A fault-tolerant controller design method for quadrotor unmanned aerial vehicles

Abstract

This invention discloses a fault-tolerant controller design method for a quadrotor unmanned aerial vehicle (UAV), relating to the field of quadrotor UAV control technology. The method includes: establishing a state-space equation; designing a state observer based on an interval-type II fuzzy logic system, and calculating the observation error between unmeasurable states and state observations; decomposing the attitude and position subsystems of the quadrotor UAV into two-order subsystems; and designing a virtual controller α for the first-order subsystem. i,1 For the second-order subsystem, a predetermined-time filter and an adaptive predetermined-time fault-tolerant controller τ based on hybrid event triggering are designed. i (t) Parameter update law and parameter update law The fault-tolerant controller designed in this invention can ensure that the actual predetermined time of the quadcopter UAV closed-loop system is stable, and the minimum upper bound of the system stability time can be determined by a controller gain, realizing the quadcopter UAV accurately tracking the reference trajectory under the conditions of only measurable output signal, saturated input and actuator failure.

Description

Technical Field

[0001] This invention relates to the field of quadcopter unmanned aerial vehicle (UAV) control technology, and more specifically to a fault-tolerant controller design method for quadcopter UAVs. Background Technology

[0002] Quadrotor unmanned aerial vehicles (UAVs) possess advantages such as vertical takeoff and landing, hovering, and high maneuverability, leading to their rapid development and widespread adoption in both military and civilian fields. Trajectory tracking control of quadrotor UAVs has received considerable attention as a fundamental research area. However, quadrotor UAVs inevitably suffer from external disturbances in real-world flight environments, and their kinematic models exhibit inherent nonlinear factors such as high complexity, strong coupling, and numerous uncertainties. In particular, actuator failures and saturated inputs also affect the stability of the quadrotor UAV closed-loop system, making trajectory tracking control research for quadrotor UAVs quite challenging.

[0003] Most current fault-tolerant controller designs for quadrotor UAVs assume that the system's state information is fully measurable. However, in real-world operating environments, it is difficult to accurately observe all the state information of a quadrotor UAV, making these controller designs unable to achieve trajectory tracking control for quadrotor UAVs with unmeasurable states. Furthermore, the onboard network communication resources of quadrotor UAVs are subject to rigid constraints due to their structure; traditional periodic sampling controller designs continuously consume communication bandwidth, causing mechanical wear on actuators. Although event-driven communication mechanisms can alleviate the communication burden and reduce the transmission of redundant data, the control design method lacks flexibility. Summary of the Invention

[0004] The present invention provides a fault-tolerant controller design method for a quadcopter unmanned aerial vehicle (UAV) to achieve accurate observation of the unmeasurable system state of the quadcopter UAV, and to ensure that the quadcopter UAV can quickly and accurately track the reference trajectory under conditions of actuator failure, saturated input, and limited network bandwidth.

[0005] The technical solution adopted in this invention is as follows:

[0006] This invention provides a fault-tolerant controller design method for a quadcopter unmanned aerial vehicle (UAV), comprising:

[0007] A dynamic model of a quadrotor UAV with saturated input and actuator failure is constructed, and a state-space equation is established based on it.

[0008] Design a state observer based on an interval type-II fuzzy logic system. Use this state observer to estimate the unmeasurable states of a quadrotor UAV, obtain state observation values, and calculate the observation error e between the unmeasurable states and the state observation values ​​of the quadrotor UAV. i ;

[0009] Based on the adaptive back-propagation control method, coordinate transformation equations are constructed by combining state observations to decompose the attitude subsystem and position subsystem of the quadcopter UAV into two-order subsystems.

[0010] Based on the coordinate transformation equation, a virtual controller α is designed for the first-order subsystems of the attitude and position subsystems of a quadrotor UAV. i,1 ;

[0011] For the second-order subsystems of the attitude and position subsystems of a quadcopter UAV, a predetermined time filter and an adaptive predetermined time fault-tolerant controller τ based on hybrid event triggering are designed. i (t), parameter update law and parameter update law This allows the closed-loop systems of the attitude and position subsystems of the quadcopter UAV to stabilize within a predetermined time, thereby obtaining the controller gain to be designed.

