A method for adaptive adjustment of flight attitude of a UAV

Abstract

This invention relates to the field of unmanned aerial vehicle (UAV) flight control technology, and discloses a method for adaptive adjustment control of UAV flight attitude. Addressing the problems of unstable flight and untimely or inaccurate attitude adjustment of UAVs in complex weather conditions and variable terrain, this method deploys multiple types of sensors on the UAV to collect flight data in real time, including information such as air pressure, wind speed and direction, and attitude angles, and transmits the data to a central processing unit (CPU). The CPU analyzes and processes the data based on a preset algorithm model. When it detects a deviation in flight attitude from a set value, it automatically adjusts the UAV's rotor speed, wing angle, and other actuators to achieve rapid adaptive adjustment of flight attitude. This method effectively improves the flight stability and reliability of UAVs in complex environments and can be widely applied in fields such as aerial surveying, material delivery, and environmental monitoring, ensuring efficient and safe operation of UAVs.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) control technology, specifically to a method for adaptive adjustment control of UAV flight attitude, which is suitable for improving the flight stability and control accuracy of UAVs in complex environments. Background Technology

[0002] With the widespread application of drone technology, it plays a vital role in aerial photography, logistics, agriculture, surveying, and other fields. However, in actual flight, drones often face challenges from complex weather conditions (such as strong winds and airflow disturbances) and varied terrain. Existing drone flight attitude control methods mostly employ fixed-parameter control strategies, failing to dynamically adjust according to real-time environmental changes and flight status. This leads to untimely and inaccurate attitude adjustments when encountering sudden interference, easily resulting in flight instability or even loss of control, significantly limiting the application scope and safety of drones. Therefore, there is an urgent need for a control method that can adaptively adjust flight attitude based on actual conditions. Summary of the Invention

[0003] The purpose of this invention is to provide an adaptive adjustment control method for the flight attitude of unmanned aerial vehicles (UAVs) to solve the problems of inflexible attitude control and poor stability of UAVs in complex environments in the prior art.

[0004] This method deploys multiple types of sensors on a drone to collect real-time flight data such as air pressure, wind speed and direction, and attitude angles, and transmits the collected data to a central processing unit (CPU). The CPU analyzes and processes the data based on a pre-defined algorithm model. When it detects that the drone's flight attitude deviates from a set value, it automatically adjusts the drone's rotor speed, wing angle, and other actuators to achieve rapid adaptive adjustment of the flight attitude. The multiple types of sensors include air pressure sensors, wind speed and direction sensors, gyroscopes, and accelerometers; the pre-defined algorithm model uses a Kalman filter algorithm to fuse the sensor data; and a PID control algorithm is used to achieve precise adjustment of the actuators.

[0005] The method provided by this invention can effectively improve the anti-interference ability and flight stability of UAVs in complex environments, and can be widely applied to various UAV operation scenarios to ensure the safe and efficient operation of UAVs. Detailed Implementation

[0006] Sensor Deployment and Data Acquisition: Barometric pressure sensors, wind speed and direction sensors, gyroscopes, and accelerometers are installed at appropriate locations on the drone's fuselage. The barometric pressure sensor collects real-time air pressure data at the drone's altitude to determine flight altitude; the wind speed and direction sensor acquires ambient wind speed and direction information; the gyroscope and accelerometer monitor real-time changes in the drone's attitude angles. Each sensor transmits the collected data to the central processing unit at a set frequency.

[0007] Data processing: After receiving sensor data, the central processing unit uses the Kalman filter algorithm to fuse multi-source data, remove noise interference, and improve the accuracy and reliability of the data. Then, based on the processed data, it analyzes whether the current flight attitude of the UAV deviates from the set value according to the preset attitude judgment rules.

[0008] Attitude adjustment: When the central processing unit determines that the UAV's flight attitude deviates from the set value, it starts the PID control algorithm. Based on the magnitude and trend of the attitude deviation, it calculates the specific parameters for adjusting the rotor speed and wing angle, and sends control commands to the corresponding actuators. The actuators respond quickly to the commands, adjust the rotor speed and wing angle, and restore the UAV's attitude to the set state, thus realizing adaptive adjustment of flight attitude.

Claims

1. A method for adaptive adjustment control of flight attitude of an unmanned aerial vehicle (UAV), characterized in that, include: Deploy multiple types of sensors on drones to collect real-time data on air pressure, wind speed and direction, and attitude angles; The data collected by the sensors is transmitted to the central processing unit (CPU). The CPU analyzes and processes the data based on a preset algorithm model. When the analysis results show that the UAV's flight attitude deviates from the set value, the CPU automatically adjusts the UAV's rotor speed and wing angle actuators to achieve adaptive adjustment of the flight attitude.

2. The UAV flight attitude adaptive adjustment control method according to claim 1, characterized in that, The preset algorithm model uses the Kalman filter algorithm to fuse the data collected by the sensor.

3. The UAV flight attitude adaptive adjustment control method according to claim 1, characterized in that, When the central processing unit adjusts the rotor speed and wing angle, it uses a PID control algorithm to achieve precise adjustment.