A refined fault-tolerant cooperative control method for swarm aircraft based on dual-event triggering

Abstract

This application discloses a refined fault-tolerant cooperative control method for swarm aircraft based on dual-event triggering. First, considering the constraints of limited communication resources, an event-triggered communication mechanism is constructed to reduce the communication frequency between swarm aircraft. Second, a distributed observer is designed to estimate the aircraft reference command signal using discontinuous neighbor state information. Then, a Nussbaum function and an adaptive neural network method are employed to handle actuator faults and parameter uncertainties. Finally, a fractional-order sliding mode fault-tolerant control strategy based on the event-triggered control mechanism is designed, reducing the number of controller updates and achieving refined control of the swarm aircraft. This invention can be applied to fault-tolerant cooperative control of swarm aircraft formations under conditions of limited communication and computing resources.

Description

Technical Field

[0001] This invention addresses the problem of actuator failures and parameter uncertainties encountered by swarm aircraft under limited communication and computing resources. It designs a refined fault-tolerant collaborative control method for swarm aircraft based on dual-event triggering, belonging to the field of fault-tolerant collaborative control. Background Technology

[0002] In recent years, swarm aircraft have played an increasingly important role in numerous fields. Compared to single aircraft, swarm aircraft have a larger exploration range and can carry more equipment, thus enabling them to perform a variety of complex tasks, such as forest fire monitoring, search and rescue, and line inspection. However, as a highly complex system, swarm aircraft carry numerous actuator components, making them more susceptible to actuator failures than single aircraft. Furthermore, when the swarm size is large, the limited onboard computer capabilities of each aircraft make it difficult for them to process large amounts of information simultaneously, thus affecting flight performance. Therefore, achieving fault-tolerant collaborative control of swarm aircraft within the constraints of limited computer capabilities is of paramount importance. Summary of the Invention

[0003] This invention addresses the problem of actuator failures and parameter uncertainties encountered by swarm aircraft under limited communication and computing resources. It designs a refined fault-tolerant collaborative control method for swarm aircraft based on dual-event triggering, ensuring that swarm aircraft can still achieve fault-tolerant collaborative control even when communication and computing resources are limited.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] A refined fault-tolerant cooperative control method for swarm aircraft based on dual-event triggering, the control method comprising the following steps:

[0006] Step 1: Establish the aircraft dynamics model.

[0007] Set up a formation of N followers and 1 leader aircraft, where the dynamics of the i-th follower aircraft (i = 1, 2, ..., N) is:

[0008]

[0009] Where, x i y i and z i Let i be the three-dimensional coordinates of the i-th aircraft. x represents i First derivative, V i For the speed of the aircraft, χ i Represents the heading angle, γi Represents the flight path angle. T i D i L i and Y i These are the aircraft's thrust, drag, lift, and lateral forces. α i It's the angle of attack, μ i It is the tilt angle, β i It is the sideslip angle. m is the mass of the aircraft, and g is the gravitational acceleration coefficient;

[0010] Thrust, drag, lift, and lateral force can be expressed as:

[0011]

[0012] Where, ρ iT It's the throttle opening, T max For maximum thrust, ρ is the air density, s i C represents the wing area. iL C iD and C iY These are the lift coefficient, drag coefficient, and lateral force coefficient, which are expressed by the following formulas:

[0013]

[0014] Among them, C iD0 C iY0 and C iL0 C is a constant. iDα C iLα and C iDα2 To match the angle of attack α i Correlation coefficient, C iYβ To the sideslip angle β i Correlation coefficient;

[0015] Define u i =[ρ iT ,α i sinμ i ,α i cosμ i ] T Input signal vector and p to the aircraft i =[x i ,y i ,z i ] T Given the aircraft position information vector, (1) can be simplified to:

[0016]

[0017] Among them, f i and g iTo simplify the resulting matrix, it is expressed as:

[0018]

[0019] Step 2: Establish a dynamic model of the aircraft under the influence of parameter uncertainty and actuator failure.

