CN-121978969-A - Self-adaptive rigid force response control method and device for carrying unmanned aerial vehicle

CN121978969ACN 121978969 ACN121978969 ACN 121978969ACN-121978969-A

Abstract

The application provides a self-adaptive rigid force response control method and device for a carrying unmanned aerial vehicle. The method comprises the steps of obtaining a real-time state of the carrying unmanned aerial vehicle in a current calculation period, generating a basic control instruction corresponding to expected attitude information according to the real-time attitude information and the basic controller, adaptively generating a feedforward compensation control instruction corresponding to a deterministic disturbance component in the rigidity force according to the deterministic disturbance force, the deterministic disturbance force moment and the deterministic disturbance feedforward controller, adaptively predicting a feedback compensation control instruction corresponding to an uncertainty disturbance component in the rigidity force in the current calculation period based on a flight state of the carrying unmanned aerial vehicle after receiving the comprehensive control instruction in the last calculation period and the feedforward compensation control instruction in the current calculation period, and fusing the basic control instruction, the feedforward compensation control instruction and the feedback compensation control instruction to generate an adaptive comprehensive control instruction of the carrying unmanned aerial vehicle. The self-adaptation is responded to the rigid force of the carrying unmanned aerial vehicle, and the flight stability and the anti-interference capability are improved.

Inventors

ZHENG CANLUN
HU TIANZHE
LI CAIYI
WANG ZHIKUN
XU JINMING
ZHAO SHIYU

Assignees

浙江大学
西湖大学

Dates

Publication Date: 20260505
Application Date: 20260407

Claims (10)

