Multi-rotor unmanned aerial vehicle stability control method suitable for multiple environments

Abstract

The invention discloses a multi-rotor unmanned aerial vehicle stability control method suitable for multiple environments, and relates to the technical field of unmanned aerial vehicle stability control, and the method comprises the following steps: obtaining the environmental factor data of the height of an unmanned aerial vehicle in a flight stage and the internal detection data of a fuselage; establishing an evaluation coefficient of height flight attitude stability of the unmanned aerial vehicle according to the factor data of the environment where the unmanned aerial vehicle is located and the fuselage internal detection data; and comparing the flight attitude stability evaluation coefficient with a preset flight stability coefficient, judging whether the unmanned aerial vehicle flies stably at the height or not, and warning and adjusting the unstable flight attitude in time. The problem that when the unmanned aerial vehicle continuously rises in the flight process, the flight attitude cannot be kept or adjusted to be stable in time is solved.

Description

Technical Field

[0001] The present invention relates to the field of multi-rotor unmanned aerial vehicle control, and more specifically, to a multi-rotor unmanned aerial vehicle stabilization control method suitable for multiple environments. Background Art

[0002] Unmanned aerial vehicles (UAVs) are unmanned aircraft that are controlled by radio remote control equipment and their own program algorithm control devices. After years of rapid development of UAV technology, the scenarios in which multi-rotor UAVs are used are becoming more and more complex, such as aerial photography, agricultural monitoring, vegetation protection, disaster relief, wildlife observation, surveying and mapping, and power inspections.

[0003] The flight control of UAVs is one of the main issues in the field of UAV research. On the technical level, the flight control of UAVs involves many aspects, including the principles, technologies and related algorithms of flight control, navigation and path planning. The basic principle of flight control is to detect the flight status and environmental information of the UAV through sensors and feed it back to the controller. The controller calculates the control instructions of the UAV based on the feedback information and task requirements and sends them to the actuator. The actuator modulates the flight attitude and speed of the UAV according to the instructions of the controller to achieve stable control of the UAV.

[0004] When the drone continues to rise in altitude during flight, it is affected by external crosswind environmental factors, causing the drone to be unable to maintain or adjust the stability of its flight attitude in time, affecting the balance and stability of the drone.

[0005] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a multi-rotor UAV stabilization control method suitable for multiple environments, which solves the problem that the UAV cannot maintain or adjust the stability of the flight attitude in time when the altitude continues to rise during the flight through the UAV stabilization control.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A multi-rotor UAV stable control method suitable for multiple environments comprises the following steps: obtaining environmental factor data of the altitude of the UAV during the flight phase and internal detection data of the fuselage; establishing an evaluation coefficient of the stability of the UAV's altitude flight attitude according to the environmental factor data of the UAV and the internal detection data of the fuselage; comparing the flight attitude stability evaluation coefficient with a pre-set flight stability coefficient to judge whether the UAV is flying stably at this altitude, and issuing a warning and making adjustments in time for unstable flight attitude.

[0009] In a preferred embodiment, the environmental factor data for obtaining the altitude of the UAV during the flight phase includes a crosswind detection coefficient, and the internal fuselage detection data includes a yaw angle parameter and a motor power stability coefficient.

[0010] In a preferred embodiment, the specific method for obtaining the flight attitude stability assessment coefficient is as follows: the flight attitude stability assessment coefficient is calculated based on a preset flight attitude stability assessment coefficient calculation formula according to the UAV crosswind detection coefficient combined with the yaw angle coefficient and the motor power stability coefficient.

[0011] In a preferred embodiment, the flight attitude stability evaluation coefficient is compared with a pre-set flight stability coefficient to determine whether the UAV is flying stably at this altitude, and a warning is issued in time and adjustments are made to the unstable flight attitude. Specifically, when it is determined that the altitude is unstable due to the influence of crosswind, the alarm sends a feedback signal and lowers the altitude. By processing the data of the deviation angle, the stable flight of the UAV is ensured and the UAV is kept on the predetermined flight trajectory; whether the UAV is stable is determined in real time, and if it is stable, the altitude reduction and the adjustment of the deviation angle are stopped.

[0012] In a preferred embodiment, the specific method for obtaining the crosswind detection coefficient is as follows: obtain the first data of the drone environment, the first environmental data including the drone height, the wind speed at each height, the lift of the drone and the lift coefficient; calculate the wind speed influence coefficient based on the first data of the drone environment based on a preset wind speed influence coefficient calculation formula; obtain the second data of the drone environment, the second data of the drone environment including the estimated wind speed that the drone can withstand for stability, the average flight speed of the drone and the reference area of ​​the drone; calculate the crosswind detection coefficient based on the preset crosswind detection coefficient calculation formula according to the wind speed influence coefficient combined with the second data of the drone environment.

