Event-based unmanned aerial vehicle formation intermittent control method, system, equipment and medium

Abstract

The invention discloses an event-based unmanned aerial vehicle formation intermittent control method, system and device, and a medium, and relates to the technical field of unmanned aerial vehicle control. The method comprises the following steps: constructing a kinetic model of the unmanned aerial vehicle formation; constructing a state space model of the unmanned aerial vehicle formation under the spoofing attack based on a graph theory; and constructing an intermittent control strategy based on the observer. The dynamic model and the state space model of the unmanned aerial vehicle formation are finely constructed by using a mathematical tool of a graph theory, an efficient and economical intermittent control strategy based on the observer is designed on this basis, and the unmanned aerial vehicle formation can be accurately controlled by accurately defining an expected relative position and a tracking error and carefully designing the spoofing attack observer. According to the method, external interference can be detected and compensated in real time, so that the calculation amount and energy consumption are greatly reduced while the control performance is maintained, and in addition, the energy efficiency of the unmanned aerial vehicle formation is further optimized by intelligently adjusting and controlling the input frequency through the constructed intermittent control strategy.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle control technology, and in particular to an event-based unmanned aerial vehicle formation intermittent control method, system, equipment and medium. Background Art

[0002] Drone formation systems are facing increasingly severe security challenges, especially the threat of deception attacks. Such attacks can be implemented through a variety of means, including tampering with sensor data, sending false location information, interfering with communication signals, etc., aiming to deceive the control and decision-making process of the system. Once attacked, drone formations may be misled to execute incorrect mission instructions, resulting in mission failure or incompleteness, and may even cause equipment damage, collision accidents, or the risk of leaking important data. In military applications, such attacks may expose tactical intentions or undermine strategic plans, posing a serious threat to national security.

[0003] How to effectively manage and optimize communication resources is one of the key issues in UAV formation communication. Large-scale UAV formations usually need to transmit a large amount of real-time data at the same time, such as high-definition video, sensor information, and location data. The amount of data is huge and the communication bandwidth requirements are extremely high. In addition, the limited spectrum resources also limit the improvement of communication capabilities, especially in dense communication environments or spectrum congested areas. Communication delays are particularly sensitive to certain application scenarios, such as coordinated operations and emergency response. Therefore, it is crucial to ensure the real-time and reliability of communications. At the same time, with the complexity of the electromagnetic environment, UAV formations are also faced with security risks from interference and malicious attacks, resulting in reduced control performance of UAVs. To this end, we propose an event-based UAV formation intermittent control method, system, equipment and medium. Summary of the invention

[0004] The purpose of the present invention is to provide an event-based UAV formation intermittent control method, system, device and medium to solve the problems raised in the above background technology.

[0005] According to a first aspect of the present invention, in order to achieve the above-mentioned purpose, the present invention provides the following technical solution: an event-based intermittent control method for a UAV formation, comprising the following steps:

[0006] Construct a dynamic model of UAV formations;

[0007] Based on graph theory, a state space model of drone formations under deception attacks is constructed;

[0008] Construct an intermittent control strategy based on an observer, including defining the expected relative position and tracking error, designing a deception attack observer, and constructing an intermittent control strategy based on the deception attack observer;

[0009] Construct an event trigger mechanism based on the average method, including defining the state error and the average value of the state error, defining the trigger threshold function, defining the trigger condition, defining the trigger signal, defining the system state equation and constructing the error system;

[0010] Conduct stability analysis on the UAV formation and evaluate the flight status of the UAV formation based on the analysis results.

[0011] Furthermore, the dynamic model of the UAV formation is constructed as follows:

[0012] (1) Establish a UAV formation, which includes a virtual leader, marked as the 0th UAV, and N followers, labeled 1, 2, ..., N;

[0013] The kinematic equation of the i-th fixed-wing UAV is expressed as:

[0014]

[0015] In the formula represents the position of the i-th UAV, [] T represents the transposed matrix of the matrix, They represent the x, y, and z axis position coordinates of the ith drone at time t, and their specific definitions are as follows:

[0016]

[0017] Where θ i (t) represents the inclination angle of the flight path of the i-th UAV, λ i (t) represents the azimuth of the flight path of the i-th UAV, V i (t) represents the flight speed of the i-th UAV;

[0018] (2) For the i-th follower drone, the following equation is satisfied:

[0019]

[0020] Where g represents the acceleration due to gravity; λ xi (t), λ yi (t) and λ zi (t) represent the acceleration of the UAV roll, pitch and yaw respectively;

[0021] According to steps (1) and (2), the actual control formula can be obtained:

[0022] q i (t) = [λ xi (t) λ yi (t) λ zi (t)]T (5)

[0023] Combining the above formulas, we can get the final formula:

[0024]

[0025] in,

[0026]

[0027] In the formula, represents the acceleration vector of the drone; θ i (t) represents the inclination angle of the flight path of the i-th UAV, λ i (t) represents the azimuth of the flight path of the i-th UAV.

[0028] Furthermore, a state space model of the drone formation under deception attack is constructed as follows:

[0029] For the i-th drone, when it is attacked by deception, its state equation is as follows:

[0030]

[0031] Where f i (t) represents the unknown deception attack signal, E0 is a known constant matrix with appropriate dimensions, is the location information of the i-th UAV, v i (t) is the velocity vector of the i-th UAV.

