A method for controlling the attitude of a quadrotor drone

Abstract

The present invention provides a quad-rotor unmanned aerial vehicle attitude control method, comprising: S1, establishing a quad-rotor unmanned aerial vehicle attitude control model; S1 includes: establishing a dynamic model according to dynamic characteristics and Newton-Euler equations; establishing an integral series system for the attitude control system of the quad-rotor unmanned aerial vehicle according to a preset improved third-order extended state observer; establishing a state space equation and discretizing the three channels of the attitude control system of the quad-rotor unmanned aerial vehicle in the integral series system; S2, constructing the quad-rotor unmanned aerial vehicle attitude control model according to a preset prediction time domain; S3, optimizing and solving the quad-rotor unmanned aerial vehicle attitude control model and obtaining the final control amount, and executing the final control amount through the power system of the quad-rotor unmanned aerial vehicle. Compared with the relevant technology, the unmanned aerial vehicle using the quad-rotor unmanned aerial vehicle attitude control method of the present invention has good dynamic performance, strong robustness and strong anti-interference ability, and low debugging difficulty.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular to a method for controlling the attitude of a quad-rotor unmanned aerial vehicle. Background Art

[0002] In recent years, drone technology has been deeply developed. The application of quadcopter drones is becoming more and more extensive. Quadcopter drones are a kind of multi-rotor helicopter that can take off and land vertically and hover freely. They have the advantages of small size, simple structure, easy maintenance, good maneuverability, etc. They have broad application prospects in military, civil and scientific research fields. At the same time, the dynamic model of quadcopter drones is highly complex. The various parameters of the quadcopter drone model are affected by uncertain factors such as motor selection, rotor size, body weight and mass distribution, which makes the model have great uncertainty, resulting in greater difficulty in modeling and controlling the quadcopter. The attitude control of quadcopter drones is the basis for quadcopter drones to perform various tasks. The quality of attitude control determines the quality of position control and upper-level control.

[0003] The attitude control schemes of quad-rotor UAVs in related technologies mainly include cascade PID control schemes or anti-disturbance control schemes.

[0004] However, the PID control scheme in the attitude control scheme of the quad-rotor drone in the related art does not rely on the model, has a simple structure, and is widely used, but it has disadvantages such as poor dynamic performance, difficult parameter adjustment, easy overshoot, and limited ability to resist large disturbances; the traditional self-disturbance rejection control has many parameters and is difficult to adjust. In addition, the parameters of the self-disturbance rejection control scheme in the related art are numerous and difficult to adjust; model predictive control relies on a high-precision model of the controlled object, but in actual situations, the model uncertainty of the quad-rotor drone is large, the parameters are difficult to determine, and it is difficult to put into practical use. Therefore, how to make the attitude control scheme of the quad-rotor drone have poor dynamic performance, weak robustness, and difficult parameter adjustment is a technical problem to be solved.

[0005] Therefore, it is necessary to provide a new method to solve the above problems. Summary of the invention

[0006] In view of the above shortcomings of the prior art, the present invention proposes a quad-rotor UAV attitude control method which has good UAV dynamic performance, strong robustness, strong anti-interference ability and low debugging difficulty.

[0007] In order to solve the above technical problems, an embodiment of the present invention provides a quad-rotor drone attitude control method, characterized in that the method comprises the following steps:

[0008] Step S1, establishing a quadrotor drone attitude control model; the step S1 specifically includes:

[0009] Step S11, establishing a dynamic model according to the dynamic characteristics of the quadrotor drone and the Newton-Euler equations;

[0010] Step S12, establishing an integral series system for the attitude control system of the quadrotor drone according to a preset improved third-order extended state observer; the improved third-order extended state observer is an extended state observer that adds a proportional calculation in the total disturbance estimation calculation;

[0011] Step S13, establishing a state space equation by combining the three channels of the attitude control system of the quadrotor drone in the integral series system, and discretizing the state space equation;

[0012] Step S2, constructing the attitude control model of the quadrotor drone according to a preset prediction time domain to achieve state prediction;

[0013] Step S3, optimizing and solving the attitude control model of the quadrotor drone to obtain a final control amount, and executing the final control amount through the power system of the quadrotor drone to control the attitude of the quadrotor drone.

