Four-rotor unmanned aerial vehicle actuator fault adaptive control method and device

Abstract

The invention discloses a four-rotor unmanned aerial vehicle actuator fault adaptive control method and device. The method comprises the following steps: establishing a four-rotor unmanned aerial vehicle height and attitude model with modeling uncertainty compensation parameters and actuator faults; according to unmanned aerial vehicle operation measurement data and the four-rotor unmanned aerial vehicle height and attitude model, tracking error variables are determined, and the tracking error variables comprise variables used for representing unmanned aerial vehicle state variable errors and virtual control signal errors; and constructing a nonlinear robust adaptive control law based on an adaptive backstepping method according to the height and attitude model of the quad-rotor unmanned aerial vehicle and the tracking error variable so as to realize adaptive control of the quad-rotor unmanned aerial vehicle according to the nonlinear robust adaptive control law. The technical effect of improving the operation stability and safety of the four-rotor unmanned aerial vehicle is achieved.

Description

Technical Field

[0001] The present application relates to the field of quadrotor UAV control, and more particularly, to a method and device for adaptive control of quadrotor UAV actuators Background Technique

[0002] With the gradual development of UAV technology, the application of UAVs in various industries has become increasingly widespread. Due to advantages such as adapting to various terrains, being flexible and maneuverable, having simple operation, and low cost, the application of quadrotor UAVs in various fields has also gradually increased.

[0003] During the flight and transportation of quadrotor UAVs, one or more simultaneous actuator failures may occur. How to perform adaptive control settings on quadrotor UAVs to improve the stability and safety of quadrotor UAV operation.

[0004] The inventors found that the altitude and attitude tracking control of quadrotor UAVs are susceptible to actuator failures and modeling uncertainties. Therefore, in the presence of multiple actuator failures and modeling uncertainties, altitude and attitude controllers with fixed gains can lead to performance degradation or even loss of stability, resulting in failures of the entire control system.

[0005] Therefore, the present application is proposed. Summary of the Invention

[0006] The main objective of the present application is to provide a method and device for adaptive control of quadrotor UAV actuators to solve the above problems and achieve the technical effect of improving the operation stability and safety of quadrotor UAVs.

[0007] To achieve the above objective, in the first aspect of the present application, a method for adaptive control of quadrotor UAV actuators is proposed, including:

[0008] Establish a quadrotor UAV altitude and attitude model with modeling uncertainty compensation parameters and actuator failures;

[0009] Determine the tracking error variables based on the UAV operation measurement data and the quadrotor UAV altitude and attitude model, where the tracking error variables include variables used to represent the UAV state variable error and the virtual control signal error;

[0010] Construct a non-linear robust adaptive control law based on the quadrotor UAV altitude and attitude model and the tracking error variables using adaptive backstepping to achieve the adaptive control of the quadrotor UAV according to the non-linear robust adaptive control law.

[0011] Furthermore, a height and attitude model of a quadrotor UAV with modeling uncertainty compensation parameters and actuator faults is established. The formula for the height and attitude model of the quadrotor UAV is as follows:

[0012]

[0013] Where, represents the differential of the first flight parameter of the quadrotor UAV. x1 is the state variable of position and attitude angle. z, Ф, θ, and φ are the flight height and the rotation angles in the three-axis directions of the transverse axis, longitudinal axis, and vertical axis, respectively. is the differential of the second flight parameter of the quadrotor UAV. x2 is the state variable of velocity and angular velocity. x2 = [v z , p, q, r] T , v z is the velocity in the height direction in the inertial coordinate system. p, q, r are the roll angular velocity, pitch angular velocity, and yaw angular velocity. M is the mapping matrix of thrust and torque to rotor angular velocity. I4 is the identity matrix. ζ s is the fault coefficient. Λ s is the 4×4 distribution matrix of faults. represents the square of the rotational speed of each rotor.

[0014] g1(x1) is the state transition matrix. g2(x1) is the input gain matrix. f2(x1,x2) is the nonlinear dynamics function. ξ * (x,t) is the compensation for modeling uncertainty, and the formula is as follows:

[0015] ξ * (x,t) = (g2(x1)M) -1 ξ(x,t),

[0017] Where, ξ0(x,t) is the unmodeled dynamic disturbance. is the translational component in the dynamics model. is the rotational component in the dynamics. R η (φ,θ) is the conversion matrix from the rotational speed in the body coordinate system to the angular velocity of the attitude angle. m is the weight of the UAV. g is the acceleration due to gravity. C d is the drag coefficient. J x , J y , J z are the moments of inertia of the UAV about the x, y, and z axes, respectively.

