An aircraft attitude adaptive stability control method and system

Abstract

The application discloses a kind of aircraft attitude self-adaptive stabilizing control method and system, it is related to aircraft attitude control technical field, including: aim at solving existing aircraft when flying in low air field, it is susceptible to airflow disturbance, attitude drift influence, leading to attitude fluctuation, poor stability, operation precision is insufficient, cannot complete accurate low-altitude patrol, low-altitude surveying and mapping etc. technical problem of task.Based on the dynamic characteristics of aircraft, fusion IMU inertial measurement, barometer, attitude sensor and vision auxiliary positioning multi-source data, using attitude drift pre-judgment+adaptive fuzzy PID control+vision auxiliary calibration+closed loop parameter iteration core architecture, realize the accurate control of aircraft attitude, the advance inhibition of attitude drift, ensure that aircraft maintains stable attitude in low air complex environment.

Description

Technical Field

[0001] This invention relates to the field of aircraft attitude control technology, and more specifically to an adaptive stability control method and system for aircraft attitude. Background Technology

[0002] The low-altitude environment is complex, with problems such as unstable airflow and large changes in light. Aircraft are easily disturbed by unstable airflow during flight, resulting in attitude fluctuations. At the same time, during long-term operation, the inertial measurement unit is prone to cumulative errors, causing attitude drift and thus affecting the accuracy of operation.

[0003] Existing attitude control algorithms for aircraft mostly employ fixed-parameter PID control, which cannot dynamically adjust control parameters according to the intensity of airflow interference and attitude drift trends. This results in problems such as attitude adjustment lag and insufficient drift suppression. Furthermore, most algorithms rely solely on inertial measurement data and do not introduce auxiliary calibration methods. After prolonged operation, the accumulated error increases, causing attitude deviations to exceed operational requirements.

[0004] Furthermore, existing algorithms lack a predictive mechanism for airflow characteristics in the low-altitude domain, often adjusting only after attitude drift occurs, failing to suppress drift in advance and further reducing the aircraft's attitude stability. No existing technology has been found to simultaneously incorporate attitude drift prediction, adaptive fuzzy PID control, visual-assisted calibration, and closed-loop parameter iteration, thus failing to effectively address attitude drift and accumulated error issues under low-altitude airflow disturbances.

[0005] Therefore, how to propose an adaptive stability control method and system for aircraft attitude to overcome the shortcomings of existing technologies is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] In view of this, the present invention provides an adaptive attitude stabilization control method and system for aircraft, overcoming the shortcomings of existing technologies such as poor attitude stability, weak resistance to airflow interference, inability to suppress attitude drift in advance, and insufficient accuracy during long-term operation. Through innovative drift prediction, adaptive control, and auxiliary calibration design, it achieves precise attitude control, early drift suppression, and effective correction of accumulated errors. To achieve the above objectives, the present invention adopts the following technical solution: An adaptive stabilization control method for aircraft attitude includes: Initially, multi-source attitude and environmental data acquisition of the aircraft was conducted. Based on the dynamic model of the aircraft and combined with the collected multi-source attitude data, an attitude error model is constructed to calculate the deviation between the current attitude and the preset attitude of the aircraft; an attitude drift prediction module is introduced to predict the amount of attitude drift. Based on the attitude error model, the predicted attitude drift and airflow interference intensity, an adaptive fuzzy PID controller is designed to dynamically adjust the PID control parameters. Collect environmental feature data, match it with a preset environmental feature library, calculate the attitude deviation of the aircraft relative to the environment, and use it as an auxiliary calibration benchmark for PID control to correct the cumulative error caused by inertial measurement. Continuously collect multi-source data and dynamically update the attitude error model, drift prediction parameters, and PID control parameters to form a closed-loop control for aircraft attitude stabilization.

[0007] Optionally, the initial acquisition of multi-source attitude and environmental data of the aircraft includes: acquiring the aircraft's attitude parameters, motion parameters, air pressure and airflow speed data in the flight environment, and visual feature data of the surrounding environment in real time through the aircraft's IMU sensor, barometer, attitude sensor and high-definition vision camera.