[0014] Preferably, in step S11, the dynamic characteristics of the quadrotor drone and the Newton-Euler equation include the following formula:

[0015]

[0016] Wherein, (x, y, z) are the spatial coordinates of the quadrotor drone; is the acceleration in the x direction, is the acceleration in the y direction, is the acceleration in the z direction; m is the mass of the quadrotor drone, g is the acceleration due to gravity; φ, θ, ψ are the roll angle, pitch angle, and heading angle, respectively; p is the angular velocity of the quadrotor drone body around the x-axis, q is the angular velocity of the quadrotor drone body around the y-axis, and r is the angular velocity of the quadrotor drone body around the z-axis; are the angular accelerations of the body around the x-axis, y-axis, and z-axis respectively; J xx , J yy , J zz are the rotational inertia of the body of the quad-rotor drone in the xx, yy, and zz directions respectively; f is the lift provided by the brushless motor of the quad-rotor drone; J RP is the moment of inertia of the entire motor rotor and propeller of the quadrotor drone around the collective rotation axis; τ x , τ x , τ xare the control quantity in the x direction, the control quantity in the y direction, and the control quantity in the z direction respectively; w1, w2, w3, and w4 are the speeds of the four motors respectively; Δx, Δy, and Δz are the unmodeled parts of the three channels respectively; Δφ, Δθ, and Δψ are the three uncertain information in the model respectively;

[0017] The kinetic model is:

[0018]

[0019] Among them, F1~F4 are the lifts generated by the rotation of the four motors in the quadrotor drone; Ω1~Ω4 are the rotation speeds of the four motors in the quadrotor drone; k1 is the lift coefficient of the rotor in the quadrotor drone, and k2 is the anti-torsion coefficient of the four motor rotors in the quadrotor drone.

[0020] Preferably, in step S12, the improved third-order extended state observer satisfies the following formula:

[0021]

[0022] Wherein, v is the system output of the quadrotor drone; z1 and z2 are the estimated values ​​of the attitude angle and angular velocity in the quadrotor drone, respectively; z3 is the total disturbance estimated value representing the sum of the internal disturbance and the external disturbance of the system; β1, β2, β3 are the gain parameters of the improved third-order extended state observer, respectively, and w o is the bandwidth of the improved third-order extended state observer;

[0023] The values ​​of β1, β2, and β3 using the pole placement method are:

[0024] β1=3ω o ,

[0025] b is a system parameter, satisfying: b≈1 / J;

[0026] Assume its estimated value is b0,ω o is a constant greater than 0, k p is a constant greater than 0, and the attitude control system of the quadrotor drone is:

[0027]

[0028] in,

[0029]

[0030]

[0031]

[0032] f1, f2, f3 are total disturbances respectively. The estimated values ​​of f1, f2, f3 are z3 calculated by the improved third-order extended state observer. By compensating this term, the integral series system is:

[0033]

[0034] Preferably, in step S13, the state space equation is:

[0035]

[0036] in, The roll angle channel meets the following requirements: u=u1; the pitch angle channel satisfies: u=u2; the heading angle channel satisfies: u=u3;

[0037] The state space equation is discretized using the forward Euler method to satisfy the following formula:

[0038]

[0039] Where T is the period, k is the current cycle.

[0040] Preferably, in step S2, the preset prediction time domain is p, and the constructed quadrotor drone attitude control model satisfies the following formula:

[0041] X k =Ψx(k)+ΘU k ;

[0042] in,

[0043] X k =[x(k+1|k) T x(k+2|k) T … x(k+p|k) T ] T ;

[0044] U k = [u(k|k) T u(k+1|k) T … u(k+p-1|k) T ] T .

[0045] Preferably, step S3 comprises the following steps:

[0046] Step S31, constructing a cost function;

[0047] Step S32, setting optimization constraints;

[0048] Step S33, solving the control quantity by quadratic programming;

[0049] Step S34: Compensate for the disturbance.

[0050] Preferably, in step S31, the reference value sequence is defined as:

[0051] R k =[r(k+1) T r(k+2) T … r(k+p) T ] T ;

[0052] Then the cost function is:

[0053]

[0054] Among them, Q is the state error penalty weight, and R is the control amount penalty weight.