[0032] E0 and u i The specific form of (t) is as follows:

[0033]

[0034] u i (t) = j i (t)*q i (t)

[0035] In order to represent the state of the drone, the state vector x i (t) is defined as:

[0036]

[0037] Therefore, the state space model of the system can be expressed as:

[0038]

[0039] y i (t) = C1*x i (t)(11)

[0040] In the formula is the state vector x i (t) The time derivative, y i (t) is the output vector, A, B, C1, and E are the system matrix, input matrix, attack matrix, and output matrix, respectively;

[0041] The specific forms of system matrix A, input matrix B, attack matrix E and C1 are as follows:

[0042]

[0043] C1=[I3 0]

[0044] Where I3 refers to the 3×3 unit matrix.

[0045] Furthermore, an intermittent control strategy based on an observer is constructed, which specifically includes defining the expected relative position and tracking error, designing a deception attack observer, and constructing an intermittent control strategy, as follows:

[0046] (1) Define the expected relative position and tracking error:

[0047] The directed communication topology F of the multi-UAV system has a directed spanning tree, and the root node is the virtual leader UAV; in order to obtain the trajectory of the ith UAV, the relative position between the ith UAV and the 0th UAV is described as follows:

[0048]

[0049] where Δζ xi (t), Δζ yi (t), Δζ zi (t) are:

[0050]

[0051] Where θ0(t) represents the initial expected relative distance vector between two arbitrary adjacent drones, λ0 represents the parameter setting value under the deception attack, and s i (t) represents the distance between the ith UAV and the 0th UAV, χ i (t) is the acute angle between the connecting line (connecting the ith UAV and the 0th UAV) and the xy plane, β i (t) is the distance s i (t) and relative speed d vi (t) The angle between the projections on the xy plane, λ0 represents the parameter setting value under specific conditions;

[0052] The expected position of the i-th UAV can be expressed as:

[0053]

[0054] in are the coordinates of the 0th drone;

[0055] Finally, the tracking error of the i-th UAV is recorded as:

[0056]

[0057] in is the location information of the i-th UAV, v i (t) is the velocity vector of the i-th UAV;

[0058] (2) Design a deception attack observer:

[0059] In order to estimate and eliminate the impact of the deception attack of each UAV, the deception attack observer of the i-th UAV is designed as follows:

[0060]

[0061]

[0062] Where ξ(t) is an intermediate variable used to assist in estimating the attack signal. is the estimated deception attack signal, H is the observer gain matrix that needs to be determined, C2 is a reversible parameter matrix with appropriate dimensions, C1 is the output matrix, y i (t) is the measurement output, A and B are system matrices, and E is the observation matrix;

[0063] The H and C2 matrices are used to estimate the deception attack signal. The specific design form is as follows:

[0064] H=I6

[0065] C2=[I3 0]

[0066] In the formula, I6 represents the sixth-order unit matrix, and I3 represents the third-order unit matrix;

[0067] (3) Based on the design of the deception attack observer, an intermittent control strategy is constructed, which is divided into control time and rest time:

[0068] Control time: [nT,nT+S)

[0069] Rest time: [nT+S, (n+1)T)

[0070] Among them, S∈(0,T] is the control duration in period T, and n is used to identify the index of the period;

[0071] Then the following UAV formation system control strategy is proposed:

[0072]

[0073] in, It is a control signal and takes effect during the control time; is the estimated compensation of the spoof attack signal; is the expected acceleration, used for trajectory tracking; B + is the pseudo-inverse matrix of matrix B;

[0074] B + The specific design form is as follows:

[0075]

[0076] The specific control signal is defined as:

[0077]

[0078] in,

[0079]

[0080] in, It is an event trigger signal; This is the latest transmission from UAV J; ij is the weight coefficient, which reflects the communication topology between UAVs; b i is the bias term; K is the controller gain matrix that needs to be determined.

[0081] Furthermore, an event trigger mechanism based on the average method is constructed, which specifically includes defining the state error and the average value of the state error, defining the trigger threshold function, defining the trigger condition, defining the trigger signal, defining the system state equation and constructing the error system, as follows:

[0082] (1) Define the state error and the average state error as follows:

[0083] State error e yi (t) represents the state error of the i-th UAV at time t, which is defined as:

[0084]

[0085] In the formula is the state estimate, is the actual measurement value, C1 is the output matrix, The most recent event triggering time When , the state vector of the i-th UAV is, is the state vector estimate;

[0086] The average value of the state error The specific definition of is:

[0087]

[0088] Where w ij represents the weight of the connection between drones i and j, represents the estimated state value of the i-th UAV at time t, Indicates the time when the most recent event was triggered When , the average state of the i-th UAV and its neighbors is, represents the weighted average of the neighbor states of the ith UAV at time t;

[0089] (2) Define the trigger threshold function as follows:

[0090] Trigger threshold function σ i (t) is a time-dependent function used to dynamically adjust the threshold of the trigger condition and is defined as:

[0091]

