i Represents drone node d i In the ground coordinate system E 地 The coordinates in the Z direction of -OXYZ.

[0077] o i Represents drone node d i In the ground coordinate system E 地 -The vector coordinates of the position in OXYZ that deviates from the position of the formation center. i =[o xi ,o yi ,,o zi ].

[0078] o xi Represents drone node d i In the ground coordinate system E 地 - The deviation in the X direction of OXYZ.

[0079] o yi Represents drone node d i In the ground coordinate system E 地 - Deviation in the Y direction of OXYZ.

[0080] o zi Represents drone node d i In the ground coordinate system E 地 - The deviation in the Z direction of OXYZ.

[0081] Therefore, drone node d i The neighboring drone node d j In inertial coordinate system E 地 The position within OXYZ and its deviation from the formation center are defined as follows:

[0082] p j Indicates the neighboring drone node d j In the ground coordinate system E 地 The position vector coordinates in -OXYZ, p j =[x j ,y j ,z j ].

[0083] x j Indicates the neighboring drone node d j In the ground coordinate system E 地 The coordinates in the X direction of -OXYZ.

[0084] y j Indicates the neighboring drone node d j In the ground coordinate system E 地 - The coordinates in the Y direction of OXYZ.

[0085] z j Indicates the neighboring drone node d j In the ground coordinate system E 地 The coordinates in the Z direction of -OXYZ.

[0086] o j represents the position of the UAV node d j in the ground coordinate system E 地 -OXYZ, and the deviation of the position of the UAV node d j from the position of the formation center is represented by a vector coordinate o xj . yj . zj .

[0087] o xj represents the position of the UAV node d j in the ground coordinate system E 地 -OXYZ, and the deviation of the position of the UAV node d

[0088] o yj represents the position of the UAV node d j in the ground coordinate system E 地 -OXYZ, and the deviation of the position of the UAV node d

[0089] o zj represents the position of the UAV node d j in the ground coordinate system E 地 -OXYZ, and the deviation of the position of the UAV node d

[0090] In the present embodiment, the position of the formation center of the UAV cluster in the ground coordinate system E 地 -OXYZ is represented by a position vector coordinate The velocity vector coordinate of the formation center in the ground coordinate system E 地 -OXYZ is represented by where x i (0), y i (0), and z i (0) are the initial position vector coordinates of the UAV node d i in the ground coordinate system E 地 -OXYZ, respectively; are the initial velocity vector coordinates of the UAV node d i in the ground coordinate system E 地 -OXYZ, respectively.

[0091] Further, the expression of the motion model of the UAV cluster formation is:

[0092]

[0093] where d i is the current UAV node, m i is the mass of the UAV node d i ; in the present example, the masses of all UAVs are the same; p iRepresents drone node d i During flight, the 3*1 position vector (x) of the inertial coordinate system is satisfied. i ,y i ,z i );v i Represents drone node d i During flight, the 3*1 velocity vector in the inertial coordinate system is satisfied. Where x i and Correspondingly, d represents the drone node. i Position and velocity information on the x-axis, similarly, y... i and Correspondingly, z i and Correspondingly, d represents the drone node d. i Position and velocity information on the y and z axes; Represents drone node d i The acceleration during flight, u i1 (t) and u i2 (t) represents the drone node d i The control laws based on local false information exchange and the control laws based on event-triggered strategies.

[0094] Furthermore, the drone node d i The expression for the control law based on local false information exchange is:

[0095]

[0096] in, and Representing the drone node d respectively i The true position and velocity information in the inertial coordinate system are transformed by a finite time-varying masking function to become false position and velocity information for information exchange; i Represents drone node d i The vector coordinates representing the deviation between the position in the ground coordinate system and the position of the formation center. Let d j For the current drone node's outgoing neighbor drone nodes, similarly, and Represent the neighboring drone node d respectively j False position and velocity information after transformation by a finite time-varying masking function; j Represents drone node d j The vector coordinates of the deviation between the position in the ground coordinate system and the position of the formation center;

[0097] Specifically The expression is:

[0098]

[0099] The expression is:

[0100]

