Quadrotor unmanned aerial vehicle large-angle adaptive sliding mode control method and system

Abstract

This invention relates to the field of unmanned aerial vehicle (UAV) technology, and more particularly to a large-angle adaptive sliding mode control method and system for a quadrotor UAV. The method includes: establishing a quaternion-based attitude kinematic model based on the quadrotor UAV's motion posture; establishing a quaternion-based error model based on the target posture angle; simplifying the error model using a nonlinear integral sliding mode function to obtain a simplified error model; establishing a terminal sliding mode controller based on the sliding mode function to control the motion posture, and controlling the simplified error model through the terminal sliding mode controller; establishing an adaptive estimation algorithm to adaptively estimate the upper bound parameters of the perturbation related to the motion posture in the control quantity; and outputting the control quantity output by the terminal sliding mode controller to the quadrotor UAV and executing it to control the motion posture of the quadrotor UAV. This invention achieves high-performance attitude control for quadrotor UAVs.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a method and system for large-angle adaptive sliding mode control of a quadcopter UAV. Background Technology

[0002] Quadrotor UAVs possess advantages such as simple structure, high maneuverability, safety, and reliability, and are widely used in military, agriculture, industry, civilian, surveying, and remote sensing fields. However, due to the inherent uncertainties in the quadrotor UAV model and the various unknown disturbances it frequently encounters during flight missions, practical applications place higher demands on the performance of quadrotor UAV controllers. Maintaining stable attitude is fundamental for quadrotor UAVs to perform various tasks; therefore, developing a method for stably achieving attitude control of quadrotor UAVs has significant engineering and application value.

[0003] Existing attitude control schemes for quadrotor UAVs commonly include PID control, Active Disturbance Rejection Control (ADRC), Sliding Mode Control (SMC), and Low-Quickness Reduction (LQR) control. However, these control schemes inevitably suffer from the following drawbacks: poor dynamic performance, limited disturbance rejection capability, insufficient control accuracy, difficulty in parameter tuning, and sensitivity of the control algorithm to model parameters, leading to significant challenges in practical applications. For example, PID control is model-independent, has a simple structure, and is widely used, but it suffers from poor dynamic performance, difficulty in parameter tuning, susceptibility to overshoot, and limited ability to resist large disturbances. ADRC has numerous control parameters, making tuning difficult. SMC is prone to causing system chattering, affecting control accuracy. LQR control relies on a high-precision model of the controlled object, but in reality, the model of a quadrotor UAV has significant uncertainty, making parameter determination difficult and hindering its practical application. Summary of the Invention

[0004] The present invention aims to solve the technical problems of existing technologies for attitude controllers of quadcopter UAVs, such as poor dynamic performance, limited anti-disturbance capability, insufficient control accuracy, difficulty in parameter tuning, and sensitivity of control algorithms to parameters.

[0005] To address the aforementioned technical problems, in a first aspect, embodiments of the present invention provide a large-angle adaptive sliding mode control method for a quadcopter unmanned aerial vehicle (UAV), the control method comprising the following steps:

[0006] S1. Based on the motion posture of the quadrotor UAV, establish an attitude kinematic model based on quaternions;

[0007] S2. Based on the target attitude angle of the quadcopter UAV, establish an error model based on quaternions;

[0008] S3. The error model is simplified based on the nonlinear integral sliding mode function to obtain a simplified error model;

[0009] S4. Establish a terminal sliding mode controller based on the sliding mode function to control the control quantity for the motion posture, and control the simplified error model through the terminal sliding mode controller;

[0010] S5. Establish an adaptive estimation algorithm to adaptively estimate and adjust the upper bound parameter of the disturbance in the control quantity related to the motion posture;

[0011] S6. The control quantity output by the terminal sliding mode controller is output to the quadcopter drone and executed to control the motion posture of the quadcopter drone.

[0012] Furthermore, in step S1, the posture kinematic model satisfies:

[0013]

[0014] Wherein, the quaternion is a unit quaternion, i.e., q0 2 +q v T q v =1, q0 is the scalar part of the unit quaternion, q v The vector part of the unit quaternion, and I 3×3 Let J represent a 3×3 identity matrix, and J be the moment of inertia matrix of the quadcopter UAV, where J = diag(J x J y J z ); u represents the control torque generated by the rotor rotation of the quadcopter UAV, and u = [u x u y u z ] T ;w b Represents the angular velocity of the drone's fuselage, and w b =[w bx w by w bz ] T ; d represents the sum of bounded external disturbances and parameter uncertainties, and d = [d x d y d z ] T .

