CN115454113A - Attitude stability control method of unmanned aerial vehicle in high-altitude environment - Google Patents
Attitude stability control method of unmanned aerial vehicle in high-altitude environment Download PDFInfo
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- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/08—Control of attitude, i.e. control of roll, pitch, or yaw
- G05D1/0808—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
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Abstract
The invention provides a method for controlling the attitude stability of an unmanned aerial vehicle in a high-altitude environment, wherein the unmanned aerial vehicle acquires the external wind speed and wind direction and the flight three-dimensional attitude of the unmanned aerial vehicle through a sensor, the information acquired by the sensor is transmitted to a flight controller, and the flight controller controls a flight actuator through an algorithm so as to control the attitude of the unmanned aerial vehicle and realize the attitude stability of the unmanned aerial vehicle in the high-altitude environment; the algorithm is a combination of a self-adaptive control algorithm and fuzzy active disturbance rejection control, the fuzzy controller can be used for adjusting nonlinear state error feedback control law parameters in real time on line, the self-adaptive controller is used for tracking and estimating the attitude of the unmanned aerial vehicle, the estimated value is used for correcting the compensation coefficient of the active disturbance rejection, and the control precision and the disturbance rejection of the system are improved. The invention solves the problem of attitude stability control of the unmanned aerial vehicle in a high-altitude environment, effectively avoids flight attitude change caused by air density change and strong wind influence, and improves the stability of the unmanned aerial vehicle.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to a method for stably controlling the posture of an unmanned aerial vehicle in a high-altitude environment.
Background
Under high-altitude environment, unmanned aerial vehicle's application is more and more extensive, for example unmanned aerial vehicle carries out aerial surveying and mapping and unmanned aerial vehicle is to high-rise building's fire rescue at the high altitude. However, in the case of high altitude difference span, the air density may change greatly, thereby affecting the aerodynamic characteristics of the drone. And the high-altitude environment has the condition of strong wind interference, and great influence is produced to the real-time attitude control of unmanned aerial vehicle, can lead to unmanned aerial vehicle's gesture to change suddenly. Therefore, the problem of attitude stability control of the unmanned aerial vehicle in the high-altitude environment needs to be solved urgently.
Disclosure of Invention
The invention provides a method for stably controlling the attitude of an unmanned aerial vehicle in a high-altitude environment in order to solve the problems of the prior art, solves the problem of stably controlling the attitude of the unmanned aerial vehicle in the high-altitude environment, effectively avoids the change of flight attitude caused by the change of air density and the influence of strong wind, and improves the stability of the unmanned aerial vehicle.
The invention provides a method for controlling the attitude stability of an unmanned aerial vehicle in a high-altitude environment, wherein the unmanned aerial vehicle acquires the external wind speed and wind direction and the flight three-dimensional attitude of the unmanned aerial vehicle through a sensor, the information acquired by the sensor is transmitted to a flight controller, and the flight controller controls a flight actuator through an algorithm so as to control the attitude of the unmanned aerial vehicle and realize the attitude stability of the unmanned aerial vehicle in the high-altitude environment; the algorithm is a combination of a self-adaptive control algorithm and fuzzy active disturbance rejection control, and the fuzzy controller can be used for adjusting the nonlinear state error feedback control law parameters in real time on line, so that the problem that the parameters in the active disturbance rejection controller are lack of on-line self-tuning is solved. And tracking and estimating the attitude of the unmanned aerial vehicle by using the self-adaptive controller, and correcting the compensation coefficient of the active disturbance rejection by using the estimated value, thereby improving the control precision and disturbance rejection of the system.
The uncertainty of strong wind interference of the unmanned aerial vehicle in the high altitude condition can cause larger deviation of a disturbance compensation coefficient in active disturbance rejection control, and the disturbance compensation coefficient is difficult to be accurately compensated, so that a self-adaptive controller is designed to ensure that the error between an ideal flight attitude and an actual flight attitude approaches to zero, and then an accurate compensation coefficient is obtained, thereby improving the disturbance compensation precision;
the tracking differentiator is expressed as follows:
wherein,
a tracking output value representing the attitude signal;
representing a differential signal; r is the stable convergence speed of the system; h is an integration step length;
the extended observer expression is as follows:
wherein β is a regulatory factor of the observer; α determines the degree of non-linearity of the function; δ determines the linear region range of the function.