[0055] Preferably, in step S32, φ max ,θ max , max are respectively the maximum roll angle, maximum pitch angle and maximum heading angle of the quadrotor drone; p max ,q max 、r max are respectively the maximum rolling angular velocity, maximum pitch angular velocity and maximum yaw angular velocity of the quadrotor drone; τ xmax , τ ymax , τ zmax They are the maximum control amount in the x direction, the maximum control amount in the y direction, and the maximum control amount in the z direction respectively;

[0056] The cost function for setting the optimization constraints includes a roll angle control target optimization function, a pitch angle control target optimization function, and a yaw angle control target optimization function;

[0057] The roll angle control objective optimization function is:

[0058]

[0059] The pitch angle control objective optimization function is:

[0060]

[0061] The yaw angle control objective optimization function is:

[0062]

[0063] Preferably, in step S33, the cost function for setting the optimization constraint is organized as:

[0064] in,

[0065] Using quadratic programming to solve, we can get J(U k )The minimum control vector U k , take U k The first element is the control value u of this cycle mpc ,u mpc As the attitude controller output of the quadrotor drone.

[0066] Preferably, in step S34, the compensation disturbance amount is the total disturbance calculated by the improved third-order extended state observer, and the final control amount is obtained;

[0067] The final control quantity satisfies the following formula: u = u mpc -z3 / b0.

[0068] Compared with the related art, the quadrotor drone attitude control method of the present invention establishes a quadrotor drone attitude control model through step S1; specifically, through step S11, a dynamic model is established according to the dynamic characteristics of the quadrotor drone and the Newton-Euler equation; step S12, the attitude control system of the quadrotor drone is established as an integral series system according to a preset improved third-order extended state observer; step S13, a state space equation is established by integrating the three channels of the attitude control system of the quadrotor drone in the integral series system, and the state space equation is discretized. By using the improved third-order extended state observer in step S1 to add a proportional calculation in the total disturbance estimation calculation, the tracking speed of the system state quantity and the total disturbance estimation speed and accuracy can be effectively improved; the attitude model of the quadcopter drone is converted into an integral series system, thereby optimizing the system model and simplifying the controller design; this allows the model predictive control to be applied to the attitude control system, and at the same time, by improving the extended state observer, the state estimation and disturbance estimation performance are improved, the influence of the controller on the uncertainty of the model parameters is reduced, the ability to resist large disturbances is enhanced, and the unknown disturbance and the uncertain part of the model are estimated and compensated, and accurate attitude control can be completed when the model parameters are uncertain and there are large external disturbances. The quadcopter drone attitude control method of the present invention constructs the quadcopter drone attitude control model according to the preset prediction time domain through step S2 to realize state prediction; thereby solving the problem that the traditional model predictive control of the related technology is sensitive to parameters and the control effect is poor, and avoiding the problems that the self-disturbance rejection controller of the related technology is not suitable for the system due to the control rate calculation method, resulting in poor system dynamic response and difficult parameter debugging. The quad-rotor drone attitude control method of the present invention optimizes and solves the quad-rotor drone attitude control model and obtains the final control amount through step S3, and executes the final control amount through the power system of the quad-rotor drone to control the attitude of the quad-rotor drone. Step S3 improves the dynamic performance of the control system by adding optimization constraints to meet specific control requirements, thereby obtaining better anti-interference performance and robustness, reducing the difficulty of parameter debugging, and enhancing parameter adaptive capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The present invention will be described in detail below in conjunction with the accompanying drawings. The above and other aspects of the present invention will become clearer and easier to understand through the detailed description made in conjunction with the following drawings. In the accompanying drawings:

[0070] Figure 1 It is a flowchart of the attitude control method of the quad-rotor UAV of the present invention;

[0071] Figure 2 It is a structural block diagram of the attitude controller of the quad-rotor UAV in the embodiment of the attitude control method of the quad-rotor UAV of the present invention;

[0072] Figure 3 Schematic diagram of the attitude control structure of a quad-rotor drone in an embodiment of the attitude control method of a quad-rotor drone of the present invention;

[0073] Figure 4 This is a flowchart of step S3 of the quadrotor drone attitude control method of the present invention. DETAILED DESCRIPTION

[0074] The specific implementation of the present invention will be described in detail below with reference to the accompanying drawings.

[0075] The specific implementation modes / embodiments recorded herein are specific implementation modes of the present invention, which are used to illustrate the concept of the present invention, are explanatory and exemplary, and should not be interpreted as limiting the implementation modes of the present invention and the scope of the present invention. In addition to the embodiments recorded herein, those skilled in the art can also adopt other obvious technical solutions based on the contents disclosed in the claims and the specification of this application, and these technical solutions include any obvious replacement and modification of the embodiments recorded herein, which are within the protection scope of the present invention.

