Unmanned aerial vehicle automatic driving hovering system

Abstract

The invention discloses an unmanned aerial vehicle automatic driving hovering system, relates to the technical field of unmanned aerial vehicle hovering control, and solves the technical problems that defects exist in the aspects of data processing, state judgment, attitude adjustment and the like, and the working requirements of high precision and high stability cannot be met. The height error of the unmanned aerial vehicle is analyzed and processed through the self-adaptive processing and analysis module by using a PID control algorithm, the output of proportion, integration and differentiation links is calculated, the total output is obtained and analyzed, a stable or unstable change signal is generated according to the change condition of the total output, attitude abnormality is further analyzed for the unstable condition, and the unmanned aerial vehicle attitude accuracy is improved. According to the method, integral coefficients are adjusted, hovering control information is generated, the integral coefficients are classified and compared, a proper integral coefficient is selected as a standard, the hovering control information is generated, and through the method of combining PID control and attitude analysis, the hovering stability of the unmanned aerial vehicle can be effectively improved, and the influence of external interference on the attitude is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of UAV hovering control, and particularly to an autonomous hovering system for UAVs. Background Technique

[0002] In the field of UAV applications, autonomous hovering is a key technology, which is widely used in many scenarios such as surveying and mapping, logistics, security monitoring, etc. However, at present, UAVs face many challenges during the process of autonomous hovering.

[0003] According to the patent application with the publication number CN108128445A, an autonomous hovering system for UAVs is disclosed, which includes a fuselage. Rotors are arranged on both sides of the fuselage. Support rods are arranged at the four corners of the bottom of the fuselage. The bottom end of each support rod is fixedly connected to a suction cup through a shock absorption device. A fixed plate is fixedly arranged inside the fuselage. A control chip and a signal transmitter are respectively arranged on both sides of the top of the fixed plate. A height sensor is arranged in the middle of the bottom end of the fuselage. By providing a control chip and a height sensor, the height sensor transmits the height signal to the control chip, and the control chip transmits the control signal to the stepper motor driver through the signal transmitter. The stepper motor driver controls the steering and speed of the stepper motor, so that the buoyancy generated by the rotation of the stepper motor is equal to the gravity of the fuselage itself, thereby realizing the autonomous hovering of the fuselage in the air.

[0004] However, when some existing hovering control systems are in use, on the one hand, during the flight of the UAV, affected by environmental factors, the captured images may deviate, making it difficult to accurately judge whether its own state meets the working requirements; on the other hand, when the UAV hovers, how to accurately control the height and maintain a stable attitude to ensure the completion of various tasks is an urgent problem to be solved. Summary of the Invention

[0005] In view of the deficiencies of the prior art, the present invention provides an autonomous hovering system for UAVs, which solves the problems of deficiencies in data processing, state judgment, attitude adjustment, etc., and being unable to meet the working requirements of high precision and high stability.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: An autonomous hovering system for UAVs, including:

[0007] An adjustment judgment and analysis module, used to compare the captured image in the real-time data with the standard image, generate a comprehensive analysis signal or an adjustment analysis signal, and transmit the comprehensive analysis signal to the adaptive processing and analysis module, and transmit the adjustment analysis signal to the adjustment analysis processing module;

[0008] The adaptive processing and analysis module analyzes the acquired comprehensive analysis signal, calculates the altitude difference of the drone, outputs through calculation of proportional output, integral output, and differential output, and combines the three to obtain the total output, judges the periodic change of the total output, generates a stable or unstable change signal. For the unstable change signal, an abnormal total output is obtained based on the relationship between the total output of the roll attitude, pitch attitude, and yaw attitude of the drone and the preset value, and the corresponding abnormal integral coefficient is obtained. At the same time, the abnormal integral coefficient is matched and judged with the normal integral coefficient to generate a secondary analysis signal or hover control information, and the secondary analysis signal is transmitted to the secondary analysis and processing module;

[0009] The secondary analysis and processing module analyzes the acquired secondary analysis signal, screens all the same total outputs and corresponding integral coefficients within the time period, and compares them with the abnormal analysis coefficient to obtain the classification coefficient. At the same time, the magnitudes of the abnormal integral coefficient and the normal integral coefficient are compared to obtain the adjustment method, calculates the difference between the selected classification coefficient and the abnormal integral coefficient, screens out the integral coefficients that meet the requirements of roll, pitch, and yaw attitudes, and takes the one with the smallest difference as the standard to generate hover control information and transmits it to the control information output module.