1. A method for adaptive rigid force response control of a delivery unmanned aerial vehicle, the method comprising: acquiring a real-time state of the carrying unmanned aerial vehicle in a current calculation period, wherein the real-time state at least comprises a deterministic disturbance force and a deterministic disturbance moment in real-time attitude information and rigidity force of the carrying unmanned aerial vehicle; Generating a basic control instruction corresponding to the expected posture information according to the real-time posture information and the basic controller; Adaptively generating a feedforward compensation control instruction corresponding to a deterministic disturbance component in the rigid force according to the deterministic disturbance force, the deterministic disturbance moment and a deterministic disturbance feedforward controller; Based on the flight state of the carrying unmanned aerial vehicle after receiving the comprehensive control instruction in the previous calculation period and the feedforward compensation control instruction in the current calculation period, adaptively predicting a feedback compensation control instruction corresponding to an uncertainty disturbance component in the rigidity force in the current calculation period, wherein the prediction is at least based on the uncertainty disturbance force and the uncertainty disturbance moment; and fusing the basic control instruction, the feedforward compensation control instruction and the feedback compensation control instruction to generate a self-adaptive comprehensive control instruction of the carrying unmanned aerial vehicle.
2. The method of claim 1, wherein the carrier drone comprises at least a carrier platform, wherein a plurality of sensors are provided on a bottom surface of the carrier platform, and wherein the acquiring the real-time status of the carrier drone for the current computing period comprises: the plurality of sensors simultaneously detect a force variation above the carrying platform; positioning the position information and the quality information of the carrying object above the carrying platform according to the stress variation; Calculating the deterministic disturbance force of the carrying unmanned aerial vehicle based on the quality information; And calculating the deterministic disturbance moment according to the position information and the deterministic disturbance force.
3. The method of claim 1, wherein generating basic control instructions corresponding to desired gesture information from the real-time gesture information and basic controller comprises: Inputting the real-time pose information and the desired pose information to the base controller; the basic controller calculates total thrust according to the stress balance relation of the carrying unmanned aerial vehicle; constructing a rotation matrix according to the stress balance relation; calculating an attitude error vector according to the rotation matrix; Calculating an angular velocity error vector according to the real-time attitude information; fusing the attitude error vector and the angular velocity error vector to generate an ideal moment; calculating a basic moment according to the difference value of the ideal moment and the deterministic disturbance moment; and taking the total thrust and the basic moment as the basic control command.
4. The method of claim 1, wherein generating feedforward compensation control instructions from the deterministic disturbance force, the deterministic disturbance torque, and a deterministic disturbance feedforward controller comprises: Inputting the deterministic disturbance force and the deterministic disturbance torque to the deterministic disturbance feedforward controller; the deterministic disturbance feedforward controller determines an allocation matrix corresponding to the unmanned aerial vehicle; and calculating a feedforward compensation control instruction based on the product of the distribution matrix and the deterministic disturbance force and the deterministic disturbance moment respectively.
5. The method of claim 1, wherein predicting the feedback compensation control command corresponding to the uncertainty disturbance in the current computing period based on the flight status of the carrying drone after receiving the integrated control command in the previous computing period and the feedforward compensation control command in the current computing period comprises: detecting the flight state of the carrying unmanned aerial vehicle after receiving the comprehensive control instruction in the last calculation period; acquiring the feedforward compensation control instruction calculated by the carrying unmanned aerial vehicle in the current calculation period; inputting the flight status and the feedforward compensation control command to a feedback compensation controller; And the feedback compensation controller removes the compensation quantity corresponding to the feedforward compensation control instruction from the total compensation quantity based on the total compensation quantity of the disturbance component estimated by the flight state, and obtains the compensation quantity corresponding to the uncertainty disturbance component.
6. The method of claim 5, wherein the feedback compensation controller removing the compensation amount corresponding to the feedforward compensation control command from the total compensation amount based on the total compensation amount of the estimated disturbance component of the flight state, to obtain the compensation amount corresponding to the uncertainty disturbance component, comprising: establishing a state predictor of the carrying unmanned aerial vehicle, wherein the state predictor is used for predicting the real-time state of the carrying unmanned aerial vehicle at the next moment based on the real-time information of the carrying unmanned aerial vehicle; Taking the difference value between the state predicted by the state predictor and the detected real-time state of the carrying unmanned aerial vehicle as the total influence margin of disturbance components; Taking the product of the update law and the real-time state information of the current carrying unmanned aerial vehicle as the self-adaptive law of the feedback compensation controller, and predicting the total disturbance component based on the total influence margin; Removing a deterministic disturbance component corresponding to the feedforward compensation control instruction from the total disturbance component to obtain a non-deterministic disturbance component; a feedback compensation control command is generated for the deterministic disturbance component based on a filter.
7. The method of claim 1, wherein the fusing the base control command, the feedforward compensation control command, and the feedback compensation control command to generate the integrated control command comprises: Determining control items in the basic control instruction, the feedforward compensation control instruction and the feedback compensation control instruction; and fusing control values corresponding to the same control items, combining all the control items, and generating the comprehensive control instruction.
8. The method of claim 1, wherein prior to the acquiring the real-time status of the drone for the current computing period, the method further comprises at least: Determining the position of a carrying object on a carrying platform; determining target detection ranges of the sensors of the carrier type object according to the positions; The union of the target detection ranges of all the sensors under the same detection shaft is equal to the area of the carrying platform, and the target detection ranges of all the sensors under the same detection shaft have no overlapping area.
9. The method of claim 4, wherein the distribution matrix comprises at least a force distribution matrix and a moment distribution matrix, and wherein the deterministic disturbance feedforward controller determines the distribution matrix corresponding to the drone, comprising: Determining multiple historical compensation information of the unmanned aerial vehicle; learning a force distribution matrix and a moment distribution matrix of the unmanned aerial vehicle according to the multiple historical compensation information; and correcting the force distribution matrix and the moment distribution matrix according to the compensation margin of the next compensation information of each historical compensation information.
10. A self-adaptive rigid force response control device for a carrying unmanned aerial vehicle, the device comprising: The acquisition module is used for acquiring the real-time state of the carrying unmanned aerial vehicle in the current calculation period, wherein the real-time state at least comprises the real-time attitude information of the carrying unmanned aerial vehicle and the deterministic disturbance force and the deterministic disturbance moment in the rigidity force; the basic control module is used for generating basic control instructions corresponding to the expected posture information according to the real-time posture information and the basic controller; the feedforward compensation module is used for adaptively generating a feedforward compensation control instruction corresponding to a deterministic disturbance component in the rigid force according to the deterministic disturbance force, the deterministic disturbance moment and a deterministic disturbance feedforward controller; The feedback compensation module is used for adaptively predicting the feedback compensation control command corresponding to the uncertainty disturbance component in the rigidity force in the current calculation period based on the flight state of the carrying unmanned aerial vehicle after receiving the comprehensive control command in the previous calculation period and the feedforward compensation control command in the current calculation period, and the prediction is at least based on the uncertainty disturbance force and the uncertainty disturbance moment; and the control module is used for fusing the basic control instruction, the feedforward compensation control instruction and the feedback compensation control instruction to generate a self-adaptive comprehensive control instruction of the carrying unmanned aerial vehicle.

