Unmanned aerial vehicle anti-wind disturbance stable control method

Abstract

The invention discloses an anti-wind disturbance stable control method for an unmanned aerial vehicle, and the method comprises the steps: carrying out the optimization on the basis of conventional active disturbance rejection control, and carrying out the fusion of a Kalman filter (KF) and an extended state observer (ESO), so as to form a composite state observer (KFESO); the ESO can ensure that the KF works when the system has deviation or the model has uncertainty, and the KF can filter the input of the ESO to weaken the influence of noise on the ESO; and meanwhile, a backstepping method is added into the active disturbance rejection controller, so that the response speed of the system can be improved, the unmanned aerial vehicle system can respond more quickly when being subjected to wind disturbance, and the stability of the system is improved. The improved active-disturbance-rejection controllers are adopted in the position ring and the attitude ring, so that better anti-interference performance can be achieved, and it is guaranteed that the unmanned aerial vehicle has the high response speed under wind disturbance; according to the method, the algorithm robustness is improved, the algorithm design is simplified, the calculation efficiency is improved, the adaptability and generalization ability are enhanced, and the practical application ability is improved.

Description

Technical Field

[0001] The invention relates to the field of unmanned aerial vehicle control, and in particular to a method for unmanned aerial vehicle anti-wind disturbance stability control. Background Art

[0002] With the rapid development of drone technology, drones have shown great application potential in many fields such as agriculture, logistics, security, and aerial photography. In the agricultural field, drones are used to spray pesticides and fertilizers, etc., which improves agricultural production efficiency; in the logistics field, drones can achieve fast and accurate cargo delivery, reducing logistics costs; in the security field, drones can perform patrol, monitoring and other tasks, improving social security. However, these application scenarios are often accompanied by complex meteorological conditions, such as strong winds and heavy rains, which put forward higher requirements on the flight stability and safety of drones. Wind field interference is one of the main challenges faced by drones in flight. Changes in the external wind field will cause the attitude and position of the drone to shift, thereby affecting its flight stability and accuracy. In strong wind conditions, drones may even crash due to loss of control. Therefore, it is of great significance to study the anti-wind disturbance stability control algorithm of drones and improve the flight stability of drones under complex wind field conditions to ensure that drones can complete their tasks safely and efficiently. With the continuous advancement of drone technology and the expansion of application scenarios, the research and application of anti-wind disturbance stability control algorithms will have broad market prospects. In the future, drones with wind resistance will be widely used in express delivery, disaster relief, agricultural spraying, environmental monitoring and other fields, bringing greater economic and social benefits to society. At the same time, with the continuous development of artificial intelligence technology, drone anti-wind disturbance stability control algorithms will also become more intelligent and autonomous, providing stronger guarantees for drone flight safety and stability.

[0003] Although the existing UAV anti-wind disturbance control algorithm has improved the stability and safety of UAVs in wind fields to a certain extent, it still has some shortcomings.

[0004] 1. High dependence on models: Some control algorithms, such as Backstepping, require high accuracy of the UAV’s dynamic model. However, in actual flight, due to changes in external factors such as wind field and air density, the UAV’s dynamic model is often difficult to accurately describe, which limits the effectiveness of the algorithm in practical applications.

[0005] 2. Susceptible to external interference: Some algorithms may experience performance degradation or even failure when affected by external factors such as strong winds and electromagnetic interference. This limits the stability and reliability of drones in complex environments.

[0006] 3. Complex design steps: For example, the design process of the LQR controller involves the optimization and adjustment of multiple parameters, which requires high professional knowledge and experience. This increases the difficulty of algorithm development and application.

[0007] 4. Large amount of computation: Some algorithms require a lot of computing resources when running, which is a challenge for drones with limited resources. Especially in application scenarios with high real-time requirements, the amount of computation of the algorithm may become a key factor restricting its performance. 5. Lack of real-time performance: Some algorithms lack real-time performance and cannot respond to changes in the external environment in a timely manner. This may cause delays or loss of control of the drone during flight.