[0020] First, consider parameter uncertainties. The dynamics of an aircraft with parameter uncertainties can be expressed as follows:

[0021]

[0022] Among them, L id D id and Y id These are the unknown lift, drag, and side force caused by parameter uncertainties, respectively. The remaining parameters are consistent with the aircraft model parameters in step one.

[0023] The unknown lift, drag, and lateral forces, along with the uncertain parameters, can be expressed as:

[0024]

[0025] Among them, C iDd C iYd and C iLd For unknown aerodynamic parameters, C iD0d C iDαd C iDα2d C iY0d C iYβd C iL0d and C iLαd For unknown coefficients;

[0026] By considering parameter uncertainties, the aircraft dynamics equation (4) in step one is changed to:

[0027]

[0028] Among them, f i and g i Consistent with the definition in step one, f id and g id The unknown matrix caused by parameter uncertainty can be represented as:

[0029]

[0030] Among them, M i As given in (5);

[0031] Considering actuator failures faced by the aircraft, the failure model is represented as follows:

[0032] u i =ρi u i0 +f ib (10)

[0033] Where, ρ i =diag{ρ i1 ,ρ i2 ,ρ i3}, ρ ij ∈(0,1] is the efficiency loss factor, j=1,2,3,f ib =[f ib1 ,f ib2 ,f ib3 ] T Represents a deviation fault, u i0 It is the aircraft control input signal;

[0034] Combining (8), the aircraft dynamics model considering parameter uncertainties and actuator failures can be expressed as:

[0035]

[0036] Where, d i =f id +g i f ib +g id (ρ i u i0 +f ib ) is a lumped unknown term resulting from the coupling of deviation faults and parameter uncertainties.

[0037] Step 3: Design an event-triggered communication mechanism and establish a distributed observer to estimate the aircraft reference command signal using discontinuous state information from neighbors.

[0038] An event-triggered communication mechanism is established to save communication resources. Using this mechanism, the i-th aircraft can only obtain the status information of its neighbors when an event is triggered. The position triggering error of the i-th aircraft is defined as... Speed ​​trigger error is in, and Position and velocity information estimated for distributed observers, and At the triggering time The estimated position and velocity information, k i =0, 1, ... The first trigger time is set to... The event-triggered communication mechanism is represented as follows:

[0039]

[0040] in, Indicates the trigger time The next triggering time, inf{*} represents the infmum of a function, χ1, χ2, ξ1, ξ2, and ξ3 are positive constants in the design, Ω i It is a time-varying parameter, and its derivative is:

[0041]

[0042] Among them, Ω i (0) represents the initial value of the time-varying parameter, and δ1 is a positive design parameter;

[0043] Because of the event-triggered communication mechanism, the state information received by the aircraft from its neighbors is discontinuous. This discontinuous state information cannot be directly used to design the aircraft's control law. Therefore, a distributed observer is designed to obtain continuous state information. The designed distributed observer is as follows:

[0044]

[0045] in, and For the position and velocity information estimated by the distributed observer, ψ i1 and ψ i2 The co-position and velocity deviation of the estimator, as defined, is expressed as:

[0046]

[0047] Among them, a ij b represents the communication weight between the i-th and j-th aircraft. i This represents the communication weight between the i-th aircraft and the leader aircraft. and It is the j-th spacecraft at the trigger time Estimated position and velocity information and The leader's aircraft at the trigger moment Position and velocity information. ij d represents the distance between the i-th and j-th aircraft. i0 This represents the distance between the i-th aircraft and the leader aircraft.

[0048] Step four: Establish an event-triggered control mechanism and a fractional sliding surface. Use the Nussbaum function and adaptive neural network method to handle actuator failures and parameter uncertainties, and design the event-triggered fault-tolerant control input signal.