[0010] As a further solution of the present invention, it further includes a drone data acquisition module and an adjustment and analysis processing module;

[0011] The drone data acquisition module is used to acquire the real-time data of the drone and simultaneously transmit the acquired real-time data to the adjustment and judgment analysis module;

[0012] The adjustment and analysis processing module is used to analyze the adjustment and analysis signal and generate altitude adjustment information by analyzing the MTF value of the captured image.

[0013] As a further solution of the present invention, the specific method for the adjustment and judgment analysis module to generate a comprehensive analysis signal or an adjustment and analysis signal is as follows:

[0014] Perform on the real-time captured image of the drone to obtain the standard image required for the work, compare its pixels with the pixels of the drone-captured image. If the pixel difference between the two is within this range, it indicates that the current state of the drone meets the work requirements and a comprehensive analysis signal is generated; if not, it indicates that the work requirements are not met and an adjustment and analysis signal is generated.

[0015] As a further solution of the present invention, the processing method for the adjustment and analysis processing module to generate altitude information is as follows:

[0016] Obtain the real-time altitude corresponding to the drone and record it as Hs. At the same time, obtain the MTF function MTF(f) of the drone lens, where f is the spatial frequency, and the formula Calculate to obtain the spatial frequency f, and then pass the obtained spatial frequency f through the formula The calculated MTF value, where fmax is the cut-off frequency;

[0017] Taking the MTF value corresponding to the standard image as the standard, substituting the MTF value corresponding to the standard image into the above formula, calculating the spatial frequency f, and reversely calculating the corresponding adjustment height H1 according to the obtained spatial frequency f. At the same time, adjusting the real-time height Hs with the adjustment height H1 as the standard, generating height adjustment information, and transmitting it to the control information output module.

[0018] As a further solution of the present invention, the specific manner in which the adaptive processing and analysis module generates stable or unstable change signals is as follows:

[0019] Obtain the real-time height Hs and the hovering height Hx of the drone, calculate the height error between the two and denote it as e, and according to the formula u p = Kp×e, calculate the proportional output u corresponding to the proportional link p , where Kp is the proportional coefficient, and the specific value is set by the operator himself;

[0020] According to the formula calculate the integral output u corresponding to the integral link i , where Ki is the integral coefficient, e(τ) represents the error value of the system at time τ, t represents the current time, and τ represents the integration variable;

[0021] According to the formula calculate the differential output u corresponding to the differential link d , where Kd is the differential coefficient, represents the change rate of the error e with respect to time t;

[0022] Substitute the calculated proportional output u p , integral output u i and differential output u d into the formula u = u p + u i + u d calculate the total output u, and analyze the obtained total output u;

[0023] Obtain the change situation of the total output u within the time period T. If the total output u is a stable change, generate a stable change signal and transmit it to the control information output module at the same time. Conversely, if the total output is an unstable change within the time period T, generate an unstable change signal.

[0024] As a further solution of the present invention, the specific manner in which the adaptive processing and analysis module analyzes the unstable change signal to generate a secondary analysis signal or hovering control information is as follows:

[0025] Obtain the total outputs corresponding to the roll direction, pitch direction, and yaw direction of the drone respectively, denoted as u1, u2, and u3, and compare them with the preset values respectively. At the same time, screen out the abnormal total outputs;

[0026] Then obtain the integral coefficient corresponding to the abnormal total output, denoted as the abnormal integral coefficient Ki′, and obtain the normal integral coefficient Ki″ corresponding to the abnormal total output under normal conditions. At the same time, judge whether the normal integral coefficient Ki″ satisfies the integral coefficient corresponding to the remaining total output. If it satisfies, adjust it with the normal integral coefficient Ki″ as the standard to generate hover control information. Otherwise, if it does not satisfy, perform secondary analysis on the abnormal integral coefficient Ki′ and generate a secondary analysis signal.

[0027] As a further solution of the present invention, the specific method for the secondary analysis processing module to analyze the secondary analysis signal to generate hover control information is as follows:

[0028] Obtain all the total outputs u within the time period T, and at the same time obtain the total outputs u with the same value, and obtain the corresponding integral coefficient Ki. Compare the integral coefficient Ki with the abnormal integral coefficient Ki′, classify the integral coefficient Ki to obtain the first integral coefficient and the second integral coefficient to obtain the classification coefficient. At the same time, compare the abnormal integral coefficient Ki′ with the normal integral coefficient Ki″ to judge the corresponding adjustment method, and then select the corresponding classification coefficient according to the adjustment method;

[0029] At the same time, calculate the numerical difference between the selected classification coefficient and the abnormal integral coefficient, and sort them from small to large according to the numerical difference. Then judge the integral coefficients that satisfy the roll attitude, pitch attitude, and yaw attitude, and select the integral coefficient with the smallest corresponding numerical difference as the standard to generate hover control information.

