Elastic safe formation method and device for unmanned aerial vehicle cluster under network attack

Abstract

The invention provides an unmanned aerial vehicle cluster elastic safe formation method and device under network attacks, and relates to the field of unmanned aerial vehicle formation control and network security, and the method comprises the steps: building an unmanned aerial vehicle cluster dynamic model considering a data spoofing attack; determining a formation task target according to a following state of a follower unmanned aerial vehicle, a state of a leader unmanned aerial vehicle and an expected time-varying formation vector in the unmanned aerial vehicle cluster dynamics model considering the data spoofing attack; determining an elastic control protocol of the unmanned aerial vehicle cluster according to the formation task target and the unmanned aerial vehicle cluster dynamic model; determining a control instruction of each follower unmanned aerial vehicle according to an unmanned aerial vehicle cluster elastic control protocol; the control instruction is used for controlling the flight path of the follower unmanned aerial vehicle, so that each follower unmanned aerial vehicle can still complete a preset formation even if subjected to data spoofing attack.

Description

Technical Field

[0001] The present application relates to the fields of drone formation control and network security, and in particular to a method and related devices for elastic and secure formation of drone clusters under network attacks. Background Art

[0002] With the development of science and technology, drone technology is also gradually improving and has been widely used in many fields. In the military field, drone swarm combat, as a new combat method, can perform multiple tasks such as reconnaissance, interference, and attack, forming a full-scale, multi-level strike capability against the enemy, thereby effectively responding to the enemy's complex air defense system and combat system. As a new combat method with high autonomy, strong coordination, and outstanding combat capability, drone swarm combat has become the focus of research and development by various countries. In future wars, drone swarm combat will play an increasingly important role and become an important part of the military power of various countries.

[0003] When a drone swarm is performing a task, it is necessary to ensure that the drone swarm can maintain the preset formation. In other words, the coordinated control of the drone swarm system is very important. At present, the coordinated control of the drone swarm system adopts a distributed control protocol. The basic assumption of the distributed control protocol is that each drone in the drone swarm can accurately, continuously and stably receive or send information from its neighboring drones. In practical applications, the group communication signals of the drone swarm system are often attacked by hostile forces. Therefore, in the past decade, control against network attacks (or resilient control) has been widely studied.

[0004] Generally speaking, network attacks are divided into two categories: denial of service (DoS) attacks and data spoofing (false data injection) attacks. If the network control system of a drone swarm is attacked by DoS, system resources will be unavailable, which will cause the drones to be unable to exchange formation tracking information with their neighboring drones. In response to DoS attacks, intermittent communication strategies can be adopted to deal with the problem of safe formation of drone swarms. However, if the network control system of a drone swarm is attacked by data spoofing, the data packets transmitted in the communication link will be tampered with, the formation tracking will be interrupted, and the overall performance of the drone swarm formation will be seriously affected. There is currently no good response strategy for data spoofing attacks on drone swarm systems. Summary of the invention

[0005] The purpose of this application is to provide a method and related devices for elastic and secure formation of drone swarms under network attacks, which can effectively resist data deception attacks on drone swarm systems.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In the first aspect, the present application provides a method for elastic and secure formation of drone swarms under network attacks, including:

[0008] A drone cluster dynamics model considering data deception attacks is established; the drone cluster dynamics model includes dynamics models of multiple follower drones and dynamics models of multiple leader drones; the parameters in the dynamics model of the follower drones include the following state of the follower drones, the controller output of the follower drones, the deception data disturbance input to the follower drone controllers, the following output of the follower drones and the deception data disturbance input to the follower drone sensors or links; the parameters in the dynamics model of the leader drone include the state of the leader drone and the output of the leader drone.

[0009] According to the following state of the follower drone, the state of the leader drone and the expected time-varying formation vector, a drone cluster flexible formation mission objective is constructed.

