Wind disturbance control method based on adaptive parameters and related device

Abstract

The invention provides a wind disturbance control method based on adaptive parameters and a related device. The wind disturbance control method comprises the steps of collecting original data; inputting the acquired position, speed and attitude data observation values into an improved EKF model for wind disturbance estimation, and estimating a wind disturbance speed component and wind disturbance intensity under a navigation coordinate system in real time; self-adaptive correction is carried out on PID parameters of the attitude loop and the position loop; according to the corrected PID parameters of the attitude ring and the position ring, calculating to obtain a three-axis torque generation and an expected total thrust; according to a four-rotor motor thrust distribution model, the expected three-axis torque and thrust are converted into the expected speed of a motor, and the expected speed is output to the motor through a PWM driving module; and a motor rotating speed feedback value and unmanned aerial vehicle state data are collected in real time, the steps are repeated, and closed-loop control of collection-estimation-adjustment-control is formed. By applying the technical scheme of the invention, the technical problems of fixed wind disturbance resistance control parameters, inaccurate wind disturbance estimation and single adaptive logic of the quad-rotor unmanned aerial vehicle in the prior art are solved.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) control technology, and in particular to a wind disturbance control method and related apparatus based on adaptive parameters. Background Technology

[0002] Quadcopter drones, with their simple structure, high maneuverability, and vertical takeoff and landing capabilities, have been widely used in fields such as power line inspection, environmental monitoring, aerial surveying and mapping, and material delivery. However, due to their light weight, low inertia, and weak aerodynamic damping, their flight performance is highly susceptible to external wind disturbances.

[0003] Existing wind disturbance control solutions for quadcopter UAVs have the following limitations:

[0004] 1. Fixed control parameters: The mainstream solution adopts traditional PID control, whose proportional (P), integral (I), and derivative (D) parameters need to be preset and fixed on the ground, and can only be adapted to specific wind conditions. When the wind disturbance intensity increases or the wind pattern changes, the fixed parameters cannot respond in time, which easily leads to "overshoot-oscillation" or "response lag", resulting in a sharp drop in control accuracy.

[0005] 2. Insufficient wind disturbance perception and estimation: Some solutions only indirectly judge wind disturbance through the attitude error of the IMU, without directly estimating key parameters such as wind speed and direction. This makes it impossible to quantify the impact of wind disturbance on the UAV, resulting in a lack of accurate basis for parameter adjustment. The wind disturbance resistance effect relies on experience rather than data-driven.

[0006] A few solutions with parameter adjustment functions only correct parameters for a single control loop (such as the attitude loop), and the adjustment rules are based on simple thresholds, without considering the coupling relationship between "wind disturbance intensity - control error - parameter adaptability". Under complex operating conditions, parameter adjustment mismatch is likely to occur, which will exacerbate flight instability.

[0007] Therefore, there is an urgent need in this field for a wind disturbance mitigation scheme that can quantify wind disturbance in real time and dynamically adjust control parameters in layers, in order to solve the problem of insufficient robustness of traditional control under changing wind conditions and ensure the reliable flight of quadcopter UAVs in complex environments. Summary of the Invention

[0008] This invention provides a wind disturbance control method and related device based on adaptive parameters, which can solve the technical problems of fixed wind disturbance control parameters, inaccurate wind disturbance estimation, and simple adaptive logic in the prior art for quadcopter UAVs.

[0009] According to one aspect of the present invention, a wind disturbance control method based on adaptive parameters is provided. The method includes: Step 1, synchronously collecting raw data using a multi-sensor module mounted on a UAV; Step 2, establishing a UAV dynamics model, obtaining an improved EKF model based on the UAV dynamics model, and inputting the position, velocity, and attitude data collected in Step 1 as observations into the improved EKF model for wind disturbance estimation, thereby estimating the wind disturbance velocity component and wind disturbance intensity in the navigation coordinate system in real time; Step 3, adaptively correcting the PID parameters of the attitude loop and position loop based on the wind disturbance intensity estimated in Step 2, the current control attitude error, and the position error; Step 4, converting the data from Step 3 into... The corrected attitude loop PID parameters are substituted into the attitude control algorithm, and the desired roll torque, pitch torque, and yaw torque in the body coordinate system are calculated by combining the desired attitude angle and the actual attitude angle. The corrected position loop PID parameters from step three are substituted into the position control algorithm, and the desired total thrust is calculated by combining the desired position and the actual position. According to the quadcopter motor thrust distribution model, the desired roll torque, pitch torque, yaw torque, and thrust are converted into the desired speeds of the four motors and output to the motors through the PWM drive module. The motor speed feedback values ​​and UAV status data are collected in real time, and steps one to four are repeated to form a closed-loop control of "acquisition-estimation-adjustment-control" to complete wind disturbance control based on adaptive parameters.

