An adaptive fault-tolerant control method for multiple unmanned aerial vehicles under time-varying communication delay

Abstract

The present invention discloses an adaptive fault-tolerant control method for multiple unmanned aerial vehicles under time-varying communication delay, and relates to the technical field of unmanned aerial vehicles. The method first considers the time-varying communication delay, designs the expected position and expected speed observers, and then transforms the dynamic model of the multiple unmanned aerial vehicles into an affine form, considers thrust failure, parameter uncertainty and external disturbance, and establishes the unmanned aerial vehicle actuator fault model. Afterwards, the lumped equivalent efficiency factor in the fault model is estimated by designing an adaptive law. Finally, a fault-tolerant controller is obtained by deduction and design based on the integral terminal sliding mode method and considering the time-varying communication delay. The designed integral terminal sliding mode fault-tolerant controller is used to achieve accurate tracking of the leader under time-varying communication delay. The present invention is used for fault-tolerant control of multiple unmanned aerial vehicle systems under time-varying communication delay.

Description

Technical Field

[0001] The present invention relates to an integral terminal sliding mode fault-tolerant control method for an unmanned aerial vehicle with reduced execution efficiency due to an actuator failure under time-varying communication delay, and belongs to the field of aircraft fault-tolerant control. Technical Background

[0002] In recent years, unmanned aerial vehicles have been widely used in many fields due to their high speed and ability to carry a variety of payloads. Especially in the military field, unmanned aerial vehicles have become an indispensable force. Most flight accidents are caused by failures in actuators, such as propeller damage, motor leakage and wear. Failures in actuators of unmanned aerial vehicles are very common. On the one hand, when performing tasks, unmanned aerial vehicles are inevitably subject to external interference and attacks. On the other hand, actual unmanned aerial vehicle systems have problems such as component degradation or damage to motors and propellers. At the same time, complex and changeable flight environments such as sudden rain and strong winds can easily cause battery short circuits and propeller damage in unmanned aerial vehicles. These greatly increase the probability of failure of unmanned aerial vehicle actuators. When an actuator fails, if it is not resolved in time, the unmanned aerial vehicle is likely to crash out of control, causing economic losses and mission failure. Therefore, in order to improve the ability of unmanned aerial vehicle systems to perform various tasks and ensure that the system has high safety and reliability, it is necessary to study the fault-tolerant control of unmanned aerial vehicles.

[0003] At the beginning of the development of formation control theory, scholars always made some ideal premises or assumptions in theoretical derivation. However, with the continuous development of theory, problems in practical combination are constantly emerging. The research on formation control problems is getting closer to specific and practical situations, and various restrictions and influencing factors are added to new problems. Various time delays are an unavoidable factor in the formation realization of multi-unmanned aerial vehicle systems, because the ideal delay-free condition is impossible to achieve in hardware implementation, and the actual multi-unmanned aerial vehicle system is a time-delay system. However, many current research results focus on the ideal multi-unmanned aerial vehicle system without delay, which may lead to unsatisfactory results when using these theories for physical simulation, and the performance indicators deviate greatly from the theoretical values. Therefore, studying the fault-tolerant control problem of multi-unmanned aerial vehicle system formation under communication delay conditions has strong practical and general significance. Summary of the invention

[0004] Purpose of the Invention

[0005] The purpose of the present invention is to provide an integral terminal sliding mode fault-tolerant control method for an unmanned aerial vehicle with reduced execution efficiency due to actuator failure under time-varying communication delay, so as to ensure that the unmanned aerial vehicle can still fly stably and track the expected trajectory and expected speed under the conditions of time-varying communication delay and actuator failure.