[0012] As a further description of the above technical solution, the dynamic model of a quadcopter UAV with saturated input and actuator failure is as follows:

[0013]

[0014] In the formula, φ, and These are the roll angle, angular velocity, and angular acceleration of the quadcopter drone; θ, and These are the pitch angle, angular velocity, and angular acceleration of the quadcopter drone; ψ, and These are the yaw angle, angular velocity, and angular acceleration of the quadcopter drone, respectively; z, and These represent the coordinates, velocity, and acceleration of the quadcopter UAV along the z-axis; x, and These represent the coordinates, velocity, and acceleration of the quadcopter UAV along the x-axis; y, and These represent the coordinates, velocity, and acceleration of the quadcopter UAV along the y-axis; τ f τ φ τ θ and τ ψ These are the total lift, roll angle control input, pitch angle control input, and yaw angle control input of the quadcopter UAV, respectively; M is the mass of the quadcopter UAV. Let g be the distance between the center of mass and the rotor of the quadcopter UAV; g is the acceleration due to gravity; J z J x and J y These are the moments of inertia along the z-axis, x-axis, and y-axis, respectively. and These are the sine and cosine functions, respectively; C k d is the drag coefficient; k For unknown external disturbances, satisfy For unknown positive constants; k = φ, θ, ψ, z, x, y;

[0015] The state-space equations are:

[0016]

[0017] In the formula: (x 1,1 ,x 2,1 ,x 3,1 ,x 4,1 ,x 5,1 ,x 6,1 (φ,θ,ψ,z,x,y),

[0018] g4=g5=g6=1 / M,I1,I2,I3=(τ φ ,τ θ ,τ ψ ),

[0019]

[0020] (d1,d2,d3,d4,d5,d6)=d φ ,d θ ,d ψ ,d z ,d x ,d y ),

[0021] The saturation input constraint experienced by the quadcopter drone is:

[0022] sat(I i ) = I i +Δ i

[0023] In the formula: and These are the upper and lower bounds of the saturated input, respectively;

[0024] The actuator failure model for a quadcopter drone is as follows:

[0025] I i =ρ i τ i +Ψ i

[0026] In the formula, τ iFor the actual control input signal of the quadcopter UAV, ρ i ∈(0,1] represents the actuator drive efficiency coefficient of the quadcopter UAV, Ψ i These are unknown time-varying deviation fault parameters.

[0027] As a further description of the above technical solution, the method also includes employing an interval-type II fuzzy logic system. Approximate nonlinear term Make it satisfy:

[0028]

[0029] In the formula, Ξ i Let ω be the ideal weight vector. i This is an approximation error. Indicates that the input is basis functions;

[0030] The state-space equations can be rewritten as follows:

[0031]

[0032] In the formula, G i =B i g i , W i =B i ω i D i =B i d i ,

[0033] The state observer design based on the interval type-2 fuzzy logic system is as follows:

[0034]

[0035] In the formula, and x i,1 x i,2 and y i The observed values, For the weight vector Ξ of the interval type II fuzzy logic system i The estimated value, k i,1 and k i,2 Positive design parameters;

[0036] Calculate the observation error between the unmeasurable state of a quadcopter UAV and the obtained state observation values. Further calculations yield the following:

[0037]

[0038] In the formula,

[0039] As a further description of the above technical solution, the coordinate transformation equation is:

[0040]

[0041] In the formula, and These are the first error surface and the second error surface, respectively. For reference trajectory, The output signal of the predetermined time filter, It is an auxiliary variable.

[0042] As a further description of the above technical solution, the virtual controller α i,1 for:

[0043]

[0044] In the formula, r = δ / 2 1+η / 2 δ=36 η / 2 , 0 < η < 1, For fractional arithmetic units, s, T r ,∈ i,1 and q i,1 For positive design parameters, For reference trajectory The derivative of .

[0045] As a further description of the above technical solution, the predetermined time filter is:

[0046]

[0047] In the formula, For filtering error, ∈ i,3 >0.