[0010] Furthermore, in step three, the adaptive correction of the attitude loop PID parameters specifically includes: designing a wind disturbance-error coupling quantization model, and using a wind disturbance intensity normalization factor S. ω Attitude error coupling factor S att and D-parameter wind disturbance adaptation factor S d A formula for correcting the nonlinear parameters of the attitude loop is established, and the adaptive correction of the PID parameters of the attitude loop is completed based on the formula.

[0011] Furthermore, the wind disturbance intensity normalization factor S ω for Attitude error coupling factor S att for D-parameter wind disturbance adaptation factor S d for Among them, V ω V represents the wind disturbance intensity. ω，lim To preset the wind disturbance limit threshold, e att e represents the current control attitude error. att,ref σ is the attitude error reference threshold, and σ is the smoothing coefficient (generally taken as 1 to 2, controlling the steepness of the curve).

[0012] Furthermore, the formula for correcting the nonlinear parameters of the attitude loop is as follows: Among them, K p0For the initial controller P parameters, K i0 For the initial controller I parameters, K d0 For the initial controller D parameters, K p For the improved P parameters, K i For the improved I parameter, K d For the improved D parameters, k1 is the wind disturbance dominant correction coefficient, k2 is the error auxiliary correction coefficient, k3 is the integral suppression coefficient, and k4 is the differential enhancement coefficient.

[0013] Furthermore, in step three, the adaptive correction of the position loop PID parameters specifically includes: defining the wind disturbance intensity factor G. ω With position error factor G pos According to the wind disturbance intensity factor G ω With position error factor G pos Calculate the corrected total weight, integrating wind disturbance dominance and error feedback; and perform differentiated corrections on the position loop PID parameters based on the corrected total weight.

[0014] Furthermore, in step three, based on the total wind disturbance intensity estimated by the improved extended Kalman filter model EKF, the wind disturbance intensity factor G is set in three segments. ω Wind disturbance intensity factor G ω for Among them, V ω Wind disturbance intensity; based on current location error The position error factor G is set in three segments. pos Position error factor G pos for Where ΔX is the position error along the X direction, ΔY is the position error along the Y direction, and ΔZ is the position error along the Z direction.

[0015] Furthermore, the position loop PID parameters are based on Differential adjustments are made, where W = 0.6G ω +0.4G pos W is the adjusted total weight, K px0 For the preset position loop reference P parameters, K ix0 K is the preset position loop reference I parameter. dx0 For the preset position loop reference D parameters, K px K is a proportional parameter. ix K is the integration parameter. dx is the differential parameter.

[0016] Furthermore, when the wind disturbance intensity estimated in step two is greater than the set wind disturbance intensity threshold, the emergency parameter mode is automatically triggered.

[0017] According to another aspect of the present invention, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to implement the steps of the wind disturbance control method based on adaptive parameters as described above.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the wind disturbance control method based on adaptive parameters as described above.

[0019] This invention provides a wind disturbance control method based on adaptive parameters. This method collects UAV status data and wind disturbance-related information in real time, uses an improved extended Kalman filter to achieve accurate wind disturbance estimation, and then dynamically adjusts the control parameters of the attitude loop and position loop based on the wind disturbance intensity and control error, ultimately generating motor control commands adapted to the wind conditions. This invention solves the problems of lag and large overshoot in traditional fixed-parameter control under varying wind conditions, significantly improving the control accuracy and flight stability of quadrotor UAVs in complex wind environments such as gusts and turbulence. Therefore, compared with existing technologies, the wind disturbance control method based on adaptive parameters provided by this invention achieves real-time accurate estimation of wind disturbance (speed, direction, type), providing a quantitative basis for parameter adjustment; it establishes a dynamic correlation logic of "wind disturbance intensity - control error - parameter correction," and adaptively adjusts control parameters layer by layer (attitude loop, position loop), improving the control response speed and accuracy under varying wind conditions. This effectively solves the shortcomings of existing technologies, such as fixed wind disturbance control parameters for quadrotor UAVs, inaccurate wind disturbance estimation, and simple adaptive logic. Attached Figure Description