[0018] Further, determining the tracking error variables according to the UAV operation measurement data and the height and attitude model of the quadrotor UAV includes:

[0019] Determining a first state variable and a second state variable according to the UAV operation measurement data and the height and attitude model of the quadrotor UAV, wherein the first state variable is used to represent the actual state variable of the UAV, and the second state variable is used to represent the desired state variable of the UAV;

[0020] Determining a first tracking error variable according to the first state variable and the second state variable, wherein the first tracking error variable is a composite tracking error variable;

[0021] Determining a second tracking error variable according to the first state variable and the virtual control signal, wherein the second tracking error variable is a variable used to represent the difference between the state variable and the virtual control signal;

[0022] Determining the tracking error variable according to the first tracking error variable and the second tracking error variable, and the tracking error variable includes the first tracking error variable and the second tracking error variable.

[0023] Further, determining a first tracking error variable according to the first state variable and the second state variable, and determining a second tracking error variable according to the first state variable and the virtual control signal, wherein the formulas of the first tracking error variable and the second tracking error variable are as follows:

[0024]

[0025] z2 = x2 - κ

[0026] wherein, z1 is the first tracking error variable, z2 is the second tracking error variable, x1 is the state variable of the position and attitude angle, x 1d is the desired height and attitude angle, c1 and c2 are design parameters in the form of diagonal matrices, adjusting the weights of the proportional term and the integral term respectively to achieve independent control of multiple degrees of freedom, κ represents the virtual control signal, used to adjust the system dynamic response, and the formula is as follows:

[0027] Parameter c1 = diag{c1 1 , c1 2 , c1 3 , c1 4}, parameter c2 = diag{c2 1 , c2 2 , c2 3 , c2 4}, and c1 i > 0, c2 i > 0, i = 1, 2, 3, 4;

[0028] The diagonal matrix \(k_1=\text{diag}\{k_1 1 ,k_1 2 ,k_1 3 ,k_1 4 \}\), and \(k_1 i >0\) to achieve the regulation of the influence of the tracking error \(z_1\) on the virtual control signal \(\kappa\).

[0029] Furthermore, based on the height and attitude model of the quadrotor UAV and the tracking error variables, a nonlinear robust adaptive control law is constructed by using the adaptive backstepping method. The nonlinear robust adaptive control law consists of a first part of the control law and a second part of the control law, and the formula is as follows:

[0030]

[0031] where \(\sigma s is the adaptive learning rate constant, and \(\sigma s >0\);

[0032] The first part of the control law is the standard control input part to achieve the tracking of the quadrotor UAV in the case of possible multiple actuator failures. The formula of the first part of the control law is:

[0033]

[0034] where \(c_1\), \(c_3\) are design parameters to adjust the dynamic response of the control law;

[0035] The second part of the control law is the robust compensation term to offset the influence of modeling uncertainties. The formula of the second part of the control law is:

[0036]

[0037] The diagonal matrix \(\text{diag}\{\text{sgn}(H)\}\) is used to compensate for modeling uncertainties or external disturbances, and \(\) is the bounding function of the modeling uncertainties.

[0038] Furthermore, after constructing the nonlinear robust adaptive control law based on the height and attitude model of the quadrotor UAV and the tracking error variables, the method further includes:

[0039] Performing a stability analysis on the nonlinear robust adaptive control law, and restricting the \(\Gamma\) parameter estimation to a predetermined known compact set \(\Theta = 0\) through the projection operator. The projection algorithm formula is:

[0040]

[0041] where χ s is a projection operator The boundaries of different state components are divided into the 0 state and other states.

[0042] According to the second aspect of the present application, an adaptive control device for a quadrotor UAV actuator is proposed, including:

[0043] A dynamic model module that establishes a height and attitude model of a quadrotor UAV with modeling uncertainty compensation parameters and actuator faults;

[0044] A tracking error variable module that determines tracking error variables based on the UAV operation measurement data and the height and attitude model of the quadrotor UAV, where the tracking error variables include variables for representing UAV state variable errors and virtual control signal errors;

[0045] A control law module that constructs a nonlinear robust adaptive control law based on the height and attitude model of the quadrotor UAV and the tracking error variables according to the adaptive backstepping method, so as to achieve the adaptive control of the quadrotor UAV according to the nonlinear robust adaptive control law.