Description

Self-adaptive rigid force response control method and device for carrying unmanned aerial vehicle Technical Field The application relates to the technical field of unmanned aerial vehicle control, in particular to a self-adaptive rigid force response control method and device for a carrying unmanned aerial vehicle. Background With the development of unmanned aerial vehicle control technology, carrying unmanned aerial vehicles become one of hot unmanned aerial vehicle types. When the carrying unmanned aerial vehicle performs tasks, the reaction force of the carried goods to the carrying unmanned aerial vehicle in the contact and flight processes is non-fixed, such as swaying liquid and hanging goods, that is to say, the carried goods can be regarded as non-fixedly connected loads of the carrying unmanned aerial vehicle or can be regarded as rigid contact with the environment. Because of the uncertainty of the position and quality change of the load, the unmanned aerial vehicle body is usually subjected to complex external forces and moments, and the forces have the characteristics of dynamics, nonlinearity and uncertainty, so that the flight stability and control accuracy of the unmanned aerial vehicle are seriously influenced, and the unmanned aerial vehicle is a key technical bottleneck for restricting the unmanned aerial vehicle to be applied in complex scenes. The prior art mainly solves the problem through two ways, namely a control method based on a specific model, which has strict requirements on load types and has poor generality, and a method based on a disturbance observer (Disturbance Observer, DOB), which regards all external acting forces as single unknown disturbance and indirectly estimates and compensates through state feedback of a carrier unmanned aerial vehicle. However, the disturbance observer-based method has inherent defects that 1) the disturbance observer is a low-pass filter in nature, phase lag exists, when the rigidity force is rapidly changed, the estimated value cannot be followed in real time, so that the compensation effect is poor, 2) the performance contradiction is that the inherent contradiction exists in the selection of the bandwidth of the disturbance observer, the tracking speed can be improved by high bandwidth, the sensor noise can be amplified, the low bandwidth is conversely difficult to consider, and 3) the information loss is that all disturbance is processed in a whole way, the structural information of a disturbance source is lost, and the optimal and fine compensation control cannot be realized. Disclosure of Invention In view of the above, the application provides a method and a device for controlling adaptive rigidity force response of a carrying unmanned aerial vehicle. The application particularly provides a self-adaptive rigid force response control method of a carrying unmanned aerial vehicle, which comprises the following steps: acquiring a real-time state of the carrying unmanned aerial vehicle in a current calculation period, wherein the real-time state at least comprises a deterministic disturbance force and a deterministic disturbance moment in real-time attitude information and rigidity force of the carrying unmanned aerial vehicle; Generating a basic control instruction corresponding to the expected posture information according to the real-time posture information and the basic controller; Adaptively generating a feedforward compensation control instruction corresponding to a deterministic disturbance component in the rigid force according to the deterministic disturbance force, the deterministic disturbance moment and a deterministic disturbance feedforward controller; Based on the flight state of the carrying unmanned aerial vehicle after receiving the comprehensive control instruction in the previous calculation period and the feedforward compensation control instruction in the current calculation period, adaptively predicting a feedback compensation control instruction corresponding to an uncertainty disturbance component in the rigidity force in the current calculation period, wherein the prediction is at least based on the uncertainty disturbance force and the uncertainty disturbance moment; and fusing the basic control instruction, the feedforward compensation control instruction and the feedback compensation control instruction to generate a self-adaptive comprehensive control instruction of the carrying unmanned aerial vehicle. A second aspect of the application provides a self-adaptive rigid force response control device for a delivery unmanned aerial vehicle, the device comprising: The acquisition module is used for acquiring the real-time state of the carrying unmanned aerial vehicle in the current calculation period, wherein the real-time state at least comprises the real-time attitude information of the carrying unmanned aerial vehicle and the deterministic disturbance force and the deterministic disturbance moment in the rigidity f