[0015] Furthermore, in step S2, the target pose angle is defined as [φ]. d θ d , ψ d ] T The target quaternion is q d It satisfies:

[0016]

[0017] Then the error quaternion satisfies:

[0018] q e =q d -1 q;

[0019] The error model satisfies:

[0020]

[0021] Among them, w e For the fuselage angular velocity error, w d For the desired angular velocity, Matrix C satisfies ||C|| = 1.

[0022] Furthermore, in step S3, the nonlinear integral sliding mode function is defined as:

[0023]

[0024] in:

[0025]

[0026] Substitute the error model into the nonlinear integral sliding mode function and calculate the second derivative of the nonlinear integral sliding mode function.

[0027]

[0028] According to the second differential The expression for the control quantity is obtained as follows:

[0029] u(t)=B(q ev ) -1 (-A(q e w e w b )+v(t));

[0030] Where v(t) is the auxiliary control variable;

[0031] definition The simplified error model satisfies:

[0032]

[0033] Furthermore, in step S4, the sliding mode function is defined to satisfy:

[0034]

[0035] Among them, sig α (x)=[|x1| α sgn(x1), |x2| α sgn(x2), |x3| α sgn(x3)] T k1 and k2 are positive numbers, and α1∈(0,1).

[0036] The sum of the uncertain parameters of the sliding mode function and the external bounded disturbances Represented as:

[0037]

[0038] In the formula, ξ = [φ, θ, ψ] T , b0, b1, and b2 are all positive constants;

[0039] Let the estimated values ​​of b0, b1, and b2 be respectively... but:

[0040]

[0041] In the formula, λ1 and λ2 are constants greater than 0, and β is greater than 0, both of which are control parameters;

[0042] According to the expression for the control quantity, the terminal sliding mode controller satisfies:

[0043]

[0044] Furthermore, in step S5, the adaptive estimation algorithm satisfies:

[0045]

[0046] Where μ0, μ1, and μ2 are all positive numbers, and It is a non-negative real number.

[0047] Secondly, embodiments of the present invention also provide a large-angle adaptive sliding mode control system for a quadcopter unmanned aerial vehicle, the control system comprising:

[0048] The kinematic model building module is used to build a quaternion-based attitude kinematic model based on the motion posture of the quadrotor UAV.

[0049] The error model construction module is used to establish a quaternion-based error model based on the target attitude angle of the quadcopter UAV.

[0050] The error model simplification module is used to simplify the error model based on a nonlinear integral sliding mode function to obtain a simplified error model;

[0051] The control quantity calculation module is used to establish a terminal sliding mode controller for controlling the motion posture based on the sliding mode function, and to control the simplified error model through the terminal sliding mode controller;

[0052] The adjustment module is used to establish an adaptive estimation algorithm to adaptively estimate and adjust the upper bound parameter of the disturbance in the control quantity related to the motion posture.

[0053] The control output module is used to output the control quantity output by the terminal sliding mode controller to the quadcopter drone and execute it to control the motion posture of the quadcopter drone.

[0054] The present invention has the following beneficial technical effects:

[0055] First, better dynamic performance. On the one hand, the control method provided by this invention adopts a quaternion-based model processing method, which makes the model still highly applicable when the quadcopter UAV is performing large-angle maneuvers, and the performance of the control system will not be affected, thus achieving better dynamic performance. On the other hand, it can ensure that the attitude of the quadcopter UAV converges to the target state in a finite time, thereby improving the following dynamic performance.

[0056] Second, it has stronger anti-disturbance capability. The control method provided by this invention has an adaptive rate that can accurately estimate the model parameters for uncertain parameters and unknown disturbances, and perform anti-disturbance control. It does not require setting an upper limit for the disturbance, is not sensitive to changes in system parameters, and has enhanced anti-disturbance capability.

[0057] Third, it has stronger stability. The adaptive sliding mode controller in the control method provided by this invention can converge in a finite time, ensuring rapid tracking of the target attitude and overall stability.

[0058] Fourth, higher control precision: Through the integral sliding mode function, the quadcopter UAV attitude can maintain a large control gain even with minimal error, thus ensuring higher control precision. At the same time, adaptive control can perform precise disturbance rejection, reduce sliding surface chattering, and improve control precision.

