A height and attitude tracking control method and system for a quadcopter unmanned aerial vehicle

Abstract

The present application belongs to the field of quadrotor unmanned aerial vehicle tracking control technology, and specifically discloses a height and attitude tracking control method and system for quadrotor unmanned aerial vehicle. In the height and attitude tracking process of the quadrotor unmanned aerial vehicle, the present application converges the tracking errors of the height subsystem and the attitude subsystem of the aircraft in the desired neighborhood by designing a virtual controller, a first-order low-pass filter, an error compensation system, and an actual control signal. The present application can not only avoid the problem that continuous-time control is affected by the computer sampling time, but also ensure that the attitude and height tracking errors of the quadrotor unmanned aerial vehicle converge to any desired neighborhood in the presence of external disturbances and system uncertainties, and all signals in the closed-loop system are bounded. In addition, the present application also provides specific examples, which well verify the effectiveness of the tracking control method.

Description

Technical Field

[0001] This invention belongs to the field of quadcopter drone tracking and control technology, and specifically relates to a method and system for tracking and controlling the altitude and attitude of a quadcopter drone. Background Technology

[0002] Quadrotor drones, with their advantages of small size, simple structure, and vertical takeoff and landing capabilities, are widely used in many fields such as control and photography, agricultural production, and fire rescue. However, quadrotor drones are characterized by underactuation, nonlinearity, and strong coupling, which makes designing high-performance quadrotor drone controllers extremely difficult.

[0003] Currently, numerous control methods have been developed for altitude and attitude tracking of quadrotor UAVs, such as sliding mode control, robust control, fuzzy logic control, and adaptive control. Most of these methods are based on continuous-time control, meaning their design is based on a continuous-time model of the quadrotor UAV. However, microprocessors, i.e., computers, can only process discrete-time signals. Therefore, when controlling a quadrotor UAV, a microprocessor needs to convert the continuous controller signal into a discrete signal, which significantly limits control methods based on the continuous-time model. In conclusion, controller methods based on discrete-time models are more practical and better suited to the computational characteristics of microprocessors.

[0004] In continuous-time, backstepping has proven effective in adaptive control of uncertain nonlinear systems and has been used in output feedback control of aircraft. However, in the design of continuous-time backstepping, the virtual controller exhibits derivatives, leading to the differential explosion problem. Dynamic surface control addresses this by incorporating a first-order filter during the design process. However, dynamic surface control does not compensate for the filtering errors generated by the filter, preventing further improvement in the system's tracking performance. Command-filtered backstepping, on the other hand, solves the differential explosion problem by approximating the derivative of the virtual signal through the output of the command filter, and further optimizes dynamic surface control by adding an error compensation mechanism, reducing the impact of filter errors and improving the system's tracking performance. Therefore, command-filtered backstepping control exhibits excellent tracking performance.

[0005] Because causal problems caused by future information can occur during the design of discrete-time backstepping control, the design steps of continuous-time backstepping control cannot be directly applied to discrete-time backstepping control. In addition, quadrotor UAVs have external disturbances and uncertainties in their own models during design. Therefore, it is of great significance to combine command filtering, fuzzy logic control and adaptive control on the basis of discrete-time backstepping to enable quadrotor UAVs to achieve excellent tracking performance. Summary of the Invention

[0006] The purpose of this invention is to propose a method for altitude and attitude tracking control of a quadrotor unmanned aerial vehicle (UAV). This method combines command filtering, fuzzy logic control, and adaptive control techniques based on the discrete-time backstepping method to solve the altitude and attitude tracking control of a quadrotor UAV under conditions of external interference and system uncertainty.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] A method for altitude and attitude tracking control of a quadcopter unmanned aerial vehicle (UAV) includes the following steps:

[0009] Step 1. Establish a discrete-time system model of the quadcopter UAV;

[0010] Step 2. Based on the obtained discrete-time system model of the quadrotor UAV, construct a discrete-time fuzzy adaptive command filtering backstepping tracking controller for the quadrotor UAV under the conditions of external disturbance and system uncertainty;

[0011] Step 3. Perform stability analysis on the discrete-time fuzzy adaptive command filter backstepping tracking controller;

[0012] Step 4. Utilize the constructed discrete-time fuzzy adaptive command filter backstepping tracking controller for the quadrotor UAV to achieve tracking control of the quadrotor UAV's altitude and attitude.