[0010] Determine elastic control protocol expressions for multiple follower drones according to the drone cluster elastic formation mission objectives and the drone cluster dynamics model considering data deception attacks;

[0011] Solving the unknown quantities in the elastic control protocol expressions of the multiple follower drones to obtain the elastic control protocols of the multiple follower drones; the unknown quantities in the elastic control protocol of the follower drones include the local formation neighbor estimation error of the follower drone, the estimation error between the follower drone and all the leader drones, the attack estimation of the follower drone controller, the first controller gain of the follower drone, the second controller gain of the follower drone and the formation compensation value of the follower drone;

[0012] The elastic control protocols of the multiple follower drones are combined to obtain an elastic control protocol for the drone cluster.

[0013] According to the UAV cluster elastic control protocol, a control instruction for each follower UAV is determined; the control instruction is used to control the flight trajectory of the follower UAV.

[0014] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of a method for resilient and secure formation of a drone cluster under a network attack as described in the first aspect.

[0015] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0016] The present application provides a method and device for elastic and secure formation of drone clusters under network attacks. The method for elastic and secure formation of drone clusters under network attacks includes: establishing a drone cluster dynamics model that takes data deception attacks into consideration; determining a formation mission objective based on the following state of the follower drones, the state of the leader drone, and the expected time-varying formation vector in the drone cluster dynamics model that takes data deception attacks into consideration; determining a flexible control protocol for the drone cluster based on the formation mission objective and the drone cluster dynamics model; determining control instructions for each follower drone based on the drone cluster flexible control protocol; the control instructions are used to control the flight trajectory of the follower drones, so that each follower drone can still complete a preset formation even if it is subjected to a data deception attack. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 This is an application environment diagram of a method for flexible and secure formation of drone clusters under network attacks in one embodiment of the present application;

[0019] Figure 2 A schematic diagram of a flow chart of a method for elastically securing formation of a drone cluster under a network attack provided in one embodiment of the present application;

[0020] Figure 3 A schematic diagram of a drone cluster network model considering data spoofing attacks provided in one embodiment of the present application;

[0021] Figure 4 A schematic diagram of attack estimation of a drone cluster provided in one embodiment of the present application;

[0022] Figure 5 A state transition diagram of a drone cluster provided in one embodiment of the present application;

[0023] Figure 6 A graph showing the formation error of a drone cluster changing over time provided in an embodiment of the present application;

[0024] Figure 7 A schematic diagram of a flow chart of a method for elastically securing formation of a drone cluster under a network attack provided by another embodiment of the present application;

[0025] Figure 8 A graph showing the relationship between the control signal triggering and time for each follower drone in the drone cluster;

[0026] Fig. 9 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0028] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0029] The embodiment of the present application provides a method for elastically securing a drone swarm formation under a network attack, which can be applied to Figure 1 In the application environment shown. Among them, the ground control station 102 communicates with the drone cluster 101 through the network. The drone cluster includes a data storage system, and the data storage system can store data that the drone cluster 101 needs to process. The ground control station 102 can send the desired formation formation signal to the drone cluster 101. After the drone cluster 101 receives the desired formation formation signal, the drone cluster 101 determines the control instructions for each follower drone according to the drone cluster elastic control protocol; the control instructions are used to control the flight trajectory of the follower drone so that the drone cluster 101 completes the desired formation formation. The drone cluster 101 can also feedback the current formation formation signal to the ground control station 102.

[0030] In an exemplary embodiment, Figure 2 As shown, a method for elastic and secure formation of drone clusters under network attacks is provided. In the embodiment of the present application, the method is applied to Figure 1 Taking the drone cluster 101 in the example as an example, the method includes the following steps 201 to 208. Among them:

[0031] Step 201, establish a drone cluster dynamics model considering data deception attacks.

[0032] Among them, the UAV cluster dynamics model includes the dynamics models of multiple follower UAVs and the dynamics model of multiple leader UAVs; the parameters in the dynamics model of the follower UAV include the following state of the follower UAV, the controller output of the follower UAV, the deception data disturbance input to the follower UAV controller, the following output of the follower UAV and the deception data disturbance input to the follower UAV sensor or link; the parameters in the dynamics model of the leader UAV include the state of the leader UAV and the output of the leader UAV.