[0020] The accompanying drawings, which form part of this specification, are provided to further illustrate embodiments of the invention and, together with the textual description, explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0021] Figure 1 A flowchart of a wind disturbance control method based on adaptive parameters according to a specific embodiment of the present invention is shown. Detailed Implementation

[0022] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. 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 a part of the embodiments of the present invention, and not all of them. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the present invention or its application or use. 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] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0024] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following figures denote similar items; therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0025] like Figure 1As shown, a wind disturbance control method based on adaptive parameters is provided according to a specific embodiment of the present invention. This method includes: Step 1, synchronously collecting raw data through a multi-sensor module mounted on a UAV; Step 2, establishing a UAV dynamics model, obtaining an improved EKF model based on the UAV dynamics model, and inputting the position, velocity, and attitude data collected in Step 1 as observations into the improved EKF model for wind disturbance estimation, thereby estimating the wind disturbance velocity component and wind disturbance intensity in the navigation coordinate system in real time; Step 3, adaptively correcting the PID parameters of the attitude loop and position loop based on the wind disturbance intensity estimated in Step 2, the current control attitude error, and the position error; Step 4, converting the data collected in Step 1 into observations, and then using the improved EKF model to estimate the wind disturbance. The attitude loop PID parameters corrected in step three are substituted into the attitude control algorithm. Combining the desired attitude angle and the actual attitude angle, the desired roll torque, pitch torque, and yaw torque in the body coordinate system are calculated. The position loop PID parameters corrected in step three are substituted into the position control algorithm. Combining the desired position and the actual position, the desired total thrust is calculated. According to the quadcopter motor thrust distribution model, the desired roll torque, pitch torque, yaw torque, and thrust are converted into the desired speeds of the four motors and output to the motors through the PWM drive module. The motor speed feedback values ​​and UAV status data are collected in real time. Steps one to four are repeated to form a closed-loop control of "acquisition-estimation-adjustment-control" to complete wind disturbance control based on adaptive parameters.

[0026] This configuration method provides a wind disturbance control approach based on adaptive parameters. This method collects real-time UAV status data and wind disturbance-related information, utilizes an improved extended Kalman filter to achieve accurate wind disturbance estimation, and then dynamically adjusts the control parameters of the attitude and position loops based on the wind disturbance intensity and control error, ultimately generating motor control commands adapted to the wind conditions. This invention solves the problems of lag and large overshoot in traditional fixed-parameter control under varying wind conditions, significantly improving the control accuracy and flight stability of quadrotor UAVs in complex wind environments such as gusts and turbulence. Therefore, compared with existing technologies, the wind disturbance control method based on adaptive parameters provided by this invention achieves real-time accurate estimation of wind disturbances (speed, direction, type), providing a quantitative basis for parameter adjustment; it establishes a dynamic correlation logic of "wind disturbance intensity - control error - parameter correction," and adaptively adjusts control parameters layer by layer (attitude loop, position loop), improving the control response speed and accuracy under varying wind conditions. This effectively solves the shortcomings of existing technologies, such as fixed wind disturbance control parameters for quadrotor UAVs, inaccurate wind disturbance estimation, and simple adaptive logic.

[0027] Specifically, in this invention, to achieve wind disturbance control based on adaptive parameters, it is first necessary to synchronously collect raw data through the multi-sensor module mounted on the UAV. In this invention, the raw data includes: UAV's own state data: attitude angles (roll angle, pitch angle, yaw angle) and angular velocity output by the IMU; GPS position (X, Y, Z) and velocity (Vx, Vy, Vz); motor speed feedback values; and wind disturbance related data: atmospheric pressure change rate and airspeed related data output by the barometer.

[0028] After obtaining the raw data, a UAV dynamics model can be established. Based on the UAV dynamics model, an improved EKF model (improved extended Kalman filter model) can be obtained. The position, velocity, and attitude data collected in step one are taken as observations and input into the improved EKF model for wind disturbance estimation. The wind disturbance velocity component in the navigation coordinate system is estimated in real time.

[0029] Furthermore, after estimating the wind disturbance velocity component in the navigation coordinate system, the PID parameters of the attitude loop and position loop can be adaptively corrected based on the wind disturbance velocity component estimated in step two, the current control attitude error, and the position error.