[0013] Furthermore, based on the altitude and attitude tracking control method for quadrotor UAVs, this invention also proposes a corresponding altitude and attitude tracking control system for quadrotor UAVs, which adopts the following technical solution:

[0014] An altitude and attitude tracking control system for a quadcopter unmanned aerial vehicle (UAV) includes:

[0015] The model building module is used to build a discrete-time system model of a quadcopter UAV.

[0016] The controller design module is used to construct a discrete-time fuzzy adaptive command filtering backstepping tracking controller for a quadrotor UAV under conditions of external disturbances and system uncertainties, based on the constructed discrete-time system model of the quadrotor UAV.

[0017] The stability analysis module is used to perform stability analysis on the discrete-time fuzzy adaptive command filter backstepping tracking controller.

[0018] It also includes an altitude and attitude tracking control module, which utilizes the discrete-time fuzzy adaptive command filtering backstepping tracking controller of the constructed quadcopter UAV to achieve altitude and attitude tracking control of the quadcopter UAV.

[0019] Furthermore, based on the aforementioned altitude and attitude tracking control method for quadcopter UAVs, this invention also proposes a computer device comprising a memory and one or more processors.

[0020] The memory stores executable code, and when the processor executes the executable code, it implements the steps of the altitude and attitude tracking control method for the quadcopter UAV described above.

[0021] Furthermore, based on the aforementioned altitude and attitude tracking control method for quadcopter UAVs, this invention also proposes a computer-readable storage medium storing a program thereon. When executed by a processor, this program is used to implement the steps of the aforementioned altitude and attitude tracking control method for quadcopter UAVs.

[0022] Compared with current tracking and control methods for quadcopter UAVs, the method of this invention has the following advantages:

[0023] As described above, this invention discloses a method for altitude and attitude tracking control of a quadcopter unmanned aerial vehicle (UAV). This method proposes a novel discrete-time backstepping method, which can solve the causal problem encountered in discrete-time backstepping. Furthermore, discrete-time control can avoid errors generated during the discretization of the continuous-time controller to obtain the actual control signal. Compared with current discrete-time control methods, this invention selects a command-filtered backstepping control method with excellent tracking performance and uses a first-order low-pass filter to solve the causal problem in discrete-time backstepping control. It also compensates for the filter's output error, reducing the impact of the filter's output error on performance and achieving good steady-state and transient performance. This invention uses a weighted Lyapunov function to expand the range of values ​​for control-related parameters. Simultaneously, this invention also considers external interference and system uncertainty, combining fuzzy logic control and adaptive control to solve these two problems, achieving altitude and attitude tracking control of the quadcopter UAV under conditions of external interference and system uncertainty. Attached Figure Description

[0024] Figure 1 This is a flowchart of the altitude and attitude tracking control method for a quadcopter UAV in an embodiment of the present invention.

[0025] Figure 2 This is a control block diagram of the altitude and attitude tracking control method for a quadcopter UAV in an embodiment of the present invention.

[0026] Figure 3 This is a motion curve of the quadcopter UAV's altitude versus the desired altitude in a simulation example of this invention.

[0027] Figure 4The figures show the motion curves of the altitude error with and without error compensation for the quadcopter UAV in the simulation example of this invention.

[0028] Figure 5 This is a motion curve of the roll angle versus the desired roll angle of the quadcopter UAV in the simulation example of this invention.

[0029] Figure 6 The figures show the motion curves of the roll angle error with and without error compensation for the quadcopter UAV in the simulation example of this invention.

[0030] Figure 7 This is a motion curve of the pitch angle versus the desired pitch angle of the quadcopter UAV in the simulation example of this invention.

[0031] Figure 8 The figures show the motion curves of the pitch angle error with and without error compensation for the quadcopter UAV in the simulation example of this invention.

[0032] Figure 9 This is a motion curve of the quadcopter UAV's yaw angle versus the desired yaw angle in a simulation example of the present invention.

[0033] Figure 10 The figures show the motion curves of the yaw angle error with and without error compensation for the quadcopter UAV in the simulation example of this invention.

[0034] Figure 11 This is a motion curve of the quadcopter drone's altitude angle versus the desired altitude angle in an experimental example of this invention.