[0033] The dynamic model of the follower UAV is:

[0034]

[0035] y i (t) = Cx i (t)+Cψ xi ;

[0036] in, is the following status of follower drone i, is the controller output of follower UAV i, ψ ui is the deception data disturbance input to the follower UAV i controller; is the follower output of follower drone i, is the system state matrix of follower UAV i, is the system interference matrix of follower UAV i, represents the system state matrix of follower UAV i, ψ xi is the deceptive data perturbation input to the sensor or link of follower drone i;

[0037] The dynamic model of the leader UAV is:

[0038]

[0039] y m (t) = Cx m (t);

[0040] in, is the state of the leader drone m, is the output of the leader drone m.

[0041] As an optional implementation, Figure 3 A schematic diagram of a drone swarm dynamics model considering data deception attacks is given. Figure 3 There are six follower drones i (i=1, 2, ..., 6) and three leader drones m (m=7, 8, 9). The figure shows three different models of controller attack, sensor attack and link attack.

[0042] Step 202, constructing the UAV swarm flexible formation mission objective according to the following state of the follower UAV, the state of the leader UAV and the expected time-varying formation vector.

[0043] The specific mission objectives of the UAV swarm flexible formation are:

[0044] In the drone swarm formation, the following state of each follower drone i satisfies:

[0045]

[0046] in, is the time-varying formation vector expected by follower UAV i, and the time-varying formation vector expected by follower UAV i is piecewise continuous and differentiable; c m is a set of constants satisfying N is the total number of drones in the drone cluster, the first M are follower drones, and the last NM are leader drones.

[0047] Step 203, according to the UAV cluster elastic formation mission goal and the UAV cluster dynamics model considering data deception attack, determine the elastic control protocol expression of multiple follower UAVs.

[0048] The elastic control protocol expression of the follower UAV is:

[0049]

[0050] Among them, u ci (t) is the elastic control protocol of follower UAV i; is the local formation neighbor estimation error of follower UAV i; is the estimation error between follower UAV i and leader UAV; is the attack estimation of the follower UAV i controller; G ei is the first controller gain of follower drone i; G ui is the second controller gain of follower drone i; v i (t) is the formation compensation value of follower UAV i.

[0051] Step 204, solving the unknowns in the elastic control protocol expressions of the multiple follower UAVs to obtain the elastic control protocols of the multiple follower UAVs.

[0052] Among them, the unknown quantities in the elastic control protocol of the follower UAV include the local formation neighbor estimation error of the follower UAV, the estimation error between the follower UAV and all leader UAVs, the attack estimation of the follower UAV controller, the first controller gain of the follower UAV, the second controller gain of the follower UAV and the formation compensation value of the follower UAV.

[0053] As an optional implementation, the calculation formula of the local formation neighbor estimation error of the follower UAV i is:

[0054]

[0055] in, is the set of drones that directly communicate with drone i in the communication topology. Figure 3 Take the follower UAV 3 (node ​​3) in the example, the UAVs that directly communicate with it include 6, 7, 8, and 9. When calculating the local formation neighbor estimation error of follower UAV 3, it is necessary to calculate the estimation errors of the four neighbor UAVs and then sum them. It should be noted that neighbor UAV j can be both a follower UAV and a leader UAV.

[0056] The topological adjacency matrix of UAV cluster communication is: , N is the number of drones in the drone cluster. ij is the element in the i-th row and j-th column of the communication topology adjacency matrix; a ij =1 means that follower UAV i has communication connection with UAV j, a ij =0 means that follower UAV i has no communication connection with UAV j.

[0057] The calculation formula of the estimated error between the follower UAV i and the leader UAV is:

[0058]

[0059] Among them, a im is the element in the i-th row and m-th column of the communication topology adjacency matrix; a im =1 means that the follower UAV i has a communication connection with the leader UAV m; a im =0 means that follower drone i has no communication connection with leader drone m.

[0060] refer to Figure 3 For example, for follower UAV 5, when calculating the estimated error between follower UAV 5 and leader UAV, it is necessary to calculate the estimated errors between follower UAV 5 and leader UAV 7, leader UAV 8 and leader UAV 9 respectively, and then sum them up.

