Unmanned aerial vehicle attitude anti-interference control method and system based on switching expansion state observer

Abstract

The invention relates to the technical field of unmanned aerial vehicle control, and discloses an unmanned aerial vehicle attitude anti-interference control method and system based on a switching extended state observer, and the method comprises the steps: 1) firstly, providing a self-adaptive switching extended state observer for the influence of the measured noise pollution of an unmanned aerial vehicle; and 2) in order to improve parameter performance of an unmanned aerial vehicle controller, on the basis of a snake optimization algorithm, strategies such as complete learning, quadratic interpolation and a Levy flight mechanism are fused with the snake optimization algorithm, and an improved snake optimization algorithm is provided. And 3) the improved snake optimization algorithm is used for parameter setting of the active-disturbance-rejection controller of the unmanned aerial vehicle, the control parameters of the unmanned aerial vehicle are optimized, and the control performance of the unmanned aerial vehicle is improved. The method is low in cost and quick in effect, unmanned aerial vehicle autonomous inspection and autonomous high-precision control of the distribution line can be realized, the dependence on high-end unmanned aerial vehicle equipment and flight operators can be reduced, the labor intensity of the operators can be reduced, and the distribution line inspection efficiency and the automation and intelligence level can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) control, and particularly to a UAV attitude disturbance rejection control method and system based on a switched extended state observer. Background Art

[0002] In recent years, UAVs have demonstrated their unique advantages and values in various application fields such as power line inspection, efficient inspection operations in narrow channels, and fire source detection in the fire protection field. When a multi-rotor UAV performs complex tasks such as power line inspection, it needs to face variable and unknown environmental risks. Especially in some specific tasks, it is also required that the UAV can operate stably for a long time in a high-intensity and complex environment, maintaining high-performance flight control of the UAV to meet the demanding flight task requirements. When the UAV performs flight tasks such as distribution network inspection, performance requirements such as its stability, durability, reliability, high-performance flight, energy economy, and reuse economy become the key guarantees for the realization of technologies such as motion planning of the UAV. Therefore, it is necessary to design a set of reliable, effective, and refined control strategies to ensure the safe and stable flight of the UAV in a complex environment. To sum up, it is necessary to develop a new UAV attitude auto-disturbance rejection control method to solve the control problems in the process of autonomous inspection of UAVs for distribution lines and the like. Summary of the Invention

[0003] The present invention provides a UAV attitude disturbance rejection control method and system based on a switched extended state observer to solve the problems of low positioning accuracy and poor sensitivity existing in the existing UAV control methods.

[0004] To achieve the above object, the present invention is realized through the following technical solutions: In a first aspect, the present invention provides a UAV attitude disturbance rejection control method based on a switched extended state observer, including the following steps: S1. Design an adaptive switched extended state observer, specifically including: Divide the attitude control loop of the multi-rotor UAV into three symmetric control channels: roll angle, pitch angle, and yaw angle; For measurement noise pollution, construct a linear extended state observer composed of a high-gain module, a low-gain module, and a switching strategy. By comparing the estimation error caused by high-frequency noise with the state estimation accuracy difference, dynamically select the output signal of the high-gain or low-gain module based on a preset switching threshold to achieve adaptive switching of the state estimation signal; Adopt a linear state error feedback control law, use the output of the observer to compensate for the total disturbance of the system, and design a proportional-derivative controller to generate a control input; S2. Improve the snake optimization algorithm, specifically including: Introduce the complete learning strategy, quadratic interpolation operator and Lévy flight mechanism into the snake optimization algorithm, initialize the population and divide it into male population and female population; Dynamically update the temperature and food parameters according to the number of iterations. Adjust the individual position through the Lévy flight coefficient in the exploration stage to enhance the global search ability; Integrate the population dimension information by combining the complete learning strategy in the transition stage to improve the population diversity; Update the individual position through the combat mode and mating mode in the exploitation stage to avoid the algorithm falling into local extrema; S3. Tune the parameters of the UAV active disturbance rejection controller based on the improved snake optimization algorithm, including: Construct a composite disturbance signal including wind disturbance, airframe vibration and modeling parameter perturbation, and use the integral absolute error as the fitness function; Optimize the controller parameters so that the UAV realizes anti-disturbance tracking control in the roll angle, pitch angle and yaw angle control channels.