[0048] As a further description of the above technical solution, an adaptive predetermined time fault-tolerant controller τ based on hybrid event triggering... i (t) is:

[0049]

[0050] In the formula, κ i (t)=γ i (t)-τ * (t), 0 < μ i (0) < 1, μ i (t)∈(0,1), and ξ i These are positive design parameters; j = 1, ..., 5;

[0051] intermediate control signal γ i (t) is defined as:

[0052]

[0053] In the formula, σ i,1 and σ i,2 Positive design parameters;

[0054] Virtual Controller α i,2 Designed as follows:

[0055]

[0056] In the formula, ∈ i,2 and q i,2 These are positive design parameters.

[0057] As a further description of the above technical solution, the parameter update law and parameter update law for:

[0058]

[0059] In the formula, λ = 2 + η,

[0060] Compared with the prior art, the beneficial effects of the present invention are:

[0061] (1) Unlike the existing design method of quadcopter UAV trajectory tracking controller that assumes that all system state information is completely measurable, this invention designs an adaptive interval type II fuzzy output feedback control strategy based on state observer. This not only ensures that the quadcopter UAV accurately tracks the reference trajectory under the condition that only the output signal is measurable, but also removes the assumption that all system signals of the quadcopter UAV are always measurable.

[0062] (2) Unlike the controller design method based on time sampling and the controller design method based on single-mode event triggering, the design method of this invention proposes a hybrid event triggering communication mechanism. This mechanism can adaptively switch the triggering protocol according to the difference in the amplitude of the system control input, which not only improves the utilization rate of communication resources, but also avoids the problems of mechanical wear of actuators and excessive control input amplitude.

[0063] (3) The adaptive predetermined time fault-tolerant controller based on hybrid event triggering designed in this invention makes the actual predetermined time of the quadcopter UAV closed-loop system stable, and the minimum upper bound of the stable time can be directly adjusted by a controller gain. Furthermore, it ensures the effectiveness and feasibility of the fault-tolerant controller design method provided in this invention under the conditions of actuator failure, saturated input, and bandwidth limitation of the quadcopter UAV.

[0064] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, embodiments of the present invention are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0065] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0066] Figure 1 This is a schematic diagram of a quadcopter drone;

[0067] Figure 2 This is a flowchart illustrating the design method of a fault-tolerant controller for a quadcopter unmanned aerial vehicle (UAV) according to the present invention.

[0068] Figure 3 This is a block diagram of the design structure of an adaptive pre-time fault-tolerant controller for a quadcopter UAV based on hybrid event triggering;

[0069] Figure 4 It is a diagram of the reference trajectory, actual trajectory, and state observation trajectory of a quadcopter UAV in three-dimensional space;

[0070] Figure 5 This is a tracking error trajectory diagram of the attitude and position subsystems of a quadcopter UAV;

[0071] Figure 6 It is a graph showing the reference signals, output signals, and observation signals of the attitude and position subsystems of a quadcopter UAV.

[0072] Figure 7 This is a diagram showing the observation errors of the attitude and position subsystems of a quadcopter UAV.

[0073] Figure 8 This is a control input curve diagram of the attitude subsystem and position subsystem of a quadcopter UAV;

[0074] Figure 9 This is a diagram showing the trigger intervals of the attitude and position subsystems of a quadcopter UAV.

[0075] Figure 10 This is an adaptive parameter trajectory diagram of the attitude and position subsystems of a quadcopter UAV. Detailed Implementation

[0076] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0077] Please refer to Figure 1 , Figure 2 and Figure 3 This invention provides a fault-tolerant controller design method for a quadcopter unmanned aerial vehicle (UAV), comprising the following steps:

[0078] Step S1, according to Figure 1 The diagram shows the structure of a quadcopter UAV. A dynamic model of the quadcopter UAV with saturated input and actuator failure is constructed as follows:

[0079]