[0061] in addition, and The corresponding actual error can be expressed by the following formula:

[0062]

[0063]

[0064] As an optional implementation, the attack estimate of the follower drone i controller The determination process is:

[0065] Design the state estimator of follower UAV i, the expression of the state estimator of follower UAV i is:

[0066] in, represents the state estimation of follower UAV i, Tx i (t), ψ ui , ψ xi , G, J, T, R are the preset estimator matrices.

[0067] Among them, the determination process of G, J, T, and R is as follows:

[0068] Defining the estimation error For ε i (t) Taking the derivative, we get:

[0069]

[0070] Among them, matrix G, matrix T, matrix J, and matrix R satisfy TA-GT-RC=0, and TB-J=0; matrix G is a Hurwitz matrix;

[0071] Then we can get in

[0072] Substitute the determined matrices G, T, J, and R into the state estimation expression of follower UAV i to obtain the attack estimation of the controller of follower UAV i and the attack estimation of the follower drone i sensor or link

[0073] Obviously, if data spoofing can be correctly detected, then when t→∞, ε ui (t) = ε xi (t) = 0, ε i (t) = 0. Further, we can set , thus Written in compact form we get:

[0074]

[0075] in,

[0076] As an optional implementation, the formation compensation value v of the follower UAV i is i (t) Must meet the following requirements:

[0077]

[0078] Among them, v i The specific process of determining (t) is as follows: Let z i (t) = x i (t)-h i (t), then the expression of “substituting the elastic control protocol expression of the follower UAV into the dynamic model of the follower UAV” (this expression can refer to step 205) can be further written as:

[0079]

[0080] make Then we have:

[0081]

[0082] in,

[0083] Define v i (t) Satisfy the conditions Then the first-order derivative of ξ(t) can be written as:

[0084]

[0085] In the process of designing attack estimation and control gain, the present application is completed with the help of stability analysis function, and the stability analysis function is:

[0086]

[0087] in, P is The solution, and Q are both positive definite matrices. and Substituting into V(t), let G ui =-I p , then the first-order derivative of V(t) is expressed as:

[0088]

[0089] if It is considered that the controller design satisfies stability, and the controller gain is then designed and proved.

[0090] As an optional implementation, the first controller gain of the follower UAV i is G ui =-I p ; The second controller gain of follower drone i is G ei =ω e R -1 B T P -1 ; Among them, ω e ≤-1 / 2λ M <0;λ M is the communication topology Laplace matrix L a The maximum eigenvalue of .

[0091] Where P is The solution, and Q are both positive definite matrices; I p represents the p×p dimensional identity matrix; ω e For the control gain coefficient, the control gain coefficient is designable.

[0092] Therefore, the first few items of stability analysis are:

[0093]

[0094] in matrix Meet U T L a1 U=J a1 , J a1 =diag{λ1,...,λ M},γ0=λ1λ min (QP -2 )>0.

[0095] According to Young's inequality, the third and fourth terms of stability analysis can be determined as:

[0096]

[0097] in,

[0098] Next, the attack estimate is designed so that the fifth, sixth, and seventh terms of the stability analysis are zero, that is, the attack estimate is designed so that

[0099] Therefore, we can further obtain

[0100] Similarly, the last few items are also designed to be zero,

[0101] From this we can get

[0102] According to the above analysis process of the stability analysis function, we can get:

[0103]

[0104] based on e i (t) and r i The definition of (t) can be obtained:

[0105]

[0106] in,

[0107] According to Young's inequality and The expression of After deduction, we can get the stability analysis results:

[0108]

[0109] in,

[0110] right and By integrating, we can get the specific design of the estimation of data deception attack by drone clusters:

[0111]

[0112]

[0113] in,

[0114] As an optional implementation, Figure 4 The attack estimation of drone swarm is given. Figure 4 a means Figure 3 The estimation of the controller attack on follower drone 1; Figure 4 b means Figure 3 The estimation of sensor attack on follower drone 6; Figure 4 c represents the estimation of the link attack suffered by follower UAV 3; Figure 4 d represents the estimation of the link attack suffered by follower UAV 5.