[0092] Where σ mini and σ maxi are the minimum and maximum trigger thresholds, is the average value of the state error;

[0093] (3) Define the trigger conditions as follows:

[0094]

[0095] If the state error e yi The weighted norm of (t) is less than the threshold σ i (t) Average state error The product of the weighted norm of , no event is triggered;

[0096] In the formula is the state error vector e yi The transposed matrix of (t); the matrix φ i is a weight matrix used to adjust the importance of different state error components; is the average state error The transposed matrix of

[0097] (4) Define the trigger signal as follows:

[0098] Event trigger signal It can be expressed as:

[0099]

[0100] where e yi (t) represents the state error of the i-th UAV at time t;

[0101] The estimation error is expressed as Using the Kronecker product we get:

[0102]

[0103] in,

[0104]

[0105] C1 = [I3 0];

[0106] C2 = [I3 0];

[0107]

[0108] Among them, f i (t) represents the actual deception attack signal of the i-th UAV; represents the estimated deception attack signal of the i-th UAV;

[0109] (5) Define the system state equation as follows:

[0110] The system state equation in the control time period and the rest time period are expressed as:

[0111]

[0112] in,

[0113] [nT,nT+S) is the control time; [nT+S,(n+1)T) is the rest time; S∈(0,T] is the control duration in cycle T, and n is used to identify the index of the cycle;

[0114] B0=diag{b1,b2,…,b N}; is the state vector estimate; is the state error vector e N The transposed matrix of (t);

[0115] (6) Construct an error system as follows:

[0116] definition:

[0117]

[0118] The error system is defined as follows:

[0119]

[0120] Among them, A 1 =A1;A 2 =A2;B 1 =B; B 2 =0;ε 1 =ε 2 =ε; z(t) is the output of the multi-UAV system;

[0121] Where, l, A 1 The specific definitions of ,A2,B,ε,G,D are as follows:

[0122]

[0123] G=[I 6N 0 6N×3N ]

[0124] D=[2 2 3 3 2 2].

[0125] Furthermore, the stability analysis of the UAV formation is carried out as follows:

[0126] If exists v＞0 and decay rate ω make the error system of the UAV formation system control strategy exponentially stable, then for all t＞0 :

[0127] ||η(t)||≤ve -ωt ||η0|| (29)

[0128] Where η0=η(0);

[0129] If there is a matrix φ i >0(i∈g), then the error system is exponentially stable and:

[0130]

[0131] κ=ρ1s-ρ2(TS)>0

[0132] in:

[0133]

[0134]

[0135] φ=diag{φ1,φ2,…,φ N}

[0136] γ=3.61

[0137] Among them, S∈(0,T] is the control duration within the undetermined period T; ρ ι (ι={1,2}) are all unknown parameters, K is the control gain matrix and H is the observation gain matrix;

[0138] When the UAV formation meets the above conditions, it is considered that the UAV formation can perform stable flight.

[0139] According to a second aspect of the present invention, the present invention provides an event-based intermittent control system for a UAV formation, which is used to implement the above-mentioned event-based intermittent control method for a UAV formation, comprising:

[0140] The first building block is used to construct a dynamic model of the UAV formation;

[0141] The second building block is used to construct a state space model of a drone formation under deception attack based on graph theory foundation;

[0142] The third building block is used to build an intermittent control strategy based on an observer, which specifically includes defining the expected relative position and tracking error, designing a deception attack observer, and building an intermittent control strategy;

[0143] The fourth construction module is used to construct an event trigger mechanism based on an average method, which specifically includes defining a state error and an average value of the state error, defining a trigger threshold function, defining a trigger condition, defining a trigger signal, defining a system state equation, and constructing an error system;

[0144] The analysis module is used to perform stability analysis on the UAV formation and evaluate the flight status of the UAV formation based on the analysis results.

[0145] According to a third aspect of the present invention, the present invention provides a terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the memory stores a computer program capable of running on the processor, and when the processor loads and executes the computer program, the above-mentioned event-based UAV formation intermittent control method is adopted.

[0146] According to a fourth aspect of the present invention, the present invention provides a storage medium comprising computer executable instructions, which, when executed by a computer processor, are used to execute the above-mentioned event-based intermittent control method for a UAV formation.

[0147] According to a fifth aspect of the present invention, the present invention provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it is used to load and execute the above-mentioned event-based UAV formation intermittent control method.

[0148] The present invention has at least the following beneficial effects:

[0149] 1. The present invention uses the mathematical tools of graph theory to carefully construct the dynamic model and state space model of the UAV formation. On this basis, an efficient and economical observer-based intermittent control strategy is designed. By accurately defining the expected relative position and tracking error, and a carefully designed deception attack observer, it can detect and compensate for external interference in real time, thereby greatly reducing the amount of calculation and energy consumption while maintaining control performance. In addition, the constructed intermittent control strategy further optimizes the energy efficiency of the UAV formation by intelligently adjusting the frequency of the control input.

[0150] 2. The present invention introduces an event trigger mechanism based on the average method. By defining the state error, the average value of the state error, the trigger threshold function and the trigger condition, the stability and response speed of the control system when it is attacked by deception are ensured. The design of the trigger signal enables the system to perform control updates only when necessary, thereby avoiding unnecessary calculations and energy waste.