[0046] Furthermore, the tracking error variable module includes:

[0047] A state variable module for determining a first state variable and a second state variable based on the UAV operation measurement data and the height and attitude model of the quadrotor UAV, where the first state variable is used to represent the actual state variable of the UAV, and the second state variable is used to represent the desired state variable of the UAV;

[0048] A first tracking error variable module for determining a first tracking error variable based on the first state variable and the second state variable, where the first tracking error variable is a composite tracking error variable;

[0049] A second tracking error variable module for determining a second tracking error variable based on the first state variable and the virtual control signal, where the second tracking error variable is a variable for representing the difference between the state variable and the virtual control signal;

[0050] Determine the tracking error variable according to the first tracking error variable and the second tracking error variable, and the tracking error variable includes the first tracking error variable and the second tracking error variable.

[0051] According to the third aspect of the present application, a computer-readable storage medium is proposed, and the computer-readable storage medium stores computer instructions for causing the computer to execute the above-mentioned adaptive control method for a quadrotor UAV actuator.

[0052] According to a fourth aspect of the present application, an electronic device is provided, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the above-mentioned quadrotor UAV actuator fault adaptive control method.

[0053] The technical solutions provided by the embodiments of the present application may include the following beneficial effects:

[0054] In the present application, a height and attitude model of a quadrotor UAV with modeling uncertainty compensation parameters and actuator faults is established; a tracking error variable is determined according to the UAV operation measurement data and the height and attitude model of the quadrotor UAV, wherein the tracking error variable includes variables for representing UAV state variable errors and virtual control signal errors; a nonlinear robust adaptive control law is constructed based on the height and attitude model of the quadrotor UAV and the tracking error variable by using an adaptive backstepping method, so as to realize the adaptive control of the quadrotor UAV according to the nonlinear robust adaptive control law.

[0055] The technical effect of improving the operation stability and safety of the quadrotor UAV is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The accompanying drawings forming a part of this application are used to provide a further understanding of the application, and make other features, objects, and advantages of the application more obvious. The schematic embodiments and descriptions thereof of the application are used to explain the application, and do not constitute an improper limitation of the application. In the drawings:

[0057] Figure 1 is a flowchart of a quadrotor UAV actuator fault adaptive control method provided by the present application;

[0058] Figure 2 is a flowchart of a quadrotor UAV actuator fault adaptive control method provided by the present application;

[0059] Figure 3 is a schematic diagram of a quadrotor UAV actuator fault adaptive control device provided by the present application;

[0060] Figure 4 is a schematic diagram of another quadrotor UAV actuator fault adaptive control device provided by the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0061] To enable those skilled in the art to better understand the solution of this application, the following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts shall fall within the scope of protection of this application.

[0062] It should be noted that the terms "first", "second", etc. in the specification, claims and the above-mentioned drawings of this application are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances for the embodiments of this application described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0063] In this application, the orientation or positional relationship indicated by the terms "upper", "lower", "left", "right", "front", "rear", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal", etc. is based on the orientation or positional relationship shown in the drawings. These terms are mainly used to better describe this application and its embodiments, and are not used to limit that the indicated devices, elements or components must have a specific orientation, or be constructed and operated in a specific orientation.

[0064] Moreover, in addition to being able to represent an orientation or positional relationship, some of the above terms may also be used to represent other meanings. For example, the term "upper" may also be used to represent a certain attachment relationship or connection relationship in some cases. For those of ordinary skill in the art, the specific meanings of these terms in this application can be understood according to specific circumstances.

[0065] In addition, the terms "installed", "set up", "provided with", "connected", "connected to", "socketed" should be understood in a broad sense. For example, "connected" can be a fixed connection, a detachable connection, or an integral structure; it can be a mechanical connection or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or there can be internal communication between two devices, elements or components. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0066] In some alternative embodiments of this application, a method for adaptive control of a quadrotor UAV actuator failure is provided.Figure 1 The flowchart of a method for adaptive control of actuator faults of a quadrotor UAV provided by this application is as follows Figure 1 As shown, this method includes the following steps:

[0067] S101: Establish a height and attitude model of the quadrotor UAV with modeling uncertainty compensation parameters and actuator faults;

[0068] In some alternative embodiments of this application, a method for adaptive control of actuator faults of a quadrotor UAV is provided, including:

[0069] The formula of the height and attitude model of the quadrotor UAV is:

[0070]

[0071] Among them, represents the differential of the first flight parameter of the quadrotor UAV, x1 is the state variable of position and attitude angle, z, Ф, θ and φ are the flight height and the rotation angles in the three-axis directions of the transverse axis, longitudinal axis and vertical axis respectively; is the differential of the second flight parameter of the quadrotor UAV, x2 is the state variable of speed and angular velocity, x2 = [v z , p, q, r] T , v z is the speed in the height direction in the inertial coordinate system, p, q, r are the roll angular velocity, pitch angular velocity and yaw angular velocity; M is the mapping matrix of thrust and torque to the rotor angular velocity, I4 is the identity matrix, ζ s is the fault coefficient, Λ s is the 4×4 distribution matrix of the fault; represents the square of the rotational speed of each rotor,

[0072] g1(x1) is the state transition matrix, g2(x1) is the input gain matrix, f2(x1,x2) is the nonlinear dynamics function, ξ * (x,t) is the compensation for modeling uncertainty, and the formula is as follows:

[0073] ξ * (x,t) = (g2(x1)M) -1 ξ(x,t),

[0075] Among them, ξ0(x,t) is the unmodeled dynamic disturbance, is the translational component in the dynamic model, is the rotational component in the dynamic model, Rη (φ, θ) is the conversion matrix from the rotational velocity in the body coordinate system to the angular velocity of the attitude angle, m is the weight of the UAV, g is the acceleration due to gravity, and C d is the drag coefficient, J x , J y , J z are the moments of inertia of the UAV about the x, y, and z axes respectively.

[0076] S102: Determine the tracking error variables according to the UAV operation measurement data and the height and attitude model of the quadrotor UAV;

[0077] Among them, the tracking error variables include variables used to represent the UAV state variable error and the virtual control signal error;

[0078] In some alternative embodiments of the present application, an adaptive control method for quadrotor UAV actuator faults is provided. Figure 2 is the flowchart of an adaptive control method for quadrotor UAV actuator faults provided by the present application. As Figure 2 shown, the method includes the following steps: including:

[0079] S201: Determine the first state variable and the second state variable according to the UAV operation measurement data and the height and attitude model of the quadrotor UAV;

[0080] The first state variable is used to represent the actual state variable of the UAV, and the second state variable is used to represent the desired state variable of the UAV;

[0081] S202: Determine the first tracking error variable according to the first state variable and the second state variable;

[0082] The first tracking error variable is a composite tracking error variable;

[0083] S203: Determine the second tracking error variable according to the first state variable and the virtual control signal;

[0084] The second tracking error variable is a variable used to represent the difference between the state variable and the virtual control signal;

[0085] S204: Determine the tracking error variable according to the first tracking error variable and the second tracking error variable.

[0086] The tracking error variable includes the first tracking error variable and the second tracking error variable.

[0087] The formulas for the first tracking error variable and the second tracking error variable are as follows:

[0088]

[0089] z2 = x2 - κ

[0090] Among them, z1 is the first tracking error variable, z2 is the second tracking error variable, x1 is the state variable of position and attitude angle, and x 1d is the desired height and attitude angle. c1 and c2 are design parameters in the form of diagonal matrices, which respectively adjust the weights of the proportional term and the integral term to achieve independent control of multiple degrees of freedom. κ represents the virtual control signal, which is used to adjust the system dynamic response. The formula is as follows:

[0091] The parameter c1 = diag{c1 1 , c1 2 , c1 3 , c1 4}, the parameter c2 = diag{c2 1 , c2 2 , c2 3 , c2 4}, and c1 i > 0, c2 i > 0, i = 1, 2, 3, 4;

[0092] The diagonal matrix k1 = diag{k1 1 , k1 2 , k1 3 , k1 4}, and k1 i > 0, to achieve adjusting the influence of the tracking error z1 on the virtual control signal κ.

[0093] S103: Construct a nonlinear robust adaptive control law based on the height and attitude model of the quadrotor UAV and the tracking error variable, so as to realize the adaptive control of the quadrotor UAV according to the nonlinear robust adaptive control law.