[0115] Step 205, combining the elastic control protocols of multiple follower drones to obtain an elastic control protocol for the drone cluster. Combining the elastic control protocols of multiple follower drones to obtain an elastic control protocol for the drone cluster specifically includes:

[0116] Substituting the elastic control protocol expression of the follower UAV into the dynamic model of the follower UAV, we can obtain:

[0117]

[0118] y i (t) = Cx i (t)+Cψ xi .

[0119] Will and i (t) can be written in compact form as:

[0120]

[0121]

[0122] Similarly, in the dynamic model of leader m and m (t) can be written in compact form:

[0123]

[0124]

[0125] in,

[0126]

[0127]

[0128] G e =diag{G e1 ,...,G eM}, G u =diag{G u1 ,...,G uM}, L a is the Laplace matrix form of the UAV cluster communication topology, L a =[L a1 L a2 ; 00], is the follower communication topology matrix, is the follower-leader communication topology matrix. a1The eigenvalues ​​of M}, where λ1 is the smallest, λ M maximum.

[0129] It should be noted that the compact form mentioned herein refers to expanding the expression of a single drone to the expression of the entire drone cluster.

[0130] Step 206, determining the control instructions for each follower drone according to the drone cluster elastic control protocol; the control instructions are used to control the flight trajectory of the follower drone.

[0131] As an optional implementation, Figure 5 The state transition diagram of the drone cluster is given, where the three red five-pointed stars represent the three drone leaders and the six blue quadrilaterals represent the six follower drones. Figure 5 a represents the initial state of the drone cluster (t=0s), where a random value is used. Figure 5 b indicates that the drone cluster completes the expected formation when it is not subjected to data deception attack (t=15s). Figure 5 c represents the state of the drone cluster when it is attacked by data spoofing (t=20s). Figure 5 d represents the state of the drone cluster completing the desired formation after being attacked by data spoofing (t = 30s). Figure 5 e and Figure 5 f represents the state when the UAV cluster stably maintains the desired formation (t = 40s and t = 50s).

[0132] It should be noted that when completing the preset formation goal, it is only necessary to make the follower drones maintain the preset formation, and there is no need to consider the relative position relationship between the leader drones. In other words, the leader drone only plays the role of assisting the follower drones in positioning, and the leader drone itself does not belong to the individual in the desired formation.

[0133] Furthermore, Figure 6 A curve chart showing the formation error of the drone cluster changing over time is given. Figure 5 and Figure 6 It can be seen that at time 0, the drone cluster is in the initial state and the formation error is the largest; around 15s, the formation error is close to 0, and the drones complete the desired formation; around 20s, the drone cluster suffers a data deception attack, and the formation error suddenly increases; at 30s, the formation error is almost reduced to 0, indicating that after suffering a data deception attack, the drone cluster only needs 10s of adjustment time to complete the desired formation.

[0134] By implementing the above steps 201 to 206, each follower drone can still complete the preset formation even if it is attacked by data spoofing.

[0135] In another exemplary embodiment, Figure 6 As shown, the method for flexible and secure formation of drone clusters under network attacks also includes:

[0136] Step 207: determine the event triggering condition of the elastic control protocol of the follower drone i and the minimum communication interval of the elastic control protocol of the follower drone i.

[0137] Figure 8 The relationship between the triggering and time of the control signal of each follower UAV in the UAV cluster is given. Figure 8 It can be seen that the data in follower drone 1 is the most dense, because Figure 3 The number of neighbor drones that communicate directly with follower drone 1 (node ​​1) is the largest, and the communication is more frequent.

[0138] As an optional implementation, the event triggering condition for follower UAV i to receive communication from its neighbor UAV j can be designed as:

[0139]

[0140]

[0141] in, is the state estimate of neighbor UAV j received by follower UAV i at time t, is the estimated state value of the follower UAV i after making an abnormal estimation on the neighbor UAV j at time t. The meaning is: when the event is triggered, the follower UAV i and its neighbor UAV j communicate and exchange state information. At this time, the state estimation value Directly assign to

[0142] Defining prediction error And according to and It can be seen that Since this is an ever-increasing estimation error term, and considering the event triggering mechanism, the designed event triggering condition needs to be smaller than the overall formation error, that is, satisfying

[0143] Therefore, the event triggering condition of the elastic control protocol of follower UAV i is designed as follows:

[0144]

[0145] in, It is the state estimation value after follower UAV i makes anomaly estimation on neighbor UAV j.

