A wind-resistant control method and system for UAV based on wind field perception

Abstract

The present invention discloses a method and system for controlling wind resistance of unmanned aerial vehicles (UAVs) based on wind field perception, and relates to the technical field of wind resistance control and wind field perception of aircraft. The method comprises: obtaining local wind field information around the aircraft in real time through distributed wind speed and direction sensors, and fusing and processing to form a three-dimensional wind field distribution; generating a feedforward control quantity based on wind field disturbance identification and short-term prediction model; fusing the feedforward control with the feedback control law of the flight control system to form a composite control strategy, thereby achieving the optimization of the flight attitude stability and control response of the UAV under complex wind disturbances. Accordingly, the system comprises: a flow field perception and data processing module, a disturbance identification and feedforward control module, and a flight control execution module, which has functions such as real-time wind field perception, disturbance prediction, and pre-deployment of control. The invention can significantly improve the wind resistance performance and autonomous flight safety of UAVs in low-altitude complex wind field environments.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) control technology, and in particular to a UAV wind resistance control method and system based on wind field perception. Background Art

[0002] With the increasing demand for drones to perform diverse tasks in low-altitude environments, their flight stability under complex wind conditions has become a key technical bottleneck.

[0003] Existing technologies primarily rely on inertial measurement units (IMUs) and GPS for flight attitude control, lacking real-time, comprehensive monitoring and data fusion of the local wind field. Traditional UAV wind control methods are mostly based on a fixed aircraft model and fail to fully account for the impact of external wind speed disturbances, leading to instability during flight. Therefore, UAV wind control technology based on flow field sensing has emerged. By sensing the wind field information around the aircraft in real time and dynamically adjusting the flight control strategy, the aircraft's wind resistance and stability can be improved. Summary of the Invention

[0004] To address the above problems, the present invention proposes a UAV wind resistance control method and system based on wind field perception. By acquiring and processing local wind speed information in real time and combining feedforward and feedback composite control strategies, the wind field disturbance can be predicted and actively compensated.

[0005] The first aspect of the present invention provides a method for controlling wind resistance of a UAV based on wind field perception, comprising the following steps:

[0006] (1) Wind field perception: Using a wind speed sensor array, local wind speed vector data around the drone is collected in real time, including wind speed magnitude and direction;

[0007] (2) Flight state estimation: Combined with the inertial measurement unit (IMU) information, the real-time flight state variables of the UAV are obtained, including speed, attitude angle, and acceleration;

[0008] (3) Relative wind speed calculation and disturbance identification: Calculate the relative wind speed vector of the UAV relative to the incoming wind, and extract the wind field disturbance characteristics based on the rate of change of the relative wind speed vector;

[0009] (4) Construction of wind-resistant control law: A composite wind-resistant control law is constructed by feedforward control quantity and feedback control quantity:

[0010] (5) Control output execution: The composite anti-wind control law is sent to the UAV flight control system to realize the control of attitude adjustment and thrust regulation, compensate for wind field disturbances, and achieve stable hovering or track tracking.

[0011] Specifically, the wind speed sensor array in step (1) is composed of a plurality of independent wind speed and direction sensor probes and a data processing module. The independent wind speed and direction sensor probes are connected to the data processing module via a data line and can be distributedly arranged on the surface of the UAV according to actual conditions to collect wind field information.

[0012] Furthermore, the wind field disturbance characteristics are extracted in step (3), specifically:

[0013] The disturbance intensity is defined as the modulus of wind speed change rate: ; Define the disturbance judgment threshold: ,determined based on experimental experience; is the rate of change of the relative wind speed vector;

[0014] like , judged to be a sudden change wind disturbance;

[0015] If both meet And it lasts for 1 second without weakening: it is judged as a gust disturbance.

[0016] Furthermore, the feedforward control quantity Based on the relative wind speed vector To calculate; assuming the dynamic model of the UAV is The relationship between the thrust and attitude control quantities and wind speed changes is described by a linear system model; the calculation formula of the feedforward control quantity is: ;in, It is a feedforward gain matrix used to describe the dynamic response of the UAV. Through the feedforward gain matrix, the feedforward control quantity can adjust the thrust of the UAV in real time according to the change of wind speed. and posture , , .

[0017] Furthermore, the feedback control quantity is used to correct the flight error of the UAV in real time to ensure that the UAV can quickly return to a stable state after being disturbed by the wind field, specifically including:

[0018] Flight state error calculation: Assume that the reference state of the drone is , the current flight status is , then the state error is: ; Among them, the state vector Including variables such as the drone's speed, attitude angle, acceleration, etc.