[0006] Technical Solution

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] The present invention is a method for adaptive fault-tolerant control of multiple unmanned aerial vehicles under time-varying communication delay, and the control method comprises the following steps:

[0009] S1, establish the unmanned aerial vehicle dynamics model;

[0010] S2, according to the dynamic model, setting the control input, position vector and velocity vector, and setting the control quantity;

[0011] S3, transforming the dynamic model of multiple UAVs into an affine form, and establishing a fault model of UAVs by considering thrust failure, parameter uncertainty and external disturbance;

[0012] S4, considering the time-varying communication delay, design the expected position and expected velocity observers;

[0013] S5, designing an adaptive law to estimate the lumped equivalent efficiency factor in the fault model;

[0014] S6, according to the estimated value of the lumped equivalent efficiency factor, a fault-tolerant controller is designed based on an integral terminal sliding mode method and taking into account time-varying communication delay.

[0015] Furthermore, the unmanned aerial vehicle dynamics model is:

[0016]

[0017] Among them, x i ,y i ,h i represents the three-axis position coordinates of the i-th UAV, V i represents the airspeed of the i-th UAV, χ i represents the heading angle of the i-th UAV, γ i represents the track angle of the i-th UAV, μ i represents the tilt angle of the i-th UAV, α i represents the angle of attack of the i-th UAV, β i represents the sideslip angle of the i-th UAV; T i is the thrust of the i-th UAV, Y i is the lateral force of the i-th UAV, D i is the resistance of the i-th UAV, L i is the lift of the i-th unmanned aerial vehicle. m iis the mass of the i-th UAV, g is the acceleration due to gravity;

[0018] The relevant forces involved in the UAV aircraft dynamics model are defined as follows:

[0019]

[0020] where s i is the wing area of ​​the i-th UAV, represents the dynamic pressure of the i-th UAV and ρ is the air density; C iL , C iD , C iY Respectively represent the total lift coefficient, total drag coefficient, and total lateral force coefficient; C iL0 , C iLα , C iD0 , C iDα , C iDα2 , C iY0 , C iYβ is the aerodynamic coefficient.

[0021] Furthermore, the control input is set as:

[0022] u i =[T i , α i sinμ i , α i cosμ i ] T , take p i =[x i ,y i ,h i ] T is the position vector, v i =[v xi , v yi , v hi ] T is the velocity vector, where v xi , v yi , v hi is the three-axis speed of the UAV, and we get

[0023]

[0024] Among them, F i is a term related to the aerodynamic parameters of the aircraft system, G i is the gain matrix;

[0025] set up

[0026]

[0027] is the rotation matrix,

[0028] but

[0029]

[0030]

[0031] Furthermore, the actuator fault model is:

[0032] The thrust fault, external disturbance and parameter inaccuracy are set as the disturbances of the actuator fault model:

[0033] u is =ζ i u i +θ i (7)

[0034] d i =[d i1 , d i2 , d i3 ] T (8)

[0035] ΔF i =[ΔF i1 , ΔF i2 , ΔF i3 ] T (9)

[0036] Among them, u is is the control input during a fault, ζ i =diag{ζ i1 ,0,0},0<ζ i1 ≤1 represents the remaining thrust ratio of the i-th UAV, θ i =[θ ip , 0, 0] T represents bias fault, θ ip is the magnitude of the bias fault of the i-th UAV;

[0037] Substituting (7) to (9) into (3), we obtain

[0038]

[0039] Let θ i +d i +ΔF i =G i ζ ib u i , ib is the equivalent efficiency factor; let ρ i =ζ i +ζ ibis the lumped equivalent efficiency factor, and equation (10) is transformed into

[0040]

[0041] Furthermore, considering the time-varying communication delay, the expected position and expected velocity observers are designed as follows:

[0042] Considering the time-varying communication delay when UAVs interact with their neighbors, and addressing the problem that some following UAVs cannot receive information from the leader, an expected position and expected velocity observer is designed to estimate its own expected position and expected velocity.

[0043] The desired velocity and desired position observers are designed as follows:

[0044]

[0045] Where N is the number of following UAVs, v0 is the leader speed, is the expected speed and expected position estimated by the i-th UAV, Ψ, Υ are the observer parameters to be designed, and τ is the time-varying communication delay; a ij is defined as: when the i-th UAV can receive the information of the j-th UAV, a ij >0, otherwise a ij =0, set a ii =0;c i Defined as: When the i-th UAV can receive the leader's information, c i =1, otherwise c i =0;h i ,h j is the expected relative distance vector between the i-th and j-th UAVs and the leader; v0 and p0 are the velocity and position vectors of the leader.