[0059] Fifth, the parameters are easier to tune. The control method provided by this invention involves fewer control parameters and fewer constraints, and the physical meaning is clear. Excellent control performance can be obtained with only simple debugging. Attached Figure Description

[0060] Figure 1 This is a flowchart illustrating the steps of the large-angle adaptive sliding mode control method for quadrotor UAVs provided in this embodiment of the invention.

[0061] Figure 2 This is a schematic diagram of the structure of the ten types of quadcopter UAVs provided in the embodiments of the present invention;

[0062] Figure 3 This is a schematic diagram of the drive signal processing process of the Type 10 quadcopter UAV provided in the embodiments of the present invention. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0064] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating the steps of a large-angle adaptive sliding mode control method for a quadcopter UAV provided in an embodiment of the present invention. The control method includes the following steps:

[0065] S1. Based on the motion posture of the quadrotor UAV, establish an attitude kinematic model based on quaternions.

[0066] Furthermore, in step S1, the posture kinematic model satisfies:

[0067]

[0068] Wherein, the quaternion is a unit quaternion, i.e., q0 2 +q v T q v =1, q0 is the scalar part of the unit quaternion, q v Let q be the vector part of the unit quaternion, and q v =[q1, q2, q3] T ;I 3×3 Let J represent a 3×3 identity matrix, and J be the moment of inertia matrix of the quadcopter UAV, where J = diag(J x J y J z ); u represents the control torque generated by the rotor rotation of the quadcopter UAV, and u = [u x ,

[0069] u y u z ] T ;w b Represents the angular velocity of the drone's fuselage, and w b =[w bx w by w bz ]T ; d represents the sum of bounded external disturbances and parameter uncertainties, and d = [d x d y d z ] T .

[0070] S2. Based on the target attitude angle of the quadcopter UAV, establish an error model based on quaternions.

[0071] Furthermore, in step S2, the target pose angle is defined as [φ]. d θ d , ψ d ] T The target quaternion is q d It satisfies:

[0072]

[0073] Then the error quaternion satisfies:

[0074] q e =q d -1 q;

[0075] The error model satisfies:

[0076]

[0077] Among them, w e For the fuselage angular velocity error, w d For the desired angular velocity, Matrix C satisfies ||C|| = 1.

[0078] S3. The error model is simplified based on the nonlinear integral sliding mode function to obtain the simplified error model.

[0079] Furthermore, in step S3, the nonlinear integral sliding mode function is defined as:

[0080]

[0081] in:

[0082]

[0083] Specifically, based on the above formula, we can obtain the differential and second-order differential of the sliding mode function:

[0084]

[0085]

[0086]

[0087] Substitute the error model into the nonlinear integral sliding mode function and calculate the second derivative of the nonlinear integral sliding mode function.

[0088]

[0089] According to the second differential The expression for the control quantity is obtained as follows:

[0090] u(t)=B(q ev ) -1 (-A(q e w e w b )+v(t));

[0091] Where v(t) is the auxiliary control quantity used for system disturbance rejection;

[0092] definition The simplified error model satisfies:

[0093]

[0094] S4. Establish a terminal sliding mode controller based on the sliding mode function to control the control quantity for the motion posture, and control the simplified error model through the terminal sliding mode controller.

[0095] Furthermore, in step S4, the sliding mode function is defined to satisfy:

[0096]

[0097] Among them, sig α (x)=[|x1| α sgn(x1), |x2| α sgn(x2), |x3| α sgn(x3)] T k1 and k2 are positive numbers, and α1∈(0,1).

[0098] Specifically, let It can be obtained

[0099] The sum of the uncertain parameters of the sliding mode function and the external bounded disturbances Represented as:

[0100]

[0101] In the formula, ξ = [φ, θ, ψ] T, b0, b1, and b2 are all positive constants;

[0102] Let the estimated values ​​of b0, b1, and b2 be respectively... but:

[0103]

[0104] In the formula, λ1 and λ2 are constants greater than 0, and β is greater than 0, both of which are control parameters;

[0105] According to the expression for the control quantity, the terminal sliding mode controller satisfies:

[0106]

[0107] S5. Establish an adaptive estimation algorithm to adaptively estimate and adjust the upper bound parameter of the disturbance in the control quantity related to the motion posture.

[0108] Specifically, in order to maintain system stability, it is necessary to estimate the upper bound of the attitude system parameters in response to the uncertainty of the parameters and external disturbances. In actual systems, the upper bound of the disturbance is difficult to determine. If the estimate is too large, it is easy to cause overshoot, oscillation and other problems. If the estimate is too small, the system may lose stability under the influence of external uncertain disturbances. Therefore, this embodiment of the invention designs an adaptive estimation algorithm to estimate the upper bound parameters of the UAV attitude system disturbance.