[0094] In some alternative embodiments of the present application, a method for adaptive control of a quadrotor UAV actuator is provided, including:

[0095] The nonlinear robust adaptive control law is composed of a first part control law and a second part control law. The formula is as follows:

[0096]

[0097]

[0098] Among them, φ s is the adaptive learning rate constant, and σ s > 0;

[0099] The first part control law It is the standard control input part to achieve tracking of the quadrotor UAV in the case of possible multiple actuator failures. The formula of the first part of the control law is:

[0100]

[0101] Among them, c1 and c3 are design parameters to adjust the dynamic response of the control law;

[0102] The second part of the control law is the robust compensation term to offset the influence of modeling uncertainty. The formula of the second part of the control law is:

[0103]

[0104] The diagonal matrix diag{sgn(H)} is used to compensate for modeling uncertainty or external disturbances, which is the bounding function of modeling uncertainty.

[0105] In some alternative embodiments of the present application, an actuator fault adaptive control method for a quadrotor UAV is provided, including:

[0106] Perform stability analysis on the non-linear robust adaptive control law. By using the projection operator, the Γ parameter estimation is restricted to a predetermined known compact set Θ = 0. The formula of the projection algorithm is:

[0107]

[0108] Among them, χ s is the projection operator Γ Θs with different state component bounds, which is divided into the 0 state and other states. By using the projection operator, the Γ parameter estimation is restricted to a predetermined known compact set, improving the stability of the adaptive algorithm in the presence of modeling uncertainty.

[0109] In an alternative embodiment of the present application, the control law consists of two parts: It can enable the quadrotor UAV to achieve good tracking performance even in the case of possible multiple actuator failures; It can achieve the robustness of the controller to model uncertainty; because the designed non-linear robust adaptive control law can achieve that all signals in the height and attitude model formula of the quadrotor UAV are bounded, and the height and attitude tracking errors asymptotically converge to 0, that is, lim t→0 (x1 - x 1d ) = 0. Therefore, the control law no longer needs to satisfy the persistent excitation condition required for parameter convergence. Even if the estimated value of the parameter does not converge to the actual value, it can still achieve good tracking performance for the height and attitude angles.

[0110] In some alternative embodiments of the present application, a fault adaptive control device for a quadrotor UAV actuator is provided. Figure 3 FIG. Figure 3 Figure 3 is a schematic diagram of a fault adaptive control device for a quadrotor UAV actuator provided by the present application. As shown, the device includes:

[0111] A dynamics model module 31 that establishes a height and attitude model of the quadrotor UAV with modeling uncertainty compensation parameters and actuator faults;

[0112] A tracking error variable module 32 that determines tracking error variables based on the UAV operation measurement data and the height and attitude model of the quadrotor UAV. Among them, the tracking error variables include variables used to represent the UAV state variable error and the virtual control signal error;

[0113] A control law module 33 that constructs a non - linear robust adaptive control law based on the height and attitude model of the quadrotor UAV and the tracking error variables according to the adaptive backstepping method, so as to realize the adaptive control of the quadrotor UAV according to the non - linear robust adaptive control law.

[0114] In some alternative embodiments of the present application, a fault adaptive control device for a quadrotor UAV actuator is provided. Figure 4 FIG. Figure 4 Figure 4 is a schematic diagram of another fault adaptive control device for a quadrotor UAV actuator provided by the present application. As shown, the tracking error variable module includes:

[0115] A state variable module 41 that determines a first state variable and a second state variable based on the UAV operation measurement data and the height and attitude model of the quadrotor UAV. Among them, the first state variable is used to represent the actual state variable of the UAV, and the second state variable is used to represent the desired state variable of the UAV;

[0116] A first tracking error variable module 42 that determines a first tracking error variable based on the first state variable and the second state variable. Among them, the first tracking error variable is a composite tracking error variable;

[0117] A second tracking error variable module 43 that determines a second tracking error variable based on the first state variable and the virtual control signal. Among them, the second tracking error variable is a variable used to represent the difference between the state variable and the virtual control signal;

[0118] Determine the tracking error variable according to the first tracking error variable and the second tracking error variable. The tracking error variable includes the first tracking error variable and the second tracking error variable.

[0119] The specific manners of the operations performed by each unit in the above embodiments have been described in detail in the embodiments related to the method, and will not be elaborated herein.