[0008] Optionally, it also includes: verifying the real-time performance and accuracy of data collection through a timestamp synchronization mechanism.

[0009] Optionally, the attitude drift prediction module employs a linear regression algorithm.

[0010] Optionally, the dynamically adjusted PID control parameters include: a proportional coefficient Kp ranging from 5.0 to 8.0, an integral coefficient Ki ranging from 0.1 to 0.3, and a derivative coefficient Kd ranging from 1.0 to 2.5.

[0011] Optionally, the fuzzy control rule of the adaptive fuzzy PID controller is as follows: with attitude deviation and attitude drift as input variables and PID parameter adjustment as output variables, a 5×5 fuzzy rule matrix is ​​established to perform real-time dynamic optimization of parameters.

[0012] Optionally, the frequency of the auxiliary calibration reference is consistent with the inertial measurement frequency, and the calibration weight is dynamically adjusted according to the ambient light intensity.

[0013] Optionally, the attitude deviation of the aircraft is controlled within ±0.3°, and the attitude drift does not exceed 0.5° every 10 minutes.

[0014] Optionally, the attitude error model adopts the Euler angle error model, and the deviation value is expressed as Δθ=θ 当前 -θ is calculated, where θ 当前 θ represents the current attitude angle of the aircraft, and θ represents the preset attitude angle.

[0015] Optionally, an adaptive stability control system for aircraft attitude includes: a data acquisition module for initially acquiring multi-source attitude and environmental data of the aircraft; Prediction module: Based on the aircraft's dynamic model and the collected multi-source attitude data, it constructs an attitude error model and calculates the deviation between the aircraft's current attitude and the preset attitude; it also introduces an attitude drift prediction module to predict the amount of attitude drift. PID control module: used to design an adaptive fuzzy PID controller based on the attitude error model, predict the attitude drift amount and airflow interference intensity, and dynamically adjust the PID control parameters; Correction module: Used to collect environmental feature data, match it with a preset environmental feature library, calculate the attitude deviation of the aircraft relative to the environment, and use it as an auxiliary calibration benchmark for PID control to correct the cumulative error caused by inertial measurement. Closed-loop control module: Used to continuously collect multi-source data, dynamically update attitude error model, drift prediction parameters and PID control parameters, and form a closed-loop control to stabilize the aircraft's attitude.

[0016] As can be seen from the above technical solution, compared with the prior art, the present invention discloses an adaptive stabilization control method and system for aircraft attitude, which has the following beneficial effects: This invention achieves (1) high attitude control accuracy: by integrating multi-source data to construct an accurate error model and combining it with visual-assisted calibration, the actual measurement comparison shows that the attitude deviation of this invention is controlled within ±0.3°, while the attitude deviation of the existing fixed parameter PID algorithm is ≥±1.0°; after long-term operation, the attitude drift of this invention is only 0.3° every 10 minutes, while the drift of the existing algorithm is ≥1.2° every 10 minutes. (2) Outstanding drift suppression capability: The drift trend is predicted 0.1-0.3s in advance, and the drift is suppressed in advance by combining adaptive PID control. According to actual measurement, the drift suppression response time of the present invention is ≤0.15s, which is more than 0.2s earlier than the existing algorithm; (3) Strong resistance to airflow interference: It can dynamically adjust the PID parameters according to the intensity of airflow interference. According to actual measurements, in an environment with an airflow speed of 1.8m / s, the attitude fluctuation of the present invention is ≤0.2°, while the attitude fluctuation of the existing fixed parameter PID algorithm is ≥0.8°. (4) Fast response and strong real-time performance, with a control response time of ≤0.1s, it is suitable for various low-altitude operation scenarios, can be directly embedded into existing control systems, and is compatible with various types of aircraft. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0018] Figure 1 This is a schematic flowchart of an adaptive stabilization control method for aircraft attitude provided by the present invention.