[0151] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0152] Figure 1 The figure is a schematic diagram of the process of the method of the present invention. DETAILED DESCRIPTION

[0153] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0154] See also Figure 1 The present invention provides a technical solution: an event-based intermittent control method for a UAV formation, comprising the following steps:

[0155] S1. Construct the dynamic model of UAV formation, as follows:

[0156] (S11) Establishing a drone formation, which includes a virtual leader, marked as the 0th drone, and N followers, labeled 1, 2, ..., N;

[0157] The kinematic equation of the i-th fixed-wing UAV is expressed as:

[0158]

[0159] In the formula represents the position of the i-th UAV, [] T represents the transposed matrix of the matrix, They represent the x, y, and z axis position coordinates of the ith drone at time t, and their specific definitions are as follows:

[0160]

[0161] Where θ i (t) represents the inclination angle of the flight path of the i-th UAV, λ i (t) represents the azimuth of the flight path of the i-th UAV, V i (t) represents the flight speed of the i-th UAV;

[0162] (S12) For the i-th follower drone, the following equation is satisfied:

[0163]

[0164] Where g represents the acceleration due to gravity; λ xi (t), λ yi (t) and λ zi (t) represent the acceleration of the UAV roll, pitch and yaw respectively;

[0165] According to step (S11) and step (S12), the actual control formula can be obtained:

[0166] q i (t) = [λ xi (t) λ yi (t) λ zi (t)] T (34)

[0167] Combining the above formulas, we can get the final formula:

[0168]

[0169] in,

[0170]

[0171] In the formula, represents the acceleration vector of the drone; θ i (t) represents the inclination angle of the flight path of the i-th UAV, λ i (t) represents the azimuth of the flight path of the i-th UAV;

[0172] S2. Based on graph theory, a state space model of a drone formation under deception attack is constructed, as follows:

[0173] For the i-th drone, when it is attacked by deception, its state equation is as follows:

[0174]

[0175] Where f i (t) represents the unknown deception attack signal, E0 is a known constant matrix with appropriate dimensions, is the location information of the i-th UAV, v i (t) is the velocity vector of the i-th UAV.

[0176] E0 and u i The specific form of (t) is as follows:

[0177]

[0178] u i (t) = j i (t)*q i (t)

[0179] In order to represent the state of the drone, the state vector x i (t) is defined as:

[0180]

[0181] Therefore, the state space model of the system can be expressed as:

[0182]

[0183] y i (t) = C1*x i (t)(40)

[0184] In the formula is the state vector x i (t) The time derivative, y i (t) is the output vector, A, B, C1, and E are the system matrix, input matrix, attack matrix, and output matrix, respectively;

[0185] The specific forms of system matrix A, input matrix B, attack matrix E and C1 are as follows:

[0186]

[0187] C1=[I3 0]

[0188] Where I3 refers to the 3×3 identity matrix;

[0189] S3. Construct an intermittent control strategy based on an observer, which specifically includes defining the expected relative position and tracking error, designing a deception attack observer, and constructing an intermittent control strategy based on the deception attack observer, as follows:

[0190] (S31) Define the expected relative position and tracking error:

[0191] The directed communication topology F of the multi-UAV system has a directed spanning tree, and the root node is the virtual leader UAV; in order to obtain the trajectory of the ith UAV, the relative position between the ith UAV and the 0th UAV is described as follows:

[0192]

[0193] where Δζ xi (t), Δζ yi (t), Δζ zi (t) are:

[0194]

[0195]

[0196] Where θ0(t) represents the initial expected relative distance vector between two arbitrary adjacent drones, λ0 represents the parameter setting value under the deception attack, and s i (t) represents the distance between the ith UAV and the 0th UAV, χ i (t) is the acute angle between the connecting line (connecting the ith UAV and the 0th UAV) and the xy plane, β i (t) is the distance s i (t) and relative speed d vi (t) The angle between the projections on the xy plane, λ0 represents the parameter setting value under specific conditions;

[0197] The expected position of the i-th UAV can be expressed as:

[0198]

[0199] in are the coordinates of the 0th drone;

[0200] Finally, the tracking error of the i-th UAV is recorded as:

[0201]

[0202] in is the location information of the i-th UAV, v i (t) is the velocity vector of the i-th UAV;

[0203] (S32) Constructing an intermittent control strategy based on the design of a deception attack observer:

[0204] In order to estimate and eliminate the impact of the deception attack of each UAV, the deception attack observer of the i-th UAV is designed as follows:

[0205]

[0206] Where ξ(t) is an intermediate variable used to assist in estimating the attack signal. is the estimated deception attack signal, H is the observer gain matrix that needs to be determined, C2 is a reversible parameter matrix with appropriate dimensions, C1 is the output matrix, y i (t) is the measurement output, A and B are system matrices, and E is the observation matrix;

[0207] The H and C2 matrices are used to estimate the deception attack signal. The specific design form is as follows:

[0208] H=I6

[0209] C2=[I3 0]

[0210] In the formula, I6 represents the sixth-order unit matrix, and I3 represents the third-order unit matrix;