[0008] 6. Limited generalization ability: Due to the diversity and complexity of the UAV flight environment, some algorithms may not be able to provide effective control strategies when facing unknown or untrained wind conditions. This reduces the autonomy and intelligence level of the UAV. Therefore, it is necessary for us to improve such a structure to overcome the above-mentioned defects. Summary of the invention

[0009] The object of the present invention is to provide a UAV anti-wind disturbance stability control algorithm combining a Kalman filter and a backstepping anti-disturbance controller to solve the problems raised in the above background technology.

[0010] To achieve the above object, the present invention provides the following technical solutions: A wind disturbance stabilization control method for UAV is optimized on the basis of traditional anti-disturbance control. The Kalman filter KF and the extended state observer ESO are integrated to form a composite state observer KFESO. ESO can ensure that KF works when there is a deviation in the system or uncertainty in the model. KF can filter the input of ESO to reduce the impact of noise on ESO. At the same time, the backstepping method is added to the anti-disturbance controller, which can improve the response speed of the system, enable the UAV system to respond faster when disturbed by wind, and improve the stability of the system.

[0011] Improved anti-disturbance controllers are used in both the position loop and the attitude loop to achieve better anti-disturbance performance, ensuring that the UAV has a higher response speed under wind disturbance while having higher stability and robustness.

[0012] Furthermore, the composite state observer KFESO is specifically: In order to solve the noise problem of ESO, the Kalman filter KF is used to pre-filter the input signal. KF can be given by the following formula: in An estimate of the system state , Lumped disturbance f The estimate, is the AURKF gain matrix.

[0013] For wind field environments, it is generally believed that the noise of drones detected by the Kalman filter under wind disturbance can be regarded as white noise. Therefore, the variance of the noise model can be calculated by the following formula: in is the simulation frequency. It can be further simplified: In the above formula is the state estimation error covariance matrix; R To measure the noise covariance, in practical applications, a white noise test can be performed to obtain the characteristic of the measured noise. R ; Q is the process noise covariance.

[0014] In steady state, it can be considered , at this time, the process noise covariance Q can be used to estimate the error covariance of the disturbance Instead, we have: get The expression is: For the design of ESO, the following extended state observer is constructed: in yes The estimate, Is the system status of estimates, and The system disturbance estimates; is the gain of the observer, The value of is .

[0015] By designing a reasonable ESO gain matrix, the lumped disturbance in the system can be accurately estimated. The error is defined as , then the derivative of the error can be defined as: In the above formula The characteristic equation of can be expressed as: Analyzing the above characteristic equation, when h Bounded and When the roots of are all located in the left half plane, the state observer can be stable, so it is assumed that , the solution can be obtained as: in is the bandwidth of the state observer. Then the ESO can be designed as: According to the description of the adaptive robust unscented Kalman filter KF and the extended state observer ESO, the expression of the composite state observer KFESO can be obtained as follows: Furthermore, the backstepping method is added to the ADRC. When the backstepping method is added to the ADRC of the UAV, since the backstepping method requires the use of a second-order differential signal, the second-order differential signal should be included in the output item of the transition process. To generate a second-order differential signal, a third-order differentiator or a cascaded second-order differentiator is required to arrange the transition process.

[0016] The third-order fast differentiator is a common third-order differentiator, and its expression is: The expression of the sat function is: Since the tracking signal and differential signal generated by the third-order fast differentiator will produce jitter when converging, a cascaded second-order differentiator is used here to obtain the required second-order differential signal.

[0017] Furthermore, a backstepping method is added to the active disturbance rejection controller; The basic idea of ​​the backstepping method is to decompose a complex nonlinear system into subsystems that do not exceed the system order, and then design partial Lyapunov functions and intermediate virtual control quantities for each subsystem, and then go back to the entire system and integrate them to complete the design of the entire control law.

[0018] The dynamic equation of the drone can be written as follows: in X It can be expressed as , It can be expressed as , and because (definition ) is the control quantity related to the position of the UAV, so it can be decomposed into Decomposition to drone X,Y,Z The three-axis position control component, the decomposed control variable It can be expressed as: In the above formula and It can be expressed by the following formula: Furthermore, the backstepping method is combined with the principle of anti-disturbance control to design the nonlinear control law of the UAV, and a single control loop in the control loop is designed. The design steps are as follows: S1. Define the desired trajectory of the drone: S2. According to the expected trajectory defined in the above formula, the first tracking error is introduced here: S3. Select the first Lyapunov function and take its derivative: In order to Convergence, this scheme defines the virtual control quantity ,by Substitute , then the derivative of the Lyapunov function can be expressed as: Also because is a number greater than 0, so It always holds true, so the constructed Lyapunov function converges stably.

