A fault-tolerant cooperative control method for UAVs against link attacks and faults

Abstract

This application discloses a fault-tolerant cooperative control method for UAVs in response to link attacks and faults, relating to the field of UAV technology. This method constructs a virtual neighbor based on the latest information received by the follower UAV before encountering a link DoS network attack. The designed virtual neighbor satisfies that the derivative of the positive direction position reference state, the lateral direction position state, and the upward direction position state remain unchanged from before the link DoS attack. Based on the virtual neighbor state, a passive communication topology switching mechanism under link DoS network attacks is designed, and a resilient fault-tolerant cooperative controller based on fault parameter estimation is further designed. This enables the follower UAV to achieve passive link switching when it encounters a link DoS network attack and cannot receive neighbor information. Simultaneously, it can perform parameter estimation and fault-tolerant control for actuator faults. This method has significant practical implications and promising application prospects in the field of multi-UAV formation cooperative fault-tolerant and resilient control.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a resilient, fault-tolerant, and collaborative control method for swarm UAVs against link DoS network attacks and actuator failures. Background Technology

[0002] With the rapid development of drone technology, swarm drone systems have been widely applied to diverse fields such as precision agriculture, urban air traffic management, and disaster relief. To achieve collaborative operations of swarm drones across multiple fields and ensure the stability and safety of mission execution, distributed collaborative control strategies have become a core focus and cutting-edge research direction in drone technology development.

[0003] In the distributed collaborative control architecture of swarm drones, drones rely on communication networks for internal information exchange, enabling each drone to obtain real-time status data from neighboring drones and thus execute tasks collaboratively. However, this communication network-dependent collaborative mechanism also faces severe external security challenges, particularly DoS (Denial of Service) network attacks. These attacks aim to sever or interfere with normal communication links between drones, hindering information transmission and preventing drones from obtaining the status of neighboring drones in a timely manner, thus disrupting the overall swarm's collaboration and posing a significant threat to the swarm's security and stability. Furthermore, actuators, as key components for executing flight commands, are responsible for driving various drone actions. Therefore, drone actuator failure is a potential and serious risk in flight missions. When an actuator malfunctions, it may fail to accurately execute commands from the flight control system, causing the drone to deviate from its planned flight path, become unstable in flight attitude, or even crash. Summary of the Invention

[0004] This application addresses the aforementioned problems and technical requirements by proposing a resilient and fault-tolerant collaborative control method for unmanned aerial vehicles (UAVs) that addresses both link DoS network attacks and actuator failures.

[0005] The technical solution of this application is as follows:

[0006] A fault-tolerant cooperative control method for unmanned aerial vehicles (UAVs) against link attacks and faults, the control method comprising the following steps:

[0007] S1 constructs a virtual neighbor drone based on information received by the follower drone before it encounters a link-DoS network attack.

[0008] S2 determines the specific value of the communication link weight based on the state design mechanism of the virtual neighbor machine under the passive switching mechanism of the communication topology under the DoS network attack.

[0009] S3, based on a passive topology communication switching mechanism and combined with the dynamic model of the UAV, obtains a collaborative resilience fault-tolerant collaborative controller for follower UAVs under the consideration of link DoS network attacks and actuator failures.

[0010] Furthermore, S1 specifically refers to:

[0011] S11 defines a swarm drone system consisting of one real or virtual leader drone and N real follower drones, where any follower drone is i, 1 ≤ i ≤ N:

[0012] Define the full-state tracking deviation of follower drone i

[0013] Among them, X 1,i X is the actual location of the follower drone i. 1,j This indicates the actual position of the j-th adjacent aircraft. X 1,j D represents the actual position of the j-th adjacent aircraft. i,j This represents the relative position between the i-th drone and the j-th drone;

[0014] Y represents the location of the j-th neighboring machine obtained when encountering a DoS attack. 1,i Y represents the roll angle, angle of attack, and sideslip angle of the drone. 1,i =[μ i ,α i ,β i ], Y 2,i The three-axis angular velocity Y of the drone 2,i =[p i ,q i ,r i ], a i,j Indicates the weight of the inter-machine communication link;

[0015] This indicates the weight of the communication link under the link switching mechanism when encountering a DoS attack. For virtual control signals;

[0016] S12 design addresses control deviations for UAV cooperative control. c3, c6, and c7 are the positive definite parameters of the controller to be designed.

[0017] Furthermore, S2 specifically refers to:

[0018] S21 uses the neighbor information obtained by the follower drone i through the cluster drone communication network when it has not encountered a link DoS network attack as the virtual neighbor information during the link DoS network attack.

