A heterogeneous unmanned aerial vehicle cluster elastic security control method, device, equipment and medium

Abstract

The application discloses a heterogeneous unmanned aerial vehicle cluster elastic security control method and device, equipment and medium, and relates to the fields of unmanned aerial vehicle cluster control and network security. The method comprises the following steps: constructing an unmanned aerial vehicle cluster dynamics model under a network attack condition based on a dynamics equation; the unmanned aerial vehicle cluster dynamics model comprises a leader unmanned aerial vehicle dynamics model and a plurality of follower unmanned aerial vehicle dynamics models; constructing an elastic security control model based on the unmanned aerial vehicle cluster dynamics model; the elastic security control model comprises a state estimator model, a heterogeneous compensator model and a security fault-tolerant control protocol model; and performing elastic security control on the heterogeneous unmanned aerial vehicle cluster under the network attack condition according to the elastic security control model. Under the influence of the network attack, the heterogeneous unmanned aerial vehicle cluster can still complete the scheduled formation flight task, and the security and stability of the unmanned aerial vehicle cluster under the network attack condition are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of unmanned aerial vehicle cluster control and network security, and particularly relates to a heterogeneous unmanned aerial vehicle cluster elastic security control method and device, equipment and medium. BACKGROUND

[0002] Under the strong driving of front-line technologies such as artificial intelligence, unmanned systems and cloud computing, various heterogeneous unmanned platforms such as unmanned aerial vehicles, unmanned vehicles and intelligent robots have been deeply integrated into multiple key fields such as military operations, disaster emergency response and smart city construction, and have shown indispensable value. In particular, at the military strategic level, unmanned combat mode has become the focus of global military powers competing with each other.

[0003] Unmanned aerial vehicle cluster formation refers to using multiple small unmanned aerial vehicles to work cooperatively to form a formation for flight based on unmanned aerial vehicle technology, so as to achieve better application effect than a single unmanned aerial vehicle, and provide a new solution and scheme for future cluster cooperative tasks. Heterogeneous unmanned aerial vehicle cluster refers to the fact that in actual application process, the unmanned aerial vehicles in the unmanned aerial vehicle cluster have different degrees of difference in hardware structure, load performance, information interaction, control mode, application characteristics and resource consumption, and present characteristics such as high heterogeneity and dynamic change. Elastic security formation refers to the fact that when the unmanned aerial vehicle cluster encounters network attacks or physical failures, the system can still remain stable and complete the initial task according to the original formation, which is a higher order fault-tolerant control.

[0004] The elastic security formation control capability of the heterogeneous unmanned aerial vehicle cluster is the core cornerstone for fully exerting the combat potential of precise perception, flexible application and rapid deployment, and its promotion and optimization have become the top priority of current research. Due to the relatively low reliability of small unmanned aerial vehicles, complex external environment, difficulty in communication between unmanned aerial vehicles and network vulnerability, the cluster network system is easily subjected to network attacks and then hijacked, disturbed or directly destroyed by the enemy, which seriously affects the overall performance of the unmanned aerial vehicle cluster formation. Therefore, it is urgent to design a new type of distributed formation control protocol with elasticity for the suffered network attacks, and to improve the reliability and operation safety of the heterogeneous unmanned aerial vehicle cluster formation. SUMMARY

[0005] The purpose of the present application is to provide a heterogeneous unmanned aerial vehicle cluster elastic security control method, device, equipment and medium, which can improve the reliability and operation safety of the heterogeneous unmanned aerial vehicle cluster formation.

[0006] To achieve the above purpose, the present application provides the following solutions.

[0007] In a first aspect, the present application provides a heterogeneous unmanned aerial vehicle cluster elastic security control method, comprising:

[0008] constructing a UAV cluster dynamics model under a network attack condition based on a dynamics equation; the UAV cluster dynamics model comprises a leader UAV dynamics model and a plurality of follower UAV dynamics models;

[0009] constructing an elastic security control model based on the UAV cluster dynamics model; the elastic security control model comprises a state estimator model, a heterogeneous compensator model and a secure fault-tolerant control protocol model; the state estimator model is configured to estimate an internal state of each UAV under a network attack condition; the heterogeneous compensator model is configured to calculate a heterogeneous state compensation value of each UAV; and the secure fault-tolerant control protocol model is configured to calculate control input information of each UAV under a network attack condition according to the state estimator model and the heterogeneous compensator model;

[0010] performing elastic security control on the heterogeneous UAV cluster under a network attack condition according to the elastic security control model.

[0011] In a second aspect, the present application provides a heterogeneous UAV cluster elastic security control device, comprising:

[0012] a dynamics model construction module configured to construct a UAV cluster dynamics model under a network attack condition based on a dynamics equation; the UAV cluster dynamics model comprises a leader UAV dynamics model and a plurality of follower UAV dynamics models;

[0013] an elastic security control model construction module configured to construct an elastic security control model based on the UAV cluster dynamics model; the elastic security control model comprises a state estimator model, a heterogeneous compensator model and a secure fault-tolerant control protocol model; the state estimator model is configured to estimate an internal state of each UAV under a network attack condition; the heterogeneous compensator model is configured to calculate a heterogeneous state compensation value of each UAV; and the secure fault-tolerant control protocol model is configured to calculate control input information of each UAV under a network attack condition according to the state estimator model and the heterogeneous compensator model;

[0014] an elastic security control execution module configured to perform elastic security control on the heterogeneous UAV cluster under a network attack condition according to the elastic security control model.