[0019] S4. We have analyzed the stability of the first tracking error and proved its stability. Now we will introduce the second tracking error and take its derivative. After taking the derivative, we get At this time, according to the above UAV dynamic expression, the expression of the second tracking error and its derivative can be obtained as follows: S5. According to the second tracking error introduced above, the second Lyapunov function applicable to the above formula is selected and differentiated: In order to make the second Lyapunov function constructed above converge, the second virtual control quantity is defined as: Then there is This also shows that the constructed second virtual control quantity can stabilize the Lyapunov function and enable the controlled object to reach the target state.

[0020] In order to design the backstepping control law, this scheme further expands the second tracking error, which can be transformed into , substitute it into the above formula and combine the terms, leaving only Finally, the backstepping control law of the UAV single channel can be obtained as: Compared with the standard form of the nonlinear error feedback control law of the ADRC, it is not difficult to find that the above control law is consistent with it in form. Therefore, the ADRC algorithm is introduced here to form the backstepping ADRC. The backstepping ADRC control law can be written as: in represents the second-order differential signal output from the third-order fast differentiator, It is given according to the above design of KFESO, and the formula can be used Perform calculations.

[0021] Compared with the prior art, the present invention has the following beneficial effects: 1. Improve algorithm robustness Complementary advantages of Kalman filter and ESO: Kalman filter is good at processing linear systems and can estimate the state of the system from noisy data. During the flight of a drone, it can help estimate the state information such as the position and speed of the drone. ESO can observe the state of the system and compensate for the total disturbance of the system, which is particularly suitable for processing nonlinear systems and unknown disturbances. Combining the two can make full use of the linear estimation ability of the Kalman filter and the nonlinear disturbance compensation ability of the ESO, and improve the robustness of the algorithm to model errors and external disturbances.

[0022] Anti-disturbance of backstepping ADRC: The backstepping ADRC algorithm is based on the backstepping design method and the ADRC concept, which can estimate and compensate for disturbances in the system in real time, thereby improving the stability and anti-disturbance of the system. The algorithm achieves high-precision control of the attitude and position of the drone by constructing a virtual control variable and a state feedback controller.

[0023] 2. Simplify algorithm design and improve computational efficiency Design of composite observer: By combining Kalman filter and ESO, a more concise and efficient composite observer can be designed. This observer can take advantage of the advantages of both algorithms and reduce the complexity of the design process. In addition, the computational efficiency of the composite observer can be further improved by optimizing algorithm parameters and reducing unnecessary calculation steps.

[0024] Simplified implementation of backstepping ADRC: The backstepping ADRC algorithm simplifies the controller design process by constructing a virtual control variable and a state feedback controller. The algorithm also improves the performance and stability of the controller while reducing the computational complexity by introducing technical means such as nonlinear state error feedback and extended state observer.

[0025] 3. Enhance adaptability and generalization Adaptability of the composite observer: The combination of the Kalman filter and the ESO enables the composite observer to adapt to different flight environments and wind conditions. By adjusting the algorithm parameters and the observer structure, accurate estimation of the UAV state under different wind conditions can be achieved. In addition, the composite observer can also handle nonlinear problems and unknown interferences during the flight of the UAV, improving the adaptability and generalization of the algorithm. Generalization of backstepping ADRC: The backstepping ADRC algorithm introduces the idea of ​​ADRC, which enables the controller to estimate and compensate for disturbances in the system in real time, thereby improving the generalization of the controller. The algorithm can also handle uncertainty problems and model errors during the flight of the UAV, further enhancing the adaptability and robustness of the controller.