[0013] In a preferred embodiment, the specific method for obtaining the yaw angle coefficient is as follows: collect the UAV yaw angle influence data, the UAV yaw angle influence data includes the external wind speed, the UAV cruising speed and the UAV wind direction angle; calculate the yaw angle coefficient based on the UAV yaw angle influence data based on a preset yaw angle coefficient calculation formula.

[0014] In a preferred embodiment, the specific method for obtaining the motor power stability coefficient is as follows: obtain the drone's own data, the drone's own data including the tilt angle during unmanned flight, the drone's airspeed, and the efficiency, gross weight, battery, and load of the motor; calculate the motor power stability coefficient by establishing a formula for calculating the motor power stability coefficient based on a big data model according to the drone's own data.

[0015] The technical effects and advantages of the multi-rotor UAV stabilization control method applicable to multiple environments of the present invention are as follows:

[0016] 1. Through a technology of multi-rotor UAV stable control method suitable for multiple environments, the unstable flight attitude of the UAV caused by the influence of the environment at high altitude is effectively solved, ensuring that the multi-rotor UAV can fly stably in complex and changeable environments.

[0017] 2. By real-time monitoring of the flight attitude stability evaluation coefficient, it provides better dynamic response and stability in a rapidly changing environment, thereby accurately controlling the attitude and position of the drone. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 The present invention is a schematic structural diagram of a multi-rotor UAV stabilization control method suitable for multiple environments. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Embodiment 1, Figure 1 The present invention provides a multi-rotor UAV stabilization control method suitable for multiple environments, comprising the following steps:

[0021] S1, obtains the environmental factor data and internal detection data of the drone during the flight phase.

[0022] In this embodiment, the environmental factor data includes the crosswind detection coefficient. When the UAV continues to rise, the change in crosswind will have many impacts on the flight performance and stability of the UAV. The internal fuselage detection data of the UAV when it is unstable includes the yaw angle parameter and the motor power stability coefficient.

[0023] By analyzing the crosswind detection coefficient, the following advantages can be achieved for the UAV to maintain a stable flight at high altitudes:

[0024] Enhance the stability of the drone: Through the crosswind detection coefficient, the drone can understand the strength and direction of the crosswind in real time. This data enables the drone to adjust its attitude and flight control to offset the interference caused by the crosswind and maintain a stable flight attitude.

[0025] Enhanced drone flight performance: When flying at high altitudes, crosswinds may have a significant impact on the flight performance of the drone. Through the crosswind detection coefficient, the drone can dynamically adjust its flight strategy and flight speed to maximize flight efficiency, allowing the drone to maintain optimal performance under different wind speeds.

[0026] Enhanced mission stability: When performing tasks at high altitudes, such as remote sensing or scientific measurements, maintaining a stable attitude is critical to ensuring data accuracy. The crosswind detection factor can help the drone maintain a stable attitude under complex wind speed conditions, thereby improving mission stability and data reliability.

[0027] Improve the system's adaptive capability: The analysis of the crosswind detection coefficient helps the UAV flight control system to adaptively adjust flight parameters to cope with crosswinds of different strengths and directions. This adaptive capability can ensure that the UAV maintains stable flight in various high-altitude environmental conditions.

[0028] The specific method for obtaining the crosswind detection coefficient is as follows:

[0029] Acquire first environmental data of the drone, wherein the first environmental data includes the altitude of the drone, wind speed at each altitude, lift of the drone, and lift coefficient;

[0030] Calculate the wind speed influence coefficient based on the preset wind speed influence coefficient calculation formula according to the drone environment data;

[0031] Acquire second drone environment data, wherein the second drone environment data includes an estimated wind speed that the drone can withstand for stability, an average flight speed of the drone, and a reference area of ​​the drone;

[0032] The crosswind detection coefficient is calculated based on the wind speed influence coefficient and the second data of the UAV environment based on the preset crosswind detection coefficient calculation formula.

[0033] The specific calculation formula of the wind speed influence coefficient is as follows:

[0034]

[0035] The calculation formula of the crosswind detection coefficient is as follows:

[0036]

[0037] In the formula, is the wind speed influence coefficient, where i represents the wind value parameter at the current height when the drone rises to a certain height, i = 1, 2, 3, ..., n is a positive integer, H is the current height of the drone, L is the lift of the drone, ρ is the air density, A is the reference area of ​​the drone, C Lis the lift coefficient, V is the crosswind detection coefficient, and M is the critical wind value of stability that the UAV can withstand. Indicates the average flight speed of the drone.