[0035] Figure 12 The figures show the motion curves of the altitude and angle errors of the quadcopter UAV with and without error compensation in the experimental examples of this invention. Detailed Implementation

[0036] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0037] Example 1

[0038] This embodiment 1 describes a method for altitude and attitude tracking control of a quadcopter unmanned aerial vehicle (UAV). This method, during the altitude and attitude tracking process of the quadcopter UAV, involves designing a virtual controller, a first-order low-pass filter, an error compensation system, and actual control signals, ensuring that the tracking errors of the aircraft's altitude and attitude subsystems converge within a desired neighborhood. This method not only avoids the problem of continuous-time control being affected by computer sampling time, but also guarantees that the attitude and altitude tracking errors of the quadcopter UAV converge to any desired neighborhood even in the presence of external disturbances and system uncertainties, and that all signals in the closed-loop system are bounded.

[0039] like Figure 1 As shown, the altitude and attitude tracking control method for a quadcopter UAV in this embodiment includes the following steps:

[0040] Step 1. Establish a discrete-time system model of a quadcopter UAV.

[0041] The continuous-time system model of a quadcopter UAV in terms of altitude and attitude is defined as follows:

[0042]

[0043] Where L represents the length of the quadcopter drone's arm, i.e., the length from the motor to the center of gravity; m represents the drone's mass; φ and θψZ represent the quadcopter drone's roll, pitch, yaw, and altitude; J x J y J z The moment of inertia of each axis of the quadcopter drone is represented by g, which represents the acceleration due to gravity.

[0044] G H This represents the air drag coefficient of the altitude subsystem. G represents the drag coefficient of the rolling subsystem. θ G represents the air drag coefficient of the pitch subsystem. ψ F represents the air resistance coefficient of the yaw subsystem. H This represents the control input of the altitude subsystem. F represents the control input of the motor in the roll subsystem. θ F represents the control input of the pitch subsystem. ψ Indicates the control input of the yaw subsystem; d H (t) represents the external disturbance function of the height subsystem. d represents the external disturbance function of the roll subsystem. θ (t) represents the external disturbance function of the pitch subsystem, d ψ (t) represents the external disturbance function of the yaw subsystem.

[0045] Let xH，1 =Z x φ，1 =φ x θ，1 =θ x ψ，1 =ψ, U H =F H U φ =F φ U θ =F θ U ψ =F ψ The continuous-time model of the quadcopter UAV can then be rewritten as:

[0046]

[0047] Among them, g H =cos(φ)cos(θ), These four items are all computable parts of the system model; These four items refer to the unknown parts of the system model; These three items refer to the parts of the system model that can be calculated.

[0048] i represents the state vector.

[0049] Based on the continuous-time model of the quadrotor UAV, i.e., formula (2), the discrete-time system model of the quadrotor UAV is designed as follows:

[0050]

[0051] Where Λ is the sampling period of the sensor; Representing the time, Is the system in The state vector at time t.

[0052] Based on the discrete-time system model of the quadcopter UAV, i.e., formula (3), it is simplified into an altitude subsystem and an attitude subsystem. For ease of calculation, when i = H, the altitude subsystem model is defined as follows:

[0053]

[0054] Meanwhile, when i = φ, θ, ψ, it is defined as the attitude subsystem, which is:

[0055]

[0056] in, G represents H gφ g θ g ψ The discretized function.

[0057] Step 2. Based on the obtained discrete-time system model of the quadrotor UAV, construct a discrete-time fuzzy adaptive command filtering backstepping tracking controller for the quadrotor UAV under the conditions of external disturbance and system uncertainty.

[0058] For a compact set x∈R p Given a discrete function f(x), there exists an approximately unknown fuzzy logic system. Using this fuzzy logic system to approximate the unknown nonlinear function f(x), for any δ > 0, there always exists a fuzzy logic system W. T S(x), denoted as f(x) = W T S(x) + δ; where δ is the approximation error; W is the ideal weight vector, W = [W1, W2, ... W N ] T W1, W2, ... W N Let S(x) represent the elements of the ideal weight vector; S(x) is the basis function, expressed as:

[0059] Where N≥1, s i (x) is the center vector σ and the width of the Gaussian function. The Gaussian function; the Gaussian function is represented as:

[0060] Step 2.1. Construct a discrete-time fuzzy adaptive command filter backstepping tracking controller for the height subsystem;

[0061] To address the causality issue in the discrete-time backstepping method, a first-order low-pass filter is introduced. This filter is used to predict future information in the virtual controller. The discrete-time model of the first-order low-pass filter is as follows:

[0062]

[0063] in, For the input of a first-order low-pass filter, For a first-order low-pass filter in and Output at time β i =π i / Λ,π i is the time constant of the first-order low-pass filter.

