Heterogeneous unmanned aerial vehicle cluster elastic safety control method, device, equipment and medium

Abstract

The invention discloses an elastic security control method, device and equipment for a heterogeneous unmanned aerial vehicle cluster and a medium, and relates to the field of unmanned aerial vehicle cluster control and network security, and the method comprises the steps: constructing an unmanned aerial vehicle cluster dynamic model under a network attack condition based on a dynamic equation; the unmanned aerial vehicle cluster dynamical model comprises a leading unmanned aerial vehicle dynamical model and a plurality of following unmanned aerial vehicle dynamical models; constructing an elastic safety control model based on the unmanned aerial vehicle cluster dynamics model; the elastic safety control model comprises a state estimator model, a heterogeneous compensator model and a safety 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. The heterogeneous unmanned aerial vehicle cluster can still complete a set formation flight task under the influence of a network attack, and the safety 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 drone cluster control and network security, and in particular to a method, device, equipment and medium for elastic security control of heterogeneous drone clusters. Background Art

[0002] Driven by cutting-edge technologies such as artificial intelligence, unmanned systems, and cloud computing, various heterogeneous unmanned platforms such as drones, unmanned vehicles, and intelligent robots have been deeply integrated into key areas such as military operations, disaster emergency response, and smart city construction, demonstrating their indispensable value. Especially at the military strategic level, unmanned combat modes have become the focus of competition among global military powers.

[0003] Drone swarm formation refers to the use of multiple small drones working together to form a formation for flight based on drone technology, thereby achieving better application effects than single drones, and providing a new solution for future cluster collaborative tasks. Heterogeneous drone swarm refers to the fact that in the actual application process, the drones in the drone swarm have different degrees of differences in hardware structure, payload performance, information interaction, control method, application characteristics, resource consumption, etc., showing characteristics such as high heterogeneity and dynamic changes. Elastic and secure formation means that when the drone swarm encounters a network attack or physical failure, it can still maintain the stability of the system and complete the initial task in the original formation form. It is a higher-order fault-tolerant control.

[0004] The elastic and safe formation control capability of heterogeneous UAV swarms is the core cornerstone for fully realizing its combat potential such as accurate perception, flexible application, and rapid deployment. Its improvement and optimization has become the top priority of current research. Due to the relatively low reliability of small UAVs, complex external environment, difficulty in communication between UAVs, and network vulnerability, the swarm network system is vulnerable to network attacks and then hijacked, disrupted, or directly destroyed by the enemy, which seriously affects the overall performance of the UAV swarm formation. Therefore, it is urgent to design a new distributed formation control protocol with elasticity to target network attacks and improve the reliability and operational safety of heterogeneous UAV swarm formations. Summary of the invention

[0005] The purpose of this application is to provide a method, device, equipment and medium for elastic safety control of heterogeneous drone clusters, which can improve the reliability and operational safety of heterogeneous drone cluster formations.

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

[0007] In a first aspect, the present application provides a heterogeneous drone cluster elastic safety control method, including:

[0008] Based on the dynamic equation, a UAV cluster dynamics model under network attack conditions is constructed; the UAV cluster dynamics model includes a leading UAV dynamics model and multiple following UAV dynamics models;

[0009] A resilient safety control model is constructed based on the UAV cluster dynamics model; the resilient safety 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 UAV under network attack conditions; the heterogeneous compensator model is used to calculate the heterogeneous state compensation value of each UAV; the safety fault-tolerant control protocol model is used to calculate the control input information of each UAV under network attack conditions according to the state estimator model and the heterogeneous compensator model;

[0010] Based on the elastic security control model, elastic security control is performed on heterogeneous drone clusters under network attack conditions.

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

[0012] A dynamic model building module is used to build a UAV cluster dynamic model under network attack conditions based on a dynamic equation; the UAV cluster dynamic model includes a leading UAV dynamic model and multiple following UAV dynamic models;

[0013] A flexible safety control model construction module is used to construct a flexible safety control model based on a UAV cluster dynamics model; the flexible safety control model includes: a state estimator model, a heterogeneous compensator model and a safe fault-tolerant control protocol model; the state estimator model is used to estimate the internal state of each UAV under network attack conditions; the heterogeneous compensator model is used to calculate the heterogeneous state compensation value of each UAV; the safe fault-tolerant control protocol model is used to calculate the control input information of each UAV under network attack conditions according to the state estimator model and the heterogeneous compensator model;

[0014] The elastic security control execution module is used to perform elastic security control on heterogeneous drone clusters under network attack conditions 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 any of the above-described heterogeneous drone cluster elastic safety control methods.