[0019] Control gain calculation: Based on the flight error vector using the LQR controller To calculate the feedback control quantity , and its calculation formula is: ,in, is the gain matrix of the LQR controller;

[0020] LQR Feedback Control Design: Defining the State Error Weight Matrix and the control input weight matrix :

[0021] ;

[0022] By solving the minimization problem of the cost function, the feedback gain matrix is ​​calculated : ;

[0023] in, is through Riccati The matrix obtained by solving the equation is, and They are the system matrix and control input matrix in the state space model of the UAV, respectively.

[0024] The second aspect of the present invention is a wind field sensing-based UAV wind resistance control system, comprising the following modules:

[0025] Wind field perception module: Through the wind speed sensor array, it collects the local wind speed vector data around the drone in real time, including wind speed magnitude and direction;

[0026] Flight state estimation module: Combined with the inertial measurement unit (IMU) information, it obtains the real-time flight state variables of the drone, including speed, attitude angle, and acceleration;

[0027] Relative wind speed calculation and disturbance identification module: Calculates the relative wind speed vector of the drone relative to the incoming wind, and extracts wind field disturbance characteristics based on the rate of change of the relative wind speed vector;

[0028] Wind-resistant control law construction module: A composite wind-resistant control law is constructed by feedforward control quantity and feedback control quantity:

[0029] Control output execution module: sends the composite anti-wind control law to the UAV flight control system to realize the control of attitude adjustment and thrust regulation, compensate for wind field disturbances, and achieve stable hovering or track tracking.

[0030] The third aspect of the present invention: an electronic device, comprising a memory and a processor, wherein the memory is coupled to the processor; wherein the memory is used to store program data, and the processor is used to execute the program data to implement the wind field perception-based UAV wind resistance control method.

[0031] The fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the wind field perception-based UAV wind resistance control method is implemented.

[0032] The beneficial effects of the present invention are as follows:

[0033] The present invention is based on a feedforward control strategy for wind field disturbances, which can adjust the flight attitude and control parameters of the UAV in real time, effectively improving its flight stability in adverse weather conditions such as strong winds and turbulence. In addition, the system design of the present invention is modular and intelligent, and can flexibly adapt to different types of UAVs. It has high scalability and adaptability, reduces dependence on external interference, and improves the reliability and accuracy of flight control. Overall, the present invention has significant advantages in improving the flight stability and wind resistance of UAVs and enhancing flight safety, and has broad prospects for engineering applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a flow chart of the wind field sensing-based UAV wind resistance control method of the present invention;

[0035] Figure 2 This is a functional principle diagram of the UAV wind-resistant control system based on wind field perception of the present invention;

[0036] Figure 3 This is a schematic diagram of the structure of the distributed wind speed and direction sensor array of the present invention;

[0037] Figure 4 This is a schematic diagram of the application of the present invention on a typical quad-rotor drone;

[0038] Figure 5 This is a schematic diagram of the application of the present invention on a typical fixed-wing UAV;

[0039] Figure numbers: 300-data processing module; 301-wind speed and direction sensor probe; 400-quadrotor UAV; 401-wind speed and direction sensor probe; 500-fixed-wing UAV; 501-right wing wind speed and direction sensor probe, 502-tail wing wind speed and direction sensor probe, 503-left wing wind speed and direction sensor probe, 504-nose wind speed and direction sensor probe. DETAILED DESCRIPTION

[0040] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0041] Please refer to Figure 1 、 Figure 2 、 Figure 3 and Figure 4 ,in Figure 1This is a flow chart of the wind field sensing-based UAV wind resistance control method of the present invention; Figure 2 This is a functional principle diagram of the UAV wind-resistant control system based on wind field perception of the present invention; Figure 3 This is a schematic diagram of the structure of the distributed wind speed and direction sensor array of the present invention; Figure 4 The figure is a schematic diagram of the application of the present invention on a typical quadrotor UAV.

[0042] First, if Figure 1 and Figure 2 As shown, the control method mainly includes the following three modules:

[0043] The flow field sensing and data processing module 200 is used to collect and process wind speed information around the drone in real time;

[0044] The disturbance identification and feedforward control module 201 is used to identify local wind disturbance characteristics based on historical and real-time wind field data, predict short-term wind speed change trends, and generate feedforward control quantities in coordination with feedback control;

[0045] The flight control execution module 202 is used to integrate feedforward control instructions with traditional feedback control to generate control outputs to drive the aircraft to maintain stable flight.

