Four-rotor plant protection unmanned aerial vehicle error tracking iterative learning control method and system

Abstract

The invention discloses a quadrotor plant protection unmanned aerial vehicle error tracking iterative learning control method and system, and relates to the technical field of plant protection unmanned aerial vehicles, and the method comprises the following steps: simplifying a kinetic model based on the trajectory error of a quadrotor plant protection unmanned aerial vehicle, and obtaining an error model; an expected error trajectory is constructed based on an attenuation function, the attenuation function is used for controlling the expected error trajectory to be smoothly attenuated from the initial error to 0, and the attenuation function comprises attenuation parameters used for adjusting the attenuation rate; obtaining a current error between the current track and the expected track of the four-rotor plant protection unmanned aerial vehicle, and calculating an error variable between the current error and the expected error track; designing a learning law and a controller through the error variable and the estimated value of the external disturbance at the previous moment; and the trajectory of the unmanned aerial vehicle is controlled through a learning law and a controller. According to the method, a novel expected error trajectory is designed, only one attenuation parameter needs to be determined, and fewer parameters are needed for calculation.

Description

Technical Field

[0001] This invention relates to the field of agricultural drone technology, and in particular to an error tracking iterative learning control method and system for a quadcopter agricultural drone. Background Technology

[0002] With the continuous growth of the global population and the acceleration of urbanization, my country's demand for food and agricultural products is also increasing. However, traditional agricultural production methods have some problems, such as labor intensity, time and labor consumption, and excessive use of pesticides. This not only increases the burden on farmers but also causes certain environmental pollution. Agricultural drones, also known as plant protection drones, are unmanned aerial vehicles used for the protection of agricultural and forestry plants. They consist of three parts: a flight platform (fixed-wing, helicopter, multi-rotor), navigation and flight control, and a spraying mechanism. Spraying operations are achieved through ground remote control or navigation and flight control, and can spray pesticides, seeds, and powders. Drones, equipped with various sensors, cameras, and spraying equipment, can achieve efficient and accurate monitoring and plant protection operations in farmland, reducing the waste of pesticides and fertilizers, effectively controlling crop pests and diseases, and improving yield and quality. Agricultural drones are of great significance in solving the problems existing in traditional agricultural production methods.

[0003] To further improve the automation technology of agricultural drones and achieve "machine replacement of human labor," there is a more direct demand for enhancing the precision and speed of these drones. On the one hand, traditional drone control technology has weak environmental perception and decision-making capabilities, failing to proactively sense and adapt to changes in the surrounding environment and make corresponding control adjustments based on real-time conditions. This limits the autonomy and intelligence of drones in complex farmland environments. On the other hand, traditional technologies have limited ability to adjust flight dynamics, making it difficult to achieve precise flight trajectory and pesticide dosage control, leading to pesticide waste and unstable application effects. Therefore, improving the control precision and intelligence of agricultural drones has become essential for supporting modern agriculture.

[0004] Iterative learning control emphasizes leveraging the iterative nature of a system to continuously optimize the controller through "learning," ultimately achieving complete tracking of the desired trajectory by the system output within a finite interval. Quadcopter agricultural drones are well-suited for iterative learning control because their mission-driven requirement is the repeated tracking of predefined trajectories. Iterative learning control can handle uncertainties in real-world system models. Compared to other systems, it requires less prior knowledge, does not rely on highly precise system models, and can handle complex control problems involving unknown parameters and model uncertainties, exhibiting strong robustness. Therefore, it holds significant research importance for systems like agricultural drones that are highly nonlinear and difficult to model.

[0005] However, within the theoretical framework of iterative learning control, initial state consistency requires that the system's initial output match the initial value of the desired trajectory, which is crucial for ensuring algorithm convergence. However, factors such as accuracy limitations and environmental uncertainties often make this condition difficult to achieve. Error tracking methods can effectively relax the initial value consistency condition, transforming the requirement for the system's initial value into a requirement for a manually designed desired error trajectory. Currently, mainstream error tracking iterative learning control methods employ polynomial forms, with parameter settings related to the system's initial value and its derivatives. Second-order systems often require four parameters that vary with the desired trajectory, resulting in a large number of parameters to be calculated. Summary of the Invention

[0006] Based on the shortcomings of the existing technology, the present invention provides a method and system for error tracking iterative learning control of a quadcopter agricultural drone, which solves the existing problems.