[0120] In summary, in this application, a height and attitude model of a quadrotor UAV with modeling uncertainty compensation parameters and actuator faults is established; a first tracking error variable and a second tracking error variable are determined according to the UAV operation measurement data and the height and attitude model of the quadrotor UAV, wherein the first tracking error variable is a composite tracking error variable, and the second tracking error variable is a variable used to represent the difference between the state variable and the virtual control signal; a nonlinear robust adaptive control law is constructed based on the height and attitude model of the quadrotor UAV, the first tracking error variable and the second tracking error variable based on the adaptive backstepping method, so as to realize the adaptive control of the quadrotor UAV according to the nonlinear robust adaptive control law, achieving the technical effect of improving the operation stability and safety of the quadrotor UAV.

[0121] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0122] Obviously, those skilled in the art should understand that the above units or steps of this application can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed on a network composed of multiple computing devices. Optionally, they can be implemented by program codes executable by the computing device, so that they can be stored in a storage device and executed by the computing device, or they can be separately made into individual integrated circuit modules, or multiple modules or steps among them can be made into a single integrated circuit module to implement. In this way, this application is not limited to any specific combination of hardware and software.

[0123] The above are only the preferred embodiments of this application and are not used to limit this application. For those skilled in the art, this application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application shall be included in the protection scope of this application.

Claims

1. An adaptive control method for actuator faults of a quadrotor UAV, characterized in that, Including: Establish a height and attitude model of a quadrotor UAV with modeling uncertainty compensation parameters and actuator faults; Determine the tracking error variables according to the UAV operation measurement data and the height and attitude model of the quadrotor UAV, where the tracking error variables include variables for representing the UAV state variable error and the virtual control signal error; Construct a nonlinear robust adaptive control law based on the adaptive backstepping method according to the height and attitude model of the quadrotor UAV and the tracking error variables, so as to realize the adaptive control of the quadrotor UAV according to the nonlinear robust adaptive control law.

2. The adaptive control method for actuator faults of a quadrotor UAV according to claim 1, characterized in that, Establish a height and attitude model of a quadrotor UAV with modeling uncertainty compensation parameters and actuator faults. The formula of the height and attitude model of the quadrotor UAV is: Among them, represents the differential of the first flight parameter of the quadrotor UAV, x1 is the state variable of position and attitude angle, z, Ф, θ, and φ are the flight altitude and the rotation angles in the directions of the three axes of the transverse axis, longitudinal axis, and vertical axis, respectively; is the differential of the second flight parameter of the quadrotor UAV, x2 is the state variable of speed and angular velocity, x2 = [v z , p, q, r] T , v z is the speed in the height direction in the inertial coordinate system, p, q, r are the roll angular velocity, pitch angular velocity, and yaw angular velocity; M is the mapping matrix of thrust and torque to the rotor angular velocity, I4 is the identity matrix, ζ s is the fault coefficient, Λ s is the 4×4 distribution matrix of faults; represents the square of the rotational speed of each rotor, $g_1(x_1)$ is the state transition matrix, $g_2(x_1)$ is the input gain matrix, $f_2(x_1,x_2)$ is the nonlinear dynamics function, and $\xi$ * (x,t) is the compensation for the modeling uncertainty, and the formula is as follows: ξ * (x,t) = (g2(x1)M) -1 ξ(x,t), where, ξ0(x,t) is the unmodeled dynamic disturbance, is the translational component in the dynamic model, is the rotational component in the dynamics, R η (φ,θ) is the conversion matrix from the rotational velocity to the angular velocity of the attitude angle in the body coordinate system, m is the weight of the UAV, g is the acceleration due to gravity, C d is the drag coefficient, J x , J y , J z are the moments of inertia of the UAV about the x, y, and z axes, respectively.

3. The adaptive control method for actuator faults of a quadrotor UAV according to claim 1, characterized in that, Determining the tracking error variables according to the UAV operation measurement data and the height and attitude model of the quadrotor UAV includes: Determine the first state variable and the second state variable according to the UAV operation measurement data and the height and attitude model of the quadrotor UAV, where the first state variable is used to represent the actual state variable of the UAV, and the second state variable is used to represent the desired state variable of the UAV; Determine the first tracking error variable according to the first state variable and the second state variable, where the first tracking error variable is a composite tracking error variable; Determine the second tracking error variable according to the first state variable and the virtual control signal, where the second tracking error variable is a variable for representing the difference between the state variable and the virtual control signal; Determine the tracking error variable according to the first tracking error variable and the second tracking error variable, and the tracking error variable includes the first tracking error variable and the second tracking error variable.