[0046] The present invention provides a method for controlling wind resistance of a UAV based on wind field perception, and the specific process includes the following steps:

[0047] Wind Field Perception 100: Using a wind speed sensor array, it collects local wind speed vector data around the drone in real time. , including wind speed and direction;

[0048] Flight State Estimation 101: Combine information from the Inertial Measurement Unit (IMU) to obtain the real-time flight state variables of the drone, including speed , attitude angle , , and acceleration ;

[0049] Relative Wind Speed ​​Calculation and Disturbance Identification 102: Calculate the relative wind speed vector of the drone relative to the incoming wind, which is defined as:

[0050] ;

[0051] according to Rate of change Extract wind field disturbance characteristics (such as gusts and sudden changes in wind);

[0052] Wind-Resistant Control Law Construction 103: Constructing a Composite Wind-Resistant Control Law , by the feedforward control quantity and feedback control quantity composition:

[0053] ;

[0054] Control output execution 104: the composite wind resistance control law The data is sent to the UAV flight control system to realize attitude adjustment and thrust regulation, compensate for wind field disturbances, and achieve stable hovering or track tracking.

[0055] Furthermore, the present invention also provides a UAV wind resistance control system based on smooth perception, which specifically includes the following modules:

[0056] Wind field perception module: Through the wind speed sensor array, it collects the local wind speed vector data around the drone in real time, including wind speed magnitude and direction;

[0057] Flight state estimation module: Combined with the inertial measurement unit (IMU) information, it obtains the real-time flight state variables of the drone, including speed, attitude angle, and acceleration;

[0058] Relative wind speed calculation and disturbance identification module: Calculates the relative wind speed vector of the drone relative to the incoming wind, and extracts wind field disturbance characteristics based on the rate of change of the relative wind speed vector;

[0059] Wind-resistant control law construction module: A composite wind-resistant control law is constructed by feedforward control quantity and feedback control quantity:

[0060] Control output execution module: sends the composite anti-wind control law to the UAV flight control system to realize the control of attitude adjustment and thrust regulation, compensate for wind field disturbances, and achieve stable hovering or track tracking.

[0061] like Figure 3 The figure shows a schematic diagram of the structure of a distributed wind speed and direction sensor array, including a data processing module 300 and eight identical wind speed and direction sensor probes 301, independent wind speed and direction sensor probes, wherein the number of wind speed sensor probes can be appropriately increased or decreased according to actual experimental conditions.

[0062] Example 1: Figure 4 The present invention is based on a typical quadcopter drone scenario, where four wind speed and direction sensor probes 401 are arranged at four positions on the front, back, left, and right sides of the quadcopter drone 400. Each wind speed sensor independently collects wind speed and direction data to form local wind speed vector information. ,in, For the The wind speed value of each sensor, For the The wind speed direction value of each sensor.

[0063] Further:

[0064] Flight State Estimation 101: Combine information from the Inertial Measurement Unit (IMU) to obtain the real-time flight state variables of the drone, including speed , attitude angle , , and acceleration ;

[0065] Relative Wind Speed ​​Calculation and Disturbance Identification 102: Calculate the relative wind speed vector of the drone relative to the incoming wind, which is defined as:

[0066] ;

[0067] according to Rate of change Extract wind field disturbance characteristics (such as gusts and sudden changes); the specific method is as follows: through multi-sensor data, use spatial interpolation methods to generalize the wind speed information at each sensor location to the entire local wind field, and derive the wind speed field model in the area. The model form is:

[0068] ;

[0069] in For time The local wind speed field under .

[0070] Calculate the wind field gradient through the spatial distribution of the local wind speed field , and further extract the wind field disturbance characteristics. Specifically, through the wind speed change rate:

[0071] ;

[0072] Extract the dynamic characteristics of wind speed changes.

[0073] Wind-Resistant Control Law Construction 103: Constructing a Composite Wind-Resistant Control Law , by the feedforward control quantity and feedback control quantity composition:

[0074] ;

[0075] Control output execution 104: the composite wind resistance control law The data is sent to the UAV flight control system to realize attitude adjustment and thrust regulation, compensate for wind field disturbances, and achieve stable hovering or track tracking.

[0076] Example 2: Figure 5As shown in the figure, the present invention is based on a typical fixed-wing UAV scenario, where several wind speed and direction sensor probes are arranged at key positions such as the wings, tail, and nose of the fixed-wing UAV 500 (i.e. Figure 5 The right wing wind speed and direction sensor probe 501, the tail wing wind speed and direction sensor probe 502, the left wing wind speed and direction sensor probe 503, and the nose wind speed and direction sensor probe 504 are shown in the figure. Each wind speed sensor independently collects wind speed and direction data to form local wind speed vector information. ,in, For the The wind speed value of each sensor, For the The wind speed direction value of each sensor.