[0101] Where μ(t) represents the time transformation scaling function, its expression is: The specific time point chosen by the designer reflects the effective range of the masking function; h is a parameter of μ(t); furthermore, γ i μ(t) -r Indicates that the drone node d i The chosen finite time-varying mask function, γ i For drone node d i The privacy coefficient is autonomously chosen by the current drone node and is not disclosed to any other drone node; r is a parameter of the finite time-varying masking function. It is important to note that the finite time-varying masking function... It exists in memory. The function disappears within the space; α > 0 is the position control coefficient, β > 0 is the velocity control coefficient, and α and β satisfy the constraints. a ij Represents drone node d i With drone node d j The adjacency matrix coefficients; it is understandable that, due to the drone d i The initial position information is sensitive, so we only need to protect the position information for a certain period of time so that other drones or attackers cannot deduce the initial position, and we do not need to protect the position information during the entire control process; because there is a risk of damage during the drone's movement. Therefore, during the control process, it is necessary to protect not only the position information of the UAV but also its speed information; since the ultimate goal of achieving the formation task is to protect the UAV node d i The position and the center position of the formation remain unchanged. i Position deviation, UAV node d i The speed of the aircraft is kept the same as that of the formation center. Therefore, in the design of the controller, it is only necessary to subtract the corresponding deviation from the position variable to achieve the formation control effect.

[0102] Furthermore, an event-triggered control law is established for drone nodes to ensure privacy protection even under the constraint of removing the existing network topology.

[0103]

[0104] Among them, t kis a series of trigger time points satisfying the event trigger control condition, which is determined by the data center according to the event trigger control condition. Assuming that the control law based on the event trigger strategy is triggered for the first time at t0=0, let ∈ i (t) = v i (t) - v i (t k ) be the privacy trigger error of the unmanned aerial vehicle node d i , and the event trigger control condition is:

[0105] wherein μ(t) represents a time transformation scale function, h is a parameter of μ(t); r represents a finite time-varying mask function parameter; h and r satisfy the relationship It can be understood that the update of u i2 (t) is determined by the data center. Each unmanned aerial vehicle node sends real speed information to the data center, and the data center determines whether to trigger according to the event trigger control condition. If triggered, t k is transmitted to each unmanned aerial vehicle, such as unmanned aerial vehicle node d i , and u i2 (t) is updated by the unmanned aerial vehicle node d i2 . In addition, since u i (t) is only known by the unmanned aerial vehicle node d i , other unmanned aerial vehicles or attackers cannot infer the initial position of the unmanned aerial vehicle node d i2 according to the observer method, thereby solving the network topology condition restriction Meanwhile, u i (t) can be regarded as the non-trivial dynamics of the unmanned aerial vehicle node d i2 itself, and its introduction does not change the original formation center position and speed, and can realize the unmanned aerial vehicle cluster formation flight task; in addition, since only the position information in a period of time needs to be protected, the same as the addition of the time period of the finite time-varying mask function, only needs to exist in , and when , u T (t) = 0.

[0106] Further, the expression of the composite control law is:

[0107]

[0108] Further, the present application further provides another embodiment, which performs formation control on a cluster composed of 5 unmanned aerial vehicles, and the 5 unmanned aerial vehicles perform formation control according to the aforementioned event-driven control method for privacy protection cooperative formation of unmanned aerial vehicles when performing a task, and the communication topology established in step one is as shown in Figure 3 It is worth noting that,Figure 3 The corresponding network topology does not satisfy the network topology condition limit required by the existing method That is However, under the design of the present application, the unmanned aerial vehicle cluster system under the network topology can achieve the privacy protection goal, and the initial positions of the five unmanned aerial vehicles are set as: p1(0) = [2, 3, 5] T m, p2(0) = [3, 4, 6] T m, p3(0) = [4, 5, 7] T m, p4(0) = [3, 3.5, 4.5] T m, p5(0) = [5.5, 5, 6] T m; the initial velocities of the five unmanned aerial vehicles are: v1(0) = [1, 2, 1] T m / s, v2(0) = [2, 3, 1.6] T m / s, v3(0) = [1.2, 2.3, 1] T m / s, v4(0) = [2.5, 3.5, 1.6] T m / s, v5(0) = [1.8, 1.7, 0.8] T m / s; the position vector trajectory of the formation center is p0 = [3.5 + 1.7*t, 4.1 + 2.5*t, 5.7 + 1.2*t]; the velocity vector coordinates of the formation center are v p0 = [1.7, 2.5, 1.2];