[0080] In the formula, φ, and These are the roll angle, angular velocity, and angular acceleration of the quadcopter drone; θ, and These are the pitch angle, angular velocity, and angular acceleration of the quadcopter drone; ψ, and These are the yaw angle, angular velocity, and angular acceleration of the quadcopter drone, respectively; z, and These represent the coordinates, velocity, and acceleration of the quadcopter UAV along the z-axis; x, and These represent the coordinates, velocity, and acceleration of the quadcopter UAV along the x-axis; y, and These represent the coordinates, velocity, and acceleration of the quadcopter UAV along the y-axis; τ f τ φ τ θ and τ ψ These are the total lift, roll angle control input, pitch angle control input, and yaw angle control input of the quadcopter UAV, respectively; M is the mass of the quadcopter UAV. Let g be the distance between the center of mass and the rotor of the quadcopter UAV; g is the acceleration due to gravity; J z J x and J y These are the moments of inertia along the z-axis, x-axis, and y-axis, respectively. and These are the sine and cosine functions, respectively; C k d is the drag coefficient; k For unknown external disturbances, satisfy For unknown positive constants; k = φ, θ, ψ, z, x, y.

[0081] The dynamic model (1) of the quadcopter UAV is transformed into the following state-space equations:

[0082]

[0083] In the formula: (x 1,1 ,x 2,1 ,x 3,1 ,x 4,1 ,x 5,1 ,x 6,1 )=(φ,θ,ψ,z,x,y),

[0084] g4=g5=g6=1 / M,I1,I2,I3=(τ φ ,τ θ ,τ ψ ),

[0085]

[0086] (d1,d2,d3,d4,d5,d6)=d φ ,d θ ,d ψ ,d z ,d x ,d y ),

[0087] The saturation input constraint experienced by the quadcopter drone is:

[0088] sat(I i ) = I i +Δ i (3) Where: and These are the upper and lower bounds of the saturated input, respectively;

[0089] The actuator failure model for a quadcopter drone is as follows:

[0090] I i =ρ i τ i +Ψ i (4)

[0091] In the formula, τ i For the actual control input signal of the quadcopter UAV, ρi ∈(0,1] represents the actuator drive efficiency coefficient of the quadcopter UAV, Ψ i These are unknown time-varying deviation fault parameters.

[0092] Step S2: For the state-space equation (2) mentioned in step S1, adopt an interval type II fuzzy logic system. Approximate nonlinear term Make it satisfy:

[0093]

[0094] In the formula, Ξ i Let ω be the ideal weight vector. i This is an approximation error. Indicates that the input is The basis functions.

[0095] Furthermore, the state-space equation (2) can be rewritten as:

[0096]

[0097] In the formula, G i =B i g i , W i =B i ω i D i =B i d i ,

[0098] Design a state observer (7) based on an interval type II fuzzy logic system. Use the state observer (7) to estimate the unmeasurable state of the quadcopter UAV and obtain the state observation value.

[0099]

[0100] In the formula, as well as x i,1 x i,2 and y i The observed values, For the weight vector Ξ of the interval type II fuzzy logic system i The estimated value, k i,1 and k i,2 These are positive design parameters.

[0101] Based on the state-space equation (6) and the state observer (7), the observation error between the unmeasurable state of the quadcopter UAV and the obtained state observation value is calculated. Further calculations yield the following:

[0102]

[0103] In the formula,

[0104] Step S3: Based on adaptive back-propagation control technology and combined with the obtained state observation values, the attitude subsystem and position subsystem of the quadcopter UAV are decomposed into two-order subsystems by constructing coordinate transformation equation (9).

[0105]

[0106] In the formula, and These are the first error surface and the second error surface, respectively. For reference trajectory, The output signal of the predetermined time filter, It is an auxiliary variable.

[0107] Step S4: Based on the coordinate transformation equations obtained in Step S3, for the first-order subsystems of the quadcopter UAV attitude subsystem and position subsystem, construct the following subsystem including the observation error e. i Lyapunov function V1:

[0108]

[0109] In the formula, P i It is a positive definite matrix. The observation error e i The transpose of .