[0016] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-mentioned heterogeneous drone cluster elastic safety control methods.

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

[0018] The present application provides a method, device, equipment and medium for elastic safety control of heterogeneous drone clusters, which constructs a drone cluster dynamics model by integrating dynamic equations with the actual situation under network attack conditions. The drone cluster dynamics model not only includes a leading drone dynamics model to lead the action direction of the entire cluster, but also includes multiple follower drone dynamics models to ensure the coordination and cooperation between the drones in the cluster. Then, a elastic safety control model including a state estimator model, a heterogeneous compensator model and a safety fault-tolerant control protocol model is constructed to perform elastic safety control on the heterogeneous drone cluster under network attack conditions. Among them, the state estimator model can accurately predict and estimate the internal state changes of each drone under network attack conditions, providing key data support for subsequent control strategies. The heterogeneous compensator model calculates the heterogeneous state compensation value of each drone for the heterogeneous problems that may exist in the drone cluster, so as to ensure that the entire cluster can still maintain consistent actions and performance under heterogeneous conditions. The safety fault-tolerant control protocol model, based on the first two, calculates the optimal control input information for each drone under network attack conditions according to the results of state estimation and heterogeneous compensation, so as to ensure that the drone cluster can still maintain stable and safe operation when facing network attacks. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] 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.

[0020] Figure 1 A schematic diagram of a flow chart of a heterogeneous drone cluster elastic safety control method provided in one embodiment of the present application;

[0021] Figure 2 A heterogeneous drone cluster network communication topology diagram provided in one embodiment of the present application;

[0022] Figure 3 A state transition diagram of the entire heterogeneous drone cluster provided in one embodiment of the present application; wherein, Figure 3 (a) is the initial state diagram of the heterogeneous UAV cluster. Figure 3 (b) is the expected formation diagram of the heterogeneous UAV cluster. Figure 3 (c) is the formation diagram of the heterogeneous UAV cluster in a stable state. Figure 3 (d) is a schematic diagram of the formation of a heterogeneous drone cluster when it is attacked by a network attack. Figure 3 (e) is controlled by a flexible security control model Figure 3 (d) The formation recovery diagram of the heterogeneous UAV cluster formation. Figure 3 (f) Figure 3 (e) The formation stability diagram of heterogeneous UAV swarm formation;

[0023] Figure 4 A schematic diagram of the performance of a heterogeneous drone cluster under network attack conditions provided in an embodiment of the present application; wherein: Figure 4 (a) is a schematic diagram of tracking errors of a heterogeneous UAV cluster under network attack conditions provided in an embodiment of the present application; Figure 4 (b) is a schematic diagram of the compensator output value of a heterogeneous drone cluster under a network attack condition provided in an embodiment of the present application;

[0024] Figure 5 A schematic diagram of the state estimation value of the state estimator for the attack situation provided by an embodiment of the present application; wherein, Figure 5 (a) Figure 5 (b) Figure 5 (c) and Figure 5 (d) Schematic diagrams showing the state estimation values ​​of a state estimator of a following UAV for an attack situation;

[0025] Figure 6 This is a diagram of the time interval of event triggering of a following drone provided in an embodiment of the present application;

[0026] Figure 7 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] In an exemplary embodiment, Figure 1As shown, a heterogeneous UAV cluster elastic safety control method is provided, including the following steps 101 to 103. Among them:

[0030] Step 101, constructing a UAV cluster dynamics model under network attack conditions based on a dynamics equation; the UAV cluster dynamics model includes a leading UAV dynamics model and multiple following UAV dynamics models.

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

[0032] Step 103, performing elastic security control on the heterogeneous drone cluster under network attack conditions according to the elastic security control model.

[0033] Implementing the above steps 101 to 103 can enable a heterogeneous drone cluster to complete a predetermined formation flight mission even under network attack conditions.