[0049] Establish an event-triggered control mechanism to save computing resources. Design the following control signal update mechanism:

[0050]

[0051] Among them, u i0 (t) is the aircraft control input signal. It is the moment the event is triggered, m i =0, 1, .... ν i These are virtual control signals designed for this purpose;

[0052] Design the following event triggering mechanism to update the control law when an event is triggered:

[0053]

[0054] in, The moment the event is triggered The next triggering time, z i (t)=ν i (t)-u i0 (t) represents the controller triggering error, 0 < α i1 <1 and β i1 >0 represents the design parameters;

[0055] Design fractional-order sliding surfaces to finely tune the control performance of swarm aircraft. Define The fractional sliding surface is as follows:

[0056]

[0057] Among them, c i1 c i2 and c i3 These are the positive parameters of the design. and For e i fractional derivative, 0 i1 <1 and 0 i2 <1 indicates a fractional operator;

[0058] Define a time-varying vector γ i =[γ i1 ,γ i2 ,γ i3 ] T The Nussbaum function used is in the form of j = 1, 2, 3, γ i The derivative is expressed as:

[0059]

[0060] Where, λ i =diag{λ i1 ,λ i2 ,λ i3 ​​} is the design matrix, λ i1 , λ i2 and λ i3 For the design of positive constants. It is an estimate of the weight matrix, φ i (p i ,v i ) are basis functions. This is an estimate of the deviation;

[0061] Define X i =[p i ,v i ] T Indicates aircraft status information, φ i (p i ,v i )=φ i (X i Let φ be a basis function containing n nodes. i (X i )=[φ i1 (X i ),φ i2 (X i ),…,φ ij (X i ),…φ in (X i )] T , where φ ij (X i Defined as Gaussian form:

[0062]

[0063] Where exp[a] represents the exponent raised to the power of a, η i For the center value, N c The width needs to be carefully adjusted;

[0064] and The derivative is expressed as:

[0065]

[0066] Where, k i2 and k i3 For positive parameters of the design;

[0067] Define a virtual time-varying vector ε i =[ε i1 ,ε i2 ,ε i3 ] T It can be expressed as:

[0068]

[0069] in, It is γ i diagonal matrix;

[0070] The designed virtual control signal is:

[0071]

[0072] Where, diag(ε i )=diag{ε i1 ,ε i2 ,ε i3} is ε i diagonal matrix, For a constant term, Λ i1 and Λ i2 The time-varying vector of the design is represented as:

[0073]

[0074] Where tanh(*) is the hyperbolic tangent function, g i1 g i2 and g i3 For matrix g in step one i The column vector, specifically g i =[g i1 ,g i2 ,g i3 ], It is a positive parameter.

[0075] Then, the actual control input signal u can be obtained through the event-triggered control mechanism. i0 .

[0076] The present invention has the following beneficial effects:

[0077] (1) This invention considers the fault-tolerant control problem of swarm aircraft encountering actuator failure and parameter uncertainty. The designed fault-tolerant cooperative control method ensures the safe cooperative flight of the swarm aircraft system.

[0078] (2) The present invention adopts a dual event triggering mechanism, namely event triggering communication and event triggering control mechanism, which reduces the communication frequency between aircraft and the update frequency of the controller, and can effectively reduce the consumption of communication and computing resources.

[0079] (3) The present invention adopts a fractional-order control method, which adds adjustable parameters at the operator level, enabling fine adjustment of the aircraft control performance. Attached Figure Description

[0080] Figure 1 A topology diagram of the swarm aircraft;

[0081] Figure 2 This is a diagram of the aircraft's control architecture.

[0082] Figure 3 This is a flight position map of the swarm aircraft;

[0083] Figure 4 A flight speed diagram of the swarm aircraft;

[0084] Figure 5 A diagram showing the tracking error of the follower aircraft to the command signal;

[0085] Figure 6 A graph showing the number of event-triggered communications and time-triggered communications for the aircraft;

[0086] Figure 7 A diagram showing the number of event-triggered control and time-triggered control operations for the aircraft;

[0087] Figure 8 Diagram of control input signals for aircraft #1;

[0088] Figure 9 Diagram of control input signals for aircraft #2;

[0089] Figure 10 Tracking error diagrams obtained by applying different fractional-order operators to aircraft #1. Detailed Implementation

[0090] The control method of the present invention will be further explained in conjunction with the accompanying drawings and tables.