[0015] In a third 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 the heterogeneous UAV cluster elastic security control method according to any one of the above.

[0016] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of the heterogeneous unmanned aerial vehicle cluster resilient security control method in any one of the above aspects.

[0017] According to the specific embodiments provided in the present application, the present application has the following technical effects:

[0018] The present application provides a heterogeneous unmanned aerial vehicle cluster resilient security control method, device, equipment and medium, by fusing the actual situation under the network attack condition and the kinetic equation, a unmanned aerial vehicle cluster kinetic model is constructed. The unmanned aerial vehicle cluster kinetic model not only contains a leader unmanned aerial vehicle kinetic model to lead the action direction of the whole cluster, but also contains multiple follower unmanned aerial vehicle kinetic models to ensure the cooperation and coordination between each unmanned aerial vehicle in the cluster. Then, a resilient security control model including a state estimator model, a heterogeneous compensator model and a safety fault-tolerant control protocol model is constructed to perform resilient security control on the heterogeneous unmanned aerial vehicle cluster under the network attack condition. The state estimator model can accurately predict and estimate the internal state change of each unmanned aerial vehicle under the network attack condition, providing key data support for the subsequent control strategy. The heterogeneous compensator model calculates the heterogeneous state compensation value of each unmanned aerial vehicle to ensure that the whole cluster can still maintain consistent action and performance under heterogeneous conditions. And the safety fault-tolerant control protocol model calculates the optimal control input information for each unmanned aerial vehicle under the network attack condition based on the state estimation and heterogeneous compensation results, so as to ensure that the unmanned aerial vehicle cluster can still maintain stable and safe operation when facing network attacks. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0020] Figure 1 A flowchart of a heterogeneous unmanned aerial vehicle cluster resilient security control method provided by an embodiment of the present application is shown in the figure.

[0021] Figure 2 A heterogeneous unmanned aerial vehicle cluster network communication topology graph provided by an embodiment of the present application is shown in the figure.

[0022] Figure 3 A state transformation graph of the whole heterogeneous unmanned aerial vehicle cluster provided by an embodiment of the present application is shown in the figure. Figure 3 (a) is the initial state graph of the heterogeneous unmanned aerial vehicle cluster,Figure 3 (b) is a desired formation graph of the heterogeneous UAV cluster, Figure 3 (c) is a formation graph of the heterogeneous UAV cluster in a stable state, Figure 3 (d) is a formation graph of the heterogeneous UAV cluster when subjected to a network attack, Figure 3 (e) is a heterogeneous UAV cluster controlled by an elastic security control model, Figure 3 (d) is a heterogeneous UAV cluster formation recovery graph, Figure 3 (f) is a heterogeneous UAV cluster, Figure 3 (e) is a heterogeneous UAV cluster formation stability graph;

[0023] Figure 4 The performance diagram of the heterogeneous UAV cluster under a network attack is provided in an embodiment of the present application; wherein, Figure 4 (a) is a tracking error diagram of the heterogeneous UAV cluster under a network attack provided in an embodiment of the present application; Figure 4 (b) is a compensator output value diagram of the heterogeneous UAV cluster under a network attack provided in an embodiment of the present application;

[0024] Figure 5 The state estimation value diagram of the state estimator of a follower UAV under an attack condition is provided in an embodiment of the present application; wherein, Figure 5 (a) is a state estimation value diagram of the state estimator of a follower UAV under an attack condition, Figure 5 (b) is a state estimation value diagram of the state estimator of a follower UAV under an attack condition, Figure 5 (c) is a state estimation value diagram of the state estimator of a follower UAV under an attack condition, and Figure 5 (d) is a state estimation value diagram of the state estimator of a follower UAV under an attack condition.

[0025] Figure 6 The event trigger time interval diagram of a follower UAV is provided in an embodiment of the present application.

[0026] Figure 7 The structural diagram of a computer device is provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] 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 of the present application. 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.

[0028] In order to make the above objectives, features and advantages of the present application more apparent, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0029] In an exemplary embodiment, as Figure 1As shown, a heterogeneous unmanned aerial vehicle cluster resilient security control method is provided, comprising the following steps 101 to 103. Among them:

[0030] Step 101, constructing a unmanned aerial vehicle cluster dynamics model under network attack based on dynamics equation; the unmanned aerial vehicle cluster dynamics model includes a leader unmanned aerial vehicle dynamics model and multiple follower unmanned aerial vehicle dynamics models.

[0031] Step 102, constructing a resilient security control model based on the unmanned aerial vehicle cluster dynamics model; the resilient security control model includes: a state estimator model, a heterogeneous compensator model and a safety fault-tolerant control protocol model; the state estimator model is used to estimate the internal state of each unmanned aerial vehicle under network attack; the heterogeneous compensator model is used to calculate the heterogeneous state compensation value of each unmanned aerial vehicle; the safety fault-tolerant control protocol model is used to calculate the control input information of each unmanned aerial vehicle under network attack according to the state estimator model and the heterogeneous compensator model.

[0032] Step 103, performing resilient security control on the heterogeneous unmanned aerial vehicle cluster under network attack according to the resilient security control model.

[0033] Implementing the above-mentioned steps 101 to 103 can enable the heterogeneous unmanned aerial vehicle cluster to still complete the scheduled formation flight task under network attack.