[0146] The minimum communication interval of the flexible control protocol of the follower UAV i is:

[0147]

[0148] in,

[0149]

[0150] in, All are pre-determined parameters;

[0151] M is the number of follower drones.

[0152] Further, The specific process of determining is as follows:

[0153] The drones are divided into two equal parts based on the most recent communication time, namely F1(t) and F2(t). The most recent communication time of the drones in F1(t) is less than that in F2(t). Define μ1+μ2=μ<1, where μ1∈(0,1), μ2∈(0,1), μ1≤μ2, and the following two inequalities can be obtained:

[0154]

[0155]

[0156] According to the event triggering conditions of the elastic control protocol of the follower UAV i, the sufficient conditions of the above inequality are further derived:

[0157]

[0158] in,

[0159] Continuing the derivation of the inequality, we can get in,

[0160] Let ε ei (t) = ε xi (t)-ε pi (t),||ε ei (t)|| / ||ξ(t)|| can be further derived as:

[0161]

[0162] in, γ5=max{γ3,γ4};

[0163] for From 0 to The lower bound of , thus we can get:

[0164]

[0165] The present application also provides an application scenario, which applies the above-mentioned method for elastic and secure formation of drone clusters under network attacks. Specifically: The method for elastic and secure formation of drone clusters under network attacks provided in this embodiment can be applied to scenarios where drone clusters are subjected to network attacks when performing tasks. The network attacks on drone clusters are mainly divided into two categories: DOS attacks and data deception attacks. The present application provides a method to solve the problem that drone clusters are difficult to maintain a preset formation when they are attacked by data deception, thereby improving the robustness of drone cluster formations.

[0166] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Fig. 9 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store a drone cluster dynamics model considering data deception attacks. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for elastic and secure formation of drone clusters under network attacks is implemented.

[0167] Those skilled in the art will understand that Fig. 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0168] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0169] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0170] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0171] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0172] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0173] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0174] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0175] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for flexible and secure formation of drone swarms under network attacks, characterized in that: The method for flexible and secure formation of drone swarms under network attacks includes: Establish a drone swarm dynamics model that takes into account data deception attacks; the drone swarm dynamics model includes dynamics models of multiple follower drones and dynamics models of multiple leader drones; the parameters in the dynamics model of the follower drones include the following state of the follower drones, the controller output of the follower drones, the deception data disturbance input to the follower drone controller, the following output of the follower drones, and the deception data disturbance input to the follower drone sensor or link; the parameters in the dynamics model of the leader drone include the state of the leader drone and the output of the leader drone; Constructing a UAV swarm flexible formation mission objective according to the following state of the follower UAV, the state of the leader UAV and the expected time-varying formation vector; Determine elastic control protocol expressions for multiple follower drones according to the drone cluster elastic formation mission objectives and the drone cluster dynamics model considering data deception attacks; Solving the unknown quantities in the elastic control protocol expressions of the multiple follower drones to obtain the elastic control protocols of the multiple follower drones; the unknown quantities in the elastic control protocol of the follower drones include the local formation neighbor estimation error of the follower drone, the estimation error between the follower drone and all the leader drones, the attack estimation of the follower drone controller, the first controller gain of the follower drone, the second controller gain of the follower drone and the formation compensation value of the follower drone; Combining the elastic control protocols of the plurality of follower drones to obtain an elastic control protocol for a drone cluster; According to the UAV cluster elastic control protocol, a control instruction for each follower UAV is determined; the control instruction is used to control the flight trajectory of the follower UAV.

2. According to the method of claim 1, a flexible and secure formation method of drone swarm under network attack is characterized in that: The dynamic model of the follower UAV is: y i (t)=Cx i (t)+Cψ xi ; in, is the following status of follower drone i, is the controller output of follower UAV i, ψ ui is the deception data disturbance input to the follower UAV i controller; is the follower output of follower drone i, is the system state matrix of follower UAV i, is the system interference matrix of follower UAV i, represents the system state matrix of follower UAV i, ψ xi is the deceptive data perturbation input to the sensor or link of follower drone i; The dynamic model of the leader UAV is: y m (t)=Cx m (t); in, is the state of the leader drone m, is the output of the leader drone m.