[0019] Figure 2 A schematic diagram of the attitude drift prediction and error modeling process provided by the present invention.

[0020] Figure 3 This is a schematic diagram of the adaptive fuzzy PID controller structure provided by the present invention.

[0021] Figure 4 A schematic diagram of the visual-assisted posture calibration logic provided by the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] This invention discloses an adaptive stabilization control method for aircraft attitude, such as... Figure 1 As shown, it includes: Initially, multi-source attitude and environmental data acquisition of the aircraft was conducted. Based on the dynamic model of the aircraft and combined with the collected multi-source attitude data, an attitude error model is constructed to calculate the deviation between the current attitude and the preset attitude of the aircraft; an attitude drift prediction module is introduced to predict the amount of attitude drift. Based on the attitude error model, the predicted attitude drift and airflow interference intensity, an adaptive fuzzy PID controller is designed to dynamically adjust the PID control parameters. Collect environmental feature data, match it with a preset environmental feature library, calculate the attitude deviation of the aircraft relative to the environment, and use it as an auxiliary calibration benchmark for PID control to correct the cumulative error caused by inertial measurement. Continuously collect multi-source data and dynamically update the attitude error model, drift prediction parameters, and PID control parameters to form a closed-loop control for aircraft attitude stabilization.

[0024] In a specific implementation, an adaptive stabilization control method for aircraft attitude includes the following steps: S1. Multi-source attitude and environment data acquisition: Through the IMU sensor, barometer, attitude sensor and high-definition vision camera on the aircraft, the attitude parameters and motion parameters of the aircraft, the air pressure and airflow speed data in the flight environment, and the visual feature data of the surrounding environment are collected in real time. S2. Attitude Drift Prediction and Error Modeling: (e.g.) Figure 2 As shown, based on the aircraft's dynamic model and combined with the collected multi-source data, an attitude error model is constructed to calculate the deviation between the aircraft's current attitude and the preset attitude. An attitude drift prediction module is introduced, which predicts the attitude drift amount within the next 0.1-0.3 seconds by analyzing the trend of airflow speed change, aircraft acceleration fluctuation, and visual feature matching deviation, providing a lead time for attitude control. The attitude drift prediction adopts a linear regression algorithm, combined with the airflow speed change and acceleration fluctuation value within the past 1 second. S3. Adaptive fuzzy PID control adjustment: such as Figure 3 As shown, based on the attitude error model, the predicted attitude drift, and the intensity of airflow interference, an adaptive fuzzy PID controller is designed to dynamically adjust the PID parameters in real time. The proportional coefficient Kp ranges from 5.0 to 8.0, the integral coefficient Ki ranges from 0.1 to 0.3, and the derivative coefficient Kd ranges from 1.0 to 2.5, achieving precise attitude adjustment and early suppression of drift. The fuzzy control rule of the adaptive fuzzy PID control is as follows: with attitude deviation and attitude drift as input variables and PID parameter adjustment as output variables, a 5×5 fuzzy rule matrix is ​​established to achieve real-time dynamic optimization of parameters. S4. Visual-assisted posture calibration: such as Figure 4 As shown, environmental feature data collected by a high-definition vision camera is matched with a preset environmental feature library to calculate the attitude deviation of the aircraft relative to the environment. This serves as an auxiliary calibration benchmark for PID control, correcting the cumulative error caused by inertial measurement and ensuring attitude accuracy during long-term operation. The frequency of visual-assisted attitude calibration is consistent with the frequency of inertial measurement, and the calibration weight is dynamically adjusted according to the ambient light intensity. The calibration weight is 0.3-0.4 when the light is sufficient and 0.15-0.25 when the light is dim. S5. Attitude stabilization closed-loop optimization: Repeat steps S1-S4, continuously collect multi-source data, dynamically update the attitude error model, drift prediction parameters and PID control parameters to form a closed-loop control, ensuring that the aircraft can maintain a stable attitude in the low-altitude domain, regardless of whether it is affected by airflow disturbances; the aircraft attitude deviation is controlled within ±0.3°, and the attitude drift does not exceed 0.5° every 10 minutes.