[0007] The present invention adopts the following technical solution: In a first aspect, the present invention provides an error tracking iterative learning control method for a quadcopter agricultural drone, comprising the following steps: A dynamic model of a quadcopter agricultural drone under external disturbance is established. The dynamic model is simplified based on the trajectory error of the quadcopter agricultural drone to obtain an error model. The trajectory error is the difference between the actual trajectory and the expected trajectory. The desired error trajectory is constructed based on the decay function, which is used to control the desired error trajectory to decay smoothly from the initial error to 0. The decay function includes decay parameters for adjusting the decay rate. Obtain the current error between the current trajectory and the expected trajectory of the quadcopter agricultural drone, and calculate the error variable between the current error and the expected error trajectory; design the learning law and controller based on the error variable and the estimated value of the external disturbance at the previous moment. The trajectory of the drone is controlled by learning laws and a controller.

[0008] Preferably, the dynamic model is as follows: ; In the formula, , and The locations of the quadcopter agricultural drones are shown below. x , y , z The second derivative, These are control quantities for different locations. , , , , and For the bounded external disturbance terms of position and attitude, The second derivative of the roll angle. The second derivative of the pitch angle. The second derivative of the yaw angle. The first derivative of the roll angle. Let be the first derivative of the pitch angle. The first derivative of the yaw angle. These are the torque components along each axis in the body coordinate system. a 1. a 2. a 3 represents the ratio of the corresponding differences in moment of inertia. b 1. b 2. b 3 represents the reciprocal of the corresponding moment of inertia.

[0009] Preferably, the expected error trajectory is as follows: ; In the formula, For the expected error trajectory, For the desired trajectory access point, Let the decay parameter be the desired error trajectory. This represents the initial error value at time 0. This represents the initial error derivative value at time 0. t For runtime, T This represents the maximum time within the interval.

[0010] Preferably, the learning law and controller are specifically as follows: ; ; In the formula, U k For the controller vectors of position and attitude, B For the controller parameter matrix, F For agricultural drone system variables, D k Let be a bounded external perturbation vector. c 2 is the control coefficient. s k As an intermediate variable, The first derivative of the error variable. The second derivative of the reference trajectory, The second derivative of the expected error trajectory. This is an estimate of the upper bound of the external disturbance. For the fully saturated learning law iteration variable, t For runtime, These are the parameters of the learning law.

[0011] Preferably, the control of the drone's trajectory through the learning law and controller specifically includes the following steps: The trajectory error and expected error trajectory from the previous moment are input into the learning law to obtain the upper bound estimate of the external disturbance at the next moment; the trajectory error includes position error and attitude error. The expected position trajectory, position error, and estimated upper bound of the external disturbance at the next moment are input to the position controller to obtain the position control quantity; the expected yaw angle trajectory and the position control quantity are decoupled and calculated to obtain the expected attitude trajectory and the resultant force of the quadcopter agricultural drone. The desired attitude trajectory, attitude error, and upper bound estimate of external disturbance are fed to the attitude controller to obtain the attitude control quantity; the attitude control quantity and resultant force are calculated to obtain the angular velocity command of the quadrotor system. The angular velocity command is input into the dynamic model to obtain the position and attitude information of the UAV at the next moment.

[0012] Secondly, the present invention provides an error tracking iterative learning control system for a quadcopter agricultural drone, comprising: A module is established to create a dynamic model of a quadcopter agricultural drone under external disturbances. The dynamic model is simplified based on the trajectory error of the quadcopter agricultural drone to obtain an error model; the trajectory error is the difference between the actual trajectory and the expected trajectory. A construction module is used to construct a desired error trajectory based on a decay function, wherein the decay function is used to control the desired error trajectory to decay smoothly from the initial error to 0, and the decay function includes decay parameters for adjusting the decay rate; The design module is used to obtain the current error between the current trajectory and the expected trajectory of the quadcopter agricultural drone, and to calculate the error variable between the current error and the expected error trajectory; the learning law and controller are designed based on the error variable and the estimated value of the external disturbance at the previous moment. The control module is used to control the trajectory of the drone through a learning law and a controller.