[0034] In another exemplary embodiment of the present application, in order to more accurately describe the impact of network attacks on the dynamic behavior of heterogeneous drone clusters and construct a drone cluster dynamics model that can reflect such impact, the above step 101 is replaced by the following steps 201-204:

[0035] Step 201, establish a drone control model under network attack conditions. Due to the vulnerability of the drone cluster system network, 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 drone cluster system. Therefore, when designing a formation security elastic control protocol, network attacks must be modeled first.

[0036] The control model of a single UAV formation under network attack is established as follows:

[0037] u(t)=u c (t)+ψ u (t) (1)

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

[0039] Step 202: Establish a UAV cluster dynamics model. Based on the dynamics equation, the dynamics model of the following UAV i in the heterogeneous UAV cluster without network attack can be expressed as:

[0040]

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

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

[0043] Step 203: Substitute the drone control model (1) under the network attack condition in step 201 into the drone dynamics models (2a) and (2b) to obtain the dynamics model of the follower drone i under the network attack condition:

[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 ith following drone at time t, x i (t) represents the internal state vector of the i-th following drone at time t, A i represents the dynamic matrix of the ith follower drone, B i represents the input matrix of the i-th following drone, u ci (t) represents the control input of the i-th following UAV at time t, ψ ui (t) represents the actuator network attack input signal received by the i-th follower drone at time t, y i (t) represents the decision state vector of the i-th following UAV at time t, ψ si (t) represents the sensor network attack input signal received by the i-th following drone at time t, C i represents the decision matrix of the i-th following drone, D i represents the transmission matrix of the i-th following UAV.

[0047] Step 204, in this application, the modeling research is mainly conducted on the formation of a single leader UAV and N follower UAVs. The above modeling is conducted on the follower UAVs, and the dynamic model of the leader UAV can be modeled as follows:

[0048]

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

[0050] in, represents the rate of change of the internal state vector of the leader UAV at time t, x0(t)∈ 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)∈ represents the decision state vector of the leader UAV at time t, A0∈ represents the dynamics matrix of the leader UAV, B0∈ represents the input matrix of the leader UAV, and C0 represents the decision matrix of the leader UAV.

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

[0052]

[0053] Then the drone swarm system can be said to be able to complete the desired formation, where Yes i The estimated value of (t), h i (t) is piecewise continuously differentiable, μ∈R ＞0 Represents a constant parameter.

[0054] In another exemplary embodiment of the present application, in order to more specifically realize the elastic safety control of the drone cluster under network attack conditions, optimize the construction process of the control model, and improve the robustness and safety 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 following drone i First, we need to design a state estimator model for estimating the network attack suffered by the following drone i:

[0056]

[0057] in, represents the rate of change of the estimated state of the i-th following UAV at time t, θ i (t) represents the initial state estimate of the i-th following UAV at time t, and They are respectively ui (t) and ψ si The estimate of (t), represents the estimated value of the actuator network attack input signal received by the i-th following drone at time t, represents the estimated value of the sensor network attack input signal received by the i-th following drone at time t, represents the state estimation value of the i-th following UAV at time t obtained by correcting the initial state estimation value and the state estimation correction matrix, F i represents the system matrix of the state estimator of the i-th following UAV, J i represents the control input matrix of the state estimator of the i-th following UAV, R i represents the output error feedback matrix of the state estimator of the i-th following UAV, H i represents the state estimation correction matrix of the state estimator of the i-th following UAV, F i , J i , R i and H i is the state estimator matrix that needs to be designed.

[0058] The state estimation error of the following UAV i is defined as:

[0059]

[0060] Among them, ε xi (t) represents the state estimation error of the i-th following UAV at time t, It represents the state estimation value of the i-th following UAV at time t obtained by correcting the initial state estimation value and the state estimation correction matrix.

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

[0062]

[0063] Among them, 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 They represent the first matrix component and the second matrix component of the numerical decomposition of the output estimation matrix of the i-th following UAV, It represents the rate of change of the state estimate of the ith following UAV at time t obtained by correcting the initial state estimate and the state estimate correction matrix. represents the rate of change of the estimated value of the sensor network attack input signal received by the i-th following drone at time t, y mi (t) represents the output state measurement value of the i-th following UAV.