[0019] S22 constructs the following intermediate variables:

[0020]

[0021] in, Indicates the communication link weight a i,j No link-DoS network attack was encountered at time t; τ represents the latest updated position status of the j-th UAV; τ0 represents the system sampling period;

[0022] S23 Design Communication Link Weights a i,j Passive handover mechanism to obtain communication link weight for:

[0023]

[0024] Among them, a i,j To determine the actual communication link weight under a link-DoS network attack, when a i,j When encountering a DoS network attack, a i,j Will be forced by a i,j =1 becomes a i,j =0, based on the actual communication link weight a i,j .

[0025] Furthermore, S3 specifically includes:

[0026] The dynamic model of the follower drone i is established by S31 as follows:

[0027]

[0028] Where x i y i , z i V represents the position of the i-th drone. i , χ i γ i μ represents the speed, trajectory angle, and ground trajectory angle of the UAV. i α i ,β i These are the roll angle, angle of attack, and sideslip angle. i q i r i Let i represent the angular velocity of the i-th drone. Let be the aerodynamic torque of the i-th UAV;

[0029]

[0030] Where, δ ia δ ie δir These represent the aileron, elevator, and rudder deflections of the i-th UAV, respectively; T i D i L i Y i Represents thrust, drag, lift, and lateral force; Q i s i b i c i This represents dynamic pressure, wing area, wingspan, and mean aerodynamic chord.

[0031] in, c i2 =(I xi -I yi +I zi )I xzi / Σ i c i3 =I zi / ∑ i c i4 =I xzi / ∑ i c i5 =(I zi -I xi ) / I yi c i6 =I xzi / I yi c i7 =1 / I yi , c i9 =I xi / Σ i , I xi I yi I zi I represents the moment of inertia. xzi For the product of rotational inertia, C iD0 C iDα C iDα2 C iY0 C iYβ C iL0 C iLα C il0 C ilβ , C ilp C ilr C im0 C imα , C imq C in0 C inβ , C inp , and C inrIndicates the relevant aerodynamic parameters;

[0032] S32 then transforms the dynamic model of the follower drone i to obtain:

[0033]

[0034] Among them, f 1,i f 2,i f 3,i f 4,i g 1,i g 2,i g 3,i g 4,i The corresponding control vector and control matrix are derived from the dynamic model of UAV i; X 1,i =[x i ,y i ,h i ] T X 2,i =[V i ,χ i ,γ i ] T ψ i =[T i ,α i sinμ i ,α i cosμ i ] T Y 1,i =[μ i ,α i ,β i ] T Y 2,i =[p i ,q i ,r i ] T u i =[δ ia ,δ ie ,δ ir ] T At the same time, there exists:

[0035]

[0036] S33 addresses the tracking error e i,1 Taking the second derivative, we get:

[0037]

[0038] in, By taking the derivative of the dynamic model, we obtain:

[0039]

[0040] Design sliding surfaces in the following forms:

[0041]

[0042] For sliding surface S i,1 Differentiation yields:

[0043]

[0044] S34 according to S i,1 The expression and the control deviation for UAV cooperative control defined in this application The following virtual control signal is designed:

[0045]

[0046] S35 based on virtual control signals The resilient and fault-tolerant collaborative controller for the follower drone i, considering link DoS network attacks and actuator failures, is as follows:

[0047]

[0048] Furthermore, based on virtual control signals The virtual control signal for the follower drone is designed as follows:

[0049]

[0050] Furthermore, the resilient fault-tolerant collaborative controller also includes actuator fault parameters. and Designed separately for estimation and The adaptive law:

[0051]

[0052] The defined variables satisfy the following relationship:

[0053]

[0054]

[0055] The beneficial technical effects of this application are:

[0056] This application discloses a resilient and fault-tolerant collaborative control method for swarm drones against link DoS network attacks and actuator failures. This method enables follower drones to passively switch links when they encounter link DoS network attacks and lose access to neighbor information, thus ensuring the stability of the swarm drones under such attacks. Simultaneously, it enables parameter estimation and fault-tolerant control for actuator failures, demonstrating significant practical value and promising application prospects in the collaborative fault-tolerant and resilient control of multi-drone formations. Attached Figure Description

[0057] Figure 1 This is a topology diagram of a clustered drone communication network in an example.