[0034] In another exemplary embodiment of the present application, in order to more accurately describe the influence of network attack on the dynamic behavior of heterogeneous unmanned aerial vehicle cluster, and to construct a unmanned aerial vehicle cluster dynamics model that can reflect this influence, the above-mentioned step 101 is replaced by the following steps 201-204:

[0035] Step 201, establishing a unmanned aerial vehicle control model under network attack. Due to the vulnerability of the network of the unmanned aerial vehicle cluster system, network attacks can be injected into the system through the cluster network, causing the formation tracking accuracy to decrease and affecting the stability of the unmanned aerial vehicle cluster system. Therefore, when designing the formation safety resilient control protocol, the network attack should be modeled first.

[0036] The single unmanned aerial vehicle formation control model under network attack is:

[0037] u(t)=u c (t)+ψ u (t) (1)

[0038] Wherein, represents the system input, represents a p-dimensional real vector, represents the controller output, ψ u =Ψ u au This represents the network attack input received by the executor, where Represents a p1-dimensional real vector. This represents a matrix that maps attacks to input data. This represents a matrix with p rows and p1 columns. In the matrix, "1" indicates that the executor has been attacked by a network, and "0" indicates that it has not been attacked.

[0039] Step 202: Establish a dynamic model of the drone swarm. Based on the dynamic equations, the dynamic model of the following drone i in a heterogeneous drone swarm under conditions without network attacks can be expressed as:

[0040]

[0041] y i (t)=C i x i (t)(2b)

[0042] in, This represents the rate of change of the internal state vector of the i-th following drone at time t. This represents the internal state vector of the i-th following drone at time t. Represents an n-dimensional real vector. This represents the control input of the i-th following drone at time t. This represents the decision state vector of the i-th following drone at time t. Represents a q-dimensional real vector. A is the known system matrix following the drone i. i Let B represent the dynamics matrix of the i-th following drone. i Let C represent the input matrix of the i-th following drone. i Let represent the decision matrix of the i-th following drone.

[0043] Step 203: Substituting the UAV control model (1) under network attack conditions from step 201, and considering the network attack on the sensor, into the UAV dynamics models (2a) and (2b), we can obtain the dynamics model of the follower UAV i under network attack conditions as follows:

[0044]

[0045] y i (t)=C i x i (t)+D i ψ si (t)(3b1)

[0046] in, represents the rate of change of the internal state vector of the i-th follower UAV at time t, x i (t) represents the internal state vector of the i-th follower UAV at time t, A i represents the dynamics matrix of the i-th follower UAV, B i represents the input matrix of the i-th follower UAV, u ci (t) represents the control input of the i-th follower UAV at time t, ψ ui (t) represents the actuator cyber attack input signal received by the i-th follower UAV at time t, y i (t) represents the decision state vector of the i-th follower UAV at time t, ψ si (t) represents the sensor cyber attack input signal received by the i-th follower UAV at time t, C i represents the decision matrix of the i-th follower UAV, D i represents the transmission matrix of the i-th follower UAV.

[0047] Step 204, in the present application, mainly for the formation of a single leader UAV and N follower UAVs are modeled, the above for the follower UAV has been modeled, leader UAV dynamics model can be modeled as:

[0048]

[0049] y0(t) = C0x0(t) (3b2)

[0050] wherein, represents the rate of change of the internal state vector of the leader UAV at time t, x0(t) e represents the internal state vector of the leader UAV at time t, u0(t) represents the control input of the leader UAV at time t, y0(t) e represents the decision state vector of the leader UAV at time t, A0 e represents the dynamics matrix of the leader UAV, B0 e represents the input matrix of the leader UAV, C0 represents the decision matrix of the leader UAV.

[0051] In another exemplary embodiment of the present application, h i (t) is the desired time-varying formation vector. If the state of each follower UAV i in the UAV cluster satisfies:

[0052]

[0053] then the UAV cluster system can be referred to as being able to complete the desired formation, wherein, is the estimate of y i (t), h i (t) is piecewise continuously differentiable, μ e R ＞0 represents a constant parameter.

[0054] In another exemplary embodiment of the present application, in order to more specifically realize the elastic security control of the UAV cluster under network attack conditions, optimize the construction process of the control model, and improve the robustness and security of the system, the above step 102 is replaced by the following steps 301-303:

[0055] Step 301, design a state estimator under network attack. In order to obtain the state output estimation of the follower UAV i First, the state estimator model for estimating the network attack received by the follower UAV i needs to be designed as:

[0056]

[0057] wherein, denotes the rate of change of the state estimation value of the i-th follower UAV at time t, θ i (t) denotes the preliminary state estimation value of the i-th follower UAV at time t, and are the estimations of ψ ui (t) and ψ si (t), respectively, denotes the estimation value of the actuator network attack input signal received by the i-th follower UAV at time t, denotes the estimation value of the sensor network attack input signal received by the i-th follower UAV at time t, denotes the state estimation value of the i-th follower UAV at time t obtained by correcting the preliminary state estimation value and the state estimation correction matrix, F i denotes the system matrix of the state estimator of the i-th follower UAV, J i denotes the control input matrix of the state estimator of the i-th follower UAV, R i denotes the output error feedback matrix of the state estimator of the i-th follower UAV, H i denotes the state estimation correction matrix of the state estimator of the i-th follower UAV, F i , J i , R i , and H i are the state estimator matrices that need to be designed.