[0091] This application provides a refined fault-tolerant cooperative control method for swarm aircraft based on dual-event triggering. First, an aircraft dynamics model is constructed, considering the impact of parameter uncertainties and actuator failures. The model is then further modified to obtain a model under the influence of parameter uncertainties and actuator failures. Second, an event-triggered communication mechanism is constructed, communicating only when specific events are triggered. A distributed observer is also built to estimate the aircraft reference command signal based on the discontinuous state information obtained from the event-triggered communication mechanism. Then, the Nussbaum function and adaptive neural network method are used to handle actuator failures and parameter uncertainties. Finally, a fractional-order sliding mode fault-tolerant control strategy based on the event-triggered control mechanism is designed, reducing computational resource consumption and achieving refined control of the swarm aircraft.

[0092] (a) Establish the aircraft dynamics model.

[0093] Set up a formation of N followers and 1 leader aircraft, where the dynamics of the i-th follower aircraft (i = 1, 2, ..., N) is:

[0094]

[0095] Where, x i y i and z i Let i be the three-dimensional coordinates of the i-th aircraft. x represents i First derivative, V i For the speed of the aircraft, χ i Represents the heading angle, γ i Represents the flight path angle. T i D i L i and Y i These are the aircraft's thrust, drag, lift, and lateral forces. α i It's the angle of attack, μ i It is the tilt angle, β i It is the sideslip angle. m is the mass of the aircraft, and g is the gravitational acceleration coefficient;

[0096] Thrust, drag, lift, and lateral force can be expressed as:

[0097]

[0098] Where, ρ iT It's the throttle opening, T max For maximum thrust, ρ is the air density, s i C represents the wing area. iL C iD and C iY These are the lift coefficient, drag coefficient, and lateral force coefficient, which are expressed by the following formulas:

[0099]

[0100] Among them, C iD0 C iY0 and C iL0 C is a constant. iDα C iLα and C iDα2 To match the angle of attack α i Correlation coefficient, C iYβ To the sideslip angle β i Correlation coefficient;

[0101] Define u i =[ρ iT ,α i sinμ i,α i cosμ i ] T Input signal vector and p to the aircraft i =[x i ,y i ,z i ] T Given the aircraft position information vector, (1) can be simplified to:

[0102]

[0103] Among them, f i and g i To simplify the resulting matrix, it is expressed as:

[0104]

[0105] (b) Establish a dynamic model of the aircraft under the influence of parameter uncertainty and actuator failure.

[0106] First, consider parameter uncertainties. The dynamics of an aircraft with parameter uncertainties can be expressed as follows:

[0107]

[0108] Among them, L id D id and Y id These represent the unknown lift, drag, and side force caused by parameter uncertainties, respectively. The remaining parameters are consistent with the aircraft model parameters in (a).

[0109] The unknown lift, drag, and lateral forces, along with the uncertain parameters, can be expressed as:

[0110]

[0111] Among them, C iDd C iYd and C iLd For unknown aerodynamic parameters, C iD0d C iDαd C iDα2d C iY0d C iYβd C iL0d and C iLαd The coefficient is unknown.

[0112] By considering parameter uncertainties, the aircraft dynamics equation (4) in step one is changed to:

[0113]

[0114] Among them, f i and g iConsistent with the definition in step one, f id and g id The unknown matrix caused by parameter uncertainty can be represented as:

[0115]

[0116] Among them, M i As given in (5).

[0117] Considering actuator failures faced by the aircraft, the failure model is represented as follows:

[0118] u i =ρ i u i0 +f ib (10)

[0119] Where, ρ i =diag{ρ i1 ,ρ i2 ,ρ i3}, ρ ij ∈(0,1] is the efficiency loss factor, j=1,2,3,f ib =[f ib1 ,f ib2 ,f ib3 ] T Represents a deviation fault, u i0 It is the aircraft control input signal.

[0120] Combining (8), the aircraft dynamics model considering parameter uncertainties and actuator failures can be expressed as:

[0121]

[0122] Where, d i =f id +g i f ib +g id (ρ i u i0 +f ib ) is a lumped unknown term resulting from the coupling of deviation faults and parameter uncertainties.

[0123] (c) Design an event-triggered communication mechanism and establish a distributed observer to estimate the aircraft reference command signal using the discontinuous state information of the neighbors.