[0025] Furthermore, in step S1, the sampling frequency of all sensors is not lower than 100Hz.

[0026] Furthermore, in step S2, the attitude error model adopts the Euler angle error model, and the deviation value is expressed as Δθ=θ 当前 -θ, calculate, where θ 当前 θ represents the current attitude angle of the aircraft, and θ represents the preset attitude angle.

[0027] In a specific embodiment, an adaptive stabilization control method for aircraft attitude includes the following steps: S11. Multi-source attitude and environmental data acquisition: Through the aircraft's onboard IMU sensors, barometers, attitude sensors, and high-definition vision cameras, the aircraft's pitch angle, roll angle, heading angle, acceleration, angular velocity, air pressure and airflow speed data in the flight environment, and visual feature data of the surrounding environment are acquired in real time; the sampling frequency of all sensors is no less than 100Hz, and the real-time performance and accuracy of data acquisition are ensured through a timestamp synchronization mechanism to avoid control errors caused by asynchronous multi-source data.

[0028] S12. Attitude Drift Prediction and Error Modeling: Based on the aircraft's dynamic model, an attitude error model is constructed using the Euler angle error model, through Δθ=θ 当前 -θ calculates the deviation between the current attitude and the preset attitude of the aircraft; an attitude drift prediction module is introduced, which uses a linear regression algorithm and combines the changes in airflow speed and acceleration fluctuations in the past 1 second to predict the attitude drift in the next 0.1-0.3 seconds, providing advance for attitude control and achieving early suppression of drift.

[0029] S13. Adaptive Fuzzy PID Control Adjustment: Based on the attitude error model, the predicted attitude drift, and the intensity of airflow interference, an adaptive fuzzy PID controller is designed. A 5×5 fuzzy rule matrix is ​​established, with attitude deviation and attitude drift as input variables and PID parameter adjustment as output variables. The PID parameters are dynamically adjusted in real time. Kp takes values ​​of 5.0-8.0, Ki takes values ​​of 0.1-0.3, and Kd takes values ​​of 1.0-2.5. When the attitude deviation is ≤0.5°, the drift is ≤0.2°, and the interference intensity is small, smaller Kp and Ki are used to ensure attitude stability. When the attitude deviation is >0.5°, the drift is >0.2°, or the interference intensity is large, Kp and Kd are automatically increased to accelerate the attitude adjustment speed and suppress the drift expansion in advance.

[0030] S14. Visual-assisted attitude calibration: Environmental feature data collected by a high-definition visual camera is matched with a preset environmental feature library to calculate the attitude deviation of the aircraft relative to the environment, which serves as an auxiliary calibration benchmark for PID control. The calibration weight is dynamically adjusted according to the ambient light intensity, with a weight of 0.3-0.4 when the light is sufficient and 0.15-0.25 when the light is dim, to correct the cumulative error caused by inertial measurement and ensure the accuracy of long-term operation.

[0031] S15. Attitude stabilization closed-loop optimization: Repeat steps S11-S14, continuously collect multi-source data, dynamically update the attitude error model, drift prediction parameters and PID control parameters to form a closed-loop control, ensure that the attitude deviation is controlled within ±0.3°, and the attitude drift does not exceed 0.5° every 10 minutes, so as to achieve stable operation in the low-altitude field.

[0032] The above scheme (1) introduces an attitude drift prediction module, adopts a linear regression algorithm, and combines airflow speed changes and acceleration fluctuations to predict the attitude drift trend 0.1-0.3s in advance. This is different from the passive control mode of the existing technology that adjusts after the drift occurs, and realizes active suppression of drift; (2) designs an adaptive fuzzy PID control, with attitude deviation and drift amount as dual inputs, establishes a 5×5 fuzzy rule matrix, and dynamically adjusts the PID parameters. This is different from the existing fixed parameter PID and single input fuzzy PID, and can adapt to different airflow interference intensities; (3) adopts a visual auxiliary calibration + closed-loop iteration mechanism, dynamically adjusts the calibration weight according to the light intensity, effectively corrects the IMU cumulative error, and continuously optimizes the parameters through closed-loop iteration to solve the problem of decreased accuracy of the existing algorithm during long-term operation; (4) deeply integrates multi-source data fusion with control strategy, can be directly embedded into the existing flight control system, and is compatible with various types of aircraft. This is different from the attitude control scheme that requires dedicated hardware support in the existing technology.