[0058] Define the state estimation error of the follower UAV i as:

[0059]

[0060] wherein, ε xi (t) denotes the state estimation error of the i-th follower UAV at time t, represents the state estimation value of the i-th follower UAV at time t obtained by correcting the preliminary state estimation value and the state estimation correction matrix.

[0061] Taking the first-order derivative of formula (6) can obtain:

[0062]

[0063] wherein, R i = R 1i + R 2i , Z i = A i -H i C i A i -R 1i C i . R 1i and R 2i respectively represent the first matrix component and the second matrix component of the output estimation matrix of the i-th follower UAV after numerical decomposition, represents the rate of change of the state estimation value of the i-th follower UAV at time t obtained by correcting the preliminary state estimation value and the state estimation correction matrix, represents the rate of change of the estimated value of the sensor network attack input signal received by the i-th follower UAV at time t, y mi (t) represents the output state measurement value of the i-th follower UAV.

[0064] Z i -F i = 0, Z i H i -R 2i = 0, B i -H i C i B i -J i = 0.

[0065] F i , J i , R i and H i are any matrices satisfying the above three conditions, and formula (7) can be simplified according to the above three conditions to obtain:

[0066]

[0067] wherein, ε ui (t) represents the estimated value error of the actuator network attack input signal received by the i-th follower UAV at time t, ε si(t) represents the estimation error of the sensor cyber attack input signal received by the i-th follower UAV at time t.

[0068] In practice, the state x i (t) of the follower UAV i is not directly available, so the output error of the UAV i is defined as:

[0069]

[0070] where ε i (t) represents the decision state vector error of the i-th follower UAV at time t, represents the estimation of the decision state vector of the i-th follower UAV at time t.

[0071] Taking the first derivative of (9) gives:

[0072]

[0073] Similarly, (10) can be simplified as:

[0074]

[0075] Design the state estimation and The Lyapunov stability analysis function is chosen as:

[0076]

[0077] where ψ si (t) represents the sensor cyber attack input signal received by the i-th follower UAV at time t, represents the rate of change of the estimation of the actuator cyber attack input signal received by the i-th follower UAV at time t, a i represents the rate of the actuator cyber attack input signal estimation, P 1i represents a unique positive definite solution matrix, Q 1i represents an arbitrary symmetric positive definite matrix, represents the rate of change of the estimation of the sensor cyber attack input signal received by the i-th follower UAV at time t, β i represents the rate of the sensor cyber attack input signal estimation, represents the pseudo-inverse of the decision matrix of the i-th follower UAV, represents the transpose of the decision state vector error of the i-th follower UAV at time t, represents the transpose of the estimation error of the actuator cyber attack input signal received by the i-th follower UAV at time t, denotes the estimation error of the sensor cyber attack input signal received by the i-th follower UAV at time t.

[0078] Taking the first derivative of (12) gives

[0079]

[0080] From (13), the design and is as follows:

[0081]

[0082] wherein, denotes the rate of change of the estimator of the i-th follower UAV at time t of the actuator cyber attack input signal, denotes the rate of change of the estimator of the i-th follower UAV at time t of the sensor cyber attack input signal. Then, from (13) (14) (15), we have

[0083]

[0084] wherein, denotes the transpose of the control input matrix of the estimator of the i-th follower UAV, denotes the transpose of the decision matrix of the i-th follower UAV, denotes the rate of change of the Lyapunov stability analysis function, which satisfies the stability according to (16).

[0085] Step 302, design an event-triggered compensator. In order to solve the heterogeneous UAV cluster formation tracking problem, first, a heterogeneous compensator needs to be constructed for each UAV. The heterogeneous compensator model of the follower UAV i and the leader UAV is:

[0086]

[0087] wherein, denotes the rate of change of the heterogeneous compensation state value of the heterogeneous compensator of the i-th follower UAV at time t, denotes the heterogeneous compensation state value of the heterogeneous compensator of the i-th follower UAV at time t, denotes the control input of the heterogeneous compensator of the i-th follower UAV at time t, denotes the rate of change of the heterogeneous compensation state value of the heterogeneous compensator of the leader UAV at time t, denotes the heterogeneous compensation state value of the heterogeneous compensator of the leader UAV at time t, denotes the controller input of the heterogeneous compensator of the leader UAV at time t, E1denotes the system matrix of the heterogeneous compensator. denotes the system matrix of the heterogeneous compensator, the input matrix of the heterogeneous compensator.

[0088] The compensator formation tracking control protocol is designed as:

[0089] u ηi (t) = Kξ i (t) + v ηi (t) (19)

[0090] u η0 (t) = K0η0(t) (20)

[0091] where u ηi (t) denotes the controller input of the heterogeneous compensator of the ith follower UAV at time t, denotes the relative state difference between the ith follower UAV and the UAVs within its communication range, j denotes the jth follower UAV within the communication range of the ith follower UAV, N denotes the total number of follower UAVs that have communication with the ith follower UAV, a ij denotes the communication weight coefficient of the ith follower UAV and the jth follower UAV within the communication range, i0 denotes the communication weight coefficient of the ith follower UAV and the leader UAV, K denotes the control gain of ξ i (t), denotes the heterogeneous cluster formation compensation vector of the ith follower UAV at time t, denotes the additional compensation value of h ηi (t), u η0 (t) denotes the controller input of the heterogeneous compensator of the leader UAV at time t, η0(t) denotes the compensation state value of the heterogeneous compensator of the leader UAV at time t, K0denotes the control gain of u η0 (t).

