CN-122018535-A - Wind disturbance control method and related device based on self-adaptive parameters

CN122018535ACN 122018535 ACN122018535 ACN 122018535ACN-122018535-A

Abstract

The invention provides a wind disturbance control method and a related device based on self-adaptive parameters, wherein the wind disturbance control method comprises the steps of collecting original data, inputting collected position, speed and attitude data bit observation values into an improved EKF model for wind disturbance estimation, estimating wind disturbance speed components and wind disturbance intensity under a navigation coordinate system in real time, carrying out self-adaptive correction on attitude loop and position loop PID parameters respectively, calculating and obtaining three-axis moment generation and expected total thrust according to the corrected attitude loop and position loop PID parameters, converting the expected three-axis moment and thrust into the expected speed of a motor according to a four-rotor motor thrust distribution model, outputting the expected three-axis moment and thrust to the motor through a PWM driving module, collecting motor rotating speed feedback values and unmanned plane state data in real time, and repeating the steps to form closed-loop control of collection, estimation, adjustment and control. By applying the technical scheme of the invention, the technical problems of fixed wind disturbance resistance control parameters, inaccurate wind disturbance estimation and single self-adaptive logic of the four-rotor unmanned aerial vehicle in the prior art are solved.

Inventors

QIN JIE
MA YUNPENG
WAN SHUANGAI
LIU XUN
CHEN LUZHAO
WANG JIACHENG

Assignees

北京自动化控制设备研究所

Dates

Publication Date: 20260512
Application Date: 20251229

Claims (10)

1. The wind disturbance control method based on the adaptive parameters is characterized by comprising the following steps of: Firstly, synchronously acquiring original data through a multi-sensor module carried by an unmanned aerial vehicle; Step two, an unmanned aerial vehicle dynamic model is established, an improved EKF model is obtained according to the unmanned aerial vehicle dynamic model, the position, speed and gesture data observation values collected in the step one are input into the improved EKF model to carry out wind disturbance estimation, and wind disturbance speed components and wind disturbance intensity under a navigation coordinate system are estimated in real time; Step three, respectively carrying out self-adaptive correction on PID parameters of the attitude loop and the position loop according to the wind disturbance intensity estimated in the step two, the current control attitude error and the position error; Substituting the PID parameters of the posture ring corrected in the step three into a posture control algorithm, combining an expected posture angle and an actual posture angle, calculating an expected rolling moment, a pitching moment and a yawing moment under a machine body coordinate system, substituting the PID parameters of the position ring corrected in the step three into a position control algorithm, combining an expected position and an actual position, calculating an expected total thrust, converting the expected rolling moment, the pitching moment, the yawing moment and the thrust into expected speeds of four motors according to a four-rotor motor thrust distribution model, outputting the expected rolling moment, the pitching moment, the yawing moment and the thrust to the motors through a PWM driving module, collecting motor rotating speed feedback values and unmanned plane state data in real time, repeating the steps one to four, forming closed loop control of collection-estimation-adjustment-control, and finishing wind disturbance control based on self-adaptive parameters.
2. The adaptive parameter-based wind disturbance control method according to claim 1, wherein in the third step, the adaptive correction of the posture ring PID parameter specifically comprises designing a wind disturbance-error coupling quantization model, establishing a posture ring nonlinear parameter correction formula through a wind disturbance intensity normalization factor S ω , a posture error coupling factor S att and a D parameter wind disturbance adaptation factor S d , and completing the adaptive correction of the posture ring PID parameter according to the nonlinear parameter correction formula.
3. The adaptive parameter-based wind disturbance control method according to claim 2, wherein the wind disturbance intensity normalization factor S ω is The attitude error coupling factor S att is The D parameter wind disturbance adaptation factor S d is Wherein V ω is wind disturbance intensity, V ω,lim is a preset wind disturbance limit threshold, e att is a current control attitude error, e att,ref is an attitude error reference threshold, and σ is a smoothing coefficient (generally 1-2 is taken, and the steepness of a control curve is controlled).
4. A wind disturbance control method according to claim 3 and wherein the attitude loop nonlinear parameter correction formula is Wherein, K p0 is an initial controller P parameter, K i0 is an initial controller I parameter, K d0 is an initial controller D parameter, K p is an improved P parameter, K i is an improved I parameter, K d is an improved D parameter, K 1 is a wind disturbance dominant correction coefficient, K 2 is an error auxiliary correction coefficient, K 3 is an integral suppression coefficient, and K 4 is a differential enhancement coefficient.
5. The adaptive parameter-based wind harassment control method according to any one of claims 1 to 4, wherein in the third step, performing adaptive correction on the position loop PID parameter specifically comprises: Defining a wind disturbance intensity factor G ω and a position error factor G pos ; Calculating a corrected total weight according to the wind disturbance intensity factor G ω and the position error factor G pos , and fusing wind disturbance dominance and error feedback; And performing differential correction on the position loop PID parameters according to the corrected total weight.
6. The adaptive parameter-based wind harassment control method according to claim 5, wherein in the third step, a wind harassment intensity factor G ω is set in three segments according to the total wind harassment intensity estimated by the improved extended kalman filter model EKF, the wind harassment intensity factor G ω being Wherein V ω is wind disturbance intensity according to current position error Setting a position error factor G pos in three steps, wherein the position error factor G pos is Where Δx is a positional error in the X direction, Δy is a positional error in the Y direction, and Δz is a positional error in the Z direction.
7. The adaptive parameter-based wind harassment control method of claim 6, wherein the position loop PID parameters are based on Performing differential correction, wherein w=0.6g ω +0.4G pos , W is the total correction weight, K px0 is a preset position loop reference P parameter, K ix0 is a preset position loop reference I parameter, K dx0 is a preset position loop reference D parameter, K px is a proportional parameter, K ix is an integral parameter, and K dx is a differential parameter.
8. The adaptive parameter-based wind harassment control method of claim 7, wherein the emergency parameter mode is automatically triggered when the wind harassment strength estimated in the step two is greater than a set wind harassment strength threshold.
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 steps of the computer program for implementing the adaptive parameter based wind harassment control method according to claims 1 to 8.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the adaptive parameter based wind harassment control method according to claims 1 to 8.