[0058] Figure 2 This is a schematic diagram of the information flow of the resilient fault-tolerant collaborative controller constructed in one embodiment of this application;

[0059] Figure 3 This is a control block diagram of a clustered UAV resilient fault-tolerant collaborative control method in one embodiment of this application;

[0060] Figure 4 This is a timing diagram of network attack signals applied to various follower drones in a simulation example;

[0061] Figure 5 yes Figure 4 Simulation diagrams of the flight trajectories of each follower drone in the simulation example;

[0062] Figure 6 yes Figure 4 The simulation example shows the fault parameter estimation results for each follower UAV.

[0063] Figure 7 yes Figure 4 The simulation results of the tracking error of each follower UAV in the simulation example are shown in the figure. Detailed Implementation

[0064] The specific embodiments of this application will be further described below with reference to the accompanying drawings.

[0065] This application discloses a resilient, fault-tolerant, and collaborative control method for swarm drones against link-DoS network attacks and actuator failures. In the swarm drone system, a communication network is established among the drones, with the leader drone at the center of the communication topology framework. Under this architecture, the leader drone acts as the central node, establishing unidirectional communication links with some follower drones, transmitting position information from the leader to the followers. The follower drones then engage in bidirectional or unidirectional communication interactions as needed. For example, in... Figure 1In this example, the swarm drones include one leader drone (denoted as UAV#Leader) and three follower drones, denoted as UAV#1, UAV#2, and UAV#3 respectively. UAV#Leader establishes one-way communication connections with UAV#1 and UAV#2 respectively, UAV#1 establishes a one-way communication connection with UAV#2, UAV#2 establishes a one-way communication connection with UAV#3, and UAV#1 establishes a two-way communication connection with UAV#3.

[0066] Please refer to Figure 2 The information flow diagram shown and Figure 3 The control block diagram shown illustrates that the resilient and fault-tolerant cooperative control method for swarm drones includes performing the following operation on any follower drone i in the swarm, where the integer parameter 1 ≤ i ≤ N:

[0067] Based on the state tracking deviation of the follower drone i For the topology of a swarm drone communication network, in Figure 1 In the example, the link weight a 0,1 =a 0,2 =a 1,2 =a 1,3 =a 2,3 =a 3,1 =1, the rest a i,j =0. For any follower drone i in a swarm of drones, its actual position X during flight is 0. 1,i =[x i ,y i ,h i ], x i y i and h i Let X represent the actual position of the i-th follower UAV on the three coordinate axes of the ground coordinate system, respectively. 1,j The actual location of other drones j is obtained through a swarm drone communication network. i,j It is the expected relative position between follower drone i and other drone j, and is a known quantity.

[0068] Considering link-based DoS network attacks, and when the communication link weight a i,j When encountering a DoS network attack, the communication link resources between follower drone i and follower drone j will be occupied by the intruder. Follower drone i will be unable to obtain the status information of drone j through the swarm drone communication network, and therefore will be unable to obtain the position reference status through the swarm drone communication network in the traditional way mentioned above.

[0069] Therefore, this application is based on the communication link weight a. i,jThe drone is designed to meet the following flight requirements of a virtual follower drone j when it is not under a DoS attack. A passive link switching mechanism is designed under DoS attack to ensure the safe flight of the drone when it is under a DoS attack.

[0070] Communication link weight a i,j When encountering a link-DoS network attack, the virtual follower drone j must meet the following flight requirements: (1) When encountering a link-DoS network attack, the virtual follower drone j will maintain its current forward direction of flight. At this time, that is, the derivative of the position reference state of the follower drone j in the positive direction remains unchanged before the link-DoS network attack. (2) When encountering a link-DoS network attack, the virtual follower drone j will not yaw or ascend / descend. That is, the derivative of the position reference state of the follower drone j in the side direction and the ascending / descending direction is 0.

[0071] Based on the above characteristics, the following intermediate variables are first constructed:

[0072]

[0073] in, Indicates the communication link weight a i,j No link-DoS network attack was encountered at time t. Let represent the latest updated position state of the j-th UAV. τ0 represents the system sampling period. Further, design the communication link weight a. i,j Passive handover mechanism to obtain communication link weight for:

[0074]

[0075] Among them, a i,j To determine the actual communication link weight under a link-DoS network attack, when a i,j When encountering a DoS network attack, a i,j Will be forced by a i,j =1 becomes a i,j =0, based on the actual communication link weight a i,j This patent designs a passive topology communication switching mechanism to achieve smooth switching of communication links. Furthermore, c1, c2, p, and q are parameters to be designed.