[0064] Define α i (0) is The initial value of x i,2c (0) is If the initial value of x is given, then x i,2c (0)=α i (0). Meanwhile, the output of the first-order low-pass filter also satisfies... This indicates the upper bound of the filter error.

[0065] Define the tracking error to be compensated for:

[0066]

[0067] in, To track errors, The tracking error is defined as the error compensation signal:

[0068]

[0069] in, Let i represent the j-th state of the i-th subsystem, where i = φ, θ, ψ, H; j = 1, 2; These are the system's desired signal and the virtual desired signal, respectively. The input is the desired signal.

[0070] Define virtual control signals and control signals for:

[0071]

[0072]

[0073] in, The parameter representing error compensation, This represents the error compensation signal. The adaptive signal representing the weight vector. Represents a basis function vector. This indicates an adaptive signal.

[0074] The error compensation signal is defined as:

[0075]

[0076] in, This represents the filter error compensation signal at the next time step after the update. This represents the filter error compensation signal at the current moment. The Lyapunov function is chosen as:

[0077]

[0078] Where, p i,1V represents the position weighting coefficient of the Lyapunov function of the i-th subsystem; according to the difference method of discrete systems, V in equation (11) i，1 Increment ΔV i，1 for:

[0079]

[0080] virtual control signal and error compensation signal Substituting into equation (12), we get:

[0081]

[0082] The Lyapunov function is chosen as follows:

[0083]

[0084] Where, p i,2 V represents the velocity weighting coefficient of the Lyapunov function of the i-th subsystem; using the difference method, in equation (14) V i,2 Increment ΔV i,2 for:

[0085]

[0086] Among them, the unknown function part Using fuzzy logic control for approximation, the unknown function part is rewritten as:

[0087]

[0088] in, W represents the ideal weight vector. Denotes the basis function, δ i This represents the approximation error.

[0089] Substituting equation (16) into equation (15), and simultaneously using adaptive control to address external disturbances, equation (15) can be rewritten as:

[0090]

[0091] control signal Substituting (17) at the same time, because Therefore, equation (17) can be rewritten as:

[0092]

[0093] in, The estimated value, W i T The ideal weight vector for the i-th subsystem; for The estimated value is obtained by scaling:

[0094]

[0095] Therefore, the adaptive laws are designed as follows:

[0096]

[0097]

[0098] Where, η i γ represents the adaptive parameter of the adaptive law of the weight vector. i o represents the adaptive parameter of the adaptive law of the weight vector. i The adaptive parameter ι represents the adaptive law of external disturbance. i The adaptive parameter represents the adaptive law of external disturbance.

[0099] Step 2.2. Construct the discrete-time fuzzy adaptive command filter backstepping tracking controller for the attitude subsystem.

[0100] Define the tracking error to be compensated for:

[0101]

[0102] in, To track errors, The error compensation signal is used; the tracking error is defined as:

[0103]

[0104] in, The output of the filter, and

[0105] Define virtual control signals and control signals for:

[0106]

[0107]

[0108] Since predicting future signals using a first-order low-pass filter will introduce errors, the error compensation signal is defined as follows:

[0109]

[0110] The Lyapunov function is chosen as follows:

[0111]

[0112] According to the difference method for discrete systems, V in equation (27) i,1 Increment ΔV i,1 for:

[0113]

[0114] virtual control signal and error compensation signal Substituting into the equation, we get:

[0115]

[0116] The Lyapunov function is chosen as follows:

[0117]

[0118] Using the difference method, V in equation (30) i,2 Increment ΔV i,2 for:

[0119]

[0120] Among them, the unknown function part Using fuzzy logic control for approximation, the unknown function part is rewritten as:

[0121]

[0122] Meanwhile, if adaptive control is used to solve the problem of external disturbances, then equation (31) can be rewritten as:

[0123]

[0124] control signal Substituting into equation (33), equation (33) can be rewritten as:

[0125]

[0126] because Therefore, equation (34) can be rewritten as:

[0127]

[0128] in, For W i T The estimated value, W i T The ideal weight vector for the i-th subsystem; for The estimated value is obtained by scaling:

[0129]

[0130] Therefore, the adaptive laws are designed as follows:

[0131]

[0132]

[0133] Step 3. Perform stability analysis on the discrete-time fuzzy adaptive command filter backstepping tracking controller constructed in Step 2. Step 3.1. Prove that the quadcopter UAV system is stable under the control method of this invention. For ease of proof, redefine i = φ, θ, ψ, H as i = 1, 2, 3, 4; choose the Lyapunov function as:

[0134]

[0135] Among them, V i,w The Lyapunov function for an adaptive system is defined as:

[0136]

[0137] Where, p i,3 p i,4 The weighting coefficients of the Lyapunov functions of the two adaptive rates of the i-th subsystem are represented.