[0013] Compared with the prior art, the above-mentioned at least one technical solution adopted by the present invention can achieve the following beneficial effects: This invention proposes an error tracking iterative learning control method for quadrotor agricultural drones with random initial states. The invention constructs a desired error trajectory based on a decay function, which controls the smooth decay of the desired error trajectory from the initial error to zero. The decay function includes a decay parameter for adjusting the decay rate. A learning law and controller are designed based on the desired error trajectory to control the drone's trajectory. This invention designs a novel desired error trajectory that only requires determining one decay parameter, requiring fewer parameters for computation compared to polynomial-based error tracking iterative learning control methods. Furthermore, the invention introduces a decay function to dynamically adjust the decay rate of the desired error trajectory and achieves suppression of maximum overshoot. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a schematic diagram of the expected error trajectory under different attenuation parameters according to the present invention; Figure 2 This is a diagram showing the position tracking effect of the quadcopter agricultural drone in the 20th iteration of the present invention; in, Figure 2 (a): Represents the position state x tracking effect of the quadcopter agricultural drone in the 20th iteration. Figure 2 (b): Represents the position state y tracking effect of the quadcopter agricultural drone in the 20th iteration. Figure 2 (c): Represents the position state z-tracking effect diagram of the quadcopter agricultural drone in the 20th iteration; Figure 3 This is a diagram showing the attitude tracking effect of the quadcopter agricultural drone under the 20th iteration of the present invention; in, Figure 3 (a): represents the attitude state of the quadcopter agricultural drone in the 20th iteration. Tracking effect diagram, Figure 3 (b): represents the attitude state of the quadcopter agricultural drone in the 20th iteration. Tracking effect diagram, Figure 3 (c): represents the attitude state of the quadcopter agricultural drone in the 20th iteration. Tracking effect diagram; Figure 4This is a diagram showing the position error effect of the quadcopter agricultural drone of the present invention in its 20th iteration; in, Figure 4 (a): Represents the error effect diagram of the position state x of the quadcopter agricultural drone in the 20th iteration. Figure 4 (b): Represents the error effect diagram of the position state y of the quadcopter agricultural drone in the 20th iteration. Figure 4 (c): Represents the error effect diagram of the position state z of the quadcopter agricultural drone in the 20th iteration; Figure 5 This is a diagram showing the attitude error effect of the quadcopter agricultural drone of the present invention during its 20th iteration. in, Figure 5 (a): represents the attitude state of the quadcopter agricultural drone in the 20th iteration. Error effect diagram, Figure 5 (b): represents the attitude state of the quadcopter agricultural drone in the 20th iteration. Error effect diagram, Figure 5 (c): represents the attitude state of the quadcopter agricultural drone in the 20th iteration. Error effect diagram; Figure 6 The image shows the position and attitude error of the quadcopter agricultural drone under different attenuation parameters in the 20th iteration of the present invention. in, Figure 6 (a): indicates the 20th iteration of the quadcopter agricultural drone. and Comparison of position status and tracking performance (x-axis). Figure 6 (b): indicates the 20th iteration of the quadcopter agricultural drone. and Comparison of position state y-tracking performance. Figure 6 (c): indicates the 20th iteration of the quadcopter agricultural drone. and Comparison of z-tracking performance in position and state. Figure 6 (d): indicates the 20th iteration of the quadcopter agricultural drone. and attitude state Comparison chart of tracking results; Figure 7 This is a 3D tracking effect diagram of the quadcopter agricultural drone of the present invention under the 20th iteration; Figure 8 This is a flowchart of an error tracking iterative learning control method for a quadcopter agricultural drone according to the present invention; Figure 9 This is a schematic diagram of the iterative process of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example 1 To address the shortcomings of existing technologies, this invention provides an error tracking iterative learning control method for quadrotor agricultural drones. Specifically, it relates to an error tracking iterative learning control method for quadrotor agricultural drones based on a decay function, which includes the following steps: S1: Establish a dynamic model of a quadcopter agricultural drone under external disturbances.

[0018] The Newton-Euler method was used to model the quadcopter agricultural drone, ignoring the spiral effect.