[0109] The motion model of the unmanned aerial vehicle cluster formation is established, and the mass of the unmanned aerial vehicle model is set as m i = 50 kg;

[0110] A control law based on local false information exchange is designed, in order to calculate the control law, the limited time r = 2.1; h = 5; the position control coefficient α = 2.9; the velocity control coefficient β = 3.1; the privacy protection coefficient γ i is γ1 = 3.04; γ2 = 3.23; γ3 = 3.5; γ4 = 3.37; γ5 = 3.57; the position deviations of the five unmanned aerial vehicles from the formation center are as follows; o1 = [1, 0, 0] T m; o2 = [3, 2, 0] T m; o3 = [5, 0, 0] T m; o4 = [4, -1, 0] T m; o5 = [2, -1, 0] T m;

[0111] The control law based on local false information exchange and the control law based on event triggering strategy are combined to obtain the compound control law of the unmanned aerial vehicle node d i .

[0112] Simulation results analysis, from Figure 4 As can be seen, the finite time-varying mask function designed in this invention makes the true initial positions and false initial positions of the five UAVs different, thus achieving the goal of protecting the initial positions. Figure 5 As shown, Figure 5 Each subplot in plot 'a' represents the trajectory of the five drones along the x-axis, y-axis, and z-axis, showing their actual and spurious positions minus the deviation. Solid lines in the subplots represent the actual position minus the deviation trajectory, while dashed lines represent the spurious position minus the deviation trajectory. From... Figure 5 a can be seen from subtracting the deviation o i The position trajectories of the last five drones are identical to those of the formation center, proving that this invention enables coordinated formation control of drone swarms. Furthermore, the false position trajectories transformed by a finite-time masking function within 0-5 seconds differ from the true position trajectories, demonstrating that this invention can protect the position trajectories for a period of time, thus preventing eavesdroppers from inferring the drones' true initial positions based on position information from other moments. Similarly, Figure 5 Each small graph in b represents the velocity trajectories of five drones along the x, y, and z axes, showing their actual and spurious speeds, respectively. Solid lines in the graphs represent the actual speed trajectories, while dashed lines represent the spurious ones. The speed trajectories of the five drones are identical to the velocity trajectory of the formation center, demonstrating that this invention enables coordinated formation control of drone swarms. Furthermore, the spurious and actual speed trajectories, transformed by a finite time-varying mask function within 0-5 seconds, differ from the actual speed trajectories, proving that this invention can protect the speed trajectories for a period of time, thus preventing eavesdroppers from inferring the drones' true position information based on the actual speed data. Figures 6-7 It can be seen that the controller of this invention triggers a finite number of times within the control time. Furthermore, a speed error is defined. The speed error demonstrated by this invention is small, which can meet the control accuracy requirements. Figure 6 The trigger frequency t of the controller based on the event-triggered strategy. k+1 -t k The time interval diagram. Since the drone swarm is triggered uniformly according to the trigger command from the data center, it represents the uniform trigger interval of the 5 drones. Figure 7 This shows the actual speed error trajectory of five drones. From... Figure 8 It can be seen that the privacy triggering error is different for each drone in this invention, proving that under the design of this invention, eavesdroppers cannot infer the initial position of the drone nodes based on the observer-based method, thus solving the topology constraints in existing designs. Figure 9 It can be seen that the drone swarm formation control effect is good and can achieve the desired formation effect.

[0113] The various embodiments described in this specification are presented for the purpose of illustrating the principles of the present application and its best mode of operation. Each of the embodiments described in this specification has been provided for the purpose of illustration and is not intended to limit the application.

[0114] The principles and operation of the present application have been explained so far with the help of specific examples. The above examples are only for the purpose of helping to understand the method of the present application and its core idea. Meanwhile, for those skilled in the art, the specific implementation and application range of the present application will be changed according to the idea of the present application. In summary, the content of the specification should not be understood as a limitation of the present application.