When the sensor data acquisition, through collecting the unmanned aerial vehicle and facing the data of moving under the strong wind interference and arrange into the data set in order, use the data set training convolution neural network, can realize that unmanned aerial vehicle's gesture under high altitude environment is stable through the convolution neural network of training.
The sensor is including the air velocity transducer who is used for measuring the wind speed, the wind direction sensor who is used for measuring the wind direction, the angular transducer who is used for measuring unmanned aerial vehicle and ground inclination and be used for measuring the attitude sensor of unmanned aerial vehicle at flight three-dimensional gesture, and the attitude sensor input is connected with air velocity transducer, angular transducer and wind direction sensor, and the output is connected with flight controller. The flight actuator adjusts the rotational speed of rotor and adjusts unmanned aerial vehicle's three-dimensional flight gesture.
The invention has the beneficial effects that:
1. the problem of unmanned aerial vehicle attitude stability control under high altitude environment is solved, effectively avoid air density change and strong wind influence to lead to the flight gesture to change, improve unmanned aerial vehicle's stability.
2. The stability of the unmanned aerial vehicle in the high altitude environment can be improved through the trained convolutional neural network.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic block diagram of the attitude control of an unmanned aerial vehicle of the present invention;
fig. 2 is a block diagram of a flight controller control system according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The technical scheme of the invention is further explained in detail by combining the attached drawings:
FIG. 1 is a schematic block diagram of the attitude control of an unmanned aerial vehicle of the present invention; fig. 2 is a block diagram of a flight controller system according to the present invention.
In a specific embodiment, a method for controlling the attitude stability of an unmanned aerial vehicle in a high-altitude environment is provided, which includes:
a wind speed sensor for measuring wind speed;
a wind direction sensor for measuring a wind direction;
the inclination angle sensor is used for measuring the inclination angle between the unmanned aerial vehicle and the ground;
the attitude sensor is used for integrating the information of the sensors and measuring the flying three-dimensional attitude of the unmanned aerial vehicle;
the flight controller collects information by a sensor and maintains stable posture by controlling an actuator;
the flight actuator is used for adjusting the rotating speed of the rotor wing and adjusting the three-dimensional flight attitude of the unmanned aerial vehicle;
when the unmanned aerial vehicle works in a high-altitude environment, the wind power and the wind direction of the unmanned aerial vehicle and the influence of the unmanned aerial vehicle are comprehensively obtained through information of the wind speed sensor, the wind direction sensor and the inclination angle sensor; the flight controller processes the information through an adaptive fuzzy active-disturbance-rejection algorithm; the flight controller controls the flight actuator to change the rotating speed of the rotor of the unmanned aerial vehicle and the flight attitude of the unmanned aerial vehicle. As shown in fig. 1.
The algorithm of the flight controller adopts a self-adaptive fuzzy active disturbance rejection control method, and the fuzzy controller can be used for adjusting the nonlinear state error feedback control law parameters in real time on line, so that the problem that parameters in the active disturbance rejection controller are lack of on-line self-tuning is solved. Then, tracking estimation is carried out on the attitude of the unmanned aerial vehicle by using the self-adaptive controller, and the compensation coefficient of active disturbance rejection is corrected by using the estimation value, so that the control precision and disturbance rejection of the system are improved;
the uncertainty of strong wind interference of the unmanned aerial vehicle in the high altitude condition can cause larger deviation of a disturbance compensation coefficient in active disturbance rejection control, and the disturbance compensation coefficient is difficult to be accurately compensated, so that a self-adaptive controller is designed to ensure that the error between an ideal flight attitude and an actual flight attitude approaches to zero, and then an accurate compensation coefficient is obtained, thereby improving the disturbance compensation precision;
the tracking differentiator is expressed as follows:
wherein,
a tracking output value representing the attitude signal;
representing a differential signal; r is the stable convergence speed of the system; h is an integration step length;
the extended observer expression is as follows:
wherein β is a regulatory factor of the observer; α determines the degree of non-linearity of the function; δ determines the linear region range of the function.