[0038] It should be noted that the lift of a drone refers to the upward force generated by aerodynamics during the flight of the drone, which enables the drone to overcome gravity and stay in the air. This lift is mainly achieved through the interaction between the drone's rotors and other propulsion devices and the air. The drone's lift data is monitored and obtained in real time through the drone's flight control system. The reference area of ​​a drone refers to the effective area of ​​the drone's wing, which is obtained through the drone's design drawings, and the air density is obtained by looking up real-time meteorological data in the current environment. The critical wind value of a drone refers to the maximum wind value that causes the stability of the drone to change during high-altitude flight as tested by machine experiments.

[0039] In drones, the yaw angle refers to the rotation angle of the drone relative to its initial heading. It indicates the degree to which the heading of the drone deviates from its initial direction. Adjusting and correcting the yaw angle plays a vital role in the stability of the drone.

[0040] Analyzing the yaw angle coefficient has the following advantages for the UAV to maintain a stable flight at high altitudes:

[0041] Optimize control direction: Monitoring the yaw coefficient enables the drone to accurately control its heading. By analyzing the yaw coefficient, the drone's control system can adjust the angles of the rudder and thruster to ensure that the drone can better maintain a stable heading at high altitudes, reduce yaw changes caused by air disturbances or improper control, and achieve precise control of flight heading and angle.

[0042] Enhance flight safety: By monitoring the yaw coefficient, drones can detect and avoid flight risks caused by heading deviation. In the presence of strong winds or air currents, drones can identify abnormal yaw angles, make timely adjustments and sound alarms, thereby improving flight safety and reducing the risk of loss of control.

[0043] Optimize energy consumption: Stable yaw control means that the drone can reduce unnecessary adjustments and corrections during flight, which helps reduce energy consumption. Especially when flying at high altitudes, energy resources are more precious. Therefore, optimizing the yaw angle coefficient can help extend the drone's flight time and improve energy efficiency.

[0044] Reduce mechanical load: Effective yaw angle control can reduce the mechanical load caused by frequent attitude adjustments. By adjusting and optimizing the yaw angle coefficient, the drone's control system can adjust the attitude in a smoother manner, reducing the mechanical burden on the motor and control system, and increasing the reliability and life of the system.

[0045] Reduce drifting posture: When flying at high altitudes, changes in wind speed and airflow cause the drone's attitude to drift. A higher yaw angle coefficient can effectively reduce this drift, allowing the drone to maintain a stable attitude in various airflow environments.

[0046] The specific method for obtaining the yaw angle coefficient is as follows:

[0047] The yaw angle influence data of the UAV is obtained, wherein the yaw angle influence data includes the external wind speed, the cruising speed of the UAV and the wind direction angle of the UAV.

[0048] Calculating the yaw angle coefficient based on the yaw angle influence data based on a preset yaw angle coefficient calculation formula;

[0049] The calculation formula of the yaw angle coefficient is as follows:

[0050]

[0051] In the formula, WA is the yaw angle coefficient, V a is the external wind speed of the drone at this time, V g is the cruising speed of the UAV, and θ is the wind direction angle between the flight direction of the UAV and the wind speed.

[0052] In drones, motor power refers to the power output provided by the motor to drive the drone's rotors or other propulsion systems. The amount of power directly affects the performance of the drone, including its flight capabilities, stability, and endurance.

[0053] Analyzing the stability coefficient of motor power to adjust the high-altitude flight stability of the UAV has the following advantages:

[0054] Evaluate the operating status of the drone: By monitoring the motor power, it is possible to promptly detect the unstable flight posture or abnormal heading of the drone caused by the crosswind at high altitude, and respond in time.

[0055] Adjustment of the current state of the drone: During high-altitude flight, stable power output helps ensure that the drone can effectively maintain its heading and flight when facing wind speed changes. When the power stability fluctuates, the output of the motor power can be adjusted to maintain the stable lift and propulsion of the drone, helping the drone to fly more stably.

[0056] Improve flight control response: The power stability factor directly affects the response speed and accuracy of the flight control system. When the motor power is stable, it can respond to flight control instructions faster and more accurately. This is especially important at high altitudes because conditions at high altitudes change dramatically. Stable power output allows the drone to adjust its attitude more effectively and maintain stable flight.

[0057] Extended motor life: Motors with high power stability coefficients can operate in a stable operating state, reducing mechanical stress caused by power fluctuations. This not only helps to maintain a stable flight attitude, but also extends the life of the motor and reduces the frequency of maintenance and replacement.