[0076] Then, based on the above passive topology communication switching mechanism, and combined with the dynamic model of UAV i, the cooperative resilience and fault-tolerant cooperative controller of follower UAV i under the consideration of link DoS network attacks and actuator failures is obtained, including the following steps:

[0077] (1) First, the dynamic model of the follower drone i is established as follows:

[0078]

[0079] Where x i y i , z i V represents the position of the i-th drone. i , χ i γ i This represents the drone's speed, flight path angle, and ground trajectory angle. μ i α i ,β i These are the roll angle, angle of attack, and sideslip angle. i q i r i Let represent the angular rate of the i-th drone. Let be the aerodynamic torque of the i-th UAV.

[0080]

[0081] Where, δ ia δ ie δ ir These represent the deflection of the aileron, elevator, and rudder of the i-th UAV, respectively.

[0082] T i D i L i Y i This represents thrust, drag, lift, and lateral force. Q i s i b i c i This represents dynamic pressure, wing area, wingspan, and mean aerodynamic chord. ρ represents the density of air. Wherein, c i2 =(I xi -I yi +I zi )I xzi / Σ i c i3 =I zi / ∑ i c i4 =I xzi / ∑ i c i5 =(I zi -I xi ) / I yi c i6 =I xzi / I yi c i7 =1 / I yi , c i9 =I xi / ∑ i , I xi I yi I zi I represents the moment of inertia. xzi C is the product of rotational inertia. iD0 C iDα C iDα2 C iY0 C iYβ C iL0 C iLα C il0 C ilβ , C ilp C ilr C im0 C imα , C imq C in0 C inβ , C inp , and C inr This indicates the relevant aerodynamic parameters.

[0083] (2) Then, the dynamic model of the follower drone i is transformed to obtain:

[0084]

[0085] Among them, f 1,i f 2,i f 3,i f 4,i g 1,i g 2,i g 3,i g 4,i X represents the corresponding control vector and control matrix, derived from the dynamic model of UAV i. 1,i =[x i ,y i ,h i ] T X 2,i =[V i ,χ i ,γ i ] T ψ i =[T i ,α i sinμ i ,α i cosμ i ] T Y1,i =[μ i ,α i ,β i ] T Y 2,i =[p i ,q i ,r i ] T u i =[δ ia ,δ ie ,δ ir ] T At the same time, the following exists:

[0086]

[0087] (3) Regarding the tracking error e i,1 Taking the second derivative, we get:

[0088]

[0089] in, The derivative of the dynamic model in (2) can be obtained as follows:

[0090]

[0091] Control deviation for UAV cooperative control as defined in this application Design sliding surfaces in the following forms:

[0092]

[0093] For sliding surface S i,1 Differentiation yields:

[0094]

[0095] (4) According to S i,1 The expression and the control deviation for UAV cooperative control defined in this application The following virtual control signal is designed:

[0096]

[0097] Furthermore, based on virtual control signals The virtual control signal for the follower drone is designed as follows:

[0098]

[0099] (5) Based on virtual control signals The resilient and fault-tolerant collaborative controller for the follower drone i, considering link DoS network attacks and actuator failures, is as follows:

[0100]

[0101] Among them, u f,i This represents the controller input u after filtering. i Based on the designed resilient fault-tolerant collaborative controller, it can be seen that it also includes actuator fault parameters. and Therefore, separate designs were also created for estimation. and The adaptive law:

[0102]

[0103] The defined variables satisfy the following relationship:

[0104]

[0105]

[0106] Once the design is complete, the communication link weights can be designed using a passive link switching mechanism during the flight of the swarm of drones. To achieve communication topology switching under link-based DoS attacks, during the attack, virtual neighbor information is used to determine the reference state of the UAV, and the UAV's flight status is used to design an actuator fault parameter observer. and Estimating fault parameters ρ i and Then, the UAV control input u is obtained according to the resilient fault-tolerant co-controller. i The system then sends control input signals to the follower drone i for control. This leverages a resilient, fault-tolerant collaborative controller to perform resilient, fault-tolerant collaborative control on the follower drone i, mitigating the impact of link DoS attacks and actuator failures on the cluster's collaborative characteristics and improving the overall security of the cluster system.

[0107] To verify the effectiveness of the method in this application, based on Figure 1 The topology of the clustered UAV communication network shown was used to construct a simulation example. Figure 1 In the simulation, the leader drone is designated V0, and the follower drones are designated V1, V2, and V3. Directed arrows indicate the direction of information flow between the drones, and dashed lines represent communication links vulnerable to DoS attacks. In the simulation instance, follower drones V1, V2, and V3 encountered actuator failures.