[0109] Furthermore, in step S5, the adaptive estimation algorithm satisfies:

[0110]

[0111] Where μ0, μ1, and μ2 are all positive numbers, and It is a non-negative real number.

[0112] In the adaptive estimation algorithm, the Lyapunov function is designed as follows:

[0113]

[0114] It can be proven that s will occur in a finite time t. r Converging to 0, and Where Γ is a positive real number, it then enters the sliding surface, and the attitude system reaches the desired state in a finite time.

[0115] S6. The control quantity output by the terminal sliding mode controller is output to the quadcopter drone and executed to control the motion posture of the quadcopter drone.

[0116] For example, please refer to Figure 2This invention takes the structure of a Type 10 quadcopter UAV using an electronically controlled motor as an example. Figure 3 This is a schematic diagram of the drive signal processing of a Type 10 quadcopter UAV. The control signal of the quadcopter UAV is PWM. In step S6, the control quantity u(t) is converted into the actual PWM control quantity u. pwm (t) Output, the relationship between the output signal and the motor speed is:

[0117] ω(t)=Kv.Vbat.u pwm (t);

[0118] In the above formula, Kv represents the motor parameters, Vbat represents the battery voltage, and ω(t) represents the rotational speed in revolutions per minute; the torque satisfies:

[0119]

[0120] Then the control signal u for each motor can be obtained. pwm for:

[0121]

[0122] Where l is the distance from the motor to the center of the machine body, and k t k d These are the aerodynamic coefficients, ω i Let u be the rotational speed of the i-th motor. pwmi For the control signal of the i-th motor, u pwm0i This is the initial control signal for the i-th motor.

[0123] The present invention has the following beneficial technical effects:

[0124] First, better dynamic performance. On the one hand, the control method provided by this invention adopts a quaternion-based model processing method, which makes the model still highly applicable when the quadcopter UAV is performing large-angle maneuvers, and the performance of the control system will not be affected, thus achieving better dynamic performance. On the other hand, it can ensure that the attitude of the quadcopter UAV converges to the target state in a finite time, thereby improving the following dynamic performance.

[0125] Second, it has stronger anti-disturbance capability. The control method provided by this invention has an adaptive rate that can accurately estimate the model parameters for uncertain parameters and unknown disturbances, and perform anti-disturbance control. It does not require setting an upper limit for the disturbance, is not sensitive to changes in system parameters, and has enhanced anti-disturbance capability.

[0126] Third, it has stronger stability. The adaptive sliding mode controller in the control method provided by this invention can converge in a finite time, ensuring rapid tracking of the target attitude and overall stability.

[0127] Fourth, higher control precision: Through the integral sliding mode function, the quadcopter UAV attitude can maintain a large control gain even with minimal error, thus ensuring higher control precision. At the same time, adaptive control can perform precise disturbance rejection, reduce sliding surface chattering, and improve control precision.

[0128] Fifth, the parameters are easier to tune. The control method provided by this invention involves fewer control parameters and fewer constraints, and the physical meaning is clear. Excellent control performance can be obtained with only simple debugging.

[0129] This invention also provides a large-angle adaptive sliding mode control system for a quadcopter unmanned aerial vehicle (UAV), the control system comprising:

[0130] The kinematic model building module is used to build a quaternion-based attitude kinematic model based on the motion posture of the quadrotor UAV.

[0131] The error model construction module is used to establish a quaternion-based error model based on the target attitude angle of the quadcopter UAV.

[0132] The error model simplification module is used to simplify the error model based on a nonlinear integral sliding mode function to obtain a simplified error model;

[0133] The control quantity calculation module is used to establish a terminal sliding mode controller for controlling the motion posture based on the sliding mode function, and to control the simplified error model through the terminal sliding mode controller;

[0134] The adjustment module is used to establish an adaptive estimation algorithm to adaptively estimate and adjust the upper bound parameter of the disturbance in the control quantity related to the motion posture.

[0135] The control output module is used to output the control quantity output by the terminal sliding mode controller to the quadcopter drone and execute it to control the motion posture of the quadcopter drone.

[0136] The large-angle adaptive sliding mode control system for quadrotor UAVs can implement the steps in the large-angle adaptive sliding mode control method for quadrotor UAVs in the above embodiments, and can achieve the same technical effect. Refer to the description in the above embodiments, which will not be repeated here.