4. The method for adaptive control of actuator faults of a quadrotor UAV according to claim 3, wherein, Determine the first tracking error variable according to the first state variable and the second state variable, and determine the second tracking error variable according to the first state variable and the virtual control signal. The formulas of the first tracking error variable and the second tracking error variable are as follows: z1 = c1(x1 - x 1d ) + c2∫0 t (x1 - x 1d )dτ z2 = x2 - k where z1 is the first tracking error variable, z2 is the second tracking error variable, x1 is the state variable of position and attitude angle, and x 1d is the desired height and attitude angle, c1 and c2 are design parameters in the form of diagonal matrices, adjusting the weights of the proportional term and the integral term respectively to achieve independent control of multiple degrees of freedom. κ represents the virtual control signal used to adjust the system dynamic response, and the formula is as follows: Parameter c1 = diag{c1 1 , c1 2 , c1 3 , c1 4}, parameter c2 = diag{c2 1 , c2 2 , c2 3 , c2 4}, and c1 i > 0, c2 i > 0, i = 1, 2, 3, 4; The diagonal matrix k1 = diag{k1 1 , k1 2 , k1 3 , k1 4}, and k1 i > 0 to achieve the adjustment of the influence of the tracking error z1 on the virtual control signal κ.

5. The adaptive control method for actuator faults of a quadrotor UAV according to claim 1, characterized in that Construct a nonlinear robust adaptive control law based on the adaptive backstepping method according to the height and attitude model of the quadrotor UAV and the tracking error variables. The nonlinear robust adaptive control law consists of a first part control law and a second part control law, and the formula is as follows: where, σ s is an adaptive learning rate constant, and σ s > 0; The first part of the control law It is the standard control input part to achieve tracking of the quadrotor UAV in the case of possible multiple actuator failures. The formula of the first part of the control law is as follows: Where c1 and c3 are design parameters to adjust the dynamic response of the control law; The second part of the control law To be the robust compensation term to offset the influence of modeling uncertainties, the formula of the second part of the control law is as follows: The diagonal matrix diag{sgn(H)} is used to compensate for modeling uncertainties or external disturbances, which is the bounding function for modeling uncertainties.

6. The adaptive control method for actuator faults of a quadrotor UAV according to claim 1, characterized in that After constructing the nonlinear robust adaptive control law based on the adaptive backstepping method according to the height and attitude model of the quadrotor UAV and the tracking error variables, the method further includes: Conduct a stability analysis on the nonlinear robust adaptive control law, and limit the Γ parameter estimation to a predetermined known compact set Θ = 0 through a projection operator. The projection algorithm formula is: Among them, χ s is a projection operator for different state component boundaries, which are divided into the 0 state and other states.

7. An adaptive control device for actuator faults of a quadrotor UAV, characterized in that, Including: A dynamics model module that establishes a height and attitude model of a quadrotor UAV with modeling uncertainty compensation parameters and actuator faults; A tracking error variable module, configured to determine a tracking error variable according to the UAV operation measurement data and the quadrotor UAV altitude and attitude model, wherein the tracking error variable includes variables for representing the UAV state variable error and the virtual control signal error; A control law module, which constructs a nonlinear robust adaptive control law based on the quadrotor UAV altitude and attitude model and the tracking error variable according to the adaptive backstepping method, so as to implement the adaptive control of the quadrotor UAV according to the nonlinear robust adaptive control law.

8. The adaptive control device for actuator faults of the quadrotor UAV according to claim 7, characterized in that, The tracking error variable module includes: A state variable module, configured to determine a first state variable and a second state variable according to the UAV operation measurement data and the quadrotor UAV altitude and attitude model, wherein the first state variable is used to represent the actual state variable of the UAV, and the second state variable is used to represent the desired state variable of the UAV; A first tracking error variable module, configured to determine a first tracking error variable according to the first state variable and the second state variable, wherein the first tracking error variable is a composite tracking error variable; A second tracking error variable module, which determines a second tracking error variable according to the first state variable and the virtual control signal, wherein the second tracking error variable is a variable for representing the difference between the state variable and the virtual control signal; The tracking error variable is determined according to the first tracking error variable and the second tracking error variable, and the tracking error variable includes the first tracking error variable and the second tracking error variable.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the quadrotor UAV actuator fault adaptive control method according to any one of claims 1-6.

10. An electronic device, characterized in that, Including: At least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to cause the at least one processor to execute the quadrotor UAV actuator fault adaptive control method according to any one of claims 1-6.

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