[0046] Furthermore, an adaptive law is designed to estimate the lumped equivalent efficiency factor in the fault model as follows:

[0047] Defining Position Tracking Error Speed ​​tracking error

[0048] in and is the expected speed and expected position of the i-th UAV, combined with the expected position and expected speed observer mentioned above, we have

[0049] Let δ i =ke ip +e iv , where k>0 is the parameter to be designed.

[0050] Select the integral terminal sliding surface

[0051]

[0052] in, are the parameters to be designed.

[0053] Considering the time-varying delay, the control law is obtained as follows:

[0054]

[0055] in η>0 is the controller parameter to be designed, is the lumped equivalent efficiency factor ρ i The estimated value of

[0056] Furthermore, according to the estimated value of the lumped equivalent efficiency factor, the fault-tolerant controller is designed based on the integral terminal sliding mode method and taking into account the time-varying communication delay:

[0057] right Using Laplace smoothing, when When setting:

[0058]

[0059] Where ξ is a very small number, k ε is a positive real number;

[0060] Design lumped equivalent efficiency factor The adaptive law is

[0061]

[0062] in is a positive real number that controls the adaptation rate.

[0063] Furthermore, according to the obtained control input u i , returning to the UAV fault model, the UAV is fault-tolerantly controlled under thrust loss conditions and the desired trajectory and desired speed are tracked.

[0064] The beneficial effects of the present invention are:

[0065] (1) The desired position and desired velocity observer designed by the present invention can still accurately estimate the desired position and desired velocity even when there is a time-varying communication delay between the following unmanned aerial vehicles.

[0066] (2) The present invention considers the fault-tolerant control problem of the UAV under time-varying communication delay when the execution efficiency is reduced due to actuator failure. Based on sliding mode control, the designed fault-tolerant control scheme not only ensures the stable flight of the UAV under actuator failure, but also enables the UAV to track the desired position and desired speed.

[0067] (3) It has great practical significance and application prospects in the fault-tolerant control of unmanned aerial vehicles.

[0068] Description of the accompanying tables and graphs

[0069] Figure 1 It is a flow chart of a method for adaptive fault-tolerant control of multiple unmanned aerial vehicles under time-varying communication delay;

[0070] Figure 2 The communication topology diagram for a multi-UAV formation;

[0071] Figure 3 is the position tracking error diagram of UAV No. 1;

[0072] Figure 4 This is the position tracking error diagram of UAV No. 2;

[0073] Figure 5 This is the position tracking error diagram of UAV No. 3;

[0074] Figure 6 This is the position tracking error diagram of UAV No. 4;

[0075] Figure 7 It is the velocity tracking error diagram of UAV No. 1;

[0076] Figure 8 It is the velocity tracking error diagram of UAV No. 2;

[0077] Fig. 9 This is the velocity tracking error diagram of UAV No. 3;

[0078] Fig.10 This is the velocity tracking error diagram of UAV No. 4;

[0079] Fig.11 It is the altitude curve diagram of four unmanned aerial vehicles;

[0080] Fig.12 This is the estimated graph of the aggregate equivalent efficiency factor of UAV No. 1;

[0081] Fig.13 This is the estimated graph of the aggregate equivalent efficiency factor of UAV No. 2;

[0082] Fig.14This is the estimated graph of the aggregate equivalent efficiency factor of UAV No. 3;

[0083] Fig.15 This is the estimated graph of the aggregate equivalent efficiency factor of UAV No. 4;

[0084] Fig.16 Thrust diagram for four unmanned aerial vehicles. DETAILED DESCRIPTION

[0085] The control method of the present invention is further explained in conjunction with the attached diagrams.