[0076] The present invention provides a method for controlling the attitude of a quad-rotor unmanned aerial vehicle. The method for controlling the attitude of a quad-rotor unmanned aerial vehicle is applied to a quad-rotor unmanned aerial vehicle. The quad-rotor unmanned aerial vehicle includes an attitude controller for implementing the method for controlling the attitude of a quad-rotor unmanned aerial vehicle.

[0077] Please refer to Figure 1 As shown, Figure 1 The figure is a flowchart of the attitude control method of the quad-rotor UAV of the present invention.

[0078] The quadrotor drone attitude control method comprises the following steps:

[0079] Step S1, establishing a quadrotor drone attitude control model.

[0080] The step S1 specifically includes:

[0081] Step S11: establishing a dynamic model according to the dynamic characteristics of the quadrotor drone and the Newton-Euler equations.

[0082] Please refer to Figure 2 As shown, Figure 2 This is a structural block diagram of the attitude controller of a quad-rotor drone in an embodiment of the quad-rotor drone attitude control method of the present invention.

[0083] In step S11, the dynamic characteristics of the quadrotor drone and the Newton-Euler equation include the following formula:

[0084]

[0085] Wherein, (x, y, z) are the spatial coordinates of the quadrotor drone; is the acceleration in the x direction, is the acceleration in the y direction, is the acceleration in the z direction; m is the mass of the quadrotor drone, g is the acceleration due to gravity; φ, θ, ψ are the roll angle, pitch angle, and heading angle, respectively; p is the angular velocity of the quadrotor drone body around the x-axis, q is the angular velocity of the quadrotor drone body around the y-axis, and r is the angular velocity of the quadrotor drone body around the z-axis; are the angular accelerations of the body around the x-axis, y-axis, and z-axis respectively; J xx , J yy , J zz are the rotational inertia of the body of the quad-rotor drone in the xx, yy, and zz directions respectively; f is the lift provided by the brushless motor of the quad-rotor drone; J RP is the moment of inertia of the entire motor rotor and propeller of the quadrotor drone around the collective rotation axis; τ x , τ x , τ x are the control quantity in the x direction, the control quantity in the y direction, and the control quantity in the z direction respectively; w1, w2, w3, and w4 are the rotation speeds of the four motors respectively; Δx, Δy, and Δz are the unmodeled parts of the three channels respectively; Δφ, Δθ, and Δψ are the three uncertain information in the model respectively.

[0086] The kinetic model is:

[0087]

[0088] Among them, F1~F4 are the lifts generated by the rotation of the four motors in the quadrotor drone; Ω1~Ω4 are the rotation speeds of the four motors in the quadrotor drone; k1 is the lift coefficient of the rotor in the quadrotor drone, and k2 is the anti-torsion coefficient of the four motor rotors in the quadrotor drone.

[0089] Please refer to Figure 3 As shown, Figure 3 The figure is a schematic diagram of the attitude control structure of a quad-rotor drone in an embodiment of the attitude control method of a quad-rotor drone of the present invention. The roll angle controller, the pitch angle controller, and the heading angle controller receive feedback of the roll angle φ, the pitch angle θ, and the heading angle ψ, and after processing, the rotor motor control distribution processing is performed to generate the speeds Ω1 to Ω4 of the four motors in the quad-rotor drone. The speeds Ω1 to Ω4 are processed by the dynamic model to output the roll angle φ, the pitch angle θ, and the heading angle ψ.

[0090] Step S12: establishing an integral series system for the attitude control system of the quadrotor drone according to a preset improved third-order extended state observer.

[0091] Step S12 converts the quadrotor drone attitude model into an integral series system, thereby optimizing the system model and simplifying the controller design.

[0092] The improved third-order extended state observer is an extended state observer that adds a proportional calculation to the total disturbance estimation calculation. By adding a proportional calculation to the total disturbance estimation calculation using the improved third-order extended state observer, the tracking speed of the system state quantity and the total disturbance estimation speed and accuracy can be effectively improved.

[0093] In step S12, the improved third-order extended state observer satisfies the following formula:

[0094]

[0095] Wherein, v is the system output of the quadrotor drone; z1 and z2 are the estimated values ​​of the attitude angle and angular velocity in the quadrotor drone, respectively; z3 is the total disturbance estimated value representing the sum of the internal disturbance and the external disturbance of the system; β1, β2, β3 are the gain parameters of the improved third-order extended state observer, respectively, and w o is the bandwidth of the improved third-order extended state observer.