[0058] Improve flight safety: The motor with a stable coefficient can effectively reduce the unexpected flight state caused by power fluctuations, enhance the safety of the UAV in high-altitude flight, and reduce the risk of flight accidents.

[0059] The specific method for obtaining the stability coefficient of the motor power is as follows:

[0060] Obtain the drone's own data, including the tilt angle during unmanned flight, the drone's airspeed, and the efficiency of the engine, total weight, battery, load, etc.

[0061] According to its own data and based on the big data model, a formula for calculating the stability coefficient of the motor power is established to calculate the stability coefficient of the motor power.

[0062] The calculation formula of the stability coefficient of the motor power is as follows:

[0063]

[0064] In the formula, P is the power stability coefficient of the UAV motor, W is the total weight of the UAV including batteries, loads, etc., θ0 is the tilt angle of flight, and V s is the airspeed of the drone and η is the efficiency of the motor.

[0065] It should be noted that the total weight of the drone and the efficiency of the motor are obtained from the drone manufacturer's data, and the flight tilt angle and the airspeed of the drone are obtained from the gyroscope and sensors.

[0066] When θ0 becomes larger and larger, the motor needs to output power in order to stabilize the UAV's smooth flight, and the coefficient of power P will become larger and larger, indicating that the stability of the UAV is affected. When the tilt angle θ0 becomes smaller and smaller, the stability coefficient of the motor power P becomes more and more stable, indicating that the UAV is affected very little, and the motor does not need too much power to maintain stable flight, and the UAV's flight attitude also tends to be stable.

[0067] S2, establish the UAV altitude flight attitude stability assessment coefficient based on the UAV environment factor data and the fuselage internal detection data;

[0068] The specific method for obtaining the evaluation coefficient of the flight attitude stability is to obtain the UAV crosswind detection coefficient, the yaw angle coefficient, the motor power stability coefficient, the UAV power standard parameters for maintaining stable flight, and the motor standard data for maintaining normal and stable flight.

[0069] The UAV altitude flight attitude stability evaluation coefficient formula is:

[0070]

[0071] In the formula, T is the standard power parameter for stable flight of the UAV, and E is the standard data for motor stability. It should be noted that, according to the formula, the larger the yaw angle coefficient WA of the UAV, the larger the motor power stability coefficient P, the larger the crosswind detection coefficient V, that is, the larger the performance value of the flight stability evaluation coefficient Q, indicating that the UAV is greatly affected by the crosswind at this time, and the flight attitude becomes unstable; the smaller the yaw angle WA of the UAV, the smaller the motor power stability coefficient P, the smaller the crosswind detection coefficient V, that is, the smaller the performance value of the flight stability evaluation coefficient Q, indicating that the UAV's flight attitude tends to be stable at this time.

[0072] S3 compares the flight attitude stability assessment coefficient with the pre-set flight stability coefficient to determine whether the UAV is flying stably at this altitude, and issues a warning and makes adjustments in time for unstable flight attitude.

[0073] When the drone is rising in the sky, it will be affected by the environment, such as crosswind, which will cause the airflow in the drone to change, causing the drone to deviate in the flight direction, resulting in a decrease in the stability of the drone and a change in the flight attitude. When the flight attitude stability evaluation coefficient is higher than the preset stable flight coefficient, it indicates that the crosswind at the height of the drone affects the stable attitude of the drone. At this time, in order to stabilize the flight attitude of the drone, the alarm will send a feedback signal, indicating that the height has reached the maximum height of the flight, and will reduce the power of the drone's rotor to reduce the height, reaching the height where the drone is little affected by the crosswind and can fly in a stable attitude. By processing the data of the deviation angle, the drone's rotor is adjusted to increase or reduce the tilt angular velocity to achieve a suitable flight posture, to ensure the stable flight of the drone and keep the drone on the predetermined flight trajectory. When the flight attitude stability evaluation coefficient is less than or equal to the preset stable flight coefficient, it indicates that the height of the drone is less affected by the crosswind at this time, and the flight attitude of the drone will not change. The drone can fly stably at this height.

[0074] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0075] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0076] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0077] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0078] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0079] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-rotor UAV stabilization control method suitable for multiple environments, characterized in that: The steps include: Obtain environmental factor data and internal fuselage inspection data of the drone during the flight phase; The evaluation coefficient of the stability of the UAV's altitude flight attitude is established based on the factor data of the UAV's environment and the internal detection data of the fuselage; The flight attitude stability assessment coefficient is compared with the pre-set flight stability coefficient to determine whether the UAV is flying stably at this altitude, and timely warnings and adjustments are issued for unstable flight attitudes.