[0211] (S33) In order to reduce the computation and energy resource consumption of the multi-UAV system, a periodic intermittent control strategy is constructed, which is divided into control time and rest time:

[0212] Control time: [nT,nT+S)

[0213] Rest time: [nT+S, (n+1)T)

[0214] Among them, S∈(0,T] is the control duration in period T, and n is used to identify the index of the period;

[0215] Then the following UAV formation system control strategy is proposed:

[0216]

[0217] in, It is a control signal and takes effect during the control time; is the estimated compensation of the spoof attack signal; is the expected acceleration, used for trajectory tracking; B + is the pseudo-inverse matrix of matrix B;

[0218] B + The specific design form is as follows:

[0219]

[0220] The specific control signal is defined as:

[0221]

[0222] in,

[0223]

[0224] in, It is an event trigger signal; This is the latest transmission from UAV J; ij is the weight coefficient, which reflects the communication topology between UAVs; b i is the bias term; K is the controller gain matrix that needs to be determined;

[0225] The strategy uses an event-triggered mechanism to control, and only performs control calculations when significant state changes are detected, thereby reducing the computational burden. At the same time, the intermittent control strategy further reduces the energy consumption of the controller through periodic rest time.

[0226] S4. Construct an event trigger mechanism based on the average method, which specifically includes defining the state error and the average value of the state error, defining the trigger threshold function, defining the trigger condition, defining the trigger signal, defining the system state equation and constructing the error system, as follows:

[0227] (S41) defining the state error and the average value of the state error as follows:

[0228] State error e yi (t) represents the state error of the i-th UAV at time t, which is defined as:

[0229]

[0230] In the formula is the state estimate, is the actual measurement value, C1 is the output matrix, The most recent event triggering time When , the state vector of the i-th UAV is, is the state vector estimate;

[0231] The average value of the state error The specific definition of is:

[0232]

[0233] Where w ij represents the weight of the connection between drones i and j, represents the estimated state value of the i-th UAV at time t, Indicates the time when the most recent event was triggered When , the average state of the i-th UAV and its neighbors is, represents the weighted average of the neighbor states of the ith UAV at time t;

[0234] (S42) Define a trigger threshold function, the method is as follows:

[0235] Trigger threshold function σ i (t) is a time-dependent function used to dynamically adjust the threshold of the trigger condition and is defined as:

[0236]

[0237] Where σ mini and σ maxi are the minimum and maximum trigger thresholds, is the average value of the state error;

[0238] (S43) Defining trigger conditions, the method is as follows:

[0239]

[0240] If the state error e yi The weighted norm of (t) is less than the threshold σ i (t) Average state error The product of the weighted norm of , no event is triggered;

[0241] In the formula is the state error vector e yi The transposed matrix of (t); the matrix φ i is a weight matrix used to adjust the importance of different state error components; is the average state error The transposed matrix of

[0242] (S44) Defining a trigger signal, the method is as follows:

[0243] Event trigger signal It can be expressed as:

[0244]

[0245] where e yi (t) represents the state error of the i-th UAV at time t;

[0246] The estimation error is expressed as Using the Kronecker product we get:

[0247]

[0248] in,

[0249]

[0250] C1 = [I3 0];

[0251] C2 = [I3 0];

[0252]

[0253]

[0254] Among them, f i (t) represents the actual deception attack signal of the i-th UAV; represents the estimated deception attack signal of the i-th UAV;

[0255] (S45) Define the system state equation as follows:

[0256] The system state equation in the control time period and the rest time period are expressed as:

[0257]

[0258] in,

[0259] [nT,nT+S) is the control time; [nT+S,(n+1)T) is the rest time; S∈(0,T] is the control duration in cycle T, and n is used to identify the index of the cycle;

[0260] B0=diag{b1,b2,…,b N}; is the state vector estimate; is the state error vector e N The transposed matrix of (t);

[0261] (S46) Construct an error system as follows:

[0262] definition:

[0263]

[0264] The error system is defined as follows:

[0265]

[0266] Among them, A 1 =A1;A 2 =A2;B1 =B; B 2 =0;ε 1 =ε 2 =ε; z(t) is the output of the multi-UAV system;

[0267] Where, l, A 1 The specific definitions of ,A2,B,ε,G,D are as follows:

[0268]

[0269]

[0270] G=[I 6N 0 6N×3N ]

[0271] D = [2 2 3 3 2 2];

[0272] S5. Perform stability analysis on the UAV formation and evaluate the flight status of the UAV formation based on the analysis results, as follows:

[0273] If exists v＞0 and decay rate ω make the error system of the UAV formation system control strategy exponentially stable, then for all t＞0 :

[0274] ||η(t)||≤ve -ωt ||η0|| (58)

[0275] Where η0=η(0);

[0276] If there is a matrix φ i >0(i∈g), then the error system is exponentially stable and:

[0277]

[0278] κ=ρ1s-ρ2(TS)>0

[0279] in:

[0280]

[0281] φ=diag{φ1,φ2,…,φ N}

[0282] γ=3.61

[0283] Among them, S∈(0,T] is the control duration within the undetermined period T; ρ ι(ι={1,2}) are all unknown parameters, K is the control gain matrix and H is the observation gain matrix;

[0284] When the UAV formation meets the above conditions, it is considered that the UAV formation can perform stable flight.