[0030] As a further solution of the present invention, it further includes a control information output module for transmitting the obtained height adjustment information and hover control information to the flight controller and adjusting them through the flight controller.

[0031] The present invention provides an unmanned aerial vehicle (UAV) autopilot hover system. Compared with the prior art, it has the following

[0032] Beneficial effects:

[0033] In the present invention, the adjustment and analysis processing module calculates the MTF value of the UAV, takes the MTF value corresponding to the standard image as the standard, reversely calculates the adjustment height, and adjusts the real-time height to generate height adjustment information and transmit it to the flight controller. This height adjustment method based on the MTF value is more accurate, can quickly and accurately adjust the UAV height, and improve work efficiency and quality.

[0034] The adaptive processing and analysis module uses the PID control algorithm to analyze and process the altitude error of the UAV, calculate the outputs of the proportional, integral, and derivative links, obtain the total output and analyze it. According to the change of the total output, a stable or unstable change signal is generated. For the unstable situation, the attitude anomaly is further analyzed, the integral coefficient is adjusted, and the hover control information is generated and transmitted to the flight controller. This method combining PID control and attitude analysis can effectively improve the stability of the UAV hover and reduce the influence of external interference on the attitude.

[0035] By classifying and comparing the integral coefficients, a suitable integral coefficient is selected as the standard to generate the hover control information. This intelligent parameter adjustment strategy can flexibly adjust the integral coefficient according to the actual situation, quickly restore the stable hover state of the UAV, and improve the adaptability and reliability of the system. Brief Description of the Drawings

[0036] Figure 1 It is a block diagram of the system principle of the present invention. Detailed Embodiments

[0037] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0038] Embodiment 1, please refer to Figure 1 , this application provides a UAV automatic flight hover system, including a UAV data acquisition module, an adjustment judgment and analysis module, an adjustment analysis and processing module, an adaptive processing and analysis module, and a control information output module, and in combination with the attached Figure 1 It can be known that the above functional modules are all connected in a one-way electrical connection.

[0039] The UAV data acquisition module is used to obtain the real-time data of the UAV and simultaneously transmit the obtained real-time data to the adjustment judgment and analysis module. Here, the real-time data mainly obtains the data corresponding to the sensors on the UAV and the corresponding UAV images. The sensors include, for example, gyroscopes, accelerometers, and GPS. The obtained sensor data includes the angular velocity and acceleration of the UAV and the position of the UAV;

[0040] Adjustment judgment and analysis module, which is used to obtain real-time data, and at the same time judge the drone-captured images in the real-time data. By obtaining the standard images in the work requirements and comparing the drone-captured images with the standard images, specifically, the pixels of the two are compared here. If the pixel difference between the captured image and the standard image is within the allowable range, and the specific value of the allowable range is set by the operator, it means that the current state of the drone can meet the work requirements and generate a comprehensive analysis signal. On the contrary, if the pixel difference between the captured image and the standard image is not within the allowable range, it means that the current state of the drone cannot meet the work requirements and generate an adjustment analysis signal;

[0041] Transmit the generated adjustment analysis signal to the adjustment analysis and processing module, and transmit the comprehensive analysis signal to the adaptive processing and analysis module.

[0042] Adjustment analysis and processing module, which is used to analyze the obtained adjustment analysis signal. Specifically, calculate the current MTF value of the drone, obtain the corresponding real-time height of the drone denoted as Hs, and at the same time obtain the MTF function (based on the modulation transfer function) MTF(f) of the drone lens, where f is the spatial frequency, and the spatial frequency f is related to the distance v and the pixels p of the imaging sensor. Specifically And the formula Calculate the spatial frequency f, and then pass the obtained spatial frequency f through the formula Calculate the MTF value, where fmax is the cut-off frequency;

[0043] Then, taking the MTF value corresponding to the standard image as the standard, adjust the current MTF value. Specifically, substitute the MTF value corresponding to the standard image into the above formula, calculate the spatial frequency f, and reversely calculate the corresponding adjustment height H1 according to the obtained spatial frequency f. At the same time, adjust the real-time height Hs with the adjustment height H1 as the standard, generate height adjustment information, and transmit it to the control information output module;

[0044] Control information output module, which is used to transmit the obtained height adjustment information to the flight controller to adjust the height through the flight controller.