[0030] Specifically, in step three, the adaptive correction of the attitude loop PID parameters includes: designing a wind disturbance-error coupling quantization model, and using a wind disturbance intensity normalization factor S. ω Attitude error coupling factor S att and D-parameter wind disturbance adaptation factor S d A formula for correcting the nonlinear parameters of the attitude loop is established, and the adaptive correction of the PID parameters of the attitude loop is completed based on the formula.

[0031] Among them, the wind disturbance intensity normalization factor S ω for Attitude error coupling factor S att for D-parameter wind disturbance adaptation factor S d for Among them, V ω V represents the wind disturbance intensity. ω，lim To preset the wind disturbance limit threshold, e att e represents the current control attitude error. att,ref Let σ be the attitude error reference threshold, and σ be the smoothing coefficient (typically taken as 1 to 2, controlling the steepness of the curve). The formula for correcting the attitude loop nonlinear parameters is as follows: Among them, K p0 For the initial controller P parameters, K i0 For the initial controller I parameters, K d0 For the initial controller D parameters, K p For the improved P parameters, K iFor the improved I parameter, K d The improved D parameter is defined as follows: k1 is the first correlation coefficient, k2 is the second correlation coefficient, k3 is the first correlation coefficient, and k4 is the first correlation coefficient.

[0032] Furthermore, in step three, the adaptive correction of the position loop PID parameters specifically includes: defining the wind disturbance intensity factor G. ω With position error factor G pos According to the wind disturbance intensity factor G ω With position error factor G pos Calculate the corrected total weight, integrating wind disturbance dominance and error feedback; and perform differentiated corrections on the position loop PID parameters based on the corrected total weight.

[0033] The total wind disturbance intensity is estimated based on the improved extended Kalman filter model EKF, and the wind disturbance intensity factor G is set in three segments. ω Wind disturbance intensity factor G ω for Among them, V ω This represents the wind disturbance intensity. Based on the current location error. The position error factor G is set in three segments. pos Position error factor G pos for Where ΔX is the position error along the X direction, ΔY is the position error along the Y direction, and ΔZ is the position error along the Z direction.

[0034] In this invention, the position loop PID parameters are based on Differential adjustments are made, where W = 0.6G ω +0.4G pos W is the adjusted total weight, K px0 For the preset position loop reference P parameters, K ix0 K is the preset position loop reference I parameter. dx0 For the preset position loop reference D parameters, K px K is a proportional parameter. ix K is the integration parameter. dx is the differential parameter.

[0035] Furthermore, after adaptively correcting the PID parameters of the attitude loop and position loop, the corrected attitude loop PID parameters from step three can be substituted into the attitude control algorithm. Combining the desired attitude angle and the actual attitude angle, the desired roll torque, pitch torque, and yaw torque in the body coordinate system are calculated. The corrected position loop PID parameters from step three are substituted into the position control algorithm. Combining the desired position and the actual position, the desired total thrust is calculated. Based on the quadcopter motor thrust distribution model, the desired roll torque, pitch torque, yaw torque, and thrust are converted into the desired speeds of the four motors and output to the motors via the PWM drive module. Real-time acquisition of motor speed feedback values ​​and UAV status data is performed, repeating steps one through four to form a closed-loop control of "acquisition-estimation-adjustment-control," completing wind disturbance control based on adaptive parameters. In this invention, when the wind disturbance intensity estimated in step two exceeds a set wind disturbance intensity threshold, an emergency parameter mode is automatically triggered.

[0036] According to another aspect of the present invention, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to implement the steps of the wind disturbance control method based on adaptive parameters as described above.

[0037] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the wind disturbance control method based on adaptive parameters as described above.

[0038] To gain a further understanding of the present invention, the following description is provided in conjunction with... Figure 1 The wind disturbance control method based on adaptive parameters provided by this invention will be described in detail.

[0039] like Figure 1 As shown, the purpose of this invention is to overcome the shortcomings of existing technologies, such as fixed wind disturbance control parameters for quadcopter UAVs, inaccurate wind disturbance estimation, and simple adaptive logic, and to provide a wind disturbance control method based on adaptive parameters, specifically achieving the following objectives:

[0040] 1) Achieve real-time and accurate estimation of wind disturbances (speed, direction, type) to provide a quantitative basis for parameter adjustment.