[0108] The values ​​of the structural parameters and aerodynamic coefficients of each UAV are as follows:

[0109] The acceleration due to gravity is g a=9.8kg / m 2 All drones have a mass of m. i =25kg, air density is ρ=1.2205kg / m³ 3 All drones have a wing area of ​​s. i =1.463m 2 Both wingspans are b i =2.8956, the mean aerodynamic chord is c i =0.451, and the moment of inertia is I. xi =9.416, I yi =6.765, I zi =16.08, the product of moment of inertia is I xzi =0.1685. The aerodynamic parameter is C. iD0 =0.0225, C iDα =0.1002, C iDα2 =1.0778, C iY0 =0, C iYβ = -0.0046, C iL0 =0.2153, C iLα =4.6333, C il0 =0, C ilβ = -0.00054, C ilp =-0.49934, C ilr =0.07246, C im0 =-0.09543, C imα =-2.8206, C imq =-13.81889, C in0 =0, C inβ =0.00115, C inp =-0.03313, C inr = -0.04552.

[0110] The control parameters in resilient fault-tolerant collaborative control are designed as follows:

[0111] p=1.4, q=0.6, c1=0.6, c2=0.4, c3=[10,15,15] T , c4=diag([3,1.8,0.8]), c5=diag([10 -4 10 -4 10 -4 c6 = [1,1,1] T c7 = [1,1,1] T, c8=0.6, K1=diag([8,1,2.3]), K2=diag([5,5,5]), K3=diag([10,10,10]), K4=diag([10,10,10]), K5=diag([10,10,10]).

[0112] The initial motion parameters for the swarm drones are set as follows:

[0113] The trajectory of the virtual leader drone is used as the reference trajectory for the swarm of drones, and the reference trajectory is X. 1,0 = [20t + 20cos(0.05t + 0.1), 0, 0] T The initial state of all follower drones is set as: X 1,1 (0) = [5, 5, 1] T X 1,2 (0) = [5, 5, 1] T X 1,3 (0) = [6, 2, -8] T X 2,1 (0) = [19, -0.4, 0.2] T X 2,2 (0) = [16, 0.15, -0.1] T X 2,3 (0) = [21, -0.1, -0.1] T Y 1,1 (0) = [1.15, 0.25, -0.05] T Y 1,2 (0) = [0.8, -0.15, 0.1] T Y 1,3 (0) = [-0.2, 0.4, 0.1] T Y 2,1 (0)=Y 2,2 (0)=Y 2,3 (0) = [0,0,0] T .

[0114] The desired relative positions between each follower drone and the leader drone are set as follows (in meters):

[0115] D 1,0 =[0,-5sin(0.2t),-5cos(0.2t)] T D 2,0 =[0,-5sin(0.2t+2π / 3),-5cos(0.2t+2π / 3)] T D 3,0=[0,-5sin(0.2t+4π / 3),-5cos(0.2t+4π / 3)] T .

[0116] The applied actuator fault is:

[0117]

[0118] The simulated flight trajectories of each follower drone in the swarm are as follows: Figure 5 As shown. The actuator fault parameter results for each follower UAV are as follows. Figure 6 As shown. The tracking error e of each follower UAV. i =[e i1 ,e i2 ,e i3 ] T The simulation curve is as follows Figure 7 As shown.

[0119] The above descriptions are merely preferred embodiments of this application, and this application is not limited to the above embodiments. It is understood that other improvements and variations that can be directly derived or conceived by those skilled in the art without departing from the spirit and concept of this application should be considered to be included within the protection scope of this application.