[0064] Design 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 is an arbitrary matrix that satisfies the above three conditions, and according to the above three conditions, formula (7) can be simplified to obtain:

[0066]

[0067] in, ε ui (t) represents the estimated value error of the actuator network attack input signal received by the ith following UAV at time t, ε si(t) represents the estimated value error of the sensor network attack input signal received by the i-th following drone at time t.

[0068] In practice, the state x of following drone i i (t) cannot be obtained directly, so the output error of UAV i is defined as:

[0069]

[0070] Among them, ε i (t) represents the decision state vector error of the i-th following UAV at time t, Represents the estimated value of the decision state vector of the i-th following UAV at time t.

[0071] Taking the first-order derivative of equation (9) yields:

[0072]

[0073] Similarly, formula (10) can be simplified to obtain:

[0074]

[0075] Design State Estimates and The Lyapunov stability analysis function is selected as:

[0076]

[0077] Among them, ψ si (t) represents the sensor network attack input signal received by the i-th following drone at time t, represents the rate of change of the estimated value of the actuator network attack input signal received by the i-th following UAV at time t, α i represents the ratio of the estimated values ​​of the actuator network attack input signal, P 1i express The only positive solution matrix, Q 1i represents an arbitrary symmetric positive definite matrix, represents the rate of change of the estimated value of the sensor network attack input signal received by the i-th following drone at time t, β i represents the ratio of the estimated value of the input signal of the sensor network attack, represents the pseudo-inverse matrix of the decision matrix of the i-th following drone, represents the transpose of the decision state vector error of the ith following UAV at time t, represents the transpose of the estimated value error of the actuator network attack input signal received by the i-th following UAV at time t, Represents the transpose of the estimated value error of the sensor network attack input signal received by the i-th following drone at time t.

[0078] Taking the first-order derivative of equation (12) we can obtain:

[0079]

[0080] According to formula (13), we design and as follows:

[0081]

[0082] in, represents the rate of change of the estimated value of the actuator network attack input signal received by the ith following UAV at time t, represents the rate of change of the estimated value of the sensor network attack input signal received by the ith following drone at time t. Then, according to equations (13), (14), (15), we can get:

[0083]

[0084] in, represents the transposed matrix of the control input matrix of the state estimator of the i-th following drone, represents the transposed matrix of the decision matrix of the i-th following drone, represents the rate of change of the Lyapunov stability analysis function. According to formula (16), it can be seen that the design satisfies stability.

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

[0086]

[0087] in, represents the rate of change of the heterogeneous compensation state value of the i-th following UAV’s heterogeneous compensator at time t, represents the heterogeneous compensation state value of the heterogeneous compensator of the i-th following drone at time t, represents the control input of the heterogeneous compensator of the i-th following UAV at time t, represents the rate of change of the heterogeneous compensation state value of the leading UAV’s heterogeneous compensator at time t, represents the heterogeneous compensation state value of the leading UAV’s heterogeneous compensator at time t, represents the controller input of the heterogeneous compensator of the leading UAV at time t, and E1 represents the system matrix of the heterogeneous compensator. represents the system matrix of the heterogeneous compensator, Input matrix of the heterogeneous compensator.

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

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

[0090] u η0 (t) = K0η0(t) (20)

[0091] Among them, u ηi (t) represents the controller input of the heterogeneous compensator of the i-th following UAV at time t, represents the relative state difference between the ith follower drone and the drones within its communication range, j represents the jth follower drone within the communication range of the ith follower drone, N represents the total number of follower drones that have communication with the ith follower drone, and a ij represents the communication weight coefficient between the ith following drone and the jth following drone within the communication range, a i0 represents the communication weight coefficient between the ith follower UAV and the leader UAV, and K represents ξ i (t) control gain, represents the compensation vector of the heterogeneous cluster formation of the i-th following UAV at time t, Indicates h ηi (t) additional compensation value, u η0 (t) represents the controller input of the heterogeneous compensator of the leading UAV at time t, η0(t) represents the compensation state value of the heterogeneous compensator of the leading UAV at time t, and K0 represents u η0 (t) is the control gain of

[0092] Event triggering technology reduces the communication frequency and the use of communication resources by designing conditional triggering communication. The event trigger of the compensator of the i-th following drone can be designed as:

[0093]

[0094] in, is η i The estimate of (t), is the sth event triggering time of the heterogeneous compensator of the i-th following UAV.