[0019] The rotation matrix T from the body coordinate system to the Earth's inertial coordinate system is: (1); in, , , These represent the roll angle, pitch angle, and yaw angle, respectively, which represent the angles of rotation of the UAV around the axes of the inertial coordinate system in sequence. , , The cosine function values ​​representing the roll angle, pitch angle, and yaw angle. , , The cosine function values ​​representing the roll angle, pitch angle, and yaw angle.

[0020] By analyzing the translational motion of a quadcopter agricultural drone using Newton's Euler method, we can obtain the following: (2); in, x, y, z These represent the positions of the UAV in the inertial coordinate system. m Indicates the quality of the drone. g Represents gravitational acceleration. mg This indicates the gravitational force acting on the drone. This represents the resultant force generated by the four rotors. , and These represent the locations of the quadcopter agricultural drones. x , y , zThe second derivative of .

[0021] Analyzing the rotation process of a quadcopter agricultural drone, we can obtain the following: (3); in, These represent the torque components along each axis in the body coordinate system. , , These represent the components of rotational inertia along each axis in the body coordinate system. The vector product of two vectors is represented as... , , These represent the attitude angular velocity components along each axis in the body coordinate system. , , These represent the attitude angular acceleration components of each axis in the body coordinate system.

[0022] Considering that drones are generally in low-speed flight or hovering, with minimal changes in attitude angle, it can be assumed that... , , , , , Then, during the rotation process, equation (3) can be expressed as equation (4): (4); Combining equations (1), (2), and (4), the dynamic model of the UAV can be obtained as shown in equation (5): (5); in, , , , , , , These are the outputs of the three position controllers.

[0023] According to equation (5), the position and attitude relationship can be decoupled and calculated, and the results are as follows: (6); in, For roll angle The expected trajectory pitch angle The expected trajectory Let be the desired trajectory for the yaw angle φ. arcsin The function represents the arcsine function. arctan The function represents the arctangent function.

[0024] S2: The dynamic model is simplified based on the trajectory error of the quadcopter agricultural drone to obtain the error model; To ensure the universality of the quadcopter agricultural drone model, an external perturbation is added to the model based on equation (5), resulting in model expression (7): (7); in, , , , , , The term represents the bounded external disturbance term for position and attitude.

[0025] Iterative learning control is a control method that continuously improves accuracy based on historical experience. To further simplify the model expression and add an iteration axis, equation (7) can be written as: (8); in, k Indicates the first k iteration , , , , D k For bounded external disturbances. It is the upper bound of external disturbances and is a scalar. F For agricultural drone system variables, B For the controller parameter matrix, U k This is the controller vector for position and attitude.

[0026] Define trajectory error ,in This represents the desired trajectory.

[0027] Equation (8) can be further rewritten in error form: (9); S3: Construct the desired error trajectory based on the decay function.

[0028] Define the expected error trajectory of the decay function: (10); in, Indicates the desired trajectory access point. This represents the decay parameter of the desired error trajectory. This represents the initial error value at time 0. This represents the initial error derivative value at time 0. For simplicity, .

[0029] From equation (10), we can obtain: (11); from Figure 1 It can be seen that the expected error trajectory decays from the initial error to 0, and at the access point... Afterwards, it remains consistently at 0. Different decay parameters will result in different decay rates of the desired error trajectory. The larger the value, the faster the decay rate. Furthermore, the introduction of the decay function also reduces the maximum overshoot.

[0030] S4: Obtain the current error between the current trajectory and the expected trajectory of the quadcopter agricultural drone, and calculate the error variable between the current error and the expected error trajectory; design the learning law and controller based on the error variable and the estimated value of the external disturbance at the previous moment.

[0031] Define variables, ,in It is a scalar, error variable The controller and learning law are designed as follows:

[0032] (12); (13); in, It is a constant greater than 0. , c 2 is the control coefficient. s k As an intermediate variable, The first derivative of the error variable. The second derivative of the reference trajectory, The second derivative of the expected error trajectory. This is an estimate of the upper bound of the external disturbance. For the iteration variables of the fully saturated learning law.

[0033] S5: Controls the drone's trajectory through learning laws and controllers.