[0026] 4. Improve practical application capabilities Real-time performance of the composite observer: The combination of the Kalman filter and the ESO enables the composite observer to estimate the state information of the UAV in real time and respond quickly to changes in the external environment. This improves the flight stability and safety of the UAV in complex environments. In addition, the real-time performance of the composite observer can be further improved by optimizing the algorithm parameters and reducing the calculation delay. Practicality of backstepping ADRC: The backstepping ADRC algorithm achieves high-precision control of the attitude and position of the UAV by constructing a virtual control quantity and a state feedback controller. This enables the UAV to maintain a stable flight attitude and position accuracy in a complex environment. In addition, the algorithm also takes into account nonlinear problems and unknown interference during the flight of the UAV, which improves the practicality and reliability of the controller. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is the framework diagram of the UAV's anti-wind disturbance control.

[0028] Figure 2 It is the response of position channel and velocity channel under wind disturbance.

[0029] Figure 3 It is the response of attitude angle channel and attitude angular rate channel under wind disturbance.

[0030] Figure 4 This is a two-dimensional diagram of the trajectory tracking response of the Martian rotorcraft under wind disturbance.

[0031] Figure 5 It is the trajectory tracking response of the position channel and velocity channel under wind disturbance.

[0032] Figure 6 It is the trajectory tracking response of attitude angle channel and attitude angular rate channel under wind disturbance. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0034] See also Figure 1-4, a UAV anti-wind disturbance stability control method, in order to improve the robustness of the UAV under wind disturbance, based on the traditional ADRC, the Kalman filter KF and the extended state observer ESO are optimized to form a composite state observer KFESO. Compared with the traditional ADRC extended state observer ESO, the composite state observer KFESO has better performance. ESO can ensure that KF works when there is a deviation in the system or uncertainty in the model. KF can filter the input of ESO to reduce the impact of noise on ESO; At the same time, the backstepping method is added to the anti-disturbance controller, which can improve the response speed of the system, enable the UAV system to respond faster when disturbed by wind, and improve the stability of the system.

[0035] The design framework of the UAV system is as follows Figure 1 shown.

[0036] Improved anti-disturbance controllers are used in both the position loop and the attitude loop to achieve better anti-disturbance performance, ensuring that the UAV has a higher response speed under wind disturbance while having higher stability and robustness.

[0037] 1. Composite State Observer KFESO The performance of traditional extended state observer ESO control depends on the speed and accuracy of ESO. High gain is usually used to achieve fast convergence of estimation. However, this will make ESO sensitive to noise. In order to solve this problem, This solution provides an extended state observer based on the Kalman filter, namely the composite state observer KFESO.

[0038] In order to solve the noise problem of ESO, the Kalman filter KF is used to pre-filter the input signal. KF can be given by the following formula: in An estimate of the system state , Lumped disturbance f The estimate, is the AURKF gain matrix.

[0039] For wind field environments, it is generally believed that the noise of drones detected by the Kalman filter under wind disturbance can be regarded as white noise. Therefore, the variance of the noise model can be calculated by the following formula: in is the simulation frequency. It can be further simplified: In the above formula is the state estimation error covariance matrix; R To measure the noise covariance, in practical applications, a white noise test can be performed to obtain the characteristic of the measured noise.R ; Q is the process noise covariance.

[0040] It can be generally considered that the state estimation obtained by the above KF algorithm is optimal, and the proof process is as follows: Proof: For the gain of KF, the gain K can be used to replace the gain in equation (5.26) ,but: The state error of KF can be expressed as ,but The derivative of can be expressed as , so for the covariance of the noise disturbance, we have: Now to Solving the equation, we can get the general solution expression: And because the covariance of the state estimation error can be expressed as , then: Derivative and simplify the above formula, assuming , then: In order to obtain the gain K, The trace of is minimized, and its cost function can be expressed as: The necessary condition to minimize the cost function of the above formula is: Therefore, there is , which proves that the state estimation corresponding to the KF gain vector selected above is optimal.

[0041] In steady state, it can be considered , at this time, the process noise covariance Q can be used to estimate the error covariance of the disturbance Instead, we have: According to the above proof process, we can get The expression is: For the design of ESO, the following extended state observer is constructed: in yes The estimate, Is the system status of estimates, and The system disturbance estimates; is the gain of the observer, The value of is .