[0092] The event-triggered technique reduces the communication frequency and reduces the use of communication resources by designing a condition to trigger communication. The event trigger of the compensator of the ith follower UAV can be designed as:

[0093]

[0094] where, is the estimation of η i (t), is the s th event trigger time of the heterogeneous compensator of the ith follower UAV.

[0095] The estimator is introduced, and equation (19) can be transformed into:

[0096]

[0097] wherein,

[0098] Substituting (23) into (17), we have

[0099]

[0100] The compact form of the heterogeneous UAV swarm can be expressed as

[0101]

[0102] wherein, η(t) = col(η1(t),…,ηN(t)) represents the state vector of the heterogeneous UAV swarm compensator at time t, N represents the state estimation vector of the heterogeneous UAV swarm compensator at time t, h η η1 ηN represents the heterogeneous swarm formation compensation vector of the heterogeneous UAV swarm at time t, v η η1 ηN represents the compensation value vector of the heterogeneous swarm formation compensation vector of the heterogeneous UAV swarm at time t, I N represents the N-dimensional identity matrix, L a1 is the communication topology matrix between the UAV swarm followers, L a2 is the communication topology matrix between the UAV swarm leaders and followers.

[0103] The estimation error can be expressed as

[0104]

[0105] Based on the stability analysis, the event-triggered condition is designed as

[0106]

[0107] wherein, εi(t) represents the state estimation error of the i-th follower UAV at time t, ηi represents the average weight of the state estimation error and the relative state difference of the i-th follower UAV, represents the estimation value of the relative state difference between the i-th follower UAV and the UAVs within its communication range, χ ∈ R ＞0 ​​​​​​denotes the state estimation error threshold.

[0108] To avoid Zeno behavior, the event-triggered minimum time interval also needs to be designed:

[0109]

[0110] wherein, is the event-triggered time interval.

[0111] Based on error analysis, the event-triggered minimum time interval is designed as follows:

[0112]

[0113] wherein, denotes the triggering time of the (s+1)th event of the heterogeneous compensator of the ith follower UAV, denotes the triggering time of the st event of the heterogeneous compensator of the ith follower UAV, denotes the time interval from the triggering time of the st event of the heterogeneous compensator of the ith follower UAV to the time when the triggering condition of the (s+1)th event is met, denotes the event-triggered minimum time interval, denotes the average value of the heterogeneous UAV formation compensation value.

[0114] Step 303, design a distributed heterogeneous safety control protocol. The purpose of designing the distributed heterogeneous safety control protocol is to enable the heterogeneous UAV cluster to still complete the scheduled formation flight task under the influence of network attacks.

[0115] Therefore, in combination with steps 101, 301 and 302, the safety fault-tolerant control protocol model of the follower UAV i in the time period is:

[0116]

[0117] wherein, u ci (t) denotes the control input of the ith follower UAV at time t, denotes the state estimation value of the ith follower UAV at time t obtained by the preliminary state estimation value and the correction matrix of the state estimation correction, G 1i denotes the gain coefficient of the compensation state estimation value, η i (t) denotes the compensation state value of the heterogeneous compensator of the ith follower UAV at time t, G 2i denotes the gain coefficient of the heterogeneous compensation value, denotes the estimated value of the actuator network attack input received by the ith follower UAV at time t, G 3i denotes gain coefficient of the i-th follower UAV, v i (t) represents the formation compensation value of the follower UAV i at time t, u0(t) represents the control input of the leader UAV at time t, G0 represents the gain coefficient of the control input of the leader UAV, and x0(t) represents the internal state vector of the leader UAV at time t.

[0118] In another exemplary embodiment of the present application, after the step 303, the method further comprises: designing a safety fault-tolerant control protocol gain.

[0119] Define the heterogeneous compensator error of the follower UAV i and the leader UAV as:

[0120]

[0121] wherein Π i represents the compensator matrix of the i-th follower UAV, Π0 represents the compensator matrix of the leader UAV, and Π i and Π0 are matrices to be designed.

[0122] If the related terms satisfy the following condition (32), the goal of (4) can be achieved:

[0123]

[0124] wherein Γ i represents the gain coefficient matrix of the i-th follower UAV, Γ i is a matrix to be designed, Π i , Π0 and Γ i are arbitrary matrices satisfying formula (32), and the following derivation is performed:

[0125] Derive formula (31a) with respect to time, and design G 2i = Γ i -G 1i Π i , Substitute the first condition B i v i (t)-Π i E2v ηi (t)=0 and the second condition A i Π i +B i Γ i -Π i E1=0 into (32) to obtain:

[0126] ˙

[0127]

[0128] Design A i +B iG 1i and A0+B0G0is a Hurwitz matrix, and K0is designed such that A0+B0G0-E1-E2K0= 0, then (33) can be obtained by substituting into (33):

[0129]

[0130] From (34), we can get and substituting into (31) can get:

[0131]

[0132] Further, we can get:

[0133]

[0134] The third condition h i (t) = C i Π i h ηi (t) in (32) is i Π i -C0Π0= 0 and (35) into (4) can get:

[0135]

[0136] where r i (t) = C0Π0e i (t), where e i (t) = η i (t) - h ηi (t) - η0(t) is bounded, so it can satisfy the condition (4).