[0041] 2) Establish a dynamic correlation logic of "wind disturbance intensity - control error - parameter correction", and adaptively adjust control parameters in layers (attitude loop, position loop) to improve the control response speed and accuracy under changing wind conditions.

[0042] The wind disturbance control method based on adaptive parameters provided in this embodiment specifically includes the following steps.

[0043] Step S1: Collect raw data synchronously using the multi-sensor module mounted on the drone.

[0044] UAV's own status data: attitude angles (roll angle, pitch angle, yaw angle) and angular velocity output by IMU; GPS position (X\Y\Z) and velocity (Vx, Vy, Vz); motor speed feedback values.

[0045] Wind disturbance related data: atmospheric pressure change rate and airspeed related data output by barometer.

[0046] Step S2: Wind disturbance estimation based on improved extended Kalman filter (EKF)

[0047] A dynamic model of a quadcopter UAV is established, incorporating wind disturbance as a state variable. The model expression is as follows:

[0048]

[0049] Among them, V ω V represents the wind disturbance intensity. b Let F be the velocity in the UAV's body coordinate system. trust F is the total thrust of the motor. drag Where m is the aerodynamic drag and m is the mass of the UAV. This is the rotation matrix from the body coordinate system to the navigation coordinate system.

[0050] An improved EKF model is obtained based on the dynamic model of the quadcopter UAV. The position, velocity, and attitude data collected in step S1 are used as observations and input into the improved EKF (which introduces an adaptive noise covariance adjustment factor to dynamically correct process noise Q and measurement noise R based on measurement error). The wind disturbance velocity component in the navigation coordinate system is estimated in real time.

[0051] Step S3: Adjustment of hierarchical adaptive control parameters

[0052] Wind disturbance intensity estimated in step S2 Error with current control attitude Position error Adaptive corrections are made to the PID parameters of the attitude loop and position loop, respectively. Here, Δφ is the roll angle deviation, Δθ is the pitch angle deviation, ΔX is the position error along the X direction, ΔY is the position error along the Y direction, and ΔZ is the position error along the Z direction.

[0053] In the adaptive correction of PID parameters in the attitude control loop, the wind disturbance intensity normalization factor S is used. ω Attitude error coupling factor S att and D-parameter wind disturbance adaptation factor S dA nonlinear parameter correction formula was established. This formula enables precise fine-tuning in weak winds, saturation protection in strong winds, and dynamic error adaptation for multiple scenarios.

[0054] (3.1) Wind disturbance intensity normalization factor S ω Quantify the impact of wind disturbance on parameters, taking into account both "linear response in weak winds" and "saturation protection in strong winds":

[0055]

[0056] Among them, V ω V represents the total wind disturbance intensity. ω，lim This is the preset wind disturbance limit threshold.

[0057] (3.2) Attitude error coupling factor S att To quantify the coupling effect of attitude error and wind disturbance, a nonlinear error weight is introduced (the smaller the error, the faster the weight decays, avoiding overcorrection of small errors):

[0058]

[0059] Among them, e att e represents the current control attitude error. att,ref This is the reference threshold for attitude error.

[0060] (3.3) D-parameter wind disturbance adaptation factor S d The design incorporates a nonlinear characteristic of "strong adaptation in weak winds and stable suppression in strong winds" for the differential element (to prevent excessive differential parameters from causing system oscillations during strong winds).

[0061]

[0062] Where σ is the smoothing coefficient (usually taken as 1 to 2, controlling the steepness of the curve).

[0063] (3.4) Based on the above factors, the optimized PID parameter correction formula is as follows:

[0064] K p =K p0 ·[1+k1·S ω +k2·S ω ·S att ]

[0065]

[0066] K d =K d0 ·[1+k4·S d ]

[0067] Among them, K p0 For the initial controller P parameters, Ki0 For the initial controller I parameters, K d0 For the initial controller D parameters, K p For the improved P parameters, K i For the improved I parameter, K d The improved D parameter is defined as follows: k1 is the first correlation coefficient, k2 is the second correlation coefficient, k3 is the first correlation coefficient, and k4 is the first correlation coefficient.

[0068] The adaptive correction of position control loop parameters adopts a two-factor piecewise correction scheme of wind disturbance and error, which eliminates the need to construct a complex reference model. Specifically, it includes:

[0069] Define wind disturbance intensity factor G ω (Total wind disturbance based on EKF estimation) and location error factor G pos (Based on the deviation between the actual position and the desired position).