Claims

1. A fault-tolerant cooperative control method for unmanned aerial vehicles (UAVs) against link attacks and faults, characterized in that, The control method includes the following steps: S1 constructs a virtual neighbor drone based on information received by the follower drone before it encounters a link-based DoS network attack; specifically, S1 is: S11 defines a swarm drone system consisting of one real or virtual leader drone and N real follower drones, where any follower drone is i, 1 ≤ i ≤ N: Define the full-state tracking deviation of follower drone i Among them, X 1,i X is the actual location of the follower drone i. 1,j This indicates the actual position of the j-th adjacent aircraft. D i,j This represents the relative position between the i-th drone and the j-th drone; Y represents the location of the j-th neighboring machine obtained when encountering a DoS attack. 1,i Y represents the roll angle, angle of attack, and sideslip angle of the drone. 1,i =[μ i ,α i ,β i ], Y 2,i The three-axis angular velocity Y of the drone 2,i =[p i ,q i ,r i ], a i,j Indicates the actual communication link weight; This indicates the communication link weight under the passive switching mechanism of the communication topology when encountering a DoS attack; For virtual control signals; S12 design addresses control deviations for UAV cooperative control. c3, c6, and c7 are the positive definite parameters of the controller to be designed. S2 determines the specific value of the communication link weight based on the state design mechanism of the virtual neighbor machine under a passive switching mechanism for the communication topology during a DoS network attack; S2 specifically includes: S21 uses the neighbor information obtained by the follower drone i through the cluster drone communication network when it has not encountered a link DoS network attack as the virtual neighbor information during the link DoS network attack. S22 constructs the following intermediate variables: in, Indicates the communication link weight a i,j No link-DoS network attack was encountered at time t; τ represents the latest updated position status of the j-th UAV; τ0 represents the system sampling period; S23 Design the actual communication link weight a i,j Passive handover mechanism to obtain communication link weight for: Among them, a i,j To determine the actual communication link weight under a link-DoS network attack, when a i,j When encountering a DoS network attack, a i,j Will be forced by a i,j =1 becomes a i,j =0, based on the actual communication link weight a i,j ; Based on the passive switching mechanism of communication topology, S3 combines the dynamic model of UAVs to obtain a collaborative resilience fault-tolerant collaborative controller for follower UAVs under the consideration of link DoS network attacks and actuator failures.

2. The method for fault-tolerant cooperative control of unmanned aerial vehicles (UAVs) against link attacks and faults according to claim 1, characterized in that, Specifically, S3 is: The dynamic model of the follower drone i is established by S31 as follows: Where, x i y i h i V represents the position of the i-th drone. i , χ i γ i μ represents the speed, trajectory angle, and ground trajectory angle of the UAV. i α i ,β i For roll angle, angle of attack, and sideslip angle; p i q i r i Let i represent the angular velocity of the i-th drone. Let be the aerodynamic torque of the i-th UAV; Where, δ ia δ ie δ ir These represent the aileron, elevator, and rudder deflections of the i-th UAV, respectively; T i D i L i Y i Represents thrust, drag, lift, and lateral force; Q i s i b i c i This represents dynamic pressure, wing area, wingspan, and mean aerodynamic chord. in, c i2 =(I xi -I yi +I zi )I xzi / Σ i c i3 =I zi / Σ i c i4 =I xzi / Σ i c i5 =(I zi -I xi ) / I yi c i6 =I xzi / I yi c i7 =1 / I yi , c i9 =I xi / Σ i , I xi I yi I zi I represents the moment of inertia. xzi For the product of rotational inertia, C iD0 C iDα C iDα2 C iY0 C iYβ C iL0 C iLα C il0 C ilβ , C ilp C ilr C im0 C imα , C imq C in0 C inβ , C inp , and C inr Indicates the relevant aerodynamic parameters; S32 then transforms the dynamic model of the follower drone i to obtain: Among them, f 1,i f 2,i f 3,i f 4,i g 1,i g 2,i g 3,i g 4,i The corresponding control vector and control matrix are derived from the dynamic model of UAV i; X 1,i =[x i ,y i ,h i ] T X 2,i =[V i ,χ i ,γ i ] T ψ i =[T i ,α i sinμ i ,α i cosμ i ] T Y 1,i =[μ i ,α i ,β i ] T Y 2,i =[p i ,q i ,r i ] T u i =[δ ia ,δ ie ,δ ir ] T At the same time, there exists: S33 addresses the tracking error e i,1 Taking the second derivative, we get: in, By taking the derivative of the dynamic model, we obtain: Design sliding surfaces in the following forms: For sliding surface S i,1 Differentiation yields: S34 according to S i,1 Expressions and control deviations for UAV cooperative control The following virtual control signal is designed: S35 based on intermediate virtual control signal The resilient and fault-tolerant collaborative controller for the follower drone i, considering link DoS network attacks and actuator failures, is as follows:

3. The method for fault-tolerant cooperative control of unmanned aerial vehicles (UAVs) against link attacks and faults according to claim 2, characterized in that, According to virtual control signals The virtual control signal for the follower drone is designed as follows:

4. The method for fault-tolerant cooperative control of unmanned aerial vehicles (UAVs) against link attacks and faults according to claim 2, characterized in that, The resilient fault-tolerant co-controller also includes actuator fault parameters. and Designed separately for estimation and The adaptive law:

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