[0137] In another exemplary embodiment of the present application, as shown in Figure 2 the heterogeneous UAV cluster network communication topology graph is given, in which UAV 0 is the leader UAV, and UAVs 1-5 are follower UAVs. Among them, UAVs {1, 2, 4} are isomorphic, UAVs {3, 5} are isomorphic, and UAVs {1, 2, 4} are heterogeneous with UAVs {3, 5}, distinguished by a circle and a regular hexagon. The red marked UAVs {1, 2, 3} are subjected to network attacks, in which UAV 1 is subjected to a sensor attack, UAV 2 is subjected to an actuator attack, and UAV 3 is subjected to both a sensor attack and an actuator attack.

[0138] As shown in Figure 3 , the state transition graph of the entire heterogeneous UAV cluster is given. Among them, Figure 3(a) is the initial state graph of 6 heterogeneous UAVs in the heterogeneous UAVs swarm, where random values are used. When there is no fault, the heterogeneous UAVs swarm system can complete the desired formation by the resilient safety control model in about 15s, as shown in Figure 3 (b). Figure 3 (c) is the formation graph of the heterogeneous UAVs swarm in the stable state. When the network attack occurs at the 20th second, the original formation of the heterogeneous UAVs swarm is destroyed, as shown in Figure 3 (d). Due to the design of the heterogeneous formation resilient control protocol, the formation can be restored in about 35s by the resilient safety control model, as shown in Figure 3 (e). The formation of the heterogeneous UAVs swarm remains stable until the 50th second, as shown in Figure 3 (f).

[0139] As shown in Figure 4 (a) and Figure 4 (b), the tracking error and the compensator output value of the heterogeneous UAVs swarm system are given respectively when the network attack occurs. It can be seen that the error is zero in about 15s, that is, the formation is completed. When the attack occurs at the 20th second, the error suddenly increases, and then it is restored to zero by the resilient control protocol. Figure 4 The compensator output value of the No. 1 follower UAV in (b) is compensator compensation value 1, the compensator output value of the No. 2 follower UAV is compensator compensation value 2, the compensator output value of the No. 3 follower UAV is compensator compensation value 3, the compensator output value of the No. 4 follower UAV is compensator compensation value 4, and the compensator output value of the No. 5 follower UAV is compensator compensation value 5.

[0140] As shown in Figure 5 (a), Figure 5 (b), Figure 5 (c), and Figure 5 (d), the state estimation value of the actuator attack on UAV 1 varying with time the state estimation value of the sensor attack on UAV 2 varying with time the state estimation value of the actuator attack on UAV 3 varying with time and the state estimation value of the sensor attack on UAV 3 varying with time

[0141] As shown in Figure 6 the time interval of the event triggered by each follower UAV is given, wherein each point indicates that the follower UAV has communicated once.

[0142] The application also provides an application scenario of the above-mentioned heterogeneous unmanned aerial vehicle cluster elastic security control method. Specifically, the heterogeneous unmanned aerial vehicle cluster elastic security control method provided in the embodiment can be applied in a modern military combat scenario. This scenario includes multiple links such as reconnaissance and intelligence collection, target tracking and attack, and battlefield situation assessment. In the reconnaissance and intelligence collection link, key intelligence information is collected, and the target tracking and attack link is entered. In this link, the heterogeneous unmanned aerial vehicle cluster tracks and locks important enemy targets according to the collected intelligence information. Through elastic security formation control, the unmanned aerial vehicle cluster can maintain a stable formation, and even in the case of network attacks or physical failures, the flight attitude and combat strategy can be quickly adjusted to ensure the smooth completion of the attack task. After the target tracking and attack link, the system enters the battlefield situation assessment link, and the commander formulates the next combat plan. The heterogeneous unmanned aerial vehicle cluster elastic security control method provided in the embodiment belongs to the target tracking and attack link in the modern military combat. Specifically, by improving the elastic security formation control capability of the heterogeneous unmanned aerial vehicle cluster, it is ensured that the unmanned aerial vehicle cluster can maintain a stable formation in a complex battlefield environment, effectively cope with network attacks and physical failures, and improve the reliability and security of the combat task.

[0143] Based on the same inventive concept, the embodiment of the application also provides a heterogeneous unmanned aerial vehicle cluster elastic security control device for implementing the above-mentioned related heterogeneous unmanned aerial vehicle cluster elastic security control method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more heterogeneous unmanned aerial vehicle cluster elastic security control device embodiments provided below can refer to the limitations of the heterogeneous unmanned aerial vehicle cluster elastic security control method described above, which will not be repeated here.

[0144] In one exemplary embodiment, as shown in Figure 5 a heterogeneous unmanned aerial vehicle cluster elastic security control device is provided, including:

[0145] The dynamics model construction module 401 is configured to construct a dynamics model of the unmanned aerial vehicle cluster under network attack based on a dynamics equation; the dynamics model of the unmanned aerial vehicle cluster includes a dynamics model of a leader unmanned aerial vehicle and dynamics models of multiple follower unmanned aerial vehicles.

[0146] The elastic security control model construction module 402 is configured to construct an elastic security control model based on the UAV cluster dynamics model; the elastic security control model comprises a state estimator model, a heterogeneous compensator model and a secure fault-tolerant control protocol model; the state estimator model is configured to estimate the internal state of each UAV under a network attack condition; the heterogeneous compensator model is configured to calculate a heterogeneous state compensation value of each UAV; and the secure fault-tolerant control protocol model is configured to calculate control input information of each UAV under a network attack condition according to the state estimator model and the heterogeneous compensator model.