[0124] An event-triggered communication mechanism is established to save communication resources. Using this mechanism, the i-th aircraft can only obtain the status information of its neighbors' aircraft when an event is triggered. The position triggering error of the i-th aircraft is defined as... Speed ​​trigger error is in, and Position and velocity information estimated for distributed observers, and At the triggering time The estimated position and velocity information, k i =0, 1, ... The first trigger time is set to... The event-triggered communication mechanism is represented as follows:

[0125]

[0126] in, Indicates the trigger time The next triggering time, inf{*} represents the infmum of a function, χ1, χ2, ξ1, ξ2, and ξ3 are positive constants in the design, Ω i It is a time-varying parameter, and its derivative is:

[0127]

[0128] Among them, Ω i (0) is the initial value of the time-varying parameter, and δ1 is a positive design parameter.

[0129] Because of the event-triggered communication mechanism, the state information received by the aircraft from its neighbors is discontinuous. This discontinuous state information cannot be directly used to design the aircraft's control law. Therefore, a distributed observer is designed to obtain continuous state information. The designed distributed observer is as follows:

[0130]

[0131] in, and For the position and velocity information estimated by the distributed observer, ψ i1 and ψ i2 The co-position and velocity deviation of the estimator, as defined, is expressed as:

[0132]

[0133] Among them, a ij b represents the communication weight between the i-th and j-th aircraft. i This represents the communication weight between the i-th aircraft and the leader aircraft. and It is the j-th spacecraft at the trigger time Estimated position and velocity information, and The leader's aircraft at the trigger moment Position and velocity information. ij d represents the distance between the i-th and j-th aircraft. i0 This represents the distance between the i-th aircraft and the leader aircraft.

[0134] (d) Establish an event-triggered control mechanism and a fractional sliding surface. Use the Nussbaum function and adaptive neural network method to handle actuator faults and parameter uncertainties, and design event-triggered fault-tolerant control input signals.

[0135] Establish an event-triggered control mechanism to save computing resources. Design the following control signal update mechanism:

[0136]

[0137] Among them, u i0 (t) is the aircraft control input signal. It is the moment the event is triggered, m i =0, 1, .... ν i It is a virtual control signal designed.

[0138] Design the following event triggering mechanism to update the control law when an event is triggered:

[0139]

[0140] in, The moment the event is triggered The next triggering time, z i (t)=ν i (t)-u i0 (t) represents the controller triggering error, 0 < α i1 <1 and β i1 >0 represents the design parameters.

[0141] Design fractional-order sliding surfaces to finely tune the control performance of swarm aircraft. Define The fractional sliding surface is as follows:

[0142]

[0143] Among them, c i1 c i2 and c i3 These are the positive parameters of the design. and For e i fractional derivative, 0 i1 <1 and 0 i2 <1 represents a fractional operator.

[0144] Define a time-varying vector γ i ​​=[γ i1 ,γ i2 ,γ i3 ] T The Nussbaum function used is in the form of j = 1, 2, 3, γ i The derivative is expressed as:

[0145]

[0146] Where, λ i =diag{λ i1 ,λ i2 ,λ i3} is the design matrix, λ i1 , λ i2 and λ i3 For the design of positive constants. It is an estimate of the weight matrix, φ i (p i ,v i ) are basis functions. This is an estimate of the deviation.

[0147] Define X i =[p i ,v i ] T Indicates aircraft status information, φ i (p i ,v i )=φ i (X i Let φ be a basis function containing n nodes. i (X i )=[φ i1 (X i ),φ i2 (X i ),…,φ ij (X i ),…φ in (X i )] T , where φ ij (X i Defined as Gaussian form:

[0148]

[0149] Where exp[a] represents the exponent raised to the power of a, η i As the center value, N c The width needs to be carefully adjusted.

[0150] and The derivative is expressed as:

[0151]

[0152] Where, k i2 and k i3 These are the positive parameters of the design.