The data set is trained by the data set, and the attitude stability of the unmanned aerial vehicle in the high-altitude environment can be realized by the trained convolutional neural network.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus embodiment, the above is only a preferred embodiment of the present invention, and since it is basically similar to the method embodiment, it is described simply, and the relevant points can be referred to the partial description of the method embodiment. The above description is only for the specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and the protection scope of the present invention should be covered by the principle of the present invention without departing from the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (4)
1. An unmanned aerial vehicle attitude stabilization control method under a high altitude environment is characterized in that: the unmanned aerial vehicle acquires external wind speed and wind direction and a flight three-dimensional attitude of the unmanned aerial vehicle through the sensor, information acquired by the sensor is transmitted to the flight controller, and the flight controller controls the flight actuator through an algorithm so as to control the attitude of the unmanned aerial vehicle and realize the attitude stability of the unmanned aerial vehicle in a high-altitude environment; the algorithm is a combination of a self-adaptive control algorithm and fuzzy active disturbance rejection control, the fuzzy controller can be used for adjusting nonlinear state error feedback control law parameters in real time on line, the self-adaptive controller is used for tracking and estimating the attitude of the unmanned aerial vehicle, the estimated value is used for correcting the compensation coefficient of the active disturbance rejection, and the control precision and the disturbance rejection of the system are improved.
2. The unmanned aerial vehicle attitude stabilization control method under the high-altitude environment according to claim 1, characterized in that: in the adaptive control algorithm and the fuzzy active disturbance rejection control, the expression form of a tracking differentiator is as follows:
wherein,
a tracking output value representing the attitude signal;
representing a differential signal; r is the stable convergence speed of the system; h is an integration step length;
the extended observer expression is as follows:
wherein β is a regulatory factor of the observer; α determines the degree of non-linearity of the function; δ determines the linear region range of the function.
3. The unmanned aerial vehicle attitude stabilization control method under the high-altitude environment according to claim 1, characterized in that: when the sensor collects data, the data set is arranged by collecting the actuation data of the unmanned aerial vehicle under the interference of strong wind, the convolutional neural network is trained by the data set, and the attitude stability of the unmanned aerial vehicle in the high-altitude environment can be realized by the trained convolutional neural network.
4. The unmanned aerial vehicle attitude stabilization control method under the high-altitude environment according to claim 1, characterized in that: the sensor is including the air velocity transducer who is used for measuring the wind speed, the wind direction sensor who is used for measuring the wind direction, the angular transducer who is used for measuring unmanned aerial vehicle and ground inclination and be used for measuring the attitude sensor of unmanned aerial vehicle at flight three-dimensional gesture, and the attitude sensor input is connected with air velocity transducer, angular transducer and wind direction sensor, and the output is connected with flight controller.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116520870A (en) * | 2023-07-04 | 2023-08-01 | 天之翼(苏州)科技有限公司 | Unmanned aerial vehicle flight attitude remote control method and system |
| CN117666368A (en) * | 2024-02-02 | 2024-03-08 | 国网湖北省电力有限公司 | Unmanned aerial vehicle multi-machine cooperation operation method and system based on Internet of things |
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| CN109542108A (en) * | 2017-09-22 | 2019-03-29 | 南京开天眼无人机科技有限公司 | A kind of unmanned plane wind resistance patrols winged system |
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| CN109542108A (en) * | 2017-09-22 | 2019-03-29 | 南京开天眼无人机科技有限公司 | A kind of unmanned plane wind resistance patrols winged system |
Non-Patent Citations (1)
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| 吴仪政: "变负载无人机的自适应模糊自抗扰控制研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》, no. 01, 15 January 2022 (2022-01-15), pages 031 - 323 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116520870A (en) * | 2023-07-04 | 2023-08-01 | 天之翼(苏州)科技有限公司 | Unmanned aerial vehicle flight attitude remote control method and system |
| CN116520870B (en) * | 2023-07-04 | 2023-09-01 | 天之翼(苏州)科技有限公司 | Unmanned aerial vehicle flight attitude remote control method and system |
| CN117666368A (en) * | 2024-02-02 | 2024-03-08 | 国网湖北省电力有限公司 | Unmanned aerial vehicle multi-machine cooperation operation method and system based on Internet of things |
| CN117666368B (en) * | 2024-02-02 | 2024-04-16 | 国网湖北省电力有限公司 | Unmanned aerial vehicle multi-machine cooperation operation method and system based on Internet of things |
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