[0005] Optionally, the mathematical model of the switching strategy in step S1 is: ; ; ; Among them, is the estimated output of the low-gain module, is the estimated output of the high-gain module, is the preset switching threshold, is the time constant of the filter in the switching strategy; The control law of the proportional-derivative controller is: ; Among them, is the expected output of the system, is the controller gain.

[0006] Optionally, the calculation formula of the Lévy flight coefficient in step S2 is: ; ; Among them, is the Lévy flight coefficient, 𝜇 and 𝑣 are random numbers from 0 to 1, and 𝛽 is a constant of 1.5.

[0007] Optionally, the initialization of the population in step S2 includes: ; In the formula, is the position of the th individual, is the lower limit of the search space, is the upper limit of the search space, and rand is a random number uniformly distributed in the interval [0, 1]; Let the population size be , then the number of male individuals is , and the number of female individuals is .

[0008] Optionally, the dynamic update of the temperature and food parameters in step S2 includes: Update the temperature using the following formula Temp : ; In the formula, is the current iteration number, is the maximum iteration number; Update the food parameter using the following formula : ; In the formula, is the current iteration number, is the maximum iteration number, is a constant of 0.5.

[0009] Optionally, the adjustment of the individual position by the Levy flight coefficient in step S2 includes: Update using the following formula ; In the formula, is a constant of 0.05, is the Levy flight coefficient; Then conduct individual position exploration through the following formula: ; Among them, and are the male position and female position respectively, and are the random male position and random female position respectively, is a constant of 0.05, represents the ability of the male to find food, represents the ability of the female to find food.

[0010] Optionally, the integration of the population dimension information by combining the complete learning strategy in step S2 includes: Introduce the binomial difference improvement strategy method in the process of individual position exploration. The formula after introducing the binomial difference is as follows: ; Combining the formula after introducing the binomial difference with the complete learning improvement strategy, the update formulas for the \(i\)-th male individual and the \(i\)-th female individual can be rewritten as: ; Optionally, in the step S2: When rand > 0.6, the combat mode of the snake is completed using the following formula:

[0011] In the formula, represents the combat ability of the male, represents the combat ability of the female; When the mating mode of the snake is completed using the following formula:

[0012] In the formula, represents the mating ability of the male, represents the mating ability of the female; Update and using the following formula:

[0013] In the formula, is the worst male individual, is the worst female individual.

[0014] Optionally, the composite interference signal in the step S3 includes: Low-frequency wind disturbance signal: ; Medium-frequency modeling perturbation signal: ; High-frequency airframe vibration signal: .

[0015] In a second aspect, an embodiment of the present application provides an attitude disturbance rejection control system for an unmanned aerial vehicle based on a switched extended state observer, including a processor and a memory; The memory is used to store a computer program; The processor, when executing the program stored in the memory, implements the method steps described in the first aspect.

[0016] Beneficial effects: The attitude disturbance rejection control method for an unmanned aerial vehicle based on a switched extended state observer provided by the present invention can effectively enhance the sensitivity of the controller, improve the tracking performance of the unmanned aerial vehicle, help reduce the dependence on high-end unmanned aerial vehicle equipment and flight control personnel, and reduce the labor intensity of operators, improve the inspection efficiency and automation and intelligence level of power distribution lines. Brief Description of the Drawings

[0017] Figure 1 It is a flowchart of the UAV attitude disturbance rejection control method based on a switched extended state observer according to a preferred embodiment of the present invention; Figure 2 It is a schematic structural diagram of the active disturbance rejection controller algorithm according to a preferred embodiment of the present invention; Figure 3 It is a schematic diagram of the tracking response curves of different adaptive algorithms for the pitch angle θ according to a preferred embodiment of the present invention. Detailed Embodiment

[0018] The technical solutions of the present invention will be described clearly and completely below. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without any creative work belong to the scope of protection of the present invention.

[0019] Unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meaning understood by those of ordinary skill in the art to which the present invention belongs. The "first", "second" and similar terms used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, the terms such as "a" or "one" do not indicate a quantity limitation, but indicate that there is at least one. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left" and "right" are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship also changes accordingly.