[0096] The values ​​of β1, β2, and β3 using the pole placement method are:

[0097] β1=3ω o ,

[0098] b is a system parameter, satisfying: b≈1 / J.

[0099] Assume its estimated value is b0,ω o is a constant greater than 0, k p is a constant greater than 0.

[0100] The attitude control system of the quad-rotor drone is:

[0101]

[0102] in,

[0103]

[0104]

[0105]

[0106] f1, f2, f3 are total disturbances respectively. The estimated values ​​of f1, f2, f3 are z3 calculated by the improved third-order extended state observer. By compensating this term, the integral series system is:

[0107]

[0108] Step S13: establishing a state space equation by combining the three channels of the attitude control system of the quadrotor drone in the integral series system, and discretizing the state space equation.

[0109] In step S13, the state space equation is:

[0110]

[0111] in, The roll angle channel meets the following requirements: u=u1; the pitch angle channel satisfies: u=u2; the heading angle channel satisfies: u=u3;

[0112] The state space equation is discretized using the forward Euler method to satisfy the following formula:

[0113]

[0114] Where T is the period, k is the current cycle.

[0115] Through the implementation of step S11 to step S13, model predictive control can be applied to the attitude control system. At the same time, by improving the extended state observer, the state estimation and disturbance estimation performance are improved, the influence of the controller caused by the uncertainty of model parameters is reduced, and the ability to resist large disturbances is enhanced. By estimating and compensating for unknown disturbances and uncertain parts of the model, accurate attitude control can be completed when the model parameters are uncertain and there are large external disturbances.

[0116] Step S2: constructing the attitude control model of the quadrotor drone according to a preset prediction time domain to achieve state prediction.

[0117] In step S2, the preset prediction time domain is p, and the constructed quadrotor drone attitude control model satisfies the following formula:

[0118] X k =Ψx(k)+ΘU k .

[0119] in,

[0120] Xk =[x(k+1|k) T x(k+2|k) T … x(k+p|k) T ] T .

[0121] U k = [u(k|k) T u(k+1|k) T … u(k+p-1|k) T ] T .

[0122] Through the implementation of step 2, the attitude control model of the quadrotor drone is constructed according to the preset prediction time domain to realize state prediction; thereby solving the problem that the traditional model predictive control of the related technology is sensitive to parameters and thus has poor control effect, and avoiding the problem that the self-disturbance rejection controller of the related technology has poor system dynamic response and difficult parameter debugging due to the control rate calculation method not being suitable for the system.

[0123] Step S3, optimizing and solving the attitude control model of the quadrotor drone to obtain a final control amount, and executing the final control amount through the power system of the quadrotor drone to control the attitude of the quadrotor drone.

[0124] Please refer to Figure 4 As shown, Figure 4 This is a flowchart of step S3 of the quadrotor drone attitude control method of the present invention.

[0125] The step S3 comprises the following steps:

[0126] Step S31, constructing a cost function.

[0127] In step S31, the reference value sequence is defined as:

[0128] R k =[r(k+1) T r(k+2) T … r(k+p) T ] T ;

[0129] Then the cost function is:

[0130]

[0131] Among them, Q is the state error penalty weight, and R is the control amount penalty weight.

[0132] Step S32: Set optimization constraints.

[0133] In step S32, φmax ,θ max , max are respectively the maximum roll angle, maximum pitch angle and maximum heading angle of the quadrotor drone; p max ,q max 、r max are respectively the maximum rolling angular velocity, maximum pitch angular velocity and maximum yaw angular velocity of the quadrotor drone; τ xmax , τ ymax , τ zmax They are the maximum control amount in the x direction, the maximum control amount in the y direction, and the maximum control amount in the z direction respectively;

[0134] The cost function for setting the optimization constraints includes a roll angle control target optimization function, a pitch angle control target optimization function, and a yaw angle control target optimization function;

[0135] The roll angle control objective optimization function is:

[0136]

[0137] The pitch angle control objective optimization function is:

[0138]

[0139] The yaw angle control objective optimization function is:

[0140]

[0141] Step S33: Solve the control quantity by quadratic programming.

[0142] In step S33, the cost function of setting the optimization constraint is summarized as:

[0143] in,

[0144] Using quadratic programming to solve, we can get J(U k )The minimum control vector U k , take U k The first element is the control value u of this cycle mpc ,u mpc As the attitude controller output of the quadrotor drone.