[0095] By introducing the estimator, equation (19) can be transformed into:

[0096]

[0097] in,

[0098] Substituting (23) into (17), we can obtain:

[0099]

[0100] The compact form of considering heterogeneous UAV clusters can be expressed as:

[0101]

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

[0103] Estimation Error It can be expressed as:

[0104]

[0105] Based on stability analysis, the event triggering conditions are designed as follows:

[0106]

[0107] Among them, ε ηi (t) represents the state estimation error of the i-th following UAV at time t, represents the average weight of the state estimation error and relative state difference of the i-th following UAV, represents the estimated value of the relative state difference between the ith following UAV and the UAVs within its communication range, χ∈R ＞0Represents the state estimation error threshold.

[0108] In order to avoid Zeno behavior, it is also necessary to design the minimum time interval for event triggering:

[0109]

[0110] in, is the time interval for event triggering.

[0111] Based on error analysis, the minimum time interval for event triggering The design is as follows:

[0112]

[0113] in, represents the triggering time of the s+1th event of the heterogeneous compensator of the i-th following drone, represents the triggering time of the sth event of the heterogeneous compensator of the i-th following drone, represents the time interval from the sth event triggering time of the heterogeneous compensator of the i-th following UAV to the time when the s+1th event triggering condition is met, Indicates the minimum time interval for event triggering. Represents the average value of the compensation value of the heterogeneous UAV formation.

[0114] Step 303: Design a distributed heterogeneous security control protocol. The purpose of designing a distributed heterogeneous security control protocol is to enable the heterogeneous UAV cluster to complete the established formation flight mission under the influence of network attacks.

[0115] Therefore, in combination with step 101, step 301 and step 302, a design is provided to follow the drone i in the time period The safety fault-tolerant control protocol model is:

[0116]

[0117] Among them, u ci (t) represents the control input of the ith following drone at time t, G represents the state estimation value of the i-th following UAV at time t obtained by correcting the initial state estimation value and the state estimation correction matrix. 1i represents the gain coefficient of the compensation state estimate, η i (t) represents the compensation state value of the heterogeneous compensator of the i-th following UAV at time t, G 2i represents the gain coefficient of the heterogeneous compensation value, represents the estimated value of the actuator network attack input received by the ith follower drone at time t, G 3i express The gain factor, v i (t) represents the formation compensation value of the follower UAV i at time t, u0(t) represents the control input of the leading UAV at time t, G0 represents the gain coefficient of the control input of the leading UAV, and x0(t) represents the internal state vector of the leading UAV at time t.

[0118] In another exemplary embodiment of the present application, after the above step 303, the following step may also be included: designing a safety fault-tolerant control protocol gain.

[0119] The heterogeneous compensator errors of the follower UAV i and the leader UAV are defined as:

[0120]

[0121] Among them, i represents the compensator matrix of the ith follower drone, Π0 represents the compensator matrix of the leading drone, and Π i and Π0 are the matrices that need to be designed.

[0122] If the design related items satisfy the following conditions (32), the goal of (4) can be achieved:

[0123]

[0124] Among them, Γ i represents the gain coefficient matrix of the i-th following drone, Γ i is a designable matrix, Π i , Π0 and Γ i For any matrix that satisfies equation (32), the following derivation is performed:

[0125] Derivate (31a) with respect to time and design G 2i =Γ i -G 1i Π i , Substitute (32) into the first condition B i v i (t)-Π i E2V ηi (t) = 0, the second condition A i Π i +B i Γ i -Π i E1=0, we get:

[0126] ˙

[0127]

[0128] Design A i +B iG 1i and A0+B0G0 are Hurwitz matrices, and K0 is designed so that A0+B0G0-E1-E2K0=0. Substituting into (33) we get:

[0129]

[0130] From formula (34), we can get Substituting it into formula (31), we can obtain:

[0131]

[0132] Furthermore, we can get:

[0133]

[0134] The third condition h in (32) is i (t) = C i Π i h ηi (t), the fourth condition C i Π i -C0Π0=0 and (35) is substituted into (4) to obtain:

[0135]

[0136] Among them, r i (t) = C0Π0e i (t), where e i (t) = η i (t)-h ηi (t)-η0(t) is bounded, so condition (4) can be satisfied.