[0020] Please refer to Figure 1 , an embodiment of the present application provides a UAV attitude disturbance rejection control method based on a switched extended state observer, including the following steps: S1. Design an adaptive switched extended state observer, specifically including: Divide the attitude control loop of the multi-rotor UAV into three symmetric control channels: roll angle, pitch angle and yaw angle; For measurement noise pollution, construct a linear extended state observer composed of a high-gain module, a low-gain module and a switching strategy. By comparing the estimation error caused by high-frequency noise with the state estimation accuracy difference, dynamically select the output signal of the high-gain or low-gain module based on a preset switching threshold to achieve adaptive switching of the state estimation signal; Adopt a linear state error feedback control law, use the output of the observer to compensate for the total disturbance of the system, and design a proportional-derivative controller to generate a control input; S2. Improve the snake optimization algorithm, specifically including: Introduce a complete learning strategy, a quadratic interpolation operator, and a Lévy flight mechanism into the snake optimization algorithm, initialize the population and divide it into a male population and a female population; Dynamically update the temperature and food parameters according to the number of iterations, and adjust the individual positions through the Lévy flight coefficient in the exploration stage to enhance the global search ability; Integrate the population dimension information by combining the complete learning strategy in the transition stage to improve the population diversity; Update the individual positions through the combat mode and the mating mode in the exploitation stage to prevent the algorithm from falling into local extrema; S3. Tune the parameters of the UAV active disturbance rejection controller based on the improved snake optimization algorithm, including: Construct a composite disturbance signal including wind disturbance, airframe vibration, and modeling parameter perturbation, and use the integral absolute error as the fitness function; Optimize the controller parameters so that the UAV realizes anti-disturbance tracking control in the roll angle, pitch angle, and yaw angle control channels.

[0021] In this embodiment, the UAV is a multi-rotor UAV. Step 1) includes: 1) First, divide the attitude control loop of the multi-rotor UAV into three control channels, namely: the roll angle control channel, the pitch angle control channel, and the yaw angle control channel. The three control channels of the UAV are highly symmetric. The linear active disturbance rejection controller designed by the present invention consists of two parts: a linear state error feedback control law (LSEF) and a linear extended state observer (LESO), as Figure 2 shown. Among them, let 𝑝, 𝑞, 𝑟 and 𝐼 𝑥 、𝐼 𝑦 、𝐼 𝑧 be the rotational angular velocities around the body axes and the moments of inertia respectively, and let , the pitch angle 𝜃 control channel of the linear extended state observer can be written in the following form:

[0022] In the formula, . b0 is a system parameter, and u a is the control input Let be the lumped disturbance, be an extended state of the system, and let , the system can be extended into the following system:

[0023] When there is measurement noise in the system, the above formula can be designed as:

[0024] In the formula, is the measurement noise; is the real-time measured value of 𝑦; is the gain of the observer and is a constant value.

[0025] Secondly, the design of the linear state error feedback control law is as follows: First, the estimated output is used to compensate for the total disturbance 𝐷(𝑡) of the UAV system. Therefore, the linear state error feedback control law can be designed as:

[0026] In the formula, is the nominal controller.

[0027] The present invention adopts a proportional-derivative controller:

[0028] In the formula, is the desired output of the system, is the controller gain.

[0029] Finally, the design of the adaptive switching disturbance rejection control is as follows: When there is measurement noise in the system, the control objective is to accurately estimate the state signal of the system while minimizing the influence of the measurement noise on the estimation result. When the working frequency band of the system is in the low-frequency band, it is necessary to make the UAV meet the requirements of the system for the estimation accuracy of the state signal, and at the same time make the control algorithm have strong noise suppression ability; when in the medium-high frequency band, it can meet the requirements of the system for the estimation accuracy of the state signal. Therefore, it is necessary to design a linear extended state observer that can switch based on the working frequency band of the system.

[0030] The present invention proposes a linear extended state observer that combines the advantages of the estimation performance of high gain and low gain and switches based on the current working frequency band of the system. The observer consists of a high-gain module, a low-gain module, and a switching strategy. The high-gain module and the low-gain module are used to provide state estimation signals with different characteristics, and the proposed switching strategy is used to make the observer output a state estimation signal that is beneficial to the operation of the system in the current working frequency band.

[0031] Considering the amplitude difference between the estimation error caused by the noise signal and the estimation error caused by different state estimation accuracies, a switching threshold is designed to distinguish between the two. Let the estimated output of the low-gain module be , and the estimated output of the high-gain module be , the estimated performance difference between modules can be affected by high-frequency noise signals for small and to represent, that is . The switching strategy of the present invention selects the operating frequency band of the system by comparing and to make the self-switching observer output an appropriate signal.