[0138] Using the difference method, V is obtained. i,w Increment ΔV i,w for:

[0139]

[0140] in, express norm, express The norm of .

[0141] because The following inequality is derived:

[0142]

[0143]

[0144]

[0145]

[0146]

[0147]

[0148]

[0149]

[0150] Where, ∈ i The parameter represents the inequality; according to inequalities (42)-(49), equation (41) can be rewritten as:

[0151]

[0152] The design parameters must satisfy the following inequality (51):

[0153]

[0154] Substituting inequalities (50), (19), (36), (13), (29), and adaptive rates (20), (21), (37), (38) into (39), we obtain:

[0155]

[0156] Therefore, equation (52) can be rewritten as:

[0157] ΔV≤-a p V+b p (112)

[0158] Among them, a p <0, If the design parameters satisfy equation (54), then V is uniformly bounded.

[0159]

[0160] Step 3.2. Prove that the error compensation signal converges in the discrete-time fuzzy adaptive command filter backstepping tracking controller.

[0161] First, we choose the Lyapunov function as:

[0162]

[0163] Using the difference method, V is obtained i,ξ Increment ΔV i,ξ for:

[0164]

[0165] Error compensation signal Substituting into (56), we get:

[0166]

[0167] because Therefore, we get:

[0168]

[0169] Equation (58) can be rewritten as follows

[0170] ΔV i,ξ ≤-a ξ V i,ξ +b ξ (118)

[0171] in, The definition of the parameters satisfies the inequality: Then we get V i,ξ It is consistent and ultimately bounded.

[0172] Step 4. Using the discrete-time fuzzy adaptive command filter backstepping tracking controller constructed in Step 2, realize the tracking control of the altitude and attitude of the quadcopter UAV, such as... Figure 2 As shown.

[0173] First, using the attitude and altitude data transmitted back by the quadcopter UAV, virtual control signals, fuzzy logic control signals, and adaptive control signals are calculated. The desired speed signal is then calculated using the virtual control signal as input to a first-order low-pass filter, and the calculated control signal is transmitted to the quadcopter UAV for control.

[0174] As can be seen from the above steps, the method of the present invention can ensure that the tracking error of the quadcopter UAV converges to the desired neighborhood. Furthermore, the method of the present invention not only effectively avoids the causal problem in the discrete-time backstepping method, but also avoids the filtering error problem caused by the introduction of filters at the end, while effectively solving the problems of external interference and system uncertainty. Therefore, the control method of the present invention not only has excellent anti-interference capability, but also has strong practicality.

[0175] The effectiveness of the altitude and attitude tracking control method for the quadcopter UAV proposed in this invention will be verified below.

[0176] In the simulation, the airframe parameters of the quadcopter UAV are shown in Table 1:

[0177] Table 1

[0178]

[0179] External disturbance is defined as: d H =d φ =d θ =d ψ =0.1sin(π*t / 20). Air resistance is defined as: G H =G φ =G θ =Gψ =0.6. The initial state is defined as: [Z(0),φ(0),θ(0),ψ(0)]=[0,0,0,0]. The expected value is defined as: Z d =1, φ d =1, φ d =sin(t), ψ d =sin(t), and set the expected rate of ascent for altitude and yaw angle to 0.5.

[0180] The parameter selection for the controller in the control method is shown in Table 2:

[0181] Table 2

[0182]

[0183] The airframe parameters of the quadcopter UAV in the experiment are shown in Table 3:

[0184] Table 3

[0185]

[0186] External disturbance is defined as: d z =0.1sin(π*t / 20). Air resistance is defined as: Gz = 0.6. The initial state is defined as: [Z(0)] = [0]. The expected value is defined as: Z d =0.7, and set the altitude ascent rate to 0.5.

[0187] The parameter selection for the controller in the control method is shown in Table 4:

[0188] Table 4

[0189]

[0190] Simulation results are as follows Figures 3 to 12 As shown, where:

[0191] Depend on Figure 3 , Figure 5 , Figure 7 , Figure 9 as well as Figure 11 The curves show that, under the control of the invented control method, quadcopter drones can quickly and well achieve the desired altitude and attitude tracking control.