[0137] In another exemplary embodiment of the present application, Figure 2 The figure shows the network communication topology of a heterogeneous drone cluster, where drone 0 is the leader drone and drones 1-5 are follower drones. Among them, drones {1, 2, 4} are isomorphic, and drones {3, 5} are isomorphic. {1, 2, 4} and {3, 5} are heterogeneous, and are distinguished by circles and regular hexagons. The red-marked drones {1, 2, 3} are attacked by the network, of which drone 1 is attacked by the sensor, drone 2 is attacked by the actuator, and drone 3 is attacked by both the sensor and the actuator.

[0138] like Figure 3 As shown in the figure, the state transformation diagram of the entire heterogeneous UAV cluster is given. Figure 3(a) is the initial state diagram of the six drones in the heterogeneous drone cluster, where random values ​​are used. When there is no fault, the heterogeneous drone cluster system can complete the desired formation in about 15 seconds through the elastic safety control model control, as shown in Figure 3 (b) as shown. Figure 3 (c) is the formation diagram of the heterogeneous UAV cluster in a stable state. When the network is interrupted at the 20th second, the original formation of the heterogeneous UAV cluster is destroyed, such as Figure 3 As shown in (d). Due to the design of the heterogeneous formation elastic control protocol, the formation can be restored in about 35 seconds through the elastic safety control model, as shown in Figure 3 (e) The formation of the heterogeneous UAV cluster remained stable for 50 seconds. Figure 3 (f) as shown.

[0139] like Figure 4 (a) and Figure 4 (b) shows the tracking error and compensator output value of the heterogeneous UAV cluster system in the case of a network attack. It can be seen that the error is zero around 15 seconds, that is, the formation is completed. When the 20s attack occurs, the error suddenly increases, and then returns to zero by the elastic control protocol. Figure 4 In (b), the compensator output value of follower drone No. 1 is compensator compensation value 1, the compensator output value of follower drone No. 2 is compensator compensation value 2, the compensator output value of follower drone No. 3 is compensator compensation value 3, the compensator output value of follower drone No. 4 is compensator compensation value 4, and the compensator output value of follower drone No. 5 is compensator compensation value 5.

[0140] like Figure 5 (a) Figure 5 (b) Figure 5 (c) and Figure 5 (d) shows the estimated state values ​​of the actuator attacks on UAV 1 over time. State estimates of drone 2 as a result of sensor attacks over time State estimates of actuator attacks on drone 3 over time and the estimated state of the sensor attack on UAV 3 over time

[0141] like Figure 6 The figure shows the time intervals of the triggering events of each follow-up drone, where each point indicates that the follow-up drone has communicated once.

[0142] The present application also provides an application scenario, which applies the above-mentioned elastic safety control method for heterogeneous drone clusters. Specifically: The elastic safety control method for heterogeneous drone clusters provided in this embodiment can be applied in modern military combat scenarios. 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 drone cluster tracks and locks important enemy targets based on the collected intelligence information. Through elastic and safe formation control, the drone cluster can maintain a stable formation form, and even in the case of network attacks or physical failures, it can quickly adjust the flight posture and combat strategy to ensure the smooth completion of the strike mission. After the target tracking and attack link, the system enters the battlefield situation assessment link, and the commander formulates the next combat plan. The elastic and safe control method for heterogeneous drone clusters provided in this embodiment belongs to the target tracking and attack link in modern military operations. Specifically, by improving the elastic and safe formation control capability of heterogeneous drone clusters, it is ensured that the drone cluster can maintain a stable formation form in a complex battlefield environment, effectively respond to network attacks and physical failures, and improve the reliability and safety of combat missions.

[0143] Based on the same inventive concept, the embodiment of the present application also provides a device for implementing the above-mentioned elastic safety control of heterogeneous drone clusters. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiments of one or more heterogeneous drone cluster elastic safety control devices provided below can refer to the limitations of the heterogeneous drone cluster elastic safety control method above, and will not be repeated here.