[0042] By designing a reasonable ESO gain matrix, the lumped disturbance in the system can be accurately estimated. The error is defined as , then the derivative of the error can be defined as: In the above formula The characteristic equation of can be expressed as: Analyzing the above characteristic equation, when h Bounded and When the roots of are all located in the left half plane, the state observer can be stable, so it is assumed that , the solution can be obtained as: in is the bandwidth of the state observer. Then the ESO can be designed as: According to the above description of the adaptive robust unscented Kalman filter KF and the extended state observer ESO, the expression of the composite state observer KFESO can be obtained as follows: 2. Cascaded second-order tracking differentiator When the backstepping method is added to the UAV's anti-disturbance control, since the backstepping method requires the use of a second-order differential signal, the second-order differential signal should be included in the output item of the transition process. To generate a second-order differential signal, a third-order differentiator or a cascaded second-order differentiator is required to arrange the transition process.

[0043] The third-order fast differentiator is a common third-order differentiator, and its expression is: The expression of the sat function is: Since the tracking signal and differential signal generated by the third-order fast differentiator will produce jitter when converging, a cascaded second-order differentiator is used here to obtain the required second-order differential signal.

[0044] 3. Backstepping Active Disturbance Rejection Control Law The basic idea of ​​the backstepping method is to decompose a complex nonlinear system into subsystems that do not exceed the system order, and then design some Lyapunov functions and intermediate virtual control quantities for each subsystem, and then go back to the entire system and integrate them to complete the design of the entire control law. The advantage of the backstepping method is that the controller and the adaptive law that can be updated at any time can be designed at the same time to improve the transient performance of the system. At the same time, the correlation of the controlled object is no longer a design problem. According to the design principle of the backstepping method, the dynamic equation of the drone can be written as follows: in X It can be expressed as , It can be expressed as , and because (definition ) is the control quantity related to the position of the UAV, so it can be decomposed into Decomposition to drone X,Y,Z The three-axis position control component, the decomposed control variable It can be expressed as: In the above formula and It can be expressed by the following formula: Next, the backstepping method is combined with the principle of anti-disturbance control to design the nonlinear control law of the UAV, and the single control loop in the control loop is designed. The design steps are as follows: (1) Define the desired trajectory of the drone: (2) According to the expected trajectory defined in the above formula, the first tracking error is introduced here: (3) Select the first Lyapunov function and take its derivative: In order to Convergence, this scheme defines the virtual control quantity ,by Substitute , then the derivative of the Lyapunov function can be expressed as: Also because is a number greater than 0, so It always holds true, so the constructed Lyapunov function converges stably.

[0045] (4) We have already analyzed the stability of the first tracking error and proved its stability. Now we will introduce the second tracking error and take its derivative. After taking the derivative, we get At this time, according to the above UAV dynamic expression, the expression of the second tracking error and its derivative can be obtained as follows: (5) According to the second tracking error introduced above, the second Lyapunov function applicable to the above equation is selected and its derivative is calculated: In order to make the second Lyapunov function constructed above converge, the second virtual control quantity is defined as: Then there is This also shows that the constructed second virtual control quantity can stabilize the Lyapunov function and enable the controlled object to reach the target state.

[0046] In order to design the backstepping control law, this scheme further expands the second tracking error, which can be transformed into , substitute it into the above formula and combine the terms, leaving only Finally, the backstepping control law of the UAV single channel can be obtained as: Compared with the standard form of the nonlinear error feedback control law of the ADRC, it is not difficult to find that the above control law is consistent with it in form. Therefore, the ADRC algorithm is introduced here to form the backstepping ADRC. The backstepping ADRC control law can be written as: in represents the second-order differential signal output from the third-order fast differentiator, It is given according to the above design of KFESO, and the formula can be used At this point, the design of the backstepping ADRC control law is completed.