Claims

1. An event-driven privacy protection method for a UAV cluster system cooperative formation, characterized in that, The application relates to a method for controlling a target unmanned aerial vehicle (UAV) cluster to form a formation, and a method for modeling a communication topology of the UAV cluster by using graph theory and determining a formation center of a strongly connected balanced topology of the UAV cluster. A motion model of a UAV cluster formation is established according to the strongly connected balanced topology of the UAV cluster and the formation center. A privacy protection control law based on false information for cooperative formation of a UAV cluster system is established according to the motion model and the control law. A privacy protection composite control law for system formation is obtained by introducing an event-triggered control law based on the motion model and the control law. The target UAV cluster is controlled to form a formation according to the designed privacy protection composite control law. The application relates to a method for modeling a communication topology of a UAV cluster by using graph theory, which comprises the following steps: The drone node d i The expression based on the local false information communication control law is: ； in, and Representing the drone node d respectively i The false position and velocity information used for information exchange can be obtained by transforming the true position and velocity information in the inertial coordinate system through a finite time-varying mask function; i Represents drone node d i The vector coordinates of the position in the ground coordinate system and the deviation from the position of the formation center; let d j For the current drone node's outgoing neighbor drone nodes, similarly, and Represent the neighboring drone node d respectively j False position and velocity information after transformation by a finite time-varying masking function; j Represents drone node d j The vector coordinates of the deviation between the position in the ground coordinate system and the position of the formation center; For drone node d i Its neighboring drone node d j The adjacency matrix coefficients; α is the position control coefficient, β is the velocity control coefficient; All UAVs are triggered at the initial time, i.e. The expression of the event-triggered strategy control law is ； wherein, and are respectively the UAV nodes d i at the time instants and are respectively the real velocity vectors of the UAV nodes d at the time instants and is a series of triggering time instants satisfying the event-triggered control condition, determined by the data center according to the event-triggered control condition; denotes the latest triggering time instant after the triggering time instant ; is a specific time instant selected by the designer, reflecting the action interval of the event-triggered protection control law.

2. The event-driven privacy protection method for the UAV swarm system cooperative formation according to claim 1, wherein, A set of UAV nodes is determined. Out-degree and in-degree of the UAV nodes are determined according to the set of UAV nodes. An adjacency matrix of a network topology structure is determined. A Laplacian matrix of the network topology is determined according to the out-degree of the UAV nodes and the adjacency matrix. A strongly connected balanced communication topology of the UAV cluster is constructed according to the Laplacian matrix and the out-degree and in-degree of the UAV nodes. The method for determining the formation center comprises the following steps:

3. The event-driven privacy protection method for the UAV swarm system cooperative formation according to claim 1, wherein, A position of an initial formation center is determined. Coordinates of the UAV nodes in an inertial coordinate system and coordinates of out-neighbor UAV nodes of a current UAV node in the inertial coordinate system are determined based on the strongly connected balanced topology. Position vector coordinates of the UAV nodes and the out-neighbor UAV nodes of the current UAV node are determined according to the coordinates of the UAV nodes in the inertial coordinate system and the coordinates of the out-neighbor UAV nodes of the current UAV node in the inertial coordinate system. First position deviation and second position deviation are determined according to the position vector coordinates of the UAV nodes, the position vector coordinates of the out-neighbor UAV nodes of the current UAV node and the position of the initial formation center, wherein the first position deviation is a vector coordinate of a position deviation between the UAV nodes and the initial formation center, and the second position deviation is a vector coordinate of a position deviation between the out-neighbor UAV nodes of the current UAV node and the initial formation center. The position of the initial formation center is adjusted by using the first position deviation and the second position deviation to determine a position of a final formation center. The expression of the motion model of the UAV cluster formation is as follows:

4. The event-driven privacy protection method for the UAV swarm system cooperative formation according to claim 1, wherein, The expression of the composite control law is as follows: ； wherein m i is the mass of the UAV node d i ; represents the 3*1 position vector of the UAV node d i in the inertial coordinate system at time t during the flight; represents the 3*1 velocity vector of the UAV node d i in the inertial coordinate system at time t during the flight, represents the 3*1 acceleration vector of the UAV node d i at time t during the flight; and represent the local false information exchange control law and the event-triggered strategy control law of the UAV node d i , respectively;d i is the current UAV node.

5. The event-driven privacy protection method for the UAV swarm system cooperative formation according to claim 1, wherein, ​ 。

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