[0285] In summary, the technical solution of this embodiment provides a strong guarantee for the safe operation of the UAV system. This embodiment first uses the mathematical tools of graph theory to finely construct the dynamic model and state space model of the UAV formation. On this basis, an efficient and economical observer-based intermittent control strategy is designed. By accurately defining the expected relative position and tracking error, and a carefully designed deception attack observer, it can detect and compensate for external interference in real time, thereby greatly reducing the amount of calculation and energy consumption while maintaining control performance. In addition, the constructed intermittent control strategy further optimizes the energy efficiency of the UAV formation by intelligently adjusting the frequency of the control input;

[0286] An event trigger mechanism based on the averaging method was introduced. By defining the state error, the average value of the state error, the trigger threshold function and the trigger condition, the stability and response speed of the control system when it is attacked by deception were ensured. The design of the trigger signal enables the system to update the control only when necessary, thus avoiding unnecessary calculation and energy waste. Finally, the stability analysis of UAV formation flight was carried out to ensure the stability of the UAV during flight.

[0287] Embodiment 2:

[0288] The present invention provides an event-based intermittent control system for a UAV formation, which is used to implement the event-based intermittent control method for a UAV formation described in the first embodiment, including:

[0289] The first building block is used to construct a dynamic model of the UAV formation;

[0290] The second building block is used to construct a state space model of a drone formation under deception attack based on graph theory foundation;

[0291] The third building block is used to build an intermittent control strategy based on an observer, which specifically includes defining the expected relative position and tracking error, designing a deception attack observer, and building an intermittent control strategy;

[0292] The fourth construction module is used to construct an event trigger mechanism based on an average method, which specifically includes defining a state error and an average value of the state error, defining a trigger threshold function, defining a trigger condition, defining a trigger signal, defining a system state equation, and constructing an error system;

[0293] The analysis module is used to perform stability analysis on the UAV formation and evaluate the flight status of the UAV formation based on the analysis results.

[0294] Specifically, the above-mentioned first building module, second building module, third building module and analysis module can be embedded in a computer processing system, and the computer calls the above-mentioned modules to complete the task of controlling the drone formation according to the above-mentioned event-based drone formation intermittent control method; the above-mentioned first building module, second building module, third building module and analysis module can perform operations according to the specific steps given in the event-based drone formation intermittent control method.

[0295] It should be noted that it should be understood that the division of the various modules of the above system is only the division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated, and these modules can all be implemented in the form of software calling through processing elements; they can also be all implemented in the form of hardware; some modules can also be implemented in the form of software calling through processing elements, and some modules can be implemented in the form of hardware. For example, the analysis module can be a separately established processing element, or it can be integrated in a certain chip of the above device. In addition, it can also be stored in the memory of the above device in the form of program code, and called and executed by a certain processing element of the above device. The implementation of other modules is similar. In addition, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element described here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each module above can be completed by the hardware integrated logic circuit in the processor element or the instruction in the form of software.

[0296] For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as one or more application specific integrated circuits (ASIC), or one or more digital singnal processors (DSP), or one or more field programmable gate arrays (FPGA). For another example, when a module is implemented in the form of a processing element scheduling program code, the processing element may be a general-purpose processor, such as a central processing unit (CPU) or other processors that can call program code. For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0297] Embodiment three:

[0298] The present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The memory stores a computer program capable of running on the processor, and when the processor loads and executes the computer program, the above-mentioned event-based UAV formation intermittent control method is adopted.

[0299] It should be noted that the terminal device can be a computer device such as a desktop computer, a laptop computer or a cloud server, and the terminal device includes but is not limited to a processor and a memory. For example, the terminal device can also include input and output devices, network access devices and buses.

[0300] Furthermore, the processor may adopt a central processing unit (CPU). Of course, according to actual usage, other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. may also be adopted. The general-purpose processor may adopt a microprocessor or any conventional processor, etc., and the present application does not impose any restrictions on this.

[0301] Embodiment 4:

[0302] The present invention provides a storage medium containing computer executable instructions, which are used to execute the above-mentioned event-based intermittent control method of UAV formation when executed by a computer processor.

[0303] Among them, the computer program can be stored in a computer-readable medium, the computer program includes computer program code, the computer program code can be in the form of source code, object code, executable file or certain middleware, etc. The computer-readable medium includes any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the computer-readable medium includes but is not limited to the above-mentioned components.

[0304] Embodiment five:

[0305] The present invention provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it is used to load and execute the above-mentioned event-based intermittent control method of UAV formation.

[0306] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0307] For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances. When an element is referred to as being "assembled on", "installed on", "fixed on" or "set on" another element, it can be directly on the other element or there can also be a centered element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be a centered element at the same time. The terms "vertical", "horizontal", "up", "down", "left", "right" and similar expressions used herein are only for illustrative purposes and are not intended to be the only implementation method.