[0070] (1) Total wind disturbance intensity estimated based on the improved EKF model The system is divided into three sections:

[0071]

[0072] Design significance: G ω The larger the value, the stronger the wind disturbance, and the more significant the parameter correction is required.

[0073] (2) Position error factor G pos (The impact of quantification positional deviation on parameters):

[0074] Based on the current position error The system is divided into three sections:

[0075]

[0076] Design significance: G pos The larger the value, the farther the position deviates, and the faster the parameters need to be corrected.

[0077] Calculate the corrected total weight W = 0.6G ω +0.4G pos It integrates wind disturbance dominance and error feedback.

[0078] Differential correction for position PID parameters: the proportional parameter increases with the increase of wind disturbance / error, the integral parameter decreases with the increase of wind disturbance / error, and the derivative parameter increases with the increase of wind disturbance / error. The parameters are stabilized through limiting and smoothing.

[0079] proportional parameter K px =K px0 ·(1+0.8W)

[0080] Integral parameter K ix =K ix0 (1-0.5W)

[0081] Differential parameter K dx =K dx0 ·(1+0.3W)

[0082] Where W is the adjusted total weight, K px0 For the preset position loop reference P parameters, K ix0 K is the preset position loop reference I parameter. dx0 For the preset position loop reference D parameters, K px K is a proportional parameter. ix K is the integration parameter. dx is the differential parameter.

[0083] Step S4: Control command generation and closed-loop execution

[0084] Substitute the attitude loop PID parameters corrected in step S3 into the attitude control algorithm, and calculate the desired roll moment, pitch moment and yaw moment in the body coordinate system by combining the desired attitude angle and the actual attitude angle.

[0085] The corrected position loop PID parameters are substituted into the position control algorithm, and the desired total thrust is calculated by combining the desired position and the actual position.

[0086] Based on the thrust distribution model of the quadcopter motor, the three-axis torque and thrust are converted into the desired speeds of the four motors and output to the motors through the PWM drive module.

[0087] Real-time acquisition of motor speed feedback values ​​and UAV status data is returned to step S1. Steps S1 to S4 are repeated to form a closed-loop control of "acquisition-estimation-adjustment-control".

[0088] When the wind disturbance intensity V estimated in step S2 is greater than 8 m / s, the emergency parameter mode is automatically triggered.

[0089] In summary, this invention discloses a wind disturbance resistance control method for quadrotor UAVs based on adaptive parameters. This method adaptively adjusts control parameters to counteract the impact of wind disturbances of varying intensities and types on the aircraft's state. The method collects UAV state data and wind disturbance-related information in real time, utilizes an improved extended Kalman filter to achieve accurate wind disturbance estimation, and then dynamically adjusts the control parameters of the attitude and position loops based on the wind disturbance intensity and control error, ultimately generating motor control commands adapted to the wind conditions. This invention solves the problems of lag and large overshoot in traditional fixed-parameter control under varying wind conditions, significantly improving the control accuracy and flight stability of quadrotor UAVs in complex wind environments such as gusts and turbulence.

[0090] For ease of description, spatial relative terms such as "above," "on top of," "on the upper surface of," "above," etc., are used herein to describe the spatial positional relationship of a device or feature as shown in the figures to other devices or features. It should be understood that spatial relative terms are intended to encompass different orientations in use or operation beyond the orientation of the device as described in the figures. For example, if the device in the figures were inverted, a device described as "above" or "on top of" other devices or structures would subsequently be positioned as "below" or "under" other devices or structures. Thus, the exemplary term "above" can include both "above" and "below." The device may also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatial relative descriptions used herein will be interpreted accordingly.

[0091] Furthermore, it should be noted that the use of terms such as "first" and "second" to define components is merely for the purpose of distinguishing the corresponding components. Unless otherwise stated, the above terms have no special meaning and therefore should not be construed as limiting the scope of protection of this invention.