[0145] Step S34: Compensate for the disturbance.

[0146] In the step S34, the compensation disturbance amount is the total disturbance calculated by the improved third-order extended state observer, and the final control amount is obtained;

[0147] The final control quantity satisfies the following formula: u = umpc -z3 / b0.

[0148] Through the implementation of step S3, the attitude control model of the quad-rotor drone is optimized and solved to obtain the final control quantity, and the final control quantity is executed by the power system of the quad-rotor drone to control the attitude of the quad-rotor drone. Step S3 improves the dynamic performance of the control system by adding optimization constraints to meet specific control requirements, thereby obtaining better anti-interference performance and robustness, reducing the difficulty of parameter debugging, and enhancing parameter adaptive capabilities.

[0149] Compared with the related art, the quadrotor drone attitude control method of the present invention establishes a quadrotor drone attitude control model through step S1; specifically, through step S11, a dynamic model is established according to the dynamic characteristics of the quadrotor drone and the Newton-Euler equation; step S12, the attitude control system of the quadrotor drone is established as an integral series system according to a preset improved third-order extended state observer; step S13, a state space equation is established by integrating the three channels of the attitude control system of the quadrotor drone in the integral series system, and the state space equation is discretized. By using the improved third-order extended state observer in step S1 to add a proportional calculation in the total disturbance estimation calculation, the tracking speed of the system state quantity and the total disturbance estimation speed and accuracy can be effectively improved; the attitude model of the quadcopter drone is converted into an integral series system, thereby optimizing the system model and simplifying the controller design; this allows the model predictive control to be applied to the attitude control system, and at the same time, by improving the extended state observer, the state estimation and disturbance estimation performance are improved, the influence of the controller on the uncertainty of the model parameters is reduced, the ability to resist large disturbances is enhanced, and the unknown disturbance and the uncertain part of the model are estimated and compensated, and accurate attitude control can be completed when the model parameters are uncertain and there are large external disturbances. The quadcopter drone attitude control method of the present invention constructs the quadcopter drone attitude control model according to the preset prediction time domain through step S2 to realize state prediction; thereby solving the problem that the traditional model predictive control of the related technology is sensitive to parameters and the control effect is poor, and avoiding the problems that the self-disturbance rejection controller of the related technology is not suitable for the system due to the control rate calculation method, resulting in poor system dynamic response and difficult parameter debugging. The quad-rotor drone attitude control method of the present invention optimizes and solves the quad-rotor drone attitude control model and obtains the final control amount through step S3, and executes the final control amount through the power system of the quad-rotor drone to control the attitude of the quad-rotor drone. Step S3 improves the dynamic performance of the control system by adding optimization constraints to meet specific control requirements, thereby obtaining better anti-interference performance and robustness, reducing the difficulty of parameter debugging, and enhancing parameter adaptive capabilities.

[0150] The above contents are only preferred embodiments of the present invention. For ordinary technicians in this field, according to the concept of the present invention, there will be changes in the specific implementation methods and application scopes. The content of this specification should not be understood as limiting the present invention.