3. According to the method of flexible and secure formation of drone swarms under network attacks in claim 2, it is characterized in that: The specific mission objectives of the UAV swarm flexible formation are: In the drone swarm formation, the following state of each follower drone i satisfies: in, is the time-varying formation vector expected by follower UAV i, and the time-varying formation vector expected by follower UAV i is piecewise continuous and differentiable; c m is a set of constants satisfying N is the total number of drones in the drone cluster, the first M are follower drones, and the last NM are leader drones.

4. According to the method of claim 3, the method is characterized in that: The elastic control protocol expression of the follower UAV is: Among them, u ci (t) is the elastic control protocol of follower UAV i; is the local formation neighbor estimation error of follower UAV i; is the estimation error between follower UAV i and leader UAV; is the attack estimation of the follower UAV i controller; G ei is the first controller gain of follower drone i; G ui is the second controller gain of follower drone i; v i (t) is the formation compensation value of follower UAV i.

5. According to the method of claim 4, a flexible and secure formation method of drone swarm under network attack is characterized in that: The calculation formula of the local formation neighbor estimation error of the follower UAV i is: in, is the set of drones that directly communicate with follower drone i in the communication topology; a ij is the element in the i-th row and j-th column of the communication topology adjacency matrix; a ij =1 means that follower UAV i has communication connection with UAV j, a ij =0 means that follower UAV i has no communication connection with UAV j; The calculation formula of the estimated error between the follower UAV i and the leader UAV is: Among them, a im is the element in the i-th row and m-th column of the communication topology adjacency matrix; a im =1 means that the follower UAV i has a communication connection with the leader UAV m; a im =0 means that follower drone i has no communication connection with leader drone m.

6. The method for flexible and secure formation of drone swarms under network attacks according to claim 4 is characterized in that: Attack estimation of follower drone i-controller The determination process is: Design the state estimator of follower UAV i, the expression of the state estimator of follower UAV i is: in, represents the state estimation of follower UAV i, Tx i (t), ψ ui , ψ xi The estimation of , G, J, T, R are the preset estimator matrices; Among them, the determination process of G, J, T, and R is as follows: Defining the estimation error For ε i (t) Taking the derivative, we get: Among them, matrix G, matrix T, matrix J, and matrix R satisfy TA-GT-RC=0, and TB-J=0; matrix G is a Hurwitz matrix; Then we can get in Substitute the determined matrices G, T, J, and R into the state estimation expression of follower UAV i to obtain the attack estimation of the controller of follower UAV i and the attack estimation of the follower drone i sensor or link 7. The method for flexible and secure formation of drone swarms under network attacks according to claim 4 is characterized in that: The formation compensation value v of the follower drone i i (t) Must meet the following requirements: The first controller gain of the follower UAV i is G ui =-I p ; The second controller gain of the follower drone i is G ei =ω e R -1 B T P -1 ; Among them, ω e ≤-1 / 2λ M <0;λ M is the communication topology Laplace matrix L a The maximum eigenvalue of The solution, and Q are both positive definite matrices; I p represents the p×p dimensional identity matrix; ω e is the control gain coefficient.

8. The method for flexible and secure formation of drone swarms under network attacks according to claim 4 is characterized in that: The method for elastically securing formation of drone clusters under network attacks also includes: Determine the event triggering condition of the elastic control protocol of the follower drone i and the minimum communication interval of the elastic control protocol of the follower drone i.

9. The method for flexible and secure formation of drone swarms under network attacks according to claim 8 is characterized in that: The event triggering condition of the elastic control protocol of the follower UAV i is: in, 0<α i <1; is the state estimation value after the follower UAV i makes an abnormal estimation on the neighbor UAV j; The minimum communication interval of the flexible control protocol of the follower UAV i is: in, in, γ5 are all predetermined parameters; 10. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for elastic and secure formation of a drone cluster under a network attack as described in any one of claims 1 to 9.

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