[0034] Reference Figure 9The trajectory error and expected error trajectory from the previous moment are input into the learning law to obtain the estimated upper bound of the external disturbance for the next moment. The expected position trajectory, position error, and the estimated upper bound of the external disturbance for the next moment are input into the position controller to obtain the position control quantity. The expected yaw angle trajectory and the position control quantity are decoupled and calculated to obtain the expected attitude trajectory and the resultant force of the quadcopter agricultural drone. The expected attitude trajectory, attitude error information, and the estimated upper bound of the external disturbance are input into the attitude controller to obtain the attitude control quantity. The attitude control quantity and the resultant force are solved to obtain the angular velocity command of the quadcopter system. The angular velocity command is input into the dynamic model to obtain the position and attitude information of the drone for the next moment. A closed-loop control system is formed.

[0035] Example 2 Stability analysis theorem: Define a class of Lyapunov functions: (14); Part One, Proof It decreases monotonically with the number of iterations.

[0036] right Taking the derivative, we get: (15); Substituting the controller (12) into equation (15), we get: (16); in, .

[0037] For two adjacent iterations By subtraction, we can obtain (17); in, .

[0038] Lemma 1: Given scalars a and b, satisfying Then the following inequality holds: ,in, Let b be the lower bound. Let be the upper bound of b, and let sat(·) be the saturation function, whose expression is as follows: According to the equation And with Lemma 1, we can obtain the following equation (18); Substituting equation (18) into equation (17), we can obtain: (19); Therefore, Lyapunov-like functions exist When the iteration count decreases, the value decreases.

[0039] Example 3 Prove that when k=0, It is bounded in [0, T].

[0040] (20); From equation (20), it can be found that (twenty one); because It is a bounded number, therefore exist It is bounded.

[0041] In summary, based on equations (19) and (21), we can obtain (twenty two); Equation (22) can be obtained by transformation. (twenty three); Based on equation (23) and the necessity of series convergence, we can conclude that... (twenty four); Therefore, as the number of iterations approaches infinity, the variable... Approaching zero. This means that the position and attitude errors of the quadcopter agricultural drone can asymptotically track the desired error trajectory. According to the definition of the desired error trajectory, the quadcopter agricultural drone achieves asymptotic tracking of the desired trajectory. Proof obtained.

[0042] Example 4 The effectiveness of the proposed method was verified through simulation, given the following parameters for a quadcopter agricultural drone: For the desired error trajectory (10), the error tracking iterative learning controller (12) and the fully saturated learning law (13) are set with parameters. , , , The desired trajectory is set to , , , , and The system runtime is calculated based on the decoupling formula (6). Seconds, the expected error trajectory access point is set to The initial values ​​for the quadcopter agricultural drone are set as follows: , , .

[0043] The numerical simulation results of the proposed method for the quadcopter agricultural drone in the 20th iteration are as follows: Figures 2-6 As shown. The position and attitude tracking performance of the quadcopter agricultural drone is as follows. Figure 2 and Figure 3 As shown. From Figure 2 and Figure 3 It can be seen that the quadcopter agricultural drone achieved high-precision tracking of the required trajectory after the access point. Figure 4 and Figure 5 This indicates that the tracking error converges to near zero along the expected error trajectory. Figure 6 Comparison and Error tracking performance was demonstrated. Results show that the proposed decay function effectively modulates the decay rate and suppresses maximum overshoot. Figure 7 The 3D trajectory tracking performance was demonstrated, illustrating that the quadcopter agricultural drone can effectively track the desired trajectory at a specified time.

[0044] Example 5 Based on the same concept, the present invention also provides an error tracking iterative learning control system for a quadcopter agricultural drone, including an establishment module, a construction module, a design module, and a control module.

[0045] The module is used to establish a dynamic model of a quadcopter agricultural drone under external disturbances. The dynamic model is simplified based on the trajectory error of the quadcopter agricultural drone to obtain an error model; the trajectory error is the difference between the actual trajectory and the expected trajectory.

[0046] The building module is used to construct the desired error trajectory based on the decay function, which is used to control the desired error trajectory to decay smoothly from the initial error to 0. The decay function includes decay parameters for adjusting the decay rate.

[0047] The design module is used to obtain the current error between the current trajectory and the expected trajectory of the quadcopter agricultural drone, and to calculate the error variable between the current error and the expected error trajectory; the learning law and controller are designed based on the error variable and the estimated value of the external disturbance at the previous moment.