[0153] Define a virtual time-varying vector ε i =[ε i1 ,ε i2 ,ε i3 ] T It can be expressed as:

[0154]

[0155] in, It is γ i diagonal matrix;

[0156] The designed virtual control signal is:

[0157]

[0158] Where, diag(ε i )=diag{ε i1 ,ε i2 ,ε i3} is ε i diagonal matrix, For a constant term, Λ i1 and Λ i2 The time-varying vector of the design is represented as:

[0159]

[0160] Where tanh(*) is the hyperbolic tangent function, g i1 g i2 and g i3 For matrix g in step one i The column vector, specifically g i =[g i1 ,g i2 ,g i3 ], It is a positive parameter.

[0161] Then, the actual control input signal u can be obtained through the event-triggered control mechanism. i0 .

[0162] (e) Use the designed fault-tolerant control law to track the reference command signal.

[0163] To verify the effectiveness of this invention, the following experiments and analyses were conducted:

[0164] Consider a formation of aircraft consisting of 4 follower aircraft and 1 leader aircraft, with the following topology: Figure 1 As shown. The topological weights are designed as follows: b1 = 1, b2 = 1, a 12 =0.7, a 13 =0.4, a 14 =0.4, a 23 =0.4, a 24 =0.4, a 34 =0.2. The control parameters are designed as follows: χ1=1, χ2=0.5, ξ1=0.01, ξ2=0.01, ξ3=0.01, δ1=0.1, α 11 =α 21 =α 31 =α 41 =0.05, β 11 =β 21 =β 31 =β 41 =0.02, c ij =0.1,i=1,2,3,4,j=1,2,3,a 11 =a 21 =a 31 =a 41 =0.35, a 12 =a 22 =a 32 =a 42 =0.65, λ 11 =λ 21 =λ 31 =5,λ 41 =10, λ 12 =λ 22 =λ 32 =20, λ 42 =10, λ 13 =λ 23 =λ 33 =λ 43 =10,k 12 =k 22 =k 32 =k 42 =10,k 13 =k 23 =k 33 =k 43 =1, The parameter uncertainty is set to 20% of the normal parameter, i.e., C. iDd =0.2C iD C iYd =0.2C iY C iLd =0.2CiL , i = 1, 2, 3, 4. At t = 60s, an actuator fault is introduced into spacecraft #1: ρ 11 =0.7, ρ 12 =1,ρ 13 =1,f 1b1 =0.2, f 1b2 =0,f 1b3 =0. The leader's flight path is: x0 = 0 + 30t, y0 = 0 - 2sin(0.1t), z0 = 1200 - 2sin(0.1t), and the preset distance between aircraft is: d 10 =[0,0,50] T d 12 =[0,0,100] T d 13 =[0,50,50] T d 14 =[0,-50,50] T d 20 =[0,0,-50] T d 23 =[0,50,-50] T d 24 =[0,-50,-50] T d 34 =[0,-100,0] T . Figure 2 The structure of the designed fault-tolerant control method is shown.

[0165] The simulation results show that the dual-event-triggered refined fault-tolerant cooperative control method designed in this invention can effectively achieve cluster cooperation and enable the aircraft to track the reference command signal. Figure 3 The position curves of the swarm aircraft are shown. As can be seen from the figure, the aircraft can fly in formation well, and the trend of the curve changes is consistent with the trend of the leader aircraft's position curve, which proves that the follower aircraft can follow the leader aircraft. Figure 4 The curve shows the speed variation of the swarm aircraft. It can be seen that the speed of the aircraft oscillates significantly in the initial stage, but as the controller takes effect, the amplitude decreases rapidly and eventually becomes consistent with the speed of the leader aircraft. Figure 5 The data shows the tracking error of the follower aircraft to the reference command signal estimated by the distributed observer. It can be seen that the designed controller can achieve good tracking of the reference signal. Even when the actuator fails, the controller can still function well, ensuring that the tracking error does not exceed a certain range and eventually converges the tracking error. Figure 6The display shows a comparison between the number of event-triggered communications and the number of time-triggered communications. It can be seen that using an event-triggered communication mechanism can effectively reduce the number of communications and save communication resources. Figure 7 and Figure 6 Similarly, the display shows the number of times the event-triggered control signal and the time-triggered control signal are updated. It is clear from this that using the event-triggered control mechanism can significantly reduce the number of controller updates and save computing resources. Figure 8 and Figure 9 The control input signals of aircraft #1 and aircraft #2 are shown respectively, demonstrating the role of event-triggered control. The signal only changes when the control input is updated; otherwise, the control input remains unchanged. Compared to aircraft #2 without actuator malfunction, the control input u of aircraft #1... 01 The rapid increase in the effect of actuator failure demonstrates that the designed fault-tolerant control method can effectively cope with sudden actuator failures. Figure 10 The study demonstrates the tracking errors obtained by aircraft #1 using different fractional-order operators, showing that fine-tuning of control performance can be achieved by adjusting the fractional-order operators.