Description

Wind disturbance control method and related device based on self-adaptive parameters Technical Field The invention relates to the technical field of unmanned aerial vehicle control, in particular to a wind disturbance control method based on self-adaptive parameters and a related device. Background The four-rotor unmanned aerial vehicle has the advantages of simple structure, strong maneuverability, vertical take-off and landing and the like, and is widely applied to the fields of electric power inspection, environment monitoring, aerial mapping, material distribution and the like. But due to the characteristics of light weight, small inertia and weak pneumatic damping, the flying state is extremely easily influenced by external wind disturbance. The wind disturbance resistant control scheme of the existing four-rotor unmanned aerial vehicle mainly has the following limitations: 1. The main flow scheme adopts traditional PID control, the proportional (P), integral (I) and differential (D) parameters are preset and solidified on the ground and can be adapted only under specific wind conditions, and when the wind disturbance intensity is increased or the wind type is changed, the fixed parameters cannot be timely corresponding, overshoot-oscillation or response lag is easy to occur, so that the control precision is suddenly reduced. 2. The wind disturbance sensing and estimation are insufficient, namely, the wind disturbance is indirectly judged only through the attitude error of the IMU in part of the scheme, key parameters such as the speed, the direction and the like of the wind are not directly estimated, the influence degree of the wind disturbance on the unmanned aerial vehicle cannot be quantized, the parameter adjustment lacks accurate basis, and the wind disturbance resisting effect depends on experience instead of data driving. A few schemes with parameter adjustment function only aim at single control ring (such as gesture ring) to carry out parameter correction, and the adjustment rule is based on simple threshold value, does not consider the coupling relation of wind disturbance intensity-control error-parameter suitability, and parameter adjustment mismatch easily appears under complex working condition, but rather aggravates flight instability. Therefore, an anti-wind disturbance scheme capable of quantifying wind disturbance in real time and dynamically adjusting control parameters in a layered manner is urgently needed in the field, so that the problem of insufficient robustness of traditional control under variable wind conditions is solved, and reliable flight of the four-rotor unmanned aerial vehicle under a complex environment is guaranteed. Disclosure of Invention The invention provides a wind disturbance control method and a related device based on self-adaptive parameters, which can solve the technical problems of fixed wind disturbance control parameters, inaccurate wind disturbance estimation and single self-adaptive logic of a four-rotor unmanned aerial vehicle in the prior art. According to one aspect of the invention, a wind disturbance control method based on self-adaptive parameters is provided, which comprises the steps that firstly, original data are synchronously collected through a multi-sensor module carried by an unmanned aerial vehicle; establishing an unmanned aerial vehicle power model, acquiring an improved EKF model according to the unmanned aerial vehicle power model, inputting the acquired position, speed and attitude data position observation values of the first step into the improved EKF model for wind disturbance estimation, estimating wind disturbance speed components and wind disturbance intensity under a navigation coordinate system in real time, respectively carrying out self-adaptive correction on attitude ring and position ring PID parameters according to the wind disturbance intensity estimated in the second step, the current control attitude error and the position error, substituting the corrected attitude ring PID parameters of the third step into an attitude control algorithm, combining an expected attitude angle with an actual attitude angle, calculating expected rolling moment, pitching moment and yawing moment under a machine coordinate system, substituting the corrected position ring PID parameters of the third step into the position control algorithm, combining the expected position with the actual position, calculating expected total thrust, converting the expected rolling moment, pitching moment and yawing moment into the expected speeds of four motors according to a four-rotor motor thrust distribution model, outputting the expected rolling moment, pitching moment and the yawing moment to the motors through a PWM driving module, acquiring motor rotating speed feedback values and machine state data in real time, repeating the first step to the fourth step, and forming a self-adaptive control based on the wind disturbance control estimated by the