[0092] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A wind disturbance control method based on adaptive parameters, characterized in that, The wind disturbance control method based on adaptive parameters includes: Step 1: Collect raw data synchronously using the multi-sensor modules mounted on the drone; Step 2: Establish a UAV dynamics model, obtain an improved EKF model based on the UAV dynamics model, input the position, velocity, and attitude data collected in Step 1 as observation values ​​into the improved EKF model for wind disturbance estimation, and estimate the wind disturbance velocity component and wind disturbance intensity in the navigation coordinate system in real time. Step 3: Based on the wind disturbance intensity, current control attitude error, and position error estimated in Step 2, adaptively correct the PID parameters of the attitude loop and position loop respectively. Step four: Substitute the attitude loop PID parameters corrected in step three into the attitude control algorithm, and calculate the desired roll torque, pitch torque, and yaw torque in the body coordinate system by combining the desired attitude angle and the actual attitude angle; substitute the position loop PID parameters corrected in step three into the position control algorithm, and calculate the desired total thrust by combining the desired position and the actual position; according to the quadcopter motor thrust distribution model, convert the desired roll torque, pitch torque, yaw torque, and thrust into the desired speeds of the four motors, and output them to the motors through the PWM drive module; collect the motor speed feedback values ​​and UAV status data in real time, and repeat steps one to four to form a closed-loop control of "acquisition-estimation-adjustment-control" to complete wind disturbance control based on adaptive parameters.

2. The wind disturbance control method based on adaptive parameters according to claim 1, characterized in that, In step three, the adaptive correction of the attitude loop PID parameters specifically includes: designing a wind disturbance-error coupling quantization model, and using a wind disturbance intensity normalization factor S. ω Attitude error coupling factor S att and D-parameter wind disturbance adaptation factor S d A formula for correcting the nonlinear parameters of the attitude loop is established, and the adaptive correction of the PID parameters of the attitude loop is completed based on the formula.

3. The wind disturbance control method based on adaptive parameters according to claim 2, characterized in that, The wind disturbance intensity normalization factor S ω for Attitude error coupling factor S att for D-parameter wind disturbance adaptation factor S d for Among them, V ω V represents the wind disturbance intensity. ω，lim To preset the wind disturbance limit threshold, e att e represents the current control attitude error. att,ref σ is the attitude error reference threshold, and σ is the smoothing coefficient (generally taken as 1 to 2, controlling the steepness of the curve).

4. The wind disturbance control method based on adaptive parameters according to claim 3, characterized in that, The formula for correcting the nonlinear parameters of the attitude loop is as follows: Among them, K p0 For the initial controller P parameters, K i0 For the initial controller I parameters, K d0 For the initial controller D parameters, K p For the improved P parameters, K i For the improved I parameter, K d For the improved D parameters, k1 is the wind disturbance dominant correction coefficient, k2 is the error auxiliary correction coefficient, k3 is the integral suppression coefficient, and k4 is the differential enhancement coefficient.

5. The wind disturbance control method based on adaptive parameters according to any one of claims 1 to 4, characterized in that, In step three, the adaptive correction of the position loop PID parameters specifically includes: Define wind disturbance intensity factor G ω With position error factor G pos ; According to the wind disturbance intensity factor G ω With the position error factor G pos Calculate the corrected total weight, integrating wind disturbance dominance and error feedback; The position loop PID parameters are differentially modified based on the modified total weight.

6. The wind disturbance control method based on adaptive parameters according to claim 5, characterized in that, In step three, the wind disturbance intensity factor G is set in three segments based on the total wind disturbance intensity estimated by the improved extended Kalman filter model EKF. ω The wind disturbance intensity factor G ω for Among them, V ω Wind disturbance intensity; based on current location error The position error factor G is set in three segments. pos Position error factor G pos for Where ΔX is the position error along the X direction, ΔY is the position error along the Y direction, and ΔZ is the position error along the Z direction.

7. The wind disturbance control method based on adaptive parameters according to claim 6, characterized in that, Position loop PID parameters based on Differential adjustments are made, where W = 0.6G ω +0.4G pos W is the adjusted total weight, K px0 For the preset position loop reference P parameters, K ix0 K is the preset position loop reference I parameter. dx0 For the preset position loop reference D parameters, K px K is a proportional parameter. ix K is the integration parameter. dx is the differential parameter.

8. The wind disturbance control method based on adaptive parameters according to claim 7, characterized in that, When the wind disturbance intensity estimated in step two is greater than the set wind disturbance intensity threshold, the emergency parameter mode is automatically triggered.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the steps of the wind disturbance control method based on adaptive parameters as described in claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the wind disturbance control method based on adaptive parameters as described in claims 1 to 8.

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