[0192] To further verify the effectiveness of the method of the present invention, the performance of the present invention was compared with that of a method without error compensation control. Height and attitude errors were selected for comparison, such as... Figure 4 , Figure 6 , Figure 8 , Figure 10 , Figure 11 and Figure 12As shown.

[0193] The comparative results show that the control method proposed in this invention can achieve better transient and steady-state performance.

[0194] Example 2

[0195] This embodiment 2 describes an altitude and attitude tracking control system for a quadcopter drone, which is based on the same inventive concept as the altitude and attitude tracking control method for a quadcopter drone in embodiment 1 above.

[0196] Specifically, the altitude and attitude tracking control system for the quadcopter UAV in this embodiment includes:

[0197] The model building module is used to build a discrete-time system model of a quadcopter UAV.

[0198] The controller design module is used to construct a discrete-time fuzzy adaptive command filtering backstepping tracking controller for a quadrotor UAV under conditions of external disturbances and system uncertainties, based on the constructed discrete-time system model of the quadrotor UAV.

[0199] The stability analysis module is used to perform stability analysis on the discrete-time fuzzy adaptive command filter backstepping tracking controller.

[0200] It also includes an altitude and attitude tracking control module, which utilizes a constructed discrete-time fuzzy adaptive command filter backstepping tracking controller to achieve altitude and attitude tracking control of the quadcopter UAV.

[0201] It should be noted that the implementation process of the functions and roles of each functional module in the altitude and attitude tracking control system of the quadcopter UAV is detailed in the implementation process of the corresponding steps in the method of the above embodiment 1, and will not be repeated here.

[0202] Example 3

[0203] This embodiment 3 describes a computer device used to implement the steps of the altitude and attitude tracking control method for a quadcopter UAV described in embodiment 1 above.

[0204] The computer device includes a memory and one or more processors. Executable code is stored in the memory, which, when executed by the processor, enables steps for implementing a method for altitude and attitude tracking control of a quadcopter unmanned aerial vehicle.

[0205] In this embodiment, the computer device can be any device or apparatus with data processing capabilities, and will not be described in detail here.

[0206] Example 4

[0207] This embodiment 4 describes a computer-readable storage medium for implementing the steps of the altitude and attitude tracking control method for a quadcopter UAV described in embodiment 1 above.

[0208] The computer-readable storage medium in this embodiment 4 stores a program that, when executed by a processor, implements the steps of a method for tracking and controlling the altitude and attitude of a quadcopter drone.

[0209] The computer-readable storage medium can be an internal storage unit of any device or apparatus with data processing capabilities, such as a hard disk or memory, or an external storage device of any device with data processing capabilities, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc.

[0210] Of course, the above description is only a preferred embodiment of the present invention. The present invention is not limited to the above-described embodiments. It should be noted that any equivalent substitutions or obvious modifications made by those skilled in the art under the guidance of this specification fall within the scope of this specification and should be protected by the present invention.

Claims

1. A method for altitude and attitude tracking control of a quadcopter unmanned aerial vehicle, characterized in that, Includes the following steps: Step 1. Establish a discrete-time system model of the quadcopter UAV; Step 2. Based on the obtained discrete-time system model of the quadrotor UAV, construct a discrete-time fuzzy adaptive command filtering backstepping tracking controller for the quadrotor UAV under the conditions of external disturbance and system uncertainty; First, the discrete-time fuzzy adaptive command filter backstepping tracking controller of the height subsystem is constructed as follows: ； ； in, The adaptive parameters represent the adaptive law of the weight vector. The adaptive parameters represent the adaptive law of the weight vector. The adaptive parameters represent the adaptive law of external disturbance. The adaptive parameters represent the adaptive law of external disturbances; Then, the discrete-time fuzzy adaptive command filter backstepping tracking controller of the attitude subsystem is constructed as follows: ； ； Step 3. Perform stability analysis on the discrete-time fuzzy adaptive command filter backstepping tracking controller constructed in Step 2; Step 4. Using the discrete-time fuzzy adaptive command filter backstepping tracking controller constructed in Step 2, the altitude and attitude tracking control of the quadcopter UAV is realized.