[0137] This invention also provides a computer device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor.

[0138] The processor calls the computer program stored in the memory to execute the steps in the large-angle adaptive sliding mode control method for quadrotor UAVs provided in this embodiment of the invention. Please refer to... Figure 1 Specifically including

[0139] S1. Based on the motion posture of the quadrotor UAV, establish an attitude kinematic model based on quaternions.

[0140] Furthermore, in step S1, the posture kinematic model satisfies:

[0141]

[0142] Wherein, the quaternion is a unit quaternion, i.e., q0 2 +q v T q v =1, q0 is the scalar part of the unit quaternion, q v Let q be the vector part of the unit quaternion, and q v =[q1, q2, q3] T ;I 3×3 Let J represent a 3×3 identity matrix, and J be the moment of inertia matrix of the quadcopter UAV, where J = diag(J x J y J z ); u represents the control torque generated by the rotor rotation of the quadcopter UAV, and u = [u x ,

[0143] u y u z ] T ;w b Represents the angular velocity of the drone's fuselage, and w b =[w bx w by w bz ] T ; d represents the sum of bounded external disturbances and parameter uncertainties, and d = [d x d y d z ] T .

[0144] S2. Based on the target attitude angle of the quadcopter UAV, establish an error model based on quaternions.

[0145] Furthermore, in step S2, the target pose angle is defined as [φ]. d θ d , ψ d ] T The target quaternion is q d It satisfies:

[0146]

[0147] Then the error quaternion satisfies:

[0148] q e =q d -1 q;

[0149] The error model satisfies:

[0150]

[0151] Among them, w e For the fuselage angular velocity error, w d For the desired angular velocity, Matrix C satisfies ||C|| = 1.

[0152] S3. The error model is simplified based on the nonlinear integral sliding mode function to obtain the simplified error model.

[0153] Furthermore, in step S3, the nonlinear integral sliding mode function is defined as:

[0154]

[0155] in:

[0156]

[0157] Substitute the error model into the nonlinear integral sliding mode function and calculate the second derivative of the nonlinear integral sliding mode function.

[0158]

[0159] According to the second differential The expression for the control quantity is obtained as follows:

[0160] u(t)=B(q ev ) -1 (-A(q e w e w b )+v(t));

[0161] Where v(t) is the auxiliary control variable;

[0162] definition The simplified error model satisfies:

[0163]

[0164] S4. Establish a terminal sliding mode controller based on the sliding mode function to control the control quantity for the motion posture, and control the simplified error model through the terminal sliding mode controller.

[0165] Furthermore, in step S4, the sliding mode function is defined to satisfy:

[0166]

[0167] Among them, sig α (x)=[|x1| α sgn(x1), |x2| α sgn(x2), |x3| α sgn(x3)] T k1 and k2 are positive numbers, and α1∈(0,1).

[0168] The sum of the uncertain parameters of the sliding mode function and the external bounded disturbances Represented as:

[0169]

[0170] In the formula, ξ = [φ, θ, ψ] T , b0, b1, and b2 are all positive constants;

[0171] Let the estimated values ​​of b0, b1, and b2 be respectively... but:

[0172]

[0173] In the formula, λ1 and λ2 are constants greater than 0, and β is greater than 0, both of which are control parameters;

[0174] According to the expression for the control quantity, the terminal sliding mode controller satisfies:

[0175]

[0176] S5. Establish an adaptive estimation algorithm to adaptively estimate and adjust the upper bound parameter of the disturbance in the control quantity related to the motion posture.

[0177] Furthermore, in step S5, the adaptive estimation algorithm satisfies:

[0178]

[0179] Where μ0, μ1, and μ2 are all positive numbers, and It is a non-negative real number.

[0180] S6. The control quantity output by the terminal sliding mode controller is output to the quadcopter drone and executed to control the motion posture of the quadcopter drone.

[0181] The computer device provided in this embodiment of the invention can implement the steps in the large-angle adaptive sliding mode control method for quadcopter UAVs as described in the above embodiments, and can achieve the same technical effect. Refer to the description in the above embodiments, which will not be repeated here.

[0182] This invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the various processes and steps of the large-angle adaptive sliding mode control method for quadcopter UAVs provided in this invention, and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0183] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0184] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0185] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0186] The embodiments of the present invention have been described above with reference to the accompanying drawings. The disclosed embodiments are merely preferred embodiments of the present invention. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many equivalent changes in form without departing from the spirit and scope of the claims of the present invention, and all such changes are within the protection scope of the present invention.