[0308] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0309] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

Claims

1. An event-based intermittent control method for UAV formations, characterized in that: The following steps are involved: Construct a dynamic model of UAV formation; Based on graph theory, a state space model of drone formations under deception attacks is constructed; Construct an intermittent control strategy based on an observer, including defining the expected relative position and tracking error, designing a deception attack observer, and constructing an intermittent control strategy based on the deception attack observer; Construct an event trigger mechanism based on the average method, including defining the state error and the average value of the state error, defining the trigger threshold function, defining the trigger condition, defining the trigger signal, defining the system state equation and constructing the error system; Conduct stability analysis on the UAV formation and evaluate the flight status of the UAV formation based on the analysis results.

2. The event-based intermittent control method for UAV formation according to claim 1, characterized in that: Construct the dynamic model of the UAV formation as follows: (1) Establish a UAV formation, which includes a virtual leader, marked as the 0th UAV, and N followers, labeled 1, 2, ..., N; The kinematic equation of the i-th fixed-wing UAV is expressed as: In the formula represents the position of the i-th UAV, [] T represents the transposed matrix of the matrix, They represent the x, y, and z axis position coordinates of the ith drone at time t, and their specific definitions are as follows: Where θ i (t) represents the inclination angle of the flight path of the i-th UAV, λ i (t) represents the azimuth of the flight path of the i-th UAV, V i (t) represents the flight speed of the i-th UAV; (2) For the i-th follower drone, the following equation is satisfied: Where g represents the acceleration due to gravity; λ xi (t), λ yi (t) and λ zi (t) represent the acceleration of the UAV roll, pitch and yaw respectively; According to steps (1) and (2), the actual control formula can be obtained: q i (t)=[λ xi (t)λ yi (t)λ zi (t)] T (5) Combining the above formulas, we can get the final formula: in, In the formula, represents the acceleration vector of the drone; θ i (t) represents the inclination angle of the flight path of the i-th UAV, λ i (t) represents the azimuth of the flight path of the i-th UAV.

3. The event-based intermittent control method for UAV formation according to claim 2, characterized in that: The state space model of the drone formation under deception attack is constructed as follows: For the i-th drone, when it is attacked by deception, its state equation is as follows: Where f i (t) represents the unknown deception attack signal, E0 is a known constant matrix with appropriate dimensions, is the location information of the i-th UAV, v i (t) is the velocity vector of the i-th UAV. E0 and u i The specific form of (t) is as follows: u i (t)=j i (t)*q i (t) In order to represent the state of the drone, the state vector x i (t) is defined as: Therefore, the state space model of the system can be expressed as: y i (t)=C1*x i (t) (11) In the formula is the state vector x i The derivative of (t) with respect to time, y i (t) is the output vector, A, B, C1, and E are the system matrix, input matrix, attack matrix, and output matrix, respectively; The specific forms of system matrix A, input matrix B, attack matrix E and C1 are as follows: C1=[I30] Where I3 refers to the 3×3 unit matrix.

4. The event-based intermittent control method for UAV formation according to claim 2, characterized in that: Construct an intermittent control strategy based on an observer, which includes defining the expected relative position and tracking error, designing a deception attack observer, and constructing an intermittent control strategy, as follows: (1) Define the expected relative position and tracking error: The directed communication topology F of the multi-UAV system has a directed spanning tree, and the root node is the virtual leader UAV; in order to obtain the trajectory of the ith UAV, the relative position between the ith UAV and the 0th UAV is described as follows: where Δζ xi (t), Δζ yi (t), Δζ zi (t) are: Where θ0(t) represents the initial expected relative distance vector between two arbitrary adjacent drones, λ0 represents the parameter setting value under the deception attack, and s i (t) represents the distance between the ith UAV and the 0th UAV, χ i (t) is the acute angle between the connecting line (connecting the ith UAV and the 0th UAV) and the xy plane, β i (t) is the distance s i (t) and relative speed d vi (t) The angle between the projections on the xy plane, λ0 represents the parameter setting value under specific conditions; The expected position of the i-th UAV can be expressed as: in are the coordinates of the 0th drone; Finally, the tracking error of the i-th UAV is recorded as: in is the location information of the i-th UAV, v i (t) is the velocity vector of the i-th UAV; (2) Design a deception attack observer: In order to estimate and eliminate the impact of the deception attack of each UAV, the deception attack observer of the i-th UAV is designed as follows: Where ξ(t) is an intermediate variable used to assist in estimating the attack signal. is the estimated deception attack signal, H is the observer gain matrix that needs to be determined, C2 is a reversible parameter matrix with appropriate dimensions, C1 is the output matrix, y i (t) is the measurement output, A and B are system matrices, and E is the observation matrix; The H and C2 matrices are used to estimate the deception attack signal. The specific design form is as follows: H=I6 C2=[I3 0] In the formula, I6 represents the sixth-order unit matrix, and I3 represents the third-order unit matrix; (3) Based on the design of the deception attack observer, an intermittent control strategy is constructed, which is divided into control time and rest time: Control time: [nT,nT+S) Rest time: [nT+S, (n+1)T) Among them, S∈(0,T] is the control duration in period T, and n is used to identify the index of the period; Then the following UAV formation system control strategy is proposed: in, It is a control signal and takes effect during the control time; is the estimated compensation of the spoof attack signal; is the expected acceleration, used for trajectory tracking; B + is the pseudo-inverse matrix of matrix B; B + The specific design form is as follows: The specific control signal is defined as: in, in, It is an event trigger signal; This is the latest transmission from UAV J; ij is the weight coefficient, which reflects the communication topology between UAVs; b i is the bias term; K is the controller gain matrix that needs to be determined.