[0144] In an exemplary embodiment, Figure 5 As shown, a heterogeneous UAV cluster elastic safety control device is provided, including:

[0145] The dynamic model construction module 401 is used to construct a UAV cluster dynamic model under network attack conditions based on dynamic equations; the UAV cluster dynamic model includes a leading UAV dynamic model and multiple following UAV dynamic models.

[0146] The elastic safety control model construction module 402 is used to construct a elastic safety control model based on the UAV cluster dynamics model; the elastic safety 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 UAV under network attack conditions; the heterogeneous compensator model is used to calculate the heterogeneous state compensation value of each UAV; the safety fault-tolerant control protocol model is used to calculate the control input information of each UAV under network attack conditions based on the state estimator model and the heterogeneous compensator model.

[0147] The elastic security control execution module 403 is used to perform elastic security control on the heterogeneous drone cluster under network attack conditions according to the elastic security control model.

[0148] 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: Figure 7 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 heterogeneous drone cluster data. 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 safe control of a heterogeneous drone cluster is implemented.

[0149] Those skilled in the art will understand that Figure 7 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. In an exemplary embodiment, a computer device is 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.

[0150] 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 method embodiments are implemented.

[0151] 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.

[0152] 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.

[0153] Those skilled 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), magnetoresistive 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).

[0154] 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.

[0155] 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.

[0156] 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 elastic safety control of heterogeneous drone clusters, characterized in that: The heterogeneous UAV cluster elastic safety control method includes: Based on the dynamic equation, a UAV cluster dynamics model under network attack conditions is constructed; the UAV cluster dynamics model includes a leading UAV dynamics model and multiple following UAV dynamics models; A resilient safety control model is constructed based on the UAV cluster dynamics model; the resilient safety 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 UAV under network attack conditions; the heterogeneous compensator model is used to calculate the heterogeneous state compensation value of each UAV; the safety fault-tolerant control protocol model is used to calculate the control input information of each UAV under network attack conditions according to the state estimator model and the heterogeneous compensator model; Based on the elastic security control model, elastic security control is performed on heterogeneous drone clusters under network attack conditions.

2. The elastic safety control method for heterogeneous UAV clusters according to claim 1 is characterized in that: The dynamic model of the following drone is: y i (t)=C i x i (t)+D i ψ si (t); in, represents the rate of change of the internal state vector of the ith following drone at time t, x i (t) represents the internal state vector of the i-th following drone at time t, A i represents the dynamic matrix of the ith follower drone, B i represents the input matrix of the i-th following drone, u ci (t) represents the control input of the i-th following UAV at time t, ψ ui (t) represents the actuator network attack input signal received by the i-th follower drone at time t, y i (t) represents the decision state vector of the i-th following UAV at time t, ψ si (t) represents the sensor network attack input signal received by the i-th following drone at time t, C i represents the decision matrix of the i-th following drone, D i represents the transmission matrix of the i-th following UAV.

3. The elastic safety control method for heterogeneous UAV clusters according to claim 1 is characterized in that: The dynamic model of the leader UAV is: y0(t)=C0x0(t); in, represents the rate of change of the internal state vector of the leader UAV at time t, x0(t)∈ 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) represents the decision state vector of the leader UAV at time t, A0∈ represents the dynamics matrix of the leader UAV, B0∈ represents the input matrix of the leader UAV, and C0 represents the decision matrix of the leader UAV.

4. The elastic safety control method for heterogeneous UAV clusters according to claim 1 is characterized in that: The state estimator model is: in, represents the rate of change of the estimated state of the i-th following UAV at time t, F i represents the system matrix of the state estimator of the i-th following UAV, θ i (t) represents the initial state estimate of the i-th following UAV at time t, J i represents the control input matrix of the state estimator of the i-th following UAV, u ci (t) represents the control input of the ith following drone at time t, represents the estimated value of the actuator network attack input signal received by the ith following UAV at time t, R i represents the output error feedback matrix of the state estimator of the i-th following UAV, y i (t) represents the decision state vector of the i-th following UAV at time t, D i represents the transmission matrix of the ith following UAV, represents the estimated value of the sensor network attack input signal received by the i-th following drone at time t, H represents the state estimation value of the i-th following UAV at time t obtained by correcting the initial state estimation value and the state estimation correction matrix. i represents the state estimation correction matrix of the state estimator of the i-th following UAV.