[0047] Figure 2 and Figure 3 The responses of the position channel and velocity channel of the position loop and the attitude angle channel and attitude angular velocity channel of the attitude loop are given respectively when the UAV is subjected to wind disturbance under hovering conditions. Figure 2 It can be seen that due to the greater horizontal wind disturbance suffered by the UAV, the UAV deviates further from the reference hovering point in the horizontal direction. The traditional ADRC benchmark control method has a larger deviation difference than the KFESOBSADRC composite control method. The maximum deviation value of the benchmark control method reaches 0.32m, while the composite control algorithm only makes the UAV deviate from the reference hovering point by 0.1m in the horizontal direction under wind disturbance. This undoubtedly makes the UAV have better stability under horizontal wind disturbance. Looking at the vertical direction, although the wind disturbance in the vertical direction is relatively small, the gap between the two control methods can still be seen. The composite control algorithm still has good stability, making the UAV's deviation in the vertical direction only 0.04m, while the benchmark control method has a vertical deviation of 0.15m. As for the speed channel, it has a similar trend to the position channel. The speed of the composite control algorithm of this scheme fluctuates less than that of the benchmark control algorithm, and the speed fluctuation amplitude is less than 0.05m / s. From Figure 3 It can be seen that the composite control algorithm is more stable in the control of the angle loop, so that when the UAV is subject to wind disturbance, the angle change amplitude is smaller than that of the traditional benchmark control algorithm, especially in the yaw channel (the yaw angle of the yaw channel of the benchmark control method reaches 6.7°, while the maximum yaw angle of the composite control algorithm of this scheme is only 2.5°). At the same time, the angular velocity changes more rapidly, which enables the UAV to quickly adjust its attitude when encountering wind disturbance, so that the UAV has smaller fluctuations in the wind field.

[0048] In order to further verify the superiority of the composite control algorithm, the trajectory tracking performance analysis of the UAV in a wind disturbance environment was added. The trajectory was designed in the form of an "8" ring, in which the X-axis and Y-axis used sine waves with frequencies of 0.5rad / s and 0.25rad / s and amplitudes of 3, respectively.

[0049] like Figure 4 The green trajectory line in the figure is the expected trajectory of the drone.

[0050] The following uses the benchmark control method and the composite control algorithm to perform a software-in-the-loop analysis on the trajectory tracking performance of the UAV in a wind field environment. The trajectory tracking effects of the two control algorithms are shown in the figure. Figure 4As shown in the figure, the blue trajectory is the trajectory tracking curve of the UAV in the wind field under the composite control algorithm, and the red curve is the trajectory tracking curve of the UAV in the wind field under the reference control algorithm. It can be seen from the figure that under the composite control algorithm of this scheme, the running trajectory of the UAV in the wind field is closer to the reference trajectory, and the deviation error is smaller, which also shows that the composite control algorithm makes the UAV closed-loop system have better anti-disturbance performance.

[0051] In the description of the present invention, it should be noted that the terms "upper", "lower", "inner", "outer", "left", "right" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the invention product is usually placed when in use, or the orientation or positional relationship commonly understood by those skilled in the art, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second" and the like are only used to distinguish the description, and cannot be understood as indicating or implying relative importance. In the description of the present invention, it should also be noted that, unless otherwise clearly specified and limited, the terms "set", "connection" and the like should be understood in a broad sense, for example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances.

Claims

1. A method for controlling the stability of an unmanned aerial vehicle against wind disturbance, characterized in that: Based on the traditional ADRC, the Kalman filter KF and the extended state observer ESO are optimized to form a composite state observer KFESO. ESO ensures that KF works when there is a deviation in the system or uncertainty in the model. KF filters the input of ESO to reduce the impact of noise on ESO. At the same time, the backstepping method is added to the ADRC to improve the response speed of the system, so that the UAV system can react faster when it is disturbed by wind, and improve the stability of the system; Improved anti-disturbance controllers are used in both the position loop and the attitude loop to achieve better anti-disturbance performance, ensuring that the UAV has a higher response speed under wind disturbance while having higher stability and robustness.