[0147] The elastic security control execution module 403 is configured to perform elastic security control on the heterogeneous UAV cluster under a network attack condition according to the elastic security control model.

[0148] In an exemplary embodiment, a computer device is provided, which can be a server or a terminal, and an internal structure diagram thereof can be as shown in Figure 7 The computer device comprises a processor, a memory, an input / output interface (I / O) and a communication interface. 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. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises 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 operating system and the computer program in the non-volatile storage medium to run. The database of the computer device is configured to store heterogeneous UAV cluster data. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with external terminals through a network connection. The computer program is executed by the processor to implement a heterogeneous UAV cluster elastic security control method.

[0149] Those skilled in the art can understand that Figure 7 The structure shown in the above embodiment is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can comprise more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, comprising a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in each of the above method embodiments.

[0150] In an exemplary embodiment, a computer readable storage medium storing a computer program is provided, the computer program, when executed by a processor, implements the steps of any of the above method embodiments.

[0151] In an exemplary embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the steps of any of the above method embodiments.

[0152] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0153] It can be understood by those skilled in the art that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware, 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 above-mentioned embodiments. Any reference to memory, database or other medium used in the embodiments provided by the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (Read-Only Memory, ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric memory (Ferroelectric Random Access Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc.

[0154] The database involved in each of the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, and the like, without being limited thereto. The processor involved in each of the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, and the like, without being limited thereto.

[0155] The technical features of the above embodiments can be combined in any manner. To make the description concise, all possible combinations of the technical features in the above embodiments are not described, but it should be considered that any combination of the technical features is within the scope of the present disclosure, as long as there is no contradiction.

[0156] The principles and implementation manners of the present application are described by using specific examples herein, and the above embodiments are only used to help understand the method of the present application and its core idea. Meanwhile, for those skilled in the art, the specific implementation manners and application ranges can be changed according to the idea of the present application. In summary, the content of the present description should not be understood as a limitation of the present application.

Claims

1. A heterogeneous UAV swarm resilient security control method, characterized in that, The heterogeneous unmanned aerial vehicle cluster elastic security control method comprises: a dynamic model of the unmanned aerial vehicle cluster under the network attack condition is constructed based on a dynamic equation; the dynamic model of the unmanned aerial vehicle cluster comprises a dynamic model of a leader unmanned aerial vehicle and dynamic models of multiple follower unmanned aerial vehicles; an elastic security control model is constructed based on the dynamic model of the unmanned aerial vehicle cluster; the elastic security control model comprises a state estimator model, a heterogeneous compensator model and a secure fault-tolerant control protocol model; the state estimator model is used for estimating the internal state of each unmanned aerial vehicle under the network attack condition; the heterogeneous compensator model is used for calculating the heterogeneous state compensation value of each unmanned aerial vehicle; the secure fault-tolerant control protocol model is used for calculating the control input information of each unmanned aerial vehicle under the network attack condition according to the state estimator model and the heterogeneous compensator model; the heterogeneous compensator model is as follows: wherein, denotes the rate of change of the heterogenous compensator state value of the i-th follower UAV at time t, η i (t) denotes the heterogenous compensator state value of the i-th follower UAV at time t, u ηi (t) denotes the control input of the heterogenous compensator of the i-th follower UAV at time t, denotes the rate of change of the heterogenous compensator state value of the leader UAV at time t, η0(t) denotes the heterogenous compensator state value of the leader UAV at time t, u η0 (t) denotes the controller input of the heterogenous compensator of the leader UAV at time t, E1 denotes the system matrix of the heterogenous compensator, E2 denotes the input matrix of the heterogenous compensator; elastic security control is performed on the heterogeneous unmanned aerial vehicle cluster under the network attack condition according to the elastic security control model.

2. The heterogeneous UAV swarm resilient security control method of claim 1, wherein, the dynamic model of the follower unmanned aerial vehicle is as follows: y i (t) = C i x i (t) + D i ψ si (t); wherein, represents the rate of change of the internal state vector of the i-th follower UAV at time t, x i (t) represents the internal state vector of the i-th follower UAV at time t, A i represents the dynamics matrix of the i-th follower UAV, B i represents the input matrix of the i-th follower UAV, u ci (t) represents the control input of the i-th follower UAV at time t, ψ ui (t) represents the actuator cyber attack input signal received by the i-th follower UAV at time t, y i (t) represents the decision state vector of the i-th follower UAV at time t, ψ si (t) represents the sensor cyber attack input signal received by the i-th follower UAV at time t, C i represents the decision matrix of the i-th follower UAV, D i represents the transmission matrix of the i-th follower UAV.

3. The heterogeneous UAV swarm resilient security control method of claim 1, wherein, the dynamic model of the leader unmanned aerial vehicle is as follows: y0(t)=C0x0(t); wherein, denotes the rate of change of the internal state vector of the leader drone at time t, x0(t) denotes the internal state vector of the leader drone at time t, u0(t) denotes the control input of the leader drone at time t, y0(t) denotes the decision state vector of the leader drone at time t, A0denotes the dynamics matrix of the leader drone, B0denotes the input matrix of the leader drone, and C0denotes the decision matrix of the leader drone.