5. The elastic safety control method for heterogeneous UAV clusters according to claim 1 is characterized in that: The heterogeneous compensator model is: in, represents the rate of change of the heterogeneous compensation state value of the i-th following UAV’s heterogeneous compensator at time t, η i (t) represents the heterogeneous compensation state value of the heterogeneous compensator of the i-th following UAV at time t, u ηi (t) represents the control input of the heterogeneous compensator of the i-th following UAV at time t, represents the rate of change of the heterogeneous compensation state value of the leading UAV’s heterogeneous compensator at time t, η0(t) represents the heterogeneous compensation state value of the leading UAV’s heterogeneous compensator at time t, u η0 (t) represents the controller input of the heterogeneous compensator of the leading UAV at time t, E1 represents the system matrix of the heterogeneous compensator, and E2 represents the input matrix of the heterogeneous compensator.

6. The elastic safety control method for heterogeneous UAV clusters according to claim 1 is characterized in that: The safety fault-tolerant control protocol model is: u0(t)=G0x0(t); Among them, u ci (t) represents the control input of the ith following drone at time t, G represents the state estimation value of the i-th following UAV at time t obtained by correcting the initial state estimation value and the state estimation correction matrix. 1i represents the gain coefficient of the compensation state estimate, η i (t) represents the compensation state value of the heterogeneous compensator of the i-th following UAV at time t, G 2i represents the gain coefficient of the heterogeneous compensation value, represents the estimated value of the actuator network attack input received by the ith follower drone at time t, G 3i express The gain factor, v i (t) represents the formation compensation value of the follower UAV i at time t, u0(t) represents the control input of the leading UAV at time t, G0 represents the gain coefficient of the control input of the leading UAV, and x0(t)∈ represents the internal state vector of the leading UAV at time t.

7. The elastic safety control method for heterogeneous UAV clusters according to claim 4 is characterized in that: The calculation formulas for the estimated values ​​of the actuator network attack inputs received by the following drone and the estimated values ​​of the sensor network attack inputs received by the following drone are as follows: Among them, ε i (t) represents the decision state vector error of the i-th following UAV at time t, represents the estimated value of the decision state vector of the ith following UAV at time t, ψ si (t) represents the sensor network attack input signal received by the i-th following drone at time t, represents the rate of change of the estimated value of the actuator network attack input signal received by the i-th following UAV at time t, α i represents the ratio of the estimated values ​​of the actuator network attack input signal, represents the transposed matrix of the control input matrix of the state estimator of the i-th following drone, represents the transposed matrix of the decision matrix of the i-th following drone, P 1i express The only positive solution matrix, Q 1i represents an arbitrary symmetric positive definite matrix, represents the rate of change of the estimated value of the sensor network attack input signal received by the i-th following drone at time t, β i represents the ratio of the estimated value of the input signal of the sensor network attack, represents the pseudo-inverse matrix of the decision matrix of the i-th following drone, R 1i represents the first matrix component of the numerical decomposition of the output estimation matrix of the i-th following UAV, represents the rate of change of the Lyapunov stability analysis function, represents the transpose of the decision state vector error of the i-th following UAV at time t.

8. A heterogeneous UAV cluster elastic safety control device, characterized in that: The heterogeneous UAV cluster elastic safety control device comprises: A dynamic model building module is used to build a UAV cluster dynamic model under network attack conditions based on a dynamic equation; the UAV cluster dynamic model includes a leading UAV dynamic model and multiple following UAV dynamic models; A flexible safety control model construction module is used to construct a flexible safety control model based on a UAV cluster dynamics model; the flexible safety control model includes: a state estimator model, a heterogeneous compensator model and a safe fault-tolerant control protocol model; the state estimator model is used to estimate the internal state of each UAV under network attack conditions; the heterogeneous compensator model is used to calculate the heterogeneous state compensation value of each UAV; the safe fault-tolerant control protocol model is used to calculate the control input information of each UAV under network attack conditions according to the state estimator model and the heterogeneous compensator model; The elastic security control execution module is used to perform elastic security control on heterogeneous drone clusters under network attack conditions according to the elastic security control model.

9. 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 elastic and secure control method for heterogeneous drone clusters as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the elastic and safe control method of a heterogeneous UAV cluster described in any one of claims 1 to 7 is implemented.

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