[0110] Furthermore, taking the time derivative of the Lyapunov function V1, since... Using Young's inequality, we can calculate:

[0111]

[0112] In the formula, p i,1 =λ min (Q i )-3||P i || 2 , λ min (Q i Q is a positive definite matrix. i The smallest eigenvalue of Q is given by any positive definite matrix Q. i and Herwitz matrix A i There will always be Established.

[0113] Design a virtual controller α i,1 for:

[0114]

[0115] In the formula, r = δ / 2 1+η / 2 δ=36 η / 2 , 0 < η < 1, For fractional arithmetic units, s, T r , i,1 and q i,1 For positive design parameters, For reference trajectory The derivative of .

[0116] The designed virtual controller α i,1 Substituting into equation (11), we can simplify to obtain:

[0117]

[0118] In the formula, κ = 0.2785.

[0119] Step S5: Based on equation (13), for the second-order subsystems of the attitude and position subsystems of the quadcopter UAV, the Lyapunov function V2 is designed as follows:

[0120]

[0121] In the formula, and They are respectively Ξ i and Θ i The estimated value is obtained.

[0122] Calculating the time derivative of the Lyapunov function V², we get:

[0123]

[0124] Constructing auxiliary variables for:

[0125]

[0126] In the formula, These are positive design parameters.

[0127] The predetermined time filter is designed as follows:

[0128]

[0129] In the formula, For filtering error, ∈ i,3 >0.

[0130] Considering the limited onboard network bandwidth resources of quadcopter UAVs, traditional time-triggered mechanisms inevitably and continuously occupy communication bandwidth, causing mechanical wear on actuators. Therefore, to solve the problem of continuous communication bandwidth occupation by time-triggered control strategies, this invention designs the following adaptive predetermined time fault-tolerant controller τ based on hybrid event triggering. i (t):

[0131]

[0132] In the formula, κ i (t)=δ i (t)-τ i (t), 0 < μ i (0) < 1, μ i (t)∈(0,1), and ξ i These are positive design parameters; j = 1, ..., 5.

[0133] Furthermore, for the designed dynamic relative threshold triggering protocol and exponential decay triggering protocol (19), a corresponding practical adaptive predetermined time fault-tolerant controller based on hybrid event triggering is designed.

[0134] The dynamic relative threshold triggering protocol is case 1 in equation (19), namely:

[0135] |τ i (t)|<ξ i .

[0136] The exponential decay triggering protocol is case 2 in equation (19), namely:

[0137] |τ i (t)|≥ξ i .

[0138] Case 1: When |τ i (t)|<ξ i At that time, a dynamic relative threshold triggering protocol is adopted to update the control input signal, and the following intermediate control signal γ is designed. i (t):

[0139]

[0140] In the formula, σ i,1 and σ i,2 For positive design parameters, the virtual controller α i,2 Designed as follows:

[0141]

[0142] In the formula, ∈ i,2 and q i,2 These are positive design parameters.

[0143] Furthermore, the adaptive predetermined time fault-tolerant controller τ based on hybrid event triggering shown in equation (18) is... i (t) redesigned as:

[0144]

[0145] In the formula, |Φ i,1 (t)|≤1,|Φ i,2 (t)|≤1.

[0146] Case 2: When |τ i (t)|≥ i At this time, an exponential decay triggering protocol is adopted to avoid the problem of excessive control input amplitude, and the intermediate control signal γ i (t) is designed as follows:

[0147]

[0148] In the formula,

[0149] Accordingly, the adaptive predetermined time fault-tolerant controller τ based on hybrid event triggering shown in equation (18) will be... i (t) redesigned as:

[0150]

[0151] In the formula, |Φ i,3 (t)|≤1.

[0152] Design the following parameter update law and parameter update law

[0153]

[0154] In the formula, λ = 2 + η,

[0155] definition The designed adaptive pre-time fault-tolerant controller τ based on hybrid event triggering i (t), parameter update law and parameter update law Substituting into equation (15), we can calculate:

[0156]

[0157] In the formula,

[0158]

[0159] The proposed adaptive predetermined time fault-tolerant controller τ based on hybrid event triggering is to be adopted. i (t), parameter update law and parameter update law This allows the closed-loop systems of the attitude and position subsystems of a quadcopter UAV to stabilize within a predetermined time, thereby obtaining the controller gain to be designed.