[0032] The mathematical model can be expressed as:

[0033]

[0034]

[0035] Among them, , is the time constant of the filter in the switching strategy.

[0036] The parameter self-tuning method of the switching threshold can realize the real-time state reconstruction of the system closed-loop loop signal. Using this algorithm for the three attitude control channels of the quadrotor UAV can realize the attitude active disturbance rejection control of the quadrotor UAV.

[0037] In this embodiment, step 2) includes: The snake optimization algorithm is a simple and effective meta-heuristic optimization algorithm. This optimization algorithm depends on two parameters, temperature and food Q. The snake optimization algorithm is designed based on a greedy strategy and has the defect of being easily trapped in local extrema. To solve this problem, a quadratic interpolation operator, a complete learning strategy, and Lévy flight are introduced to improve the snake optimization algorithm, so as to solve the defect that the snake optimization algorithm is easily trapped in local extrema.

[0038] The algorithm flow of the improved snake optimization algorithm is as follows: (1) Initialize the population First, generate an initial population in the entire search space, and set the male population and the female population.

[0039]

[0040] Among them, is the position of the th individual, is the lower limit of the search space, is the upper limit of the search space, and rand is a random number uniformly distributed in the interval [0,1]. Then, divide the initial population into a male population and a female population, and the number of individuals in the two populations is the same. Let the population size be , then the number of male individuals is , and the number of female individuals is 。

[0041] (2) while (k ≤ K max ) Evaluate each population and 。

[0042] Find the best male individual and the best female individual 。

[0043] Update using the following formula Temp :

[0044] is the current iteration number, is the maximum iteration number.

[0045] Update using the following formula :

[0046] is a constant with a value of 0.5.

[0047] (3) When Q < 0.25 Update using the following formula :

[0048] where is the Lévy flight coefficient:

[0049]

[0050] where μ and v are random numbers from 0 to 1, and β is a constant with a value of 1.5. The small step size change of retains the exploration ability that the SO algorithm itself has in the early stage of iteration,

[0051] and the large step size change with a small probability can prevent the population from falling into local extrema and also avoid the phenomenon of premature convergence of the algorithm.

[0052] where and are the male position and female position respectively, and are the random male position and random female position respectively, is a constant with a value of 0.05. Indicates the ability of the male to find food, Indicates the ability of the female to find food, and the calculation formulas are respectively

[0053] Among them, is 's fitness, is 's fitness.

[0054] Update using Equation (3.48):

[0055] Among them, is the fluctuation amplitude coefficient = 0.05, is the fluctuation rate coefficient = 5, is the fluctuation reference coefficient = 0.1.

[0056] When rand <

[0057] Execute quadratic interpolation using the following formula. A method of introducing a binomial interpolation improvement strategy in the exploration stage of the snake optimization algorithm, and the optimization ability of the binomial interpolation algorithm in the exploration stage, which will help enhance the convergence ability in the later stage of the algorithm.

[0058]

[0059] (3) When Temp > 0.6 Update

[0060]

[0061] is a male student, is a female student. and are generated by the learning probability of each student. is a random integer from 1 to , is a constant equal to 0, is a constant equal to 1, is a constant equal to 2.

[0062] Then update and ​​​

[0063] Combined with the complete learning improvement strategy: This strategy enhances the diversity of the population by integrating the dimensional information of the entire population. In the transition stage of the snake optimization algorithm, the update formulas for the \(i\)-th male individual and the \(i\)-th female individual can be rewritten as:

[0064] (4) When rand > 0.6 Use the following formula to complete the combat mode of the snake

[0065] represents the combat ability of the male, represents the combat ability of the female When

[0066] Use the following formula to complete the mating mode of the snake

[0067] represents the mating ability of the male, represents the mating ability of the female Update using the following formula and

[0068]

[0069] is the worst male individual, is the worst female individual In this embodiment, in step 3), the parameter tuning of the UAV auto-disturbance rejection controller, and the optimization of the UAV control parameters include: Through numerical simulation experiments on the attitude model of the multi-rotor UAV, let its initial state be:

[0070] For simplicity, only observe the tracking response effect of the pitch angle \(\theta\) here, and set the desired attitude angle to:

[0071]

[0072] The overall interference signal of the UAV system will be constructed by superimposing three typical interference signals (wind interference, airframe self-vibration, modeling parameter perturbation), and then this signal will be used to comprehensively evaluate the anti-interference ability of the UAV system.