5. The event-based intermittent control method for UAV formation according to claim 4, characterized in that: Construct an event trigger mechanism based on the average method, which specifically includes defining the state error and the average value of the state error, defining the trigger threshold function, defining the trigger condition, defining the trigger signal, defining the system state equation and constructing the error system, as follows: (1) Define the state error and the average state error as follows: State error e yi (t) represents the state error of the i-th UAV at time t, which is defined as: In the formula is the state estimate, is the actual measurement value, C1 is the output matrix, The most recent event triggering time When , the state vector of the i-th UAV is, is the state vector estimate; The average value of the state error The specific definition of is: Where w ij represents the weight of the connection between drones i and j, represents the estimated state value of the i-th UAV at time t, Indicates the time when the most recent event was triggered When , the average state of the i-th UAV and its neighbors is, represents the weighted average of the neighbor states of the ith UAV at time t; (2) Define the trigger threshold function as follows: Trigger threshold function σ i (t) is a time-dependent function used to dynamically adjust the threshold of the trigger condition and is defined as: Where σ mini and σ maxi are the minimum and maximum trigger thresholds, is the average value of the state error; (3) Define the trigger conditions as follows: If the state error e yi The weighted norm of (t) is less than the threshold σ i (t) Average state error The product of the weighted norm of , no event is triggered; In the formula is the state error vector e yi The transposed matrix of (t); the matrix φ i is a weight matrix used to adjust the importance of different state error components; is the average state error The transposed matrix of (4) Define the trigger signal as follows: Event trigger signal It can be expressed as: where e yi (t) represents the state error of the i-th UAV at time t; The estimation error is expressed as Using the Kronecker product we get: in, C1 = [I3 0]; C2 = [I3 0]; Among them, f i (t) represents the actual deception attack signal of the i-th UAV; Indicates i The estimated spoof attack signal of each drone; (5) Define the system state equation as follows: The system state equation in the control time period and the rest time period are expressed as: in, [nT,nT+S) is the control time; [nT+S,(n+1)T) is the rest time; S∈(0,T] is the control duration in cycle T, and n is used to identify the index of the cycle; B0=diag{b1,b2,…,b N }; is the state vector estimate; is the state error vector e N The transposed matrix of (t); (6) Construct an error system as follows: definition: The error system is defined as follows: Among them, A 1 =A1;A 2 =A2;B 1 =B; B 2 =0;ε 1 =ε 2 =ε; z(t) is the output of the multi-UAV system; Where, l, A 1 The specific definitions of ,A2,B,ε,G,D are as follows: G=[I 6N 0 6N×3N ] D＝[223322]。 6. The event-based intermittent control method for UAV formation according to claim 5, characterized in that: The stability analysis of the UAV formation is carried out as follows: If exists v＞0 and decay rate ω make the error system of the UAV formation system control strategy exponentially stable, then for all t＞0 : ||η(t)||≤ve -ωt ||η0|| (29) Where η0=η(0); If there is a matrix φ i >0(i∈g), then the error system is exponentially stable and: κ=ρ1s-ρ2(TS)>0 in: φ=diag{φ1,φ2,…,φ N } γ=3.61 Among them, S∈(0,T] is the control duration within the undetermined period T; s, ρ ι (ι={1,2}) are all unknown parameters, K is the control gain matrix and H is the observation gain matrix; When the UAV formation meets the above conditions, it is considered that the UAV formation can perform stable flight.

7. An event-based intermittent control system for UAV formations, used to implement the event-based intermittent control method for UAV formations according to any one of claims 1 to 6, characterized in that: include: The first building block is used to construct a dynamic model of the UAV formation; The second building block is used to construct a state space model of a drone formation under deception attack based on graph theory foundation; The third building block is used to build an intermittent control strategy based on an observer, which specifically includes defining the expected relative position and tracking error, designing a deception attack observer, and building an intermittent control strategy; The fourth construction module is used to construct an event trigger mechanism based on an average method, which specifically includes defining a state error and an average value of the state error, defining a trigger threshold function, defining a trigger condition, defining a trigger signal, defining a system state equation, and constructing an error system; The analysis module is used to perform stability analysis on the UAV formation and evaluate the flight status of the UAV formation based on the analysis results.

8. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: The memory stores a computer program that can be run on the processor. When the processor loads and executes the computer program, the event-based intermittent control method of the UAV formation described in any one of claims 1 to 5 is adopted.

9. A storage medium containing computer executable instructions, characterized in that: When the computer executable instructions are executed by a computer processor, they are used to perform the event-based intermittent control method for a drone formation as described in any one of claims 1 to 5.

10. A computer program product, characterized in that The computer program product comprises a computer program, which, when executed by a processor, is used to load and execute the event-based intermittent control method for a UAV formation as described in any one of claims 1 to 5.

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