[0045] Embodiment 2, this embodiment is implemented on the basis of Embodiment 1, and the difference from Embodiment 1 is as follows:

[0046] Adaptive processing and analysis module, which is used to process the obtained comprehensive analysis signal, obtain the real-time height Hs and the hovering height Hx of the drone, calculate the height error between the two denoted as e, and then calculate the output results corresponding to the PID link respectively;

[0047] Calculate the proportional output of the proportional link, according to the formula up The proportional output u corresponding to the proportional link is calculated as u = Kp × e p , where Kp is the proportional coefficient, and the specific value is set by the operator himself;

[0048] For example, if Kp = 0.5 and the height error is calculated to be e = 5 meters after calculation, the proportional output corresponding to the proportional link is calculated according to the formula u p = 0.5 × 5 = 2.5, and this output will be converted into a control signal for the motor, causing the motor to increase its output and making the drone rise to reduce the height error.

[0049] Calculate the integral output corresponding to the integral link. According to the formula the integral output u corresponding to the integral link is calculated i , where Ki is the integral coefficient, the specific value is set by the operator, e(τ) represents the error value of the system at time τ, specifically referring to the difference between the target hover position and the current position of the drone, t represents the current time, and τ represents the integration variable;

[0050] For example, within a period of time t, the height error is always 2 meters, Ki = 0.1, assuming the integration time t = 10 seconds, then the integral output u corresponding to the integral link i is calculated to be 2 according to the formula.

[0051] Calculate the differential output corresponding to the differential link. According to the formula the differential output u corresponding to the differential link is calculated d , where Kd is the differential coefficient, and the specific value is set by the operator, represents the rate of change of the error e with respect to time t, specifically the change speed of the error;

[0052] For example, when the drone is rising, the height error e decreases from 5 meters to 3 meters, and the time interval is 1 second, then the corresponding rate of change of the error is -2 meters / second. If Kd is 0.2, then the differential output of the corresponding differential link is calculated according to the formula to be u d = -0.4.

[0053] Substitute the calculated proportional output u p , integral output u i and differential output u d into the formula u = u p + u i + u d to calculate the total output u, and analyze the obtained total output u;

[0054] Next, taking time T as the period, analyze the change of the total output u. If within the time period T, the total output u changes stably, and this stable change means that the numerical difference of the total output u is within the preset difference range, and the range of the preset difference is set by the operator, then generate a stable change signal and transmit it to the control information output module at the same time. On the contrary, if within the time period T, the total output changes unstably, and the specific numerical difference is not within the preset difference range, then generate an unstable change signal and further analyze the unstable change signal;

[0055] Obtain the generated unstable change signal. At the same time, analyze the roll attitude, pitch attitude and yaw attitude of the UAV, and respectively obtain the total outputs corresponding to the roll direction, pitch direction and yaw direction of the UAV, denoted as u1, u2 and u3, and compare them with the preset values respectively. The specific values of the preset values are set by the operator, and at the same time, filter out the abnormal total output;

[0056] Next, obtain the integration coefficient corresponding to the abnormal total output, denoted as the abnormal integration coefficient Ki′. Here, the abnormal total output is represented by any one of u1, u2 and u3, and obtain the normal integration coefficient Ki″ corresponding to the abnormal total output under normal circumstances. At the same time, judge whether the normal integration coefficient Ki″ satisfies the integration coefficients corresponding to the remaining total outputs. Here, the satisfaction means judging based on the current normal integration coefficient. If the abnormal total output is abnormal in the roll direction, judge whether the normal integration coefficient satisfies the integration coefficients in the pitch direction and yaw direction. If it satisfies, adjust according to the normal integration coefficient Ki″ as the standard to generate hover control information. On the contrary, if it does not satisfy, perform secondary analysis processing on the abnormal integration coefficient Ki′, generate a secondary analysis signal, and transmit it to the secondary analysis processing module at the same time.