[0073] Use a low-frequency perturbation signal to simulate the external low-frequency wind interference:

[0074] A 5Hz signal is used to simulate the disturbance caused by the uncertainty of the modeling parameters:

[0075] A 60Hz high-frequency signal is used to simulate the influence of airframe vibration on the attitude control system:

[0076] Figure 3 The tracking response curve of the pitch angle 𝜃 subsystem of the multi-rotor UAV under the adaptive algorithm is given. The adaptive algorithm can enable the UAV system to effectively track the desired input, verifying the effectiveness of the adaptive algorithm. For the parameter optimization process, the integral absolute error of the tracking response of the multi-rotor UAV attitude active disturbance rejection control based on the switched extended state observer is used as the fitness function.

[0077] The UAV control performance is tested in different dimensions. The multi-rotor attitude model affected by measurement noise has 4 parameters: and 𝑃 noise , by randomly adjusting the four parameters in the multi-rotor UAV attitude control system, the influence of system parameter changes on system performance can be explored within a large range. Even if the parameters of the multi-rotor attitude system change randomly, the control algorithm can ensure its convergence. Even if the measurement noise power changes to a certain extent during flight, the UAV roll angle, pitch angle, and yaw angle control systems have satisfactory tracking performance, which also verifies that the proposed algorithm can be applied to control systems with different measurement noise power levels. The results show the good control performance of the multi-rotor attitude system by adjusting parameters through the control algorithm.

[0078] In summary, currently, when using UAVs for high-efficiency inspection operations such as power line inspection, narrow passage inspection, and fire source detection in the fire protection field, in order to ensure the reliability and safety of UAVs, precise control of UAVs is required. Aiming at the characteristics of high-efficiency inspection operations such as power line inspection, narrow passage inspection, and fire source detection in the fire protection field, the multi-rotor UAV attitude active disturbance rejection control method based on the switched extended state observer in this embodiment is used for UAV operations, which improves the control accuracy of UAVs, has a good suppression effect on output chattering, realizes high-performance attitude control, helps to reduce the dependence on high-end UAV equipment and flight operators, reduces the labor intensity of operators, improves the operation efficiency, and enhances the automation and intelligence level of UAV operations.

[0079] This application embodiment also provides a UAV attitude disturbance rejection control system based on a switched extended state observer, including a processor and a memory; A memory for storing computer programs; A processor, when executing the programs stored in the memory, implements the method steps described in the first aspect.

[0080] The above-mentioned UAV attitude disturbance rejection control system based on a switched extended state observer can implement various embodiments of the above-mentioned UAV attitude disturbance rejection control method based on a switched extended state observer, and can achieve the same beneficial effects, which will not be elaborated here.

[0081] The preferred specific embodiments of the present invention have been described in detail above. It should be understood that those of ordinary skill in the art can make many modifications and variations according to the concept of the present invention without creative labor. Therefore, all technical solutions that can be obtained by those skilled in the art in the technical field based on the concept of the present invention through logical analysis, reasoning or limited experiments on the basis of the prior art should fall within the protection scope determined by the claims.

Claims

1. A method for attitude disturbance rejection control of an unmanned aerial vehicle based on a switched extended state observer, characterized in that, It includes the following steps: S1. Design an adaptive switching extended state observer, specifically including: Divide the attitude control loop of the multi-rotor UAV into three symmetric control channels: roll angle, pitch angle, and yaw angle; Aiming at measurement noise pollution, construct a linear extended state observer composed of a high-gain module, a low-gain module, and a switching strategy. By comparing the estimation error caused by high-frequency noise with the state estimation accuracy difference, dynamically select the output signal of the high-gain or low-gain module based on a preset switching threshold to achieve adaptive switching of the state estimation signal; Adopt a linear state error feedback control law, use the output of the observer to compensate for the total system disturbance, and design a proportional-derivative controller to generate the control input; S2. Improve the snake optimization algorithm, specifically including: Introduce a complete learning strategy, a quadratic interpolation operator, and a Lévy flight mechanism into the snake optimization algorithm, initialize the population and divide it into a male population and a female population; Dynamically update the temperature and food parameters according to the number of iterations. Adjust the individual position through the Lévy flight coefficient in the exploration stage to enhance the global search ability; Integrate the population dimension information by combining the complete learning strategy in the transition stage to improve the population diversity; Update the individual position through the combat mode and mating mode in the exploitation stage to avoid the algorithm falling into local extrema; S3. Tune the parameters of the UAV active disturbance rejection controller based on the improved snake optimization algorithm, including: Construct a composite disturbance signal including wind disturbance, airframe vibration, and modeling parameter disturbance, and use the integral absolute error as the fitness function; Optimize the controller parameters to enable the UAV to achieve anti-disturbance tracking control in the roll angle, pitch angle, and yaw angle control channels.