[0086] This embodiment provides a method for adaptive fault-tolerant control of multiple unmanned aerial vehicles under time-varying communication delay, and the control method comprises the following steps:

[0087] S1, establish the unmanned aerial vehicle dynamics model;

[0088] S2, according to the dynamic model, setting the control input, position vector and velocity vector, and setting the control quantity;

[0089] S3, transforming the dynamic model of multiple UAVs into an affine form, and establishing a fault model of UAVs by considering thrust failure, parameter uncertainty and external disturbance;

[0090] S4, considering the time-varying communication delay, design the expected position and expected velocity observers;

[0091] S5, designing an adaptive law to estimate the lumped equivalent efficiency factor in the fault model;

[0092] S6, according to the estimated value of the lumped equivalent efficiency factor, a fault-tolerant controller is designed based on an integral terminal sliding mode method and taking into account time-varying communication delay.

[0093] Specifically, the above control method is implemented through the following process:

[0094] (a) Establish the UAV dynamics model:

[0095]

[0096] Among them, x i ,y i ,h i represents the three-axis position coordinates of the i-th UAV, V i represents the airspeed of the i-th UAV, χ i represents the heading angle of the i-th UAV, γ i represents the track angle of the i-th UAV, μ i represents the tilt angle of the i-th UAV, α irepresents the angle of attack of the i-th UAV, β i T represents the sideslip angle of the i-th UAV. i is the thrust of the i-th UAV, Y i is the lateral force of the i-th UAV, D i is the resistance of the i-th UAV, L i is the lift of the i-th unmanned aerial vehicle. m i is the mass of the i-th UAV, and g is the acceleration due to gravity.

[0097] The relevant forces are defined as follows:

[0098]

[0099] where s i is the wing area of ​​the i-th UAV, represents the dynamic pressure of the i-th UAV and ρ is the air density. iL , C iD , C iY Represents the total lift coefficient, total drag coefficient, and total lateral force coefficient. iL0 , C iLα , C iD0 , C iDα , C iDα2 , C iY0 , C iYβ is the aerodynamic coefficient.

[0100] (b) Take the control input u i =[T i , α i sinμ i , α i cosμ i ] T , take p i =[x i ,y i ,h i ] T is the position vector, v i =[v xi , v yi , v hi ] T is the velocity vector, where v xi , v yi , v hi is the three-axis speed of the UAV, and we get

[0101]

[0102] definition

[0103]

[0104] but

[0105]

[0106]

[0107] (c) Establishing actuator fault model:

[0108] Consider thrust failure, external disturbances and parameter inaccuracies:

[0109] u is =ζ i u i +θ i (7)

[0110] d i =[d i1 , d i2 , d i3 ] T (8)

[0111] ΔF i =[ΔF i1 , ΔF i2 , ΔF i3 ] T (9)

[0112] Among them, u is is the control input during a fault, ζ i =diag{ζ i1 ,0,0},0<ζ i1 ≤1 represents the remaining thrust ratio of the i-th UAV, θ i =[θ ip , 0, 0] T represents bias fault, θ ip is the magnitude of the bias fault of the i-th UAV.

[0113] Substituting (7) to (9) into (3), we obtain

[0114]

[0115] Let θ i +d i +ΔF i =G i ζ ib u i , ib is the equivalent efficiency factor. Let ρ i =ζ i +ζ ib is the lumped equivalent efficiency factor, and formula (10) is transformed into

[0116]

[0117] (d) Design the expected position and expected observer

[0118] Considering the time-varying communication delay when UAVs interact with their neighbors, and to address the problem that some following UAVs cannot receive information from the leader, an expected position and expected velocity observer is designed to estimate their own expected position and expected velocity.