2. The altitude and attitude tracking control method for a quadcopter UAV according to claim 1, characterized in that, Step 1 specifically involves: The continuous-time system model of a quadcopter UAV in terms of altitude and attitude is defined as follows: (1) Where L represents the length of the quadcopter drone's arm, i.e., the length from the motor to the center of gravity; m represents the drone's mass. , , , These represent the roll angle, pitch angle, yaw angle, and altitude of a quadcopter drone. , , The moment of inertia of each axis of the quadcopter drone is represented by g, which represents the acceleration due to gravity. This represents the air drag coefficient of the altitude subsystem. This represents the air drag coefficient of the roll subsystem. This represents the air drag coefficient of the pitch subsystem. This represents the air resistance coefficient of the yaw subsystem. This represents the control input of the altitude subsystem. This indicates the control input of the motor in the roll subsystem. This represents the control input of the pitch subsystem. This represents the control input of the yaw subsystem; This represents the external disturbance function of the altitude subsystem. This represents the external disturbance function of the roll subsystem. This represents the external disturbance function of the pitch subsystem. This represents the external disturbance function of the yaw subsystem; make , , , , , , , , , , , The continuous-time model of the quadcopter UAV can then be rewritten as: (2) in, , , , All four items are computable parts of the system model; , , , These four items refer to the unknown parts in the system model; , , These three items refer to the computable parts of the system model; , Represents the state vector; Based on the continuous-time model of the quadrotor UAV, i.e., formula (2), the discrete-time system model of the quadrotor UAV is designed as follows: (3) in, The sampling period of the sensor; Representing the time, Is the system in The state vector at any given time; Based on the discrete-time system model of the quadrotor UAV, i.e., formula (3), it is simplified into an altitude subsystem and an attitude subsystem. For ease of calculation, we define... When the model is a high-level subsystem, it is: (4) At the same time, define , , At that time, it is the attitude subsystem, which is: (5) in, express , , , The discretized function.

3. The altitude and attitude tracking control method for a quadcopter UAV according to claim 1, characterized in that, For a compact set and a discrete function There exists an approximately unknown fuzzy logic system, which utilizes the fuzzy logic system to approximate an unknown nonlinear function. For any There is always a fuzzy logic system , represented as ;in, This is an approximation error; For the ideal weight vector, , Elements representing the ideal weight vector; Let be a basis function, expressed as: ； in, , The center vector is The width of the Gaussian function is The Gaussian function.

4. The altitude and attitude tracking control method for a quadcopter UAV according to claim 2, characterized in that, Step 2 specifically involves: Step 2.

1. Construct a discrete-time fuzzy adaptive command filter backstepping tracking controller for the height subsystem; To address the causality issue in the discrete-time backstepping method, a first-order low-pass filter is introduced. This filter is used to predict future information in the virtual controller. The discrete-time model of the first-order low-pass filter is as follows: (6) in, This is the input to a first-order low-pass filter. , For a first-order low-pass filter in and Output at any moment; , The time constant of the first-order low-pass filter; definition for initial value, for The initial value is then ; At the same time, the output of the first-order low-pass filter also satisfies , Indicates the upper bound of the filter error; Define the tracking error to be compensated for: (7) in, To track errors, The tracking error is defined as the error compensation signal: ； in, This represents the j-th state of the i-th subsystem. , , , , 2, , These are the system's desired signal and the virtual desired signal, respectively. , The input is the desired signal; Define virtual control signals and control signals for: (8) (9) in, Parameters representing error compensation, This represents the error compensation signal. The adaptive signal representing the weight vector. Represents a basis function vector. Indicates an adaptive signal; The error compensation signal is defined as: (10) in, This represents the filter error compensation signal at the next time step after the update. This represents the filter error compensation signal at the current moment; the Lyapunov function is chosen as: (11) in, The Lyapunov function of the i-th subsystem is represented by the position weighting coefficient; according to the difference method of discrete systems, in equation (11) Increment for: (12) virtual control signal and error compensation signal Substituting into equation (12), we get: (13) The Lyapunov function is chosen as follows: (14) in, The velocity weighting coefficients of the Lyapunov function of the i-th subsystem are represented by the difference method, where (14) Increment for: (15) Among them, the unknown function part Using fuzzy logic control for approximation, the unknown function part is rewritten as: (16) in, , Represents the ideal weight vector. Denotes basis functions. Indicates the approximation error; Substituting equation (16) into equation (15), and simultaneously using adaptive control to address external disturbances, equation (15) can be rewritten as: (17) control signal Substituting (17) at the same time, because Therefore, equation (17) can be rewritten as: (18) in, , , for The estimated value, The ideal weight vector for the i-th subsystem; for The estimated value is obtained by scaling: (19) Therefore, the adaptive laws are designed as follows: (20) (21) in, The adaptive parameters represent the adaptive law of the weight vector. The adaptive parameters represent the adaptive law of the weight vector. The adaptive parameters represent the adaptive law of external disturbance. The adaptive parameters represent the adaptive law of external disturbances; Step 2.