[0160] Based on Lyapunov's predetermined time stability theory, the effectiveness of the invented fault-tolerant controller design method is proven, ensuring that a quadcopter UAV with unpredictable state can accurately track the reference trajectory under actuator failure and saturated input conditions.

[0161] Based on equation (27), the following can be calculated:

[0162]

[0163] The present invention discloses a fault-tolerant controller design method for a quadcopter unmanned aerial vehicle, comprising designing a state observer (7), a predetermined time filter (17), and a virtual controller α based on an interval type-II fuzzy logic system. i,1 Virtual controller α i,2 Parameter update law Parameter update law and an adaptive pre-time fault-tolerant controller τ based on hybrid event triggering i (t), based on Lyapunov's predetermined-time stability theory, the fault-tolerant controller for the quadcopter UAV designed using the method of this invention can guarantee the stability of the attitude and position subsystems of the quadcopter UAV within the actual predetermined time. All signals of the closed-loop system will be stable within the predetermined time. Converging inward to the following interval:

[0164]

[0165] According to equations (17) and (18), achievable in, This is an unknown positive parameter. Given κ... i (t k,i ) = 0 and So the system's trigger time interval Therefore, the Zeno phenomenon will not occur.

[0166] The effectiveness and feasibility of the fault-tolerant controller design method proposed in this invention are illustrated below through simulation experiments conducted in MATLAB R2020a / SIMULINK simulation software.

[0167] The reference trajectory is set to:

[0168]

[0169] The external unknown disturbance setting is:

[0170]

[0171] The initial conditions for the quadcopter drone were selected as follows:

[0172] [φ(0),θ(0),ψ(0)]=[0,0,0], [z(0),x(0),y(0)]=[-0.2,1.1,-0.2], μ i (0) = 0.3,

[0173] The gain of the state observer in the interval-based fuzzy logic system is selected as:

[0174]

[0175] The controller gain is selected as follows:

[0176] η = 2 / 11, T r =3, s=0.8,

[0177]

[0178] σ １,j =σ 2,j =25, 3, j 10, σ 4,1 =σ 4,2 =σ 5,1 =σ 5,2 =σ 6,1 =σ 6,2 =1, ξ i =10, q i,1 =2, q i,2 =3.

[0179] The upper and lower bounds of the saturated input are set as follows:

[0180] The actuator fault parameters are selected as follows:

[0181]

[0182] The model parameters for the quadcopter UAV are provided in Table 1.

[0183] Table 1

[0184]

[0185] The simulation results were plotted on Figures 4 to 10 . Figure 4 The reference trajectory, actual trajectory, and state observer trajectory of the quadcopter UAV in three-dimensional space were plotted respectively. Figure 5 The simulation results show the tracking error trajectories of the attitude and position subsystems of a quadcopter UAV. It converges inward to a small region near the origin. Figure 6 The response curves of the reference trajectory, actual trajectory, and observed trajectory of the attitude subsystem and position subsystem of the quadcopter UAV were plotted respectively. Figure 7 The simulation diagram shows the observation error trajectory of the attitude and position subsystems of a quadcopter UAV. It can be seen from the simulation diagram that the state observer based on the interval type II fuzzy logic system designed in this invention can effectively estimate the unmeasurable system state of the quadcopter UAV. Figure 8 The control input signals for the attitude and position subsystems of the quadcopter UAV are given. Simulation results show that the control input signals of the controlled UAV are strictly constrained to the saturation region. Figure 9 The diagram shows the trigger time intervals for the control inputs of the attitude and position subsystems of a quadcopter UAV, effectively avoiding the Zeno phenomenon. Figure 10 The diagram shows the adaptive parameter trajectory. Simulation results demonstrate that the fault-tolerant controller design method of this invention ensures the stability of the actual predetermined time of the quadrotor UAV closed-loop system, and guarantees that the quadrotor UAV with actuator failure and saturated input can still accurately track the reference trajectory even when only the output signal is observable.