[0119] In the main follower formation structure, the communication network between N identical follower UAVs can be represented by a strongly connected directed graph To describe, Represents a set of vertices. The node number of is represented by I = {1, 2, ... N}, where the communication links between multiple UAVs are represented by edges, and the set of edges is represented by To describe, (υ j , v i )∈ε represents the unmanned aerial vehicle v j Transmit its own status information to the unmanned aerial vehicle v i , at this time we will v j Treated as y i The neighbor of v i The neighbor set of i = {v i ∈V:(υ j , v i )∈ε} description. In particular, the provisions and Defining the weighted adjacency matrix where a ij ≥0 is the connection weight. If (v j , i )∈ε, then a ij >0, like Then a ij =0, In particular, a ii =0, In addition, the virtual leader is labeled 0, and c=diag{c1, c2, ...c N} to represent its adjacency matrix, and when the i-th UAV can receive the leader's information, c i =1, otherwise c i = 0. In this patent, we assume that the virtual leader is not affected by any follower UAVs.

[0120] The desired position and desired velocity observers are designed as follows:

[0121]

[0122]

[0123] Among them, v0 is the leader speed, is the expected speed and expected position estimated by the i-th UAV, Ψ, Υ are the observer parameters to be designed, and τ is the time-varying communication delay. i ,h j is the expected relative distance vector between the i-th and j-th UAVs and the leader. v0, p0 are the velocity and position vectors of the leader.

[0124] (e) When the lumped equivalent efficiency factor is unknown in the fault model, the adaptive law is designed to calculate the lumped equivalent efficiency factor ρ i Make an estimate, then quote the estimate and combine it with the integral terminal sliding mode deduction to design a fault-tolerant controller;

[0125] Defining Position Tracking Error Speed ​​tracking error

[0126] in and is the expected speed and expected position of the i-th UAV, combined with the expected position and expected speed observer mentioned above, we have

[0127] Let δ i =ke ip +e iv , where k>0 is the parameter to be designed.

[0128] Select the integral terminal sliding surface

[0129]

[0130] in, are the parameters to be designed.

[0131] Considering the time-varying delay, the control law is obtained as follows:

[0132]

[0133] Among them, 0<ζ<1, η>0 are the controller parameters to be designed, is the lumped equivalent efficiency factor ρ i The estimated value of

[0134] To avoid Strange situation, yes Using Laplace smoothing, when season

[0135]

[0136] Where ξ is a very small number, k ε is a positive real number.

[0137] Design lumped equivalent efficiency factor The adaptive law is

[0138]

[0139] in is a positive real number that controls the adaptation rate.

[0140] (f) According to the obtained control input u i , returning to the UAV fault model, the UAV is fault-tolerantly controlled under thrust loss conditions and the desired trajectory and desired speed are tracked.

[0141] The effectiveness of the present invention is verified by performing simulation below:

[0142] The model parameters of the unmanned aerial vehicle are m = 25kg, g = 9.81kg / m 3 ,ρ=1.225kg / m 3 , s = 1.463m 2 The relevant parameters of the expected speed and expected position observers are Ψ=2, Υ=5. The relevant parameters of the integral terminal sliding mode are k=5, The relevant parameters of the controller are η=7,ξ=0.0001,k ξ =4, adaptive rate The trajectory of the virtual leader is: p0 = [30tm; 0m; 1000m] T , when t ≥ 25s, the height of the virtual leader is increased from 1000m to 1020m, and a filter is used to generate a smooth trajectory. The transfer function of the filter is ξ n =0.9,ω n =0.3. Among them, the initial state settings of each unmanned aerial vehicle are shown in the following table:

[0143] Table 1

[0144] UAV initial status

[0145] Unmanned aerial vehicle Initial position (m) Initial speed (m / s) Expected relative distance (m) UAV#1 <![CDATA[p1(0)=[-30;39.9;1000.1] T ]]> <![CDATA[ v1 (0)=[30;0;0] T ]]> <![CDATA[h1=[30;-40;0] T ]]> UAV#2 <![CDATA[p2(0)=[-30;-40.1;1000.1] T ]]> <![CDATA[v2(0)=[30;0;0] T ]]> <![CDATA[h2=[30;40;0] T ]]> UAV#3 <![CDATA[p3(0)=[30;-40.1;1000.1] T ]]> <![CDATA[v3(0)=[30;0;0] T ]]> <![CDATA[h3=[-30;40;0] T ]]> UAV#4 <![CDATA[p4(0)=[30;39.9;1000.1] T ]]> <![CDATA[v4(0)=[30;0;0] T ]]> <![CDATA[h4=[-30;-40;0] T ]]>