[0057] The secondary analysis processing module is used to analyze the obtained secondary analysis signal. Specifically, obtain all the total outputs u within the time period T, at the same time obtain the total outputs u with the same value, and obtain the corresponding integration coefficient Ki, and compare the integration coefficient Ki with the abnormal integration coefficient Ki′. Classify the integration coefficient Ki to obtain the first integration coefficient and the second integration coefficient to obtain the classification coefficient. Here, the first integration coefficient is greater than the current abnormal integration coefficient, and the second integration coefficient is less than the current abnormal integration coefficient. At the same time, compare the abnormal integration coefficient Ki′ with the normal integration coefficient Ki″ to judge the corresponding adjustment method, and the adjustment method specifically represents an increase adjustment or a decrease adjustment. Then select the corresponding classification coefficient according to the adjustment method. Here, the classification coefficient represents the first integration coefficient or the second integration coefficient; specifically, if the adjustment method is an increase adjustment, then select the first integration coefficient correspondingly. On the contrary, if the adjustment method is a decrease adjustment, then select the second integration coefficient.

[0058] Meanwhile, calculate the numerical difference between the selected classification coefficient and the abnormal integral coefficient, and sort them in ascending order according to the numerical difference. Then, judge the integral coefficients that satisfy the roll attitude, pitch attitude, and yaw attitude, and select the integral coefficient with the smallest corresponding numerical difference as the standard. Specifically, judge the integral coefficients in the selected classification coefficients against the integral coefficients of the roll attitude, pitch attitude, and yaw attitude. If the conditions are met, obtain the corresponding integral coefficients, and analyze the integral coefficients in the selected classification coefficients in this way. Then, obtain the numerical difference between the selected integral coefficients and the abnormal integral coefficient, and select the integral coefficient with the smallest numerical difference as the condition standard to generate hover control information, and at the same time transmit it to the control information output module.

[0059] The control information output module is used to transmit the obtained hover control information to the flight controller for adjustment by the flight controller.

[0060] Embodiment 3. As Embodiment 3 of the present invention, the key lies in combining the implementation processes of Embodiment 1 and Embodiment 2.

[0061] For some data in the above formula, only their numerical values are taken for calculation, and the parameter units are not substituted for calculation. At the same time, the content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0062] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An unmanned aerial vehicle (UAV) autopilot hovering system, characterized in that, Including: An adjustment judgment and analysis module, which is used to compare the captured image in the real-time data with the standard image, generate a comprehensive analysis signal or an adjustment analysis signal, transmit the comprehensive analysis signal to the adaptive processing and analysis module, and transmit the adjustment analysis signal to the adjustment analysis and processing module; An adaptive processing and analysis module, which analyzes the obtained comprehensive analysis signal, calculates the height difference of the UAV, outputs through calculating the proportion, integral and differential, and obtains the total output by synthesizing the three, judges the periodic change of the total output, generates a stable or unstable change signal. For the unstable change signal, based on the relationship between the total output of the roll attitude, pitch attitude and yaw attitude of the UAV and the preset value, an abnormal total output is obtained, and the corresponding abnormal integral coefficient is obtained. At the same time, the abnormal integral coefficient and the normal integral coefficient are matched and judged to generate a secondary analysis signal or a hover control message, and the secondary analysis signal is transmitted to the secondary analysis and processing module; A secondary analysis and processing module, which analyzes the obtained secondary analysis signal, screens all the same total outputs and the corresponding integral coefficients within the time period, compares them with the abnormal analysis coefficient to obtain the classification coefficient, and at the same time compares the magnitudes of the abnormal integral coefficient and the normal integral coefficient to obtain the adjustment method, calculates the difference between the selected classification coefficient and the abnormal integral coefficient, screens out the integral coefficients that meet the requirements of the roll, pitch and yaw attitudes, and takes the one with the smallest difference as the standard to generate a hover control message and transmit it to the control information output module.

2. The UAV automatic flight hover system according to claim 1, characterized in that, It also includes a UAV data acquisition module and an adjustment analysis and processing module; The UAV data acquisition module is used to acquire the real-time data of the UAV and transmit the acquired real-time data to the adjustment judgment and analysis module at the same time; The adjustment analysis and processing module is used to analyze the adjustment analysis signal and generate height adjustment information by analyzing the MTF value of the captured image.

3. An unmanned aerial vehicle automatic driving hovering system according to claim 1, characterized in that, The specific way for the adjustment judgment and analysis module to generate a comprehensive analysis signal or an adjustment analysis signal is: Perform on the real-time captured image of the UAV to obtain the standard image required for the work, compare its pixels with the pixels of the UAV captured image. If the pixel difference between the two is within this range, it indicates that the current state of the UAV meets the work requirements and a comprehensive analysis signal is generated; if it is not within the range, it indicates that the work requirements are not met and an adjustment analysis signal is generated.