[0186] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A fault-tolerant controller design method for a quadcopter unmanned aerial vehicle (UAV), characterized in that, include: A dynamic model of a quadrotor UAV with saturated input and actuator failure is constructed, and a state-space equation is established based on it. Design a state observer based on an interval type-II fuzzy logic system. Use this state observer to estimate the unmeasurable states of a quadrotor UAV, obtain state observation values, and calculate the observation error between the unmeasurable states and the state observation values ​​of the quadrotor UAV. ; Based on the adaptive back-propagation control method, coordinate transformation equations are constructed by combining state observations to decompose the attitude subsystem and position subsystem of the quadcopter UAV into two-order subsystems. Based on the coordinate transformation equation, a virtual controller is designed for the first-order subsystems of the attitude and position subsystems of a quadrotor UAV. ; For the second-order subsystems of the attitude and position subsystems of a quadcopter UAV, a pre-time filter and an adaptive pre-time fault-tolerant controller based on hybrid event triggering are designed. Parameter update law and parameter update law This allows the closed-loop systems of the attitude and position subsystems of the quadcopter UAV to stabilize at a predetermined time, thereby obtaining the controller gain to be designed. The dynamic model of a quadcopter UAV with saturated input and actuator failure is as follows: In the formula, , and These are the roll angle, angular velocity, and angular acceleration of the quadcopter drone, respectively. , and These are the pitch angle, angular velocity, and angular acceleration of the quadcopter drone; , and These are the yaw angle, angular velocity, and angular acceleration of the quadcopter drone, respectively. , and Quadrone drones Coordinates, velocity, and acceleration along the axial direction; , and Quadrone drones Coordinates, velocity, and acceleration along the axial direction; , and Quadrone drones Coordinates, velocity, and acceleration along the axial direction; , , and These are the total lift, roll angle control input, pitch angle control input, and yaw angle control input for the quadcopter UAV. The mass of the quadcopter drone; The distance between the center of mass and the rotor of a quadcopter drone; It is the acceleration due to gravity; , and They are respectively axis, shaft and Moment of inertia along the axial direction; and These are the sine and cosine functions, respectively. This is the drag coefficient; For external unknown disturbances, satisfy , For unknown positive constants; ; The state-space equations are: In the formula: , ; ， ， ； ， ； ； ； The saturation input constraint experienced by the quadcopter drone is: In the formula: , and These are the upper and lower bounds of the saturated input, respectively; The actuator failure model for a quadcopter drone is as follows: In the formula, This provides the actual control input signal for the quadcopter drone. This represents the actuator drive efficiency coefficient of a quadcopter drone. These are unknown time-varying deviation fault parameters; The method also includes the use of an interval-type II fuzzy logic system. Approximate nonlinear term To satisfy: In the formula, For the ideal weight vector, This is an approximation error. Indicates that the input is basis functions; The state-space equations can be rewritten as follows: In the formula, , , , , , , , ; The state observer design based on the interval type-2 fuzzy logic system is as follows: In the formula, , and They are respectively , and The observed values, Weight vector of interval type II fuzzy logic system The estimated value, and Positive design parameters; Calculate the observation error between the unmeasurable state of a quadcopter UAV and the obtained state observation values. Further calculations yield the following: In the formula, ; The coordinate transformation equation is: In the formula, and These are the first error surface and the second error surface, respectively. For reference trajectory, The output signal of the predetermined time filter, As an auxiliary variable; Virtual Controller for: In the formula, , , , , , , It is a fractional arithmetic unit. , , as well as For positive design parameters, For reference trajectory The derivative; The predetermined time filter is: In the formula, , For filtering error, , ; Hybrid event-triggered adaptive pre-time fault-tolerant controller for: In the formula, , , , , and Positive design parameters; ; intermediate control signal Defined as: In the formula, , , and Positive design parameters; Virtual Controller Designed as follows: In the formula, , , and These are positive design parameters.

2. The fault-tolerant controller design method for a quadcopter UAV according to claim 1, characterized in that, Parameter update law and parameter update law for: In the formula, , .

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