[0077] Further:

[0078] Flight State Estimation 101: Combine information from the Inertial Measurement Unit (IMU) to obtain the real-time flight state variables of the drone, including speed , attitude angle , , and acceleration ;

[0079] Relative Wind Speed ​​Calculation and Disturbance Identification 102: Calculate the relative wind speed vector of the drone relative to the incoming wind, which is defined as: ;

[0080] according to Rate of change Extract wind field disturbance characteristics (such as gusts and sudden changes); the specific method is as follows: through multi-sensor data, use spatial interpolation methods to generalize the wind speed information at each sensor location to the entire local wind field, and derive the wind speed field model in the area. The model form is: ;

[0081] in For time The local wind speed field under .

[0082] Calculate the wind field gradient through the spatial distribution of the local wind speed field , and further extract the wind field disturbance characteristics. Specifically, through the wind speed change rate:

[0083] ;

[0084] Extract the dynamic characteristics of wind speed changes.

[0085] Wind-Resistant Control Law Construction 103: Constructing a Composite Wind-Resistant Control Law , by the feedforward control quantity and feedback control quantity composition: ;

[0086] Control output execution 104: the composite wind resistance control law The data is sent to the UAV flight control system to realize attitude adjustment and thrust regulation, compensate for wind field disturbances, and achieve stable hovering or track tracking.

[0087] Therefore, the wind-resistance control method and system for drones based on flow field sensing, provided by this invention, accurately identifies and classifies wind field disturbances (such as gusts and sudden changes) by monitoring the rate of change of relative wind speed in real time. This significantly improves the accuracy and real-time performance of wind field sensing. Compared to traditional methods, this invention utilizes high-frequency sampling and a sensor array to achieve comprehensive wind field sensing and precise control, enhancing the drone's wind resistance capabilities in complex wind environments.

[0088] In addition, the present invention also provides an electronic device, including a memory and a processor, wherein the memory is coupled to the processor; wherein the memory is used to store program data, and the processor is used to execute the program data to implement the wind field perception-based UAV wind resistance control method.

[0089] An embodiment of the present invention further provides a computer-readable storage medium having a program stored thereon. When the program is executed by a processor, the wind field perception-based UAV wind resistance control method in the above embodiment is implemented.

[0090] The computer-readable storage medium may be an internal storage unit of any device with data processing capabilities described in any of the aforementioned embodiments, such as a hard disk or memory. The computer-readable storage medium may also be any device with data processing capabilities, such as a plug-in hard disk, a smart media card (SMC), an SD card, a flash card, etc. equipped on the device. Furthermore, the computer-readable storage medium may also include both an internal storage unit of any device with data processing capabilities and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capabilities, and may also be used to temporarily store data that has been output or is to be output.

[0091] 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 scope of protection of the present invention.

[0092] The above embodiments are intended only to illustrate the design concepts and features of the present invention. Their purpose is to enable those skilled in the art to understand the contents of the present invention and implement them accordingly. The scope of protection of the present invention is not limited to the above embodiments. Therefore, any equivalent changes or modifications made based on the principles and design concepts disclosed in the present invention are within the scope of protection of the present invention.

Claims

1. A wind-resistant control method for UAV based on wind field perception, characterized in that: The following steps are involved: (1) Wind field perception: Using a wind speed sensor array, local wind speed vector data around the drone is collected in real time, including wind speed magnitude and direction; (2) Flight state estimation: Combined with the inertial measurement unit (IMU) information, the real-time flight state variables of the UAV are obtained, including speed, attitude angle, and acceleration; (3) Relative wind speed calculation and disturbance identification: Calculate the relative wind speed vector of the UAV relative to the incoming wind, and extract the wind field disturbance characteristics based on the rate of change of the relative wind speed vector; (4) Construction of wind-resistant control law: A composite wind-resistant control law is constructed by feedforward control quantity and feedback control quantity; the feedforward control quantity u ff (t) is the relative wind speed vector To calculate; assuming the dynamic model of the UAV is M uav (t), the relationship between the thrust and attitude control variables and wind speed changes is described by a linear system model; the calculation formula of the feedforward control variable is: Among them, K ff is the feedforward gain matrix, which is used to describe the dynamic response of the UAV. Through the feedforward gain matrix, the feedforward control quantity can adjust the thrust T(t) and attitude θ(t), φ(t), ψ(t) of the UAV in real time according to the change of wind speed. The feedback control quantity is used to correct the flight error of the UAV in real time to ensure that the UAV can quickly return to a stable state after being disturbed by the wind field. Specifically, it includes: Flight state error calculation: Assume that the reference state of the drone is The current flight status is Then the state error is: Among them, the state vector Including the speed, attitude angle, and acceleration variables of the drone; Control gain calculation: Based on the flight error vector using the LQR controller To calculate the feedback control quantity u fb (t), which is calculated as follows: Where K is the gain matrix of the LQR controller; LQR feedback control design: Define the state error weight matrix Q and the control input weight matrix R: By solving the minimization problem of the cost function, the feedback gain matrix K is calculated: K=(B T PB+R) -1 B T PA Where P is the matrix obtained by solving the Riccati equation, A and B are the system matrix and control input matrix in the state space model of the UAV, respectively; (5) Control output execution: The composite anti-wind control law is sent to the UAV flight control system to realize the control of attitude adjustment and thrust regulation, compensate for wind field disturbances, and achieve stable hovering or track tracking.