2. The method for attitude disturbance rejection control of an unmanned aerial vehicle based on a switched extended state observer according to claim 1, characterized in that The mathematical model of the switching strategy in step S1 is: ； ； ； wherein, is the estimated output of the low-gain module, is the estimated output of the high-gain module, is the preset switching threshold, is the time constant of the filter in the switching strategy; The control law of the proportional-derivative controller is: ； wherein, is the system expected output, is the controller gain.

3. The method for attitude disturbance rejection control of an unmanned aerial vehicle based on a switched extended state observer according to claim 1, characterized in that The calculation formula of the Lévy flight coefficient in step S2 is: ； ； Among them, is the Lévy flight coefficient, 𝜇 and 𝑣 are random numbers from 0 to 1, and 𝛽 is a constant of 1.

5.

4. The method for attitude disturbance rejection control of an unmanned aerial vehicle based on a switched extended state observer according to claim 1, wherein, The initialization of the population in step S2 includes: ； wherein, is the position of the th individual, is the lower limit of the search space, is the upper limit of the search space, and rand is a random number uniformly distributed in the interval [0, 1]; Let the population size be , then the number of male individuals is , and the number of female individuals is .

5. The method for attitude disturbance rejection control of an unmanned aerial vehicle based on a switched extended state observer according to claim 1, characterized in that The dynamic update of the temperature and food parameters in step S2 includes: Update the temperature using the following formula Temp :[[]]END]] ； wherein, is the current iteration number, is the maximum iteration number; Update the food parameters using the following formula : ； In the formula, is the current iteration number, is the maximum iteration number, is a constant with a value of 0.

5.

6. The method for attitude disturbance rejection control of an unmanned aerial vehicle based on a switched extended state observer according to claim 1, wherein The adjustment of the individual position through the Lévy flight coefficient in step S2 includes: Update using the following formula : ； In the formula, is a constant with a value of 0.05, is the Lévy flight coefficient; Then perform individual position exploration through the following formula: ； Among them, and are the male position and the female position respectively, and are the random male position and the random female position respectively, is a constant of 0.05, represents the ability of the male to search for food, represents the ability of the female to search for food.

7. The method for attitude disturbance rejection control of an unmanned aerial vehicle based on a switched extended state observer according to claim 1, characterized in that The integration of the population dimension information by combining the complete learning strategy in step S2 includes: Introduce a binomial difference improvement strategy method in the exploration process of the individual position. The formula after introducing the binomial difference is as follows: ； Combine the formula after introducing the binomial difference with the complete learning improvement strategy. The update formulas of the \(i\)th male individual and the \(i\)th female individual can be rewritten as: ； In the formula, and are the male position and the female position, respectively.

8. The method for attitude disturbance rejection control of an unmanned aerial vehicle based on a switched extended state observer according to claim 1, wherein In step S2: When rand > 0.6, the snake's battle mode is completed using the following formula: In the formula, represents the fighting ability of males, represents the fighting ability of females; When The mating pattern of the snake is completed using the following formula: In the formula, represents the mating ability of the male, represents the mating ability of the female; Update using the following formula and : wherein, is the worst male individual, is the worst female individual.

9. The method for attitude disturbance rejection control of an unmanned aerial vehicle based on a switched extended state observer according to claim 1, wherein, The composite disturbance signal in step S3 includes: Low-frequency wind disturbance signal: ; Intermediate frequency modeling perturbation signal: ; High-frequency fuselage vibration signal: .

10. A UAV attitude anti-disturbance control system based on a switching extended state observer, including a processor and a memory; The memory is used to store a computer program; The processor is used to implement the method steps described in claims 1-9 when executing the program stored in the memory.

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