Claims

1. A quadrotor drone attitude control method, characterized in that: The method comprises the following steps: Step S1, establishing a quadrotor drone attitude control model; the step S1 specifically includes: Step S11, establishing a dynamic model according to the dynamic characteristics of the quadrotor drone and the Newton-Euler equations; Step S12, establishing an integral series system for the attitude control system of the quadrotor drone according to a preset improved third-order extended state observer; the improved third-order extended state observer is an extended state observer that adds a proportional calculation in the total disturbance estimation calculation; Step S13, establishing a state space equation by connecting three channels of the attitude control system of the quadrotor drone in the integral series system, and discretizing the state space equation; Step S2, constructing the attitude control model of the quadrotor drone according to a preset prediction time domain to achieve state prediction; Step S3, optimizing and solving the attitude control model of the quad-rotor drone to obtain a final control amount, and executing the final control amount through the power system of the quad-rotor drone to control the attitude of the quad-rotor drone; In step S11, the dynamic characteristics of the quadrotor drone and the Newton-Euler equation include the following formula: ； in, is the spatial coordinate of the quadrotor drone; for The acceleration in the direction, for The acceleration in the direction, for Acceleration in direction; is the mass of the quadrotor drone, is the acceleration due to gravity; They are roll angle, pitch angle and heading angle respectively; The body of the four-rotor drone is The angular velocity of the axis, The body of the four-rotor drone is The angular velocity of the axis, The body of the four-rotor drone is Angular velocity of the axis; The body is axis, axis, Angular acceleration of the axis; 、 、 The body of the four-rotor UAV is respectively 、 、 Directional moment of inertia; The lift provided to the brushless motor of the quad-rotor drone; is the moment of inertia of the entire motor rotor and propeller of the quadrotor drone around the collective rotation axis; 、 、 They are Direction control amount, Direction control amount, The amount of control of direction; 、 、 、 They are the speeds of the four motors respectively; 、 、 They are the unmodeled parts of the three channels respectively; are three uncertain information in the model; Ω represents the algebraic sum of the motor speed; The kinetic model is: ; Among them, ~ The lifts are respectively generated by the rotation of four motors in the quad-rotor drone; ~ are the rotation speeds of the four motors in the quad-rotor drone respectively; is the lift coefficient of the rotor in the quadrotor drone, is the anti-torsion coefficient of the four motor rotors in the quadrotor drone; In step S12, the improved third-order extended state observer satisfies the following formula: ； in The system output of the quadrotor drone; are respectively an estimation of the attitude angle and an estimation of the angular velocity in the quadrotor drone; is the total disturbance estimator representing the sum of the internal disturbance and the external disturbance of the system; are the gain parameters of the improved third-order extended state observer respectively; e represents the observer error, u Indicates the control amount, , , They represent the first-order differentials of the three state quantities of the extended state observer respectively; The values ​​are: ； is the system parameter, satisfying: ; J represents the moment of inertia; Assume that the estimated value of the system parameter is is a constant greater than 0, is a constant greater than 0, and the attitude control system of the quadrotor drone is: ； in, , , Represent the second-order differentials of the roll angle, pitch angle, and yaw angle, respectively. , Respectively represent the control amount of the channel corresponding to the roll angle, pitch angle and yaw angle; ； ； ； , are the total disturbance, , The estimated value is calculated by the improved third-order extended state observer , by compensating this term, the integral series system is: 。 2. The quadrotor drone attitude control method according to claim 1, characterized in that: In step S13, the state space equation is: ； in, , ; The roll angle channel satisfies: , ; The pitch angle channel satisfies: , ; The heading angle channel satisfies: , ; The state space equation is discretized using the forward Euler method to satisfy the following formula: ； in, For the cycle, , , The current cycle.

3. The quad-rotor drone attitude control method according to claim 2, characterized in that: In step S2, the preset prediction time domain is , the constructed quadrotor drone attitude control model satisfies the following formula: ； in, , ; ； ; in, Both represent intermediate variables.

4. The quad-rotor drone attitude control method according to claim 3, characterized in that: The step S3 comprises the following steps: Step S31, constructing a cost function; Step S32, setting optimization constraints; Step S33, solving the control quantity by quadratic programming; Step S34: Compensate for the disturbance.

5. The quad-rotor drone attitude control method according to claim 4, characterized in that: In step S31, the reference value sequence is defined as: ； in, represents a sequence of reference values, Represents discrete reference value points; Then the cost function is: ； Among them, Q is the state error penalty weight, and R is the control amount penalty weight.

6. The quad-rotor drone attitude control method according to claim 5, characterized in that: In the step S32, are respectively the maximum roll angle, maximum pitch angle and maximum heading angle of the quadrotor drone; are respectively the maximum rolling angular velocity, the maximum pitch angular velocity and the maximum yaw angular velocity of the quadrotor drone; They are Maximum control amount of direction, The maximum control amount of direction and The maximum control amount of direction; The cost function for setting the optimization constraints includes a roll angle control target optimization function, a pitch angle control target optimization function, and a yaw angle control target optimization function; The roll angle control objective optimization function is: ； The pitch angle control objective optimization function is: ； The yaw angle control objective optimization function is: 。 7. The quad-rotor drone attitude control method according to claim 6, characterized in that: In step S33, the cost function of setting the optimization constraint is summarized as: ； in, ; ； Using quadratic programming to solve, we can get Minimum control quantity ,Pick The first element is the control value of this cycle , As the attitude controller output of the quadrotor drone, , Represents an intermediate variable.

8. The quad-rotor drone attitude control method according to claim 7, characterized in that: In the step S34, the compensation disturbance amount is the total disturbance calculated by the improved third-order extended state observer, and the final control amount is obtained; The final control quantity satisfies the following formula: .

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