Claims

1. A large-angle adaptive sliding mode control method for a quadrotor unmanned aerial vehicle, characterized in that, The control method includes the following steps: S1. Based on the motion posture of the quadrotor UAV, establish an attitude kinematic model based on quaternions; S2. Based on the target attitude angle of the quadcopter UAV, establish an error model based on quaternions; S3. The error model is simplified based on the nonlinear integral sliding mode function to obtain a simplified error model; S4. Establish a terminal sliding mode controller based on the sliding mode function to control the control quantity for the motion posture, and control the simplified error model through the terminal sliding mode controller; S5. Establish an adaptive estimation algorithm to adaptively estimate and adjust the upper bound parameter of the disturbance in the control quantity related to the motion posture; S6. Output the control quantity output by the terminal sliding mode controller to the quadcopter drone and execute it to control the motion posture of the quadcopter drone; In step S3, the nonlinear integral sliding mode function is defined as: ； in: ； Substitute the error model into the nonlinear integral sliding mode function and calculate the second derivative of the nonlinear integral sliding mode function. : ； According to the second differential The expression for the control quantity is obtained as follows: ； in, For auxiliary control quantity; definition , The simplified error model satisfies: ； In step S4, the sliding mode function is defined to satisfy: ； in, , , It is a positive number. , ; The sum of the uncertain parameters of the sliding mode function and the external bounded disturbances Represented as: ； In the formula, , , , All are positive constants; Definition , The estimated values ​​are respectively ,but: ； In the formula A constant greater than 0 Anything greater than 0 is a control parameter; According to the expression for the control quantity, the terminal sliding mode controller satisfies: 。 2. The large-angle adaptive sliding mode control method for quadrotor UAVs as described in claim 1, characterized in that, In step S1, the posture kinematic model satisfies: ； Wherein, the quaternion is a unit quaternion, that is , The scalar part of the unit quaternion. The vector part of the unit quaternion, and ; represent The identity matrix, Let be the rotational inertia matrix of the quadcopter UAV, and ; This represents the control torque generated by the rotor rotation of the quadcopter drone, and ; Represents the angular velocity of the drone's fuselage, and ; This represents the sum of bounded external disturbances and parameter uncertainties, and .

3. The large-angle adaptive sliding mode control method for quadrotor UAVs as described in claim 2, characterized in that, In step S2, the target pose angle is defined as... The target quaternion is It satisfies: ； Then the error quaternion satisfies: ； The error model satisfies: ； in, For the fuselage angular velocity error, For the desired angular velocity, , ,matrix satisfy .

4. The large-angle adaptive sliding mode control method for quadrotor UAVs as described in claim 3, characterized in that, In step S5, the adaptive estimation algorithm satisfies: ； in, All are positive numbers, and It is a non-negative real number.

5. A large-angle adaptive sliding mode control system for a quadcopter unmanned aerial vehicle, characterized in that, The control system includes: The kinematic model building module is used to build a quaternion-based attitude kinematic model based on the motion posture of the quadrotor UAV. The error model construction module is used to establish a quaternion-based error model based on the target attitude angle of the quadcopter UAV. The error model simplification module is used to simplify the error model based on a nonlinear integral sliding mode function to obtain a simplified error model; The control quantity calculation module is used to establish a terminal sliding mode controller for controlling the motion posture based on the sliding mode function, and to control the simplified error model through the terminal sliding mode controller; The adjustment module is used to establish an adaptive estimation algorithm to adaptively estimate and adjust the upper bound parameter of the disturbance in the control quantity related to the motion posture. The control output module is used to output the control quantity output by the terminal sliding mode controller to the quadcopter drone and execute it to control the motion posture of the quadcopter drone; The nonlinear integral sliding mode function is defined as follows: ； in: ； Substitute the error model into the nonlinear integral sliding mode function and calculate the second derivative of the nonlinear integral sliding mode function. : ； According to the second differential The expression for the control quantity is obtained as follows: ； in, For auxiliary control quantity; definition , The simplified error model satisfies: ； The sliding mode function is defined to satisfy: ； in, , , It is a positive number. , ; The sum of the uncertain parameters of the sliding mode function and the external bounded disturbances Represented as: ； In the formula, , , , All are positive constants; Definition , The estimated values ​​are respectively ,but: ； In the formula A constant greater than 0 Anything greater than 0 is a control parameter; According to the expression for the control quantity, the terminal sliding mode controller satisfies: 。

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