[0166] In summary, when actuator failures and parameter uncertainties occur in a swarm aircraft system with limited communication and computing resources, the refined fault-tolerant collaborative control method based on dual-event triggering designed in this invention can save communication and computing resources while achieving safe collaboration of swarm aircraft.

Claims

1. A refined fault-tolerant cooperative control method for swarm aircraft based on dual-event triggering, characterized in that: The method includes the following steps: Step 1: Establish the aircraft dynamics model; Step 2: Establish a dynamic model of the aircraft under the influence of parameter uncertainties and actuator failures; Step 3: Design an event-triggered communication mechanism and establish a distributed observer to estimate the aircraft reference command signal using discontinuous state information from neighbors; Step four involves establishing an event-triggered control mechanism and fractional sliding mode, employing the Nussbaum function and adaptive neural network method to handle actuator failures and parameter uncertainties, and designing event-triggered fault-tolerant control input signals; specifically, step four includes: Establish an event-triggered control mechanism: Design the following control signal update mechanism: Among them, u i0 (t) is the aircraft control input signal. It is the moment the event is triggered, m i =0,1,…;ν i These are virtual control signals designed for this purpose; Design the following event triggering mechanism to update the control law when an event is triggered: in, The moment the event is triggered The next triggering time, z i (t)=ν i (t)-u i0 (t) represents the controller triggering error, 0 < α i1 <1 and β i1 >0 represents the design parameters; Design a fractional-order sliding surface and define... The fractional sliding surface is as follows: Among them, S i For the designed sliding surface, c i1 c i2 and c i3 For positive parameters of the design; and Represents e i fractional derivative, 0 i1 <1 and 0 i2 <1 indicates a fractional operator; p i This is the vector of the aircraft's position information. Location information estimated for distributed observers;​​ Define a time-varying vector γ i =[γ i1 ,γ i2 ,γ i3 ] T The Nussbaum function used is in the form of γ i The derivative is expressed as: Where, λ i =diag{λ i1 ,λ i2 ,λ i3 } is the diagonal matrix of the design, λ i1 , λ i2 and λ i3 For the design of positive constants; It is an estimate of the weight matrix, φ i (p i ,v i ) are basis functions. f is an estimate of the deviation; i To simplify the resulting matrix; Define X i =[p i ,v i ] T Indicates aircraft status information, φ i (p i ,v i )=φ i (X i Let φ be a basis function containing n nodes. i (X i )=[φ i1 (X i ),φ i2 (X i ),…,φ ij (X i ),…φ in (X i )] T , where φ ij (X i (in Gaussian form) Where exp[a] represents the exponent raised to the power of a, η i For the center value, N c Width; and The derivative is expressed as: Where, k i2 and k i3 For positive parameters of the design; Define a virtual time-varying vector ε i =[ε i1 ,ε i2 ,ε i3 ] T Its expression is: in, It is γ i diagonal matrix; The virtual control signal is designed as follows: Where, diag(ε i )=diag{ε i1 ,ε i2 ,ε i3 } is ε i diagonal matrix, For a constant term, Λ i1 and Λ i2 The time-varying vector of the design is represented as: Where tanh(*) is the hyperbolic tangent function, g i1 g i2 and g i3 For matrix g in step one i The column vector, specifically g i =[g i1 ,g i2 ,g i3 ], It is a positive parameter.