2. Construct the discrete-time fuzzy adaptive command filter backstepping tracking controller for the attitude subsystem; Define the tracking error to be compensated for: (22) in, To track errors, The error compensation signal is used; the tracking error is defined as: (23) in, The output of the filter, and ; Define virtual control signals and control signals for: (24) (25) Since predicting future signals using a first-order low-pass filter will introduce errors, the error compensation signal is defined as follows: (26) The Lyapunov function is chosen as follows: (27) According to the difference method for discrete systems, in equation (27) Increment for: (28) virtual control signal and error compensation signal Substituting into the equation, we get: (29) The Lyapunov function is chosen as follows: (30) Using the difference method, in equation (30) Increment for: (31) Among them, the unknown function part Using fuzzy logic control for approximation, the unknown function part is rewritten as: (32) Meanwhile, if adaptive control is used to solve the problem of external disturbances, then equation (31) can be rewritten as: (33) control signal Substituting into equation (33), equation (33) can be rewritten as: (34) because Therefore, equation (34) can be rewritten as: (35) in, , , for The estimated value, The ideal weight vector for the i-th subsystem; for The estimated value is obtained by scaling: (36) Therefore, the adaptive laws are designed as follows: (37) (38)。 5. The altitude and attitude tracking control method for a quadcopter UAV according to claim 4, characterized in that, Step 3 specifically involves: Step 3.

1. Prove that the quadcopter UAV system is stable under the control of the discrete-time fuzzy adaptive command filter backstepping tracking controller; Step 3.

2. Prove that the error compensation signal converges in the discrete-time fuzzy adaptive command filter backstepping tracking controller.

6. The altitude and attitude tracking control method for a quadcopter UAV according to claim 5, characterized in that, Step 3.1 specifically involves: Will , , , Redefining , , , The Lyapunov function is chosen as follows: (39) in, The Lyapunov function for an adaptive system is defined as: (40) in, , The weighting coefficients of the Lyapunov functions of the two adaptive rates of the i-th subsystem are represented. Using the difference method, we obtain Increment for: (41) in, express norm, express The norm; because , The following inequality is derived: (42) (43) (44) (45) (46) (47) (48) (49) in, The parameter represents the inequality; according to inequalities (42)-(49), equation (41) can be rewritten as: (50) The design parameters must satisfy the following inequality (51): ， ， ， ， (51) Substituting inequalities (50), (19), (36), (13), (29), and adaptive rates (20), (21), (37), (38) into (39), we obtain: (52) Therefore, equation (52) can be rewritten as: (53) in, , If the designed parameters satisfy equation (54), then... Consistent and ultimately bounded; (54)。 7. The altitude and attitude tracking control method for a quadcopter UAV according to claim 6, characterized in that, Step 3.2 specifically involves: First, we choose the Lyapunov function as: (55) Using the difference method, the following was obtained Increment for: (56) Error compensation signal Substituting into (56), we get: (57) because Therefore, we get: (58) Equation (58) can be rewritten as follows (59) in, , The parameters are defined to satisfy the inequality: , Then we get It is consistent and ultimately bounded.

8. A quadcopter altitude and attitude tracking control system for implementing the altitude and attitude tracking control method of the quadcopter UAV as described in claim 1, characterized in that, The altitude and attitude tracking control system of the quadcopter UAV includes: The model building module is used to build a discrete-time system model of a quadcopter UAV. The controller design module is used to construct a discrete-time fuzzy adaptive command filtering backstepping tracking controller for a quadrotor UAV under conditions of external disturbances and system uncertainties, based on the constructed discrete-time system model of the quadrotor UAV. The stability analysis module is used to perform stability analysis on the discrete-time fuzzy adaptive command filter backstepping tracking controller. It also includes an altitude and attitude tracking control module, which utilizes the discrete-time fuzzy adaptive command filtering backstepping tracking controller of the constructed quadcopter UAV to achieve altitude and attitude tracking control of the quadcopter UAV.

9. A computer device comprising a memory and one or more processors, wherein the memory stores executable code, characterized in that, When the processor executes the executable code, it implements the steps of the altitude and attitude tracking control method for a quadcopter unmanned aerial vehicle as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the altitude and attitude tracking control method for a quadcopter unmanned aerial vehicle as described in any one of claims 1 to 7.

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