[0146] The parameter uncertainties, disturbances and faults suffered by each UAV are shown in Table 2:

[0147] Table 2

[0148] Parameter uncertainties, disturbances, and failures experienced by UAVs

[0149]

[0150] The simulation results show that the adaptive fault-tolerant control method designed by the present invention for the situation where the execution efficiency of the unmanned aerial vehicle is reduced due to actuator failure under time-varying communication delay can better handle the fault problem under time-varying communication delay and has a better control effect. Figure 2 The communication topology diagram of the unmanned aerial vehicle. Figure 3 , Figure 4 , Figure 5 , Figure 6 The speed tracking errors of UAVs UAV#1 to UAV#4 show that at 25 seconds, the height tracking error increases due to the virtual leader's height increase, and then quickly reaches a new height value under control. At 40 seconds and 60 seconds, the tracking error increases slightly due to disturbances and faults, but quickly returns to stability and eventually approaches zero under fault-tolerant control. Figure 7 , Figure 8 , Fig. 9 , Fig.10 It is the velocity tracking error of unmanned aerial vehicles UAV#1~UAV#4. Similar to the position tracking error, the velocity tracking error will increase slightly under the influence of maneuvering, disturbance and failure, but then it will quickly return to stability under fault-tolerant control and eventually tend to zero. Fig.11 This is a graph of the altitude of four unmanned aerial vehicles. It can be seen that the four aircraft can smoothly follow the virtual leader to increase altitude. Fig.12 , Fig.13 , Fig.14 , Fig.15 This is the estimation curve of the aggregate equivalent efficiency factor of the unmanned aerial vehicles UAV#1 to UAV#4. It can be seen that the adaptive law designed in the present invention can accurately and quickly estimate the aggregate equivalent efficiency factor. Fig.16 It shows that the thrust signal obtained by the designed control law is ultimately stable and bounded.

[0151] In summary, for the situation where the execution efficiency of the unmanned aerial vehicle is reduced due to actuator failure under time-varying communication delay, the method of the present invention can effectively realize fault-tolerant control and tracking control of the unmanned aerial vehicle.

Claims

1. A method for adaptive fault-tolerant control of multiple unmanned aerial vehicles under time-varying communication delay, characterized in that: The control method comprises the following steps: S1, establish the UAV dynamics model; S2, according to the dynamic model, setting the control input, position vector and velocity vector, and setting the control quantity; S3, transforming the dynamic model of multiple UAVs into an affine form, and establishing a fault model of UAVs by considering thrust failure, parameter uncertainty and external disturbance; S4, considering the time-varying communication delay, design the expected position and expected speed observer; the design of the expected position and expected speed observer is specifically as follows: Considering the time-varying communication delay when UAVs interact with their neighbors, and addressing the problem that some following UAVs cannot receive information from the leader, an expected position and expected velocity observer is designed to estimate its own expected position and expected velocity. The desired velocity and desired position observers are designed as follows: Where N is the number of following UAVs, v0 is the leader speed, is the expected speed and expected position estimated by the i-th UAV, Ψ, γ are the observer parameters to be designed, and τ is the time-varying communication delay; a ij is defined as: when the i-th UAV can receive the information of the j-th UAV, a ij > 0, otherwise a ij =0, set a ii =0;c i Defined as: When the i-th UAV can receive the leader's information, c i =1, otherwise c i =0;h i ,h j is the expected relative distance vector between the i-th and j-th UAVs and the leader; v0, p0 are the velocity and position vectors of the leader; S5, designing an adaptive law to estimate the lumped equivalent efficiency factor in the fault model; designing an adaptive law to estimate the lumped equivalent efficiency factor in the fault model is specifically: Defining Position Tracking Error Speed ​​tracking error in and is the expected speed and expected position of the i-th UAV, combined with the expected position and expected speed observer mentioned above, we have Let δ i =ke ip +e iv , where k>0 is the parameter to be designed; Select the integral terminal sliding surface in, is the parameter to be designed; Considering the time-varying delay, the control law is obtained as follows: in η>0 is the controller parameter to be designed, is the lumped equivalent efficiency factor ρ i The estimated value of S6, according to the estimated value of the lumped equivalent efficiency factor, a fault-tolerant controller is designed based on an integral terminal sliding mode method and taking into account time-varying communication delay.