[0033] In a specific embodiment, taking a low-altitude mapping scenario as an example, the aircraft weighs 2kg, flies at a speed of 1.0-2.0m / s, and is equipped with an MPU6050 IMU sensor, an HMC5883L attitude sensor, a BMP280 barometer, and a 1080P high-definition visual camera. The preset pitch and roll angles are 0°, the heading angle is 90°, the operating altitude is 8-15m, and the operating environment has unstable airflow disturbances and moderate light intensity. The specific implementation steps are as follows: S21. Multi-source attitude and environmental data acquisition: The aircraft's attitude and motion data, air pressure and airflow speed data in the low-altitude environment, and ground environmental characteristic data are acquired in real time through four sensors; the sampling frequency of all sensors is set to 100Hz, and the data synchronization mechanism is used to ensure accurate synchronization of multi-source data with a data synchronization error ≤0.001s.

[0034] S22. Attitude Drift Prediction and Error Modeling: Based on the aircraft dynamics model, the Euler angle error model is used to calculate the attitude deviation, through Δθ=θ 当前 -θ is used to calculate the real-time deviations of pitch angle, roll angle, and heading angle; a linear regression algorithm is used, combined with the change in airflow velocity and acceleration fluctuation value in the past 1 second, to predict the attitude drift in the next 0.2 seconds. When the predicted drift is greater than 0.2°, the PID parameter fast adjustment mechanism is triggered.

[0035] S23. Adaptive Fuzzy PID Control Adjustment: A fuzzy control algorithm is used to adjust the PID parameters, establishing a 5×5 fuzzy rule matrix. Kp takes values ​​of 5.0-8.0, Ki takes values ​​of 0.1-0.3, and Kd takes values ​​of 1.0-2.5. When the attitude deviation is 0.3°, the drift is 0.15°, and the airflow velocity is 1.2m / s, Kp=5.5, Ki=0.15, and Kd=1.2 are adjusted to ensure stable attitude with attitude fluctuation ≤0.1°. When the airflow velocity is 1.8m / s, the attitude deviation is 0.8°, and the drift is 0.3°, Kp=7.0, Ki=0.25, and Kd=2.0 are automatically adjusted, and the attitude deviation quickly drops to within 0.2° after adjustment.

[0036] S24. Visual Assisted Attitude Calibration: Based on the low-altitude ambient light intensity, the visual calibration weight is set to 0.3. A high-definition visual camera is used to collect fixed feature points on the ground and match them with a preset environmental feature library to calculate the attitude deviation of the aircraft relative to the environment. This deviation is used as an auxiliary calibration signal and input into the PID controller to correct the cumulative error of the IMU sensor. After calibration, the attitude deviation is still controlled within ±0.25° after long-term operation, which is more than 60% more accurate than the uncalibrated state.

[0037] S25. Attitude Stabilization Closed-Loop Optimization: Repeat steps S21-S24, continuously collect multi-source data, and dynamically update the attitude error model, linear regression parameters for drift prediction, and PID control parameters; when airflow interference weakens and drift drops to 0.1°, adjust the PID parameters to Kp=5.5, Ki=0.15, and Kd=1.2 to maintain stable attitude; after testing, the aircraft operated continuously for 1 hour, and the attitude deviation was always controlled within ±0.25°, with an attitude drift of only 0.3° every 10 minutes. Compared with the existing fixed-parameter PID algorithm, the operational accuracy was improved by more than 75%, and the stability was improved by more than 60%.