4. The UAV automatic flight hover system according to claim 2, characterized in that, The processing method for the adjustment analysis and processing module to generate height information is also: Obtain the real-time height corresponding to the drone and record it as Hs. At the same time, obtain the MTF function MTF(f) of the drone's lens, where f is the spatial frequency, and the formula Calculate to obtain the spatial frequency f. Then, pass the obtained spatial frequency f through the formula Calculate to obtain the MTF value, where fmax is the cut-off frequency; Taking the MTF value corresponding to the standard image as the standard, substituting the MTF value corresponding to the standard image into the above formula, calculating the spatial frequency f, and reversely calculating the corresponding adjustment height H1 according to the obtained spatial frequency f. At the same time, taking the adjustment height H1 as the standard to adjust the real-time height Hs, generating height adjustment information and transmitting it to the control information output module.

5. An unmanned aerial vehicle automatic driving and hovering system according to claim 1, characterized in that, The specific way for the adaptive processing and analysis module to generate a stable or unstable change signal is: Obtain the real-time height \(H_s\) and hovering height \(H_x\) of the drone, and calculate the height error between the two, denoted as \(e\). According to the formula \(u\) p \(= K_p\times e\), calculate the proportional output \(u\) corresponding to the proportional link p , where \(K_p\) is the proportional coefficient, and the specific value is set by the operator himself; According to the formula the integral output u corresponding to the integral link is calculated i , where Ki is the integral coefficient, e(τ) represents the error value of the system at time τ, t represents the current time, and τ represents the integral variable; According to the formula the differential output u corresponding to the differential link is calculated d , where Kd is the differential coefficient, represents the rate of change of the error e with respect to time t; Output the calculated ratio u p , the integral output u i and the differential output u d into the formula u = u p + u i + u d Calculate the total output u, and analyze the obtained total output u; Obtain the change situation of the total output u within the time period T. If the total output u is a stable change, a stable change signal is generated and transmitted to the control information output module at the same time. On the contrary, if the total output is an unstable change within the time period T, an unstable change signal is generated.

6. The automatic flight hovering system for a drone according to claim 1, characterized in that, The specific method for the adaptive processing and analysis module to analyze the unstable change signal to generate a secondary analysis signal or hover control information is as follows: Obtain the total outputs corresponding to the roll direction, pitch direction, and yaw direction of the UAV, denoted as u1, u2, and u3 respectively, and compare them with the preset values respectively. At the same time, screen out the abnormal total outputs; Then, obtain the integral coefficient corresponding to the abnormal total output, denoted as the abnormal integral coefficient Ki′, and obtain the normal integral coefficient Ki″ corresponding to the abnormal total output under normal circumstances. At the same time, judge whether the normal integral coefficient Ki″ satisfies the integral coefficient corresponding to the remaining total output. If it satisfies, adjust it with the normal integral coefficient Ki″ as the standard to generate hover control information. Otherwise, if it does not satisfy, perform secondary analysis processing on the abnormal integral coefficient Ki′ and generate a secondary analysis signal.

7. A drone autopilot hovering system according to claim 1, wherein The specific method for the secondary analysis processing module to analyze the secondary analysis signal to generate hover control information is as follows: Obtain all the total outputs u within the time period T, and at the same time obtain the total outputs u with the same value, and obtain the corresponding integral coefficient Ki. Compare the integral coefficient Ki with the abnormal integral coefficient Ki′, classify the integral coefficient Ki to obtain the first integral coefficient and the second integral coefficient to obtain the classification coefficient. At the same time, compare the abnormal integral coefficient Ki′ with the normal integral coefficient Ki″ to judge the corresponding adjustment method, and then select the corresponding classification coefficient according to the adjustment method; At the same time, calculate the numerical difference between the selected classification coefficient and the abnormal integral coefficient, and sort them from small to large according to the numerical difference. Then, judge the integral coefficients that satisfy the roll attitude, pitch attitude, and yaw attitude, and select the integral coefficient with the smallest corresponding numerical difference as the standard to generate hover control information.

8. The UAV automatic flight hovering system according to claim 4 or 7, characterized in that, It further includes a control information output module, which is used to transmit the obtained altitude adjustment information and hover control information to the flight controller and perform adjustment through the flight controller.

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