2. The method for adaptive fault-tolerant control of multiple unmanned aerial vehicles under time-varying communication delay according to claim 1 is characterized in that: The unmanned aerial vehicle dynamics model is: Among them, x i ,y i ,h i represents the three-axis position coordinates of the i-th UAV, V i represents the airspeed of the i-th UAV, χ i represents the heading angle of the i-th UAV, γ i represents the track angle of the i-th UAV, μ i represents the tilt angle of the i-th UAV, α i represents the angle of attack of the i-th UAV, β i represents the sideslip angle of the i-th UAV; T i is the thrust of the i-th UAV, Y i is the lateral force of the i-th UAV, D i is the resistance of the i-th UAV, L i is the lift of the i-th UAV; m i is the mass of the i-th UAV, g is the acceleration due to gravity; The relevant forces involved in the UAV dynamics model are defined as follows: where s i is the wing area of ​​the i-th UAV, represents the dynamic pressure of the i-th UAV and ρ is the air density; C iL , C iD , C iY Respectively represent the total lift coefficient, total drag coefficient, and total lateral force coefficient; C iL0 , C iLα , C iD0 , C iDα , C iDα2 , C iY0 , C iYβ is the aerodynamic coefficient.

3. The method for adaptive fault-tolerant control of multiple unmanned aerial vehicles under time-varying communication delay according to claim 1, characterized in that: Set the control input to: u i =[T i , α i sinμ i , α i cosμ i ] T , take p i =[x i ,y i ,h i ] T is the position vector, v i =[v xi , v yi , v hi ] T is the velocity vector, where v xi , v yi , v hi is the three-axis speed of the UAV, and we get Among them, F i is a term related to the aerodynamic parameters of the aircraft system, G i is the gain matrix; set up is the rotation matrix, but 4. The method for adaptive fault-tolerant control of multiple unmanned aerial vehicles under time-varying communication delay according to claim 1, characterized in that: The actuator fault model is: The thrust fault, external disturbance and parameter inaccuracy are set as the disturbances of the actuator fault model: you is =ζ i you i +θ i (10) d i =[d i1 ,d i2 ,d i3 ] T (11) ΔF i =[ΔF i1 ,ΔF i2 ,ΔF i3 ] T (12) Among them, u is is the control input during a fault, ζ i =diag{ζ i1 ,0,0},0<ζ i1 ≤1 represents the remaining thrust ratio of the i-th UAV, θ i =[θ ip , 0, 0] T represents bias fault, θ ip is the magnitude of the bias fault of the i-th UAV; Substituting (10) to (12) into (6), we obtain Let θ i +d i +ΔF i =G i ζ ib u i , ib is the equivalent efficiency factor; let ρ i =ζ i +ζ ib is the lumped equivalent efficiency factor, and equation (13) is transformed into 5. The method for adaptive fault-tolerant control of multiple unmanned aerial vehicles under time-varying communication delay according to claim 1, characterized in that: According to the estimated value of the lumped equivalent efficiency factor, the fault-tolerant controller is designed based on the integral terminal sliding mode method and taking into account the time-varying communication delay: right Using Laplace smoothing, when When setting: Where ξ is a very small number, k ε is a positive real number; Design lumped equivalent efficiency factor The adaptive law is in is a positive real number that controls the adaptation rate.

6. The method for adaptive fault-tolerant control of multiple unmanned aerial vehicles under time-varying communication delay according to claim 1, characterized in that: According to the obtained control input u i , returning to the UAV fault model, the UAV is fault-tolerantly controlled under thrust loss conditions and the desired trajectory and desired speed are tracked.

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