[0038] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0039] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An adaptive stabilization control method for aircraft attitude, characterized in that, include: Initially, multi-source attitude and environmental data acquisition of the aircraft was conducted. Based on the dynamic model of the aircraft and combined with the collected multi-source attitude data, an attitude error model is constructed to calculate the deviation between the current attitude and the preset attitude of the aircraft; an attitude drift prediction module is introduced to predict the amount of attitude drift. Based on the attitude error model, the predicted attitude drift and airflow interference intensity, an adaptive fuzzy PID controller is designed to dynamically adjust the PID control parameters. Collect environmental feature data, match it with a preset environmental feature library, calculate the attitude deviation of the aircraft relative to the environment, and use it as an auxiliary calibration benchmark for PID control to correct the cumulative error caused by inertial measurement. Continuously collect multi-source data and dynamically update the attitude error model, drift prediction parameters, and PID control parameters to form a closed-loop control for aircraft attitude stabilization.

2. The adaptive stabilization control method for aircraft attitude according to claim 1, characterized in that, The initial acquisition of multi-source attitude and environmental data of the aircraft includes: real-time acquisition of the aircraft's attitude parameters, motion parameters, air pressure and airflow speed data in the flight environment, and visual feature data of the surrounding environment through the aircraft's onboard IMU sensor, barometer, attitude sensor and high-definition vision camera.

3. The adaptive stabilization control method for aircraft attitude according to claim 2, characterized in that, Also includes: The real-time performance and accuracy of data collection are verified through a timestamp synchronization mechanism.

4. The adaptive stabilization control method for aircraft attitude according to claim 1, characterized in that, The attitude drift prediction module uses a linear regression algorithm.

5. The adaptive stabilization control method for aircraft attitude according to claim 1, characterized in that, The dynamically adjusted PID control parameters include: a proportional coefficient Kp ranging from 5.0 to 8.0, an integral coefficient Ki ranging from 0.1 to 0.3, and a derivative coefficient Kd ranging from 1.0 to 2.

5.

6. The adaptive stabilization control method for aircraft attitude according to claim 1, characterized in that, The fuzzy control rule of the adaptive fuzzy PID controller is as follows: with attitude deviation and attitude drift as input variables and PID parameter adjustment as output variables, a 5×5 fuzzy rule matrix is ​​established to perform real-time dynamic optimization of parameters.

7. The adaptive stabilization control method for aircraft attitude according to claim 1, characterized in that, The frequency of the auxiliary calibration reference is consistent with the inertial measurement frequency, and the calibration weight is dynamically adjusted according to the ambient light intensity.

8. The adaptive stabilization control method for aircraft attitude according to claim 1, characterized in that, The attitude deviation of the aircraft is controlled within ±0.3°, and the attitude drift does not exceed 0.5° every 10 minutes.

9. The adaptive stabilization control method for aircraft attitude according to claim 1, characterized in that, The attitude error model adopts the Euler angle error model, and the deviation value is expressed as Δθ=θ 当前 -θ is calculated, where θ 当前 θ represents the current attitude angle of the aircraft, and θ represents the preset attitude angle.

10. An adaptive attitude stabilization control system for an aircraft, characterized in that, Includes: Acquisition module: used for initial acquisition of multi-source attitude and environmental data of the aircraft; Prediction module: Based on the aircraft's dynamic model and the collected multi-source attitude data, it constructs an attitude error model and calculates the deviation between the aircraft's current attitude and the preset attitude; it also introduces an attitude drift prediction module to predict the amount of attitude drift. PID control module: used to design an adaptive fuzzy PID controller based on the attitude error model, predict the attitude drift amount and airflow interference intensity, and dynamically adjust the PID control parameters; Correction module: Used to collect environmental feature data, match it with a preset environmental feature library, calculate the attitude deviation of the aircraft relative to the environment, and use it as an auxiliary calibration benchmark for PID control to correct the cumulative error caused by inertial measurement. Closed-loop control module: Used to continuously collect multi-source data, dynamically update attitude error model, drift prediction parameters and PID control parameters, and form a closed-loop control to stabilize the aircraft's attitude.

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