2. The method according to claim 1, characterized in that In step (1), the wind speed sensor array is composed of a plurality of independent wind speed and direction sensor probes and a data processing module. The independent wind speed and direction sensor probes are connected to the data processing module via a data line and can be distributedly arranged on the surface of the drone according to actual conditions to collect wind field information.

3. The method according to claim 1, characterized in that The wind field disturbance characteristics are extracted in step (3), specifically: The disturbance intensity is defined as the modulus of wind speed change rate: Define the disturbance judgment threshold: ∈ gust ,determined based on experimental experience; is the rate of change of the relative wind speed vector; If ΔV rel >∈ gust , judged to be a sudden change wind disturbance; If ΔV is satisfied at the same time rel >∈ gust And it lasts for 1 second without weakening: it is judged as a gust disturbance.

4. A UAV wind resistance control system based on wind field perception, characterized in that: Includes the following modules: Wind field perception module: Through the wind speed sensor array, it collects the local wind speed vector data around the drone in real time, including wind speed magnitude and direction; Flight state estimation module: Combined with the inertial measurement unit (IMU) information, it obtains the real-time flight state variables of the drone, including speed, attitude angle, and acceleration; Relative wind speed calculation and disturbance identification module: Calculates the relative wind speed vector of the drone relative to the incoming wind, and extracts wind field disturbance characteristics based on the rate of change of the relative wind speed vector; Wind resistance control law construction module: a composite wind resistance control law is constructed by feedforward control quantity and feedback control quantity; the feedforward control quantity u ff (t) is the relative wind speed vector To calculate; assuming the dynamic model of the UAV is M uav (t), the relationship between the thrust and attitude control variables and wind speed changes is described by a linear system model; the calculation formula of the feedforward control variable is: Among them, K ff is the feedforward gain matrix, which is used to describe the dynamic response of the UAV. Through the feedforward gain matrix, the feedforward control quantity can adjust the thrust T(t) and attitude θ(t), φ(t), ψ(t) of the UAV in real time according to the change of wind speed. The feedback control quantity is used to correct the flight error of the UAV in real time to ensure that the UAV can quickly return to a stable state after being disturbed by the wind field. Specifically, it includes: Flight state error calculation: Assume that the reference state of the drone is The current flight status is Then the state error is: Among them, the state vector Including the speed, attitude angle, and acceleration variables of the drone; Control gain calculation: Based on the flight error vector using the LQR controller To calculate the feedback control quantity u fb (t), which is calculated as follows: Where K is the gain matrix of the LQR controller; LQR feedback control design: Define the state error weight matrix Q and the control input weight matrix R: By solving the minimization problem of the cost function, the feedback gain matrix K is calculated: K=(B T PB+R) -1 B T PA Where P is the matrix obtained by solving the Riccati equation, A and B are the system matrix and control input matrix in the state space model of the UAV, respectively; Control output execution module: sends the composite anti-wind control law to the UAV flight control system to realize the control of attitude adjustment and thrust regulation, compensate for wind field disturbances, and achieve stable hovering or track tracking.

5. An electronic device comprising a memory and a processor, wherein: The memory is coupled to the processor; wherein the memory is used to store program data, and the processor is used to execute the program data to implement the wind field perception-based UAV wind resistance control method described in any one of claims 1-3 above.

6. A computer-readable storage medium having a computer program stored thereon, wherein: When the program is executed by the processor, the wind field sensing-based UAV wind resistance control method as described in any one of claims 1 to 3 is implemented.

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