[0048] The control module is used to control the trajectory of the drone through a learning law and a controller.

[0049] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0050] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A quadrotor agricultural unmanned aerial vehicle error tracking iterative learning control method, characterized in that, The method comprises the following steps: A dynamic model of the quadrotor plant protection unmanned aerial vehicle under external disturbance is established, and an error model is obtained by simplifying the dynamic model based on a trajectory error of the quadrotor plant protection unmanned aerial vehicle; the trajectory error is a difference between an actual trajectory and an expected trajectory; An expected error trajectory is constructed based on a decay function, the decay function is used to control the expected error trajectory to be smoothly attenuated from an initial error to 0, and the decay function comprises a decay parameter used to adjust an attenuation rate; A current error between a current trajectory of the quadrotor plant protection unmanned aerial vehicle and the expected trajectory is obtained, and an error variable between the current error and the expected error trajectory is calculated; A learning law and a controller are designed through the error variable and an estimated value of the external disturbance at a previous moment; The trajectory of the unmanned aerial vehicle is controlled through the learning law and the controller. 2.The error tracking iterative learning control method for a quad-rotor unmanned aerial vehicle for plant protection according to claim 1, wherein The dynamic model is specifically as follows: ； wherein , and are the second derivatives of the quadrotor agricultural unmanned aerial vehicle position x , y , z , are the control variables at different positions, , , , , and are the bounded external disturbance terms of the position and attitude, is the second derivative of the roll angle, is the second derivative of the pitch angle, is the second derivative of the yaw angle, is the first derivative of the roll angle, is the first derivative of the pitch angle, is the first derivative of the yaw angle, are the respective axis torque components in the body coordinate system, a 1, a 2, a 3 are the ratios of the corresponding moment of inertia difference values, b 1, b 2, b 3 are the reciprocals of the corresponding moments of inertia.

3. The error tracking iterative learning control method for a quad-rotor unmanned aerial vehicle for plant protection according to claim 1, wherein The expected error trajectory is specifically as follows: ； wherein is a desired error trajectory, is a desired trajectory access point, is a decay parameter of the desired error trajectory, denotes an initial error value at time 0, denotes an initial error derivative value at time 0, t is a run time, T is an interval maximum time.

4. The error tracking iterative learning control method for a quad-rotor unmanned aerial vehicle for plant protection according to claim 1, wherein The learning law and the controller are specifically as follows: ； ； wherein, U k is the position and attitude controller vector, B is the controller parameter matrix, F is the plant protection UAV system variable, D k is the bounded external disturbance vector, c 2is the control coefficient, s k is the intermediate variable, is the first derivative of the error variable, is the second derivative of the reference trajectory, is the second derivative of the desired error trajectory, is the estimate of the external disturbance upper bound, is the full-saturation learning law iteration variable, t is the run time, is the learning law parameter.

5. The error tracking iterative learning control method for a quad-rotor unmanned aerial vehicle for plant protection according to claim 4, characterized in that, The trajectory of the unmanned aerial vehicle is controlled through the learning law and the controller, and the method specifically comprises the following steps: The trajectory error at the previous moment and the expected error trajectory are input into the learning law to obtain an estimated value of an upper limit of the external disturbance at a next moment; the trajectory error comprises a position error and an attitude error; The position expected trajectory, the position error and the estimated value of the upper limit of the external disturbance at the next moment are input into a position controller to obtain a position control amount; the yaw angle expected trajectory and the position control amount are decoupled to obtain the attitude expected trajectory and a resultant force of the quadrotor plant protection unmanned aerial vehicle; The attitude expected trajectory, the attitude error and the estimated value of the upper limit of the external disturbance are input into an attitude controller to obtain an attitude control amount; the attitude control amount and the resultant force are calculated to obtain an angular velocity instruction of the quadrotor system; The angular velocity instruction is input into the dynamic model to obtain position and attitude information of the unmanned aerial vehicle at the next moment.

6. A quadrotor agricultural unmanned aerial vehicle error tracking iterative learning control system, characterized in that, The method comprises: The method comprises: The method comprises: The method comprises: The method comprises: The method comprises:

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