2. The anti-wind disturbance stability control method for an unmanned aerial vehicle according to claim 1, characterized in that: The composite state observer KFESO is specifically: In order to solve the noise problem of ESO, the Kalman filter KF is used to pre-filter the input signal. KF is given by the following formula: in An estimate of the system state , Lumped disturbance f The estimate, is the AURKF gain matrix; For wind field environments, it is generally believed that the noise of drones detected by the Kalman filter under wind disturbance is regarded as white noise. Therefore, the variance of the noise model is calculated by the following formula: in is the simulation frequency; it can be further simplified: In the above formula is the state estimation error covariance matrix; R In order to measure the noise covariance, in practical applications, a white noise test is performed to obtain the characteristic of the measurement noise. R ; Q is the process noise covariance; In steady state, it is considered , at this time, the process noise covariance Q can be used to estimate the error covariance of the disturbance Instead, we have: get The expression is: For the design of ESO, the following extended state observer is constructed: in yes The estimate, Is the system status of estimates, and The system disturbance estimates; is the gain of the observer, The value of is ; By designing a reasonable ESO gain matrix, the lumped disturbance in the system is accurately estimated; the error is defined as , then the derivative of the error is defined as: In the above formula The characteristic equation of is expressed as: Analyzing the above characteristic equation, when h Bounded and When the roots of are all located in the left half plane, the state observer is stable, so it is assumed that , and the solution is: in is the bandwidth of the state observer; then the ESO can be designed as: According to the description of the adaptive robust unscented Kalman filter KF and the extended state observer ESO, the expression of the composite state observer KFESO is obtained as follows: .

3. The anti-wind disturbance stability control method for a UAV according to claim 1 is characterized in that: Backstepping is added to the ADRC. When backstepping is added to the ADRC of the UAV, since the backstepping method requires the use of the second-order differential signal, the output item of the transition process should include the second-order differential signal. To generate the second-order differential signal, a third-order differentiator or a cascaded second-order differentiator is required to arrange the transition process. The third-order fast differentiator is a common third-order differentiator, and its expression is: The expression of the sat function is: Since the tracking signal and differential signal generated by the third-order fast differentiator will produce jitter when converging, a cascaded second-order differentiator is used here to obtain the required second-order differential signal.

4. The anti-wind disturbance stability control method for a UAV according to claim 3 is characterized in that: The backstepping method is added to the active disturbance rejection controller; The basic idea of ​​the backstepping method is to decompose a complex nonlinear system into subsystems that do not exceed the system order, and then design some Lyapunov functions and intermediate virtual control quantities for each subsystem, and then go back to the entire system and integrate them to complete the design of the entire control law. The dynamic equation of the drone is written as follows: in X It can be expressed as , It can be expressed as , and because (definition ) is the control quantity related to the position of the UAV, so it can be decomposed into Decomposition to drone X,Y,Z The three-axis position control component, the decomposed control variable It can be expressed as: In the above formula and It is expressed by the following formula: .

5. The anti-wind disturbance stability control method for a UAV according to claim 4 is characterized in that: The backstepping method is combined with the principle of anti-disturbance control to design the nonlinear control law of the UAV. The single control loop in the control loop is designed. The design steps are as follows: S1. Define the desired trajectory of the drone: S2. According to the expected trajectory defined in the above formula, the first tracking error is introduced here: S3. Select the first Lyapunov function and take its derivative: In order to Convergence, this scheme defines the virtual control quantity ,by Substitute , then the derivative of the Lyapunov function is expressed as: Also because is a number greater than 0, so Always holds true, so the constructed Lyapunov function converges stably; S4. We have analyzed the stability of the first tracking error and proved its stability. Now we will introduce the second tracking error and take its derivative. After taking the derivative, we get At this time, according to the above UAV dynamic expression, the expression of the second tracking error and its derivative is: S5. According to the second tracking error introduced above, the second Lyapunov function applicable to the above formula is selected and differentiated: In order to make the second Lyapunov function constructed above converge, the second virtual control quantity is defined as: Then there is , which also shows that the constructed second virtual control quantity makes the Lyapunov function stable and can make the controlled object reach the target state; Further expanding the second tracking error, it is transformed into Substitute it into the above formula and combine the terms, keeping only Finally, the backstepping control law of the UAV single channel can be obtained as: Compared with the standard form of the nonlinear error feedback control law of the ADRC, it is not difficult to find that the above control law is consistent with it in form. Therefore, the ADRC algorithm is introduced here to form the backstepping ADRC. The backstepping ADRC control law can be written as: represents the second-order differential signal output from the third-order fast differentiator, It is given according to the above design of KFESO, and the formula can be used Perform calculations.

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