2. A multi-rotor UAV stabilization control method suitable for multiple environments as claimed in claim 1, characterized in that: The environmental factor data of the drone includes first environmental data and second environmental data, and the internal detection data of the fuselage includes yaw angle parameters and motor power stability coefficient.

3. A multi-rotor UAV stabilization control method suitable for multiple environments as claimed in claim 1, characterized in that: The specific method for obtaining the flight attitude stability evaluation coefficient is as follows: The flight attitude stability evaluation coefficient is calculated based on the preset flight attitude stability evaluation coefficient calculation formula according to the UAV crosswind detection coefficient combined with the yaw angle coefficient and the motor power stability coefficient.

4. A multi-rotor UAV stabilization control method suitable for multiple environments as claimed in claim 1, characterized in that: The flight attitude stability evaluation coefficient is compared with the pre-set flight stability coefficient to determine whether the UAV is flying stably at this altitude, and a warning is issued in time for unstable flight attitude and adjustments are made, specifically: When it is judged that the altitude is unstable, the alarm sends a feedback signal and lowers the altitude. By processing the data of the deviation angle, the stable flight of the UAV is ensured and the UAV is kept on the predetermined flight trajectory. Determine in real time whether the drone is stable. If it is stable, stop lowering the altitude and adjusting the deviation angle.

5. A multi-rotor UAV stabilization control method suitable for multiple environments as claimed in claim 3, characterized in that: The specific method for obtaining the crosswind detection coefficient is as follows: Acquire first environmental data of the drone, wherein the first environmental data includes the altitude of the drone, wind speed at each altitude, lift of the drone, and lift coefficient; Calculate the wind speed influence coefficient based on the first data of the drone environment and based on a preset wind speed influence coefficient calculation formula; Acquire second drone environment data, where the second drone environment data includes an estimated wind speed that the drone can withstand for stability, an average flight speed of the drone, and a reference area of ​​the drone; The crosswind detection coefficient is calculated based on the wind speed influence coefficient and the second data of the UAV environment based on the preset crosswind detection coefficient calculation formula.

6. A multi-rotor UAV stabilization control method applicable to multiple environments as claimed in claim 3, characterized in that: The specific method for obtaining the yaw angle coefficient is as follows: Collecting the yaw angle impact data of the UAV, wherein the yaw angle impact data of the UAV includes the external wind speed, the cruising speed of the UAV and the wind direction angle of the UAV; The yaw angle coefficient is calculated based on the preset yaw angle coefficient calculation formula according to the UAV yaw angle influence data.

7. A multi-rotor UAV stabilization control method applicable to multiple environments as claimed in claim 3, characterized in that: The specific method for obtaining the motor power stability coefficient is as follows: Obtaining the drone's own data, including the tilt angle during unmanned flight, the drone's airspeed, and the efficiency of the engine, gross weight, battery, and load; According to the UAV's own data and based on the big data model, the stability coefficient calculation formula of the motor power is established to calculate the stability coefficient of the motor power.

8. A multi-rotor UAV stabilization control method applicable to multiple environments as claimed in claim 5, characterized in that: The specific calculation formula of the crosswind detection coefficient is as follows: The specific calculation formula of the wind speed influence coefficient is as follows: The specific calculation formula of the crosswind detection coefficient is as follows: In the formula, is the wind speed influence coefficient, where i represents the wind value parameter at the current height when the drone rises to a certain height, i = 1, 2, 3, ..., n is a positive integer, H is the current height of the drone, L is the lift of the drone, ρ is the air density, A is the reference area of ​​the drone, X L is the lift coefficient, V is the crosswind detection coefficient, M is the critical wind value of stability that the UAV can withstand, A is the reference area of ​​the UAV, Indicates the average flight speed of the drone.

9. The method for stabilizing and controlling a multi-rotor UAV applicable to multiple environments as claimed in claim 6 is characterized in that: The specific calculation formula of the yaw angle coefficient is as follows: In the formula, WA is the yaw angle coefficient, V a is the external wind speed of the drone at this time, V g is the cruising speed of the UAV, and θ is the wind direction angle between the flight direction of the UAV and the wind speed.

10. The method for stabilizing and controlling a multi-rotor UAV applicable to multiple environments as claimed in claim 7, characterized in that: The specific calculation formula of the motor power stability coefficient is as follows: In the formula, P is the power stability coefficient of the UAV motor, W is the total weight of the UAV, θ0 is the tilt angle of flight, V s is the airspeed of the drone and η is the efficiency of the motor.

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