4. The heterogeneous UAV swarm resilient security control method of claim 1, wherein, the state estimator model is as follows: wherein, denotes the rate of change of the state estimation value of the i-th follower UAV at time t, F i denotes the system matrix of the state estimator of the i-th follower UAV, θ i denotes the preliminary state estimation value of the i-th follower UAV at time t, J i denotes the control input matrix of the state estimator of the i-th follower UAV, u ci denotes the control input of the i-th follower UAV at time t, denotes the estimation value of the actuator cyber attack input signal received by the i-th follower UAV at time t, R i denotes the output error feedback matrix of the state estimator of the i-th follower UAV, y i denotes the decision state vector of the i-th follower UAV at time t, D i denotes the transmission matrix of the i-th follower UAV, denotes the estimation value of the sensor cyber attack input signal received by the i-th follower UAV at time t, denotes the state estimation value of the i-th follower UAV at time t obtained by correction of the preliminary state estimation value and the state estimation correction matrix, H i denotes the state estimation correction matrix of the state estimator of the i-th follower UAV.

5. The heterogeneous UAV swarm resilient security control method of claim 1, wherein, the secure fault-tolerant control protocol model is as follows: u0(t)=G0x0(t); wherein u ci (t) denotes the control input of the i-th follower UAV at time t, denotes the state estimation value of the i-th follower UAV at time t obtained by the preliminary state estimation value and the state estimation correction matrix, G 1i denotes the gain coefficient of the compensation state estimation value, η i (t) denotes the compensation state value of the heterogeneous compensator of the i-th follower UAV at time t, G 2i denotes the gain coefficient of the heterogeneous compensation value, denotes the estimation value of the actuator network attack input received by the i-th follower UAV at time t, G 3i denotes the gain coefficient of , v i (t) denotes the formation compensation value of the follower UAV i at time t, u0(t) denotes the control input of the leader UAV at time t, G0denotes the gain coefficient of the leader UAV control input, x0(t) denotes the internal state vector of the leader UAV at time t.

6. The heterogeneous UAV swarm resilient security control method of claim 4, wherein, the calculation formulae of the estimated value of the actuator network attack input received by the follower unmanned aerial vehicle and the estimated value of the sensor network attack input received by the follower unmanned aerial vehicle are as follows: where ε i (t) denotes the decision state vector error of the i-th follower UAV at time t, denotes the estimated value of the decision state vector of the i-th follower UAV at time t, ψ si (t) denotes the sensor cyber attack input signal received by the i-th follower UAV at time t, denotes the rate of change of the estimated value of the actuator cyber attack input signal received by the i-th follower UAV at time t, a i denotes the rate of the actuator cyber attack input signal estimate, denotes the transpose of the control input matrix of the state estimator of the i-th follower UAV, denotes the transpose of the decision matrix of the i-th follower UAV, P 1i denotes the unique positive definite solution matrix of , Q 1i denotes an arbitrary symmetric positive definite matrix, denotes the rate of change of the estimated value of the sensor cyber attack input signal received by the i-th follower UAV at time t, β i denotes the rate of the sensor cyber attack input signal estimate, denotes the pseudo-inverse of the decision matrix of the i-th follower UAV, R 1i denotes the first matrix component of the numerical decomposition of the output estimation matrix of the i-th follower UAV, denotes the rate of change of the Lyapunov stability analysis function, denotes the transpose of the decision state vector error of the i-th follower UAV at time t.

7. A heterogeneous UAV swarm resilient security control apparatus, characterized in that, The heterogeneous unmanned aerial vehicle cluster elastic security control device comprises: a dynamic model construction module, configured to construct a dynamic model of an unmanned aerial vehicle cluster under a network attack condition based on a dynamic equation; the dynamic model of the unmanned aerial vehicle cluster comprises a dynamic model of a leader unmanned aerial vehicle and dynamic models of multiple follower unmanned aerial vehicles; an elastic security control model construction module, configured to construct an elastic security control model based on the dynamic model of the unmanned aerial vehicle cluster; the elastic security control model comprises a state estimator model, a heterogeneous compensator model and a secure fault-tolerant control protocol model; the state estimator model is used for estimating the internal state of each unmanned aerial vehicle under the network attack condition; the heterogeneous compensator model is used for calculating the heterogeneous state compensation value of each unmanned aerial vehicle; the secure fault-tolerant control protocol model is used for calculating the control input information of each unmanned aerial vehicle under the network attack condition according to the state estimator model and the heterogeneous compensator model; the heterogeneous compensator model is as follows: wherein, denotes the rate of change of the hetero-compensator state value of the i-th follower UAV at time t, η i (t) denotes the hetero-compensator state value of the i-th follower UAV at time t, u ηi (t) denotes the control input of the hetero-compensator of the i-th follower UAV at time t, denotes the rate of change of the hetero-compensator state value of the leader UAV at time t, η0(t) denotes the hetero-compensator state value of the leader UAV at time t, u η0 (t) denotes the controller input of the hetero-compensator of the leader UAV at time t, E1 denotes the system matrix of the hetero-compensator, E2 denotes the input matrix of the hetero-compensator; an elastic security control execution module, configured to perform elastic security control on the heterogeneous unmanned aerial vehicle cluster under the network attack condition according to the elastic security control model.

8. A computer device comprising: The computer program is executed by the processor to implement the heterogeneous unmanned aerial vehicle cluster elastic security control method in any one of claims 1-6.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the heterogeneous unmanned aerial vehicle cluster elastic security control method in any one of claims 1-6.

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