2. The method according to claim 1, characterized in that: The aircraft dynamics model established in step one is as follows: Set up a formation of N follower aircraft and 1 leader aircraft, where the i-th aircraft has the following dynamics: Where, x i y i and z i Let i be the three-dimensional coordinates of the i-th aircraft. x represents i First derivative, V i For the speed of the aircraft, χ i Represents the heading angle, γ i Represents the flight path angle; T i D i L i and Y i These are the aircraft's thrust, drag, lift, and lateral force; α i It's the angle of attack, μ i It is the tilt angle, β i It is the sideslip angle; m is the mass of the aircraft, and g is the gravitational acceleration coefficient. The thrust, drag, lift, and lateral force are expressed as follows: Where, ρ iT It's the throttle opening, T max For maximum thrust, ρ is the air density, and C is the wing area; iL C iD and C iY These are the lift coefficient, drag coefficient, and lateral force coefficient, which are expressed by the following formulas: Among them, C iD0 C iY0 and C iL0 C is a constant. iDα C iLα and To match the angle of attack α i Correlation coefficient, C iYβ To the sideslip angle β i Correlation coefficient; Define u i =[ρ iT ,α i sinμ i ,α i cosμ i ] T Input signal vector and p to the aircraft i =[x i ,y i ,z i ] T Given the aircraft position information vector, equation (1) can be simplified to obtain: Among them, f i and g i To simplify the resulting matrix, it is expressed as:

3. The method according to claim 2, characterized in that: The aircraft dynamics model established in step two, under the influence of parameter uncertainties and actuator failures, is as follows: The dynamics of an aircraft with parameter uncertainties are defined as follows: Among them, L id D id and Y id These are the unknown lift, drag, and lateral forces caused by parameter uncertainties, respectively. The unknown lift, drag, and lateral forces, along with the uncertain parameters, are represented as follows: Among them, C iDd C iYd and C iLd For unknown aerodynamic parameters, C iD0d C iDαd , C iY0d C iYβd C iL0d and C iLαd For unknown coefficients; Change the aircraft dynamics equation (4) in step one to: Among them, f i and g i Consistent with the definition in step one, f id and g id The unknown matrix caused by parameter uncertainty is represented as: Among them, M i As given in (5); Considering actuator failures faced by the aircraft, the failure model is represented as follows: in i =ρ i in i0 +f ib (10) Where, ρ i =diag{ρ i1 ,ρ i2 ,ρ i3 }, ρ ij ∈(0,1] is the efficiency loss factor, j=1,2,3,f ib =[f ib1 ,f ib2 ,f ib3 ] T Represents a deviation fault, u i0 It is the aircraft control input signal; Combining (8), the aircraft dynamics model considering parameter uncertainties and actuator failures is expressed as: Where, d i =f id +g i f ib +g id (ρ i u i0 +f ib ) is a lumped unknown term resulting from the coupling of deviation faults and parameter uncertainties.

4. The method according to claim 3, characterized in that: The event-triggered communication mechanism and distributed observer established in step three are as follows: Design an event-triggered communication mechanism: The i-th aircraft can only obtain the status information of its neighbors' aircraft when the event is triggered; the position triggering error of the i-th aircraft is defined as... And speed trigger error is in, and Position and velocity information estimated for distributed observers, and At the triggering time The estimated position and velocity information, k i =0, 1, ...; the first trigger time is set to The event-triggered communication mechanism is as follows: in, Indicates the trigger time The next triggering time, inf{*} represents the infmum of a function, χ1, χ2, ξ1, ξ2, and ξ3 are positive constants in the design, Ω i It is a time-varying parameter, and its derivative is: Among them, Ω i (0) represents the initial value of the time-varying parameter, and δ1 is a positive design parameter; Design a distributed observer to obtain continuous state information. The distributed observer is as follows: in, and For the position and velocity information estimated by the distributed observer, ψ i1 and ψ i2 The co-position and velocity deviation of the estimator, as defined, is expressed as: Among them, a ij b represents the communication weight between the i-th and j-th aircraft. i This represents the communication weight between the i-th aircraft and the leader aircraft. and It is the j-th spacecraft at the trigger time Estimated position and velocity information, and The leader's aircraft at the trigger moment Position and velocity information; d ij d represents the distance between the i-th and j-th aircraft. i0 This represents the distance between the i-th aircraft and the leader aircraft.

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