Unmanned aerial vehicle wind speed estimation method

Abstract

The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle wind speed estimation method, which comprises the following steps: acquiring the relationship between the windward area and the body resistance coefficient of an unmanned aerial vehicle and the horizontal attitude angle of the unmanned aerial vehicle; the unmanned aerial vehicle is controlled to be automatically braked and stopped at different speeds, and induced resistance parameters are identified according to the acceleration in the braking and stopping process; according to the induced resistance parameters and the obtained relation, a wind speed model for calculating the wind speed is obtained; controlling the flight of the unmanned aerial vehicle, and collecting flight parameters of the unmanned aerial vehicle in the flight process in real time; and calculating the wind speed in the flight process of the unmanned aerial vehicle by combining the obtained wind speed model and the flight parameters. According to the method, induced resistance parameters are calculated off line through a parameter identification method, so that the method can be more effectively close to a real flight state and has better accuracy.

Description

Technical Field

[0001] The invention belongs to the technical field of unmanned aerial vehicles, and in particular relates to a method for estimating wind speed of an unmanned aerial vehicle. Background Art

[0002] With the continuous development of the drone industry, drones have been widely used in military and civilian fields. At present, there are three main types of drones: rotary-wing drones, fixed-wing drones, and compound-wing drones. Most fixed-wing drones and compound-wing drones use airspeed sensors to observe wind speed; however, this method is rarely used on rotary-wing drones because their direction of movement is not necessarily the direction of the nose and the airflow of the propeller will interfere with the measurement of the pitot tube, so airspeed sensors are basically not installed to observe wind speed.

[0003] In response to the above needs, there are many relevant solutions at home and abroad.

[0004] The Chinese patent publication number is CN106885918A, and the publication date is June 23, 2017. In the invention patent application named "A multi-information fusion real-time wind speed estimation method for multi-rotor aircraft", the airborne sensor data is first collected, and the multi-rotor dynamics model is constructed to obtain the relationship between resistance and wind speed. The Kalman filter is used to estimate the wind speed in real time based on the established state equation and measurement equation. This method is too dependent on the accuracy of the UAV dynamics model. Summary of the invention

[0005] In view of this, the invention aims to provide a method for estimating wind speed of a drone, which calculates the induced drag parameters offline through parameter identification, and calculates the real-time wind speed in combination with the induced drag parameters during flight. In the actual test process, good experimental results were obtained.

[0006] To achieve the above object, the technical solution created by the present invention is implemented as follows: A method for estimating wind speed using a drone, comprising: S1: Establish a wind speed model to calculate the wind speed during the flight of the UAV. The wind speed model contains the induced drag parameters of the UAV. S2: obtaining a first relationship between the windward area of ​​the UAV and the horizontal attitude angle of the UAV, and a second relationship between the body drag coefficient of the UAV and the horizontal attitude angle; S3: Controlling the UAV to fly at different initial speeds, and determining a third relationship between the induced drag parameter and the ground speed of the UAV according to the wind speed model obtained in step S1; S4: Control the flight of the drone and collect the flight parameters of the drone in real time during the flight; S5: Calculate the wind speed according to the wind speed model obtained in step S1, the first relationship and the second relationship obtained in step S2, the third relationship obtained in step S3, and the flight parameters obtained in step S4.

[0007] Furthermore, in step S1, the wind speed model is: ; in, Indicates wind speed, Indicates ground speed, m Indicates the quality of the drone, K represents the induced drag parameter, a Indicates the acceleration of the drone in the horizontal direction, ρ represents the air density, S represents the windward area, C D Represents the drag coefficient of the aircraft.

[0008] Further, in step S1, the first relationship is as follows: S=f 1( β ) =a 1 ×β 2 +b 1 ×β+c 1; in, f 1( · ) represents the first relation, β represents the horizontal attitude angle, S represents the windward area, a 1. b 1 and c 1 represents the weight in the first relationship; The second relationship is as follows: C D =f 2( β ) =a 2 ×β 2 +b 2 ×β+c 2; in, f 2(·) represents the second relation, C D is the body drag coefficient, a 2. b 2 and c 2 represents the weight in the second relationship.

[0009] Furthermore, in step S3, it includes: S31: After the drone is controlled to fly a certain distance against the wind at an arbitrary initial speed at the origin, the target speed of the drone in the horizontal direction is canceled until the drone is blown back to the origin by the wind in the horizontal direction; S32: Record the acceleration, wind speed and ground speed of the drone during the process of being blown back, and bring them into the wind speed model to obtain the induced drag parameter corresponding to the current ground speed; S33: changing the flight speed of the UAV, and repeating steps S31 to S32 to obtain a plurality of ground speeds and an induced drag parameter corresponding to each ground speed; S34: Fitting the ground speed and induced drag parameters obtained in step S33 to obtain a third relationship.

[0010] Furthermore, in step S4, the flight parameters include horizontal attitude angle, ground speed and acceleration.

[0011] Further, in step S5: Substitute the horizontal attitude angle into the first and second relations to obtain the frontal area and the body drag coefficient; According to the ground speed, the corresponding induced drag parameter is determined in combination with the third relationship; The acceleration, induced drag parameter, frontal area and body drag coefficient are introduced into the wind speed model to obtain the wind speed.

[0012] Compared with the prior art, the invention can achieve the following beneficial effects: The method for estimating wind speed of a drone created by the present invention does not need to rely too much on the model. The method combined with parameter identification can be more effectively close to the actual flight state. The real-time wind speed is calculated by combining the induced drag parameter during flight. In the actual test process, good experimental results are obtained. The method for estimating wind speed of a drone created by the present invention is not limited to use on multi-rotors, but also has reference significance for drones such as fuselage vertical take-off drones that cannot be equipped with airspeed sensors or when the airspeed sensors cannot function at certain stages. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The drawings constituting part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments and descriptions of the present invention are used to explain the present invention and do not constitute an improper limitation on the present invention. In the drawings: Figure 1 A schematic diagram of a flow chart of a method for estimating wind speed of a drone according to an embodiment of the present invention; Figure 2 A schematic diagram of the measured flow of the method for estimating wind speed of a drone according to an embodiment of the present invention; Figure 3 The present invention creates a curve diagram of the ground speed and horizontal acceleration described in the embodiment. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical solution and advantages of the invention more clear, the invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described here are only used to explain the invention and do not constitute a limitation of the invention.

[0015] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0016] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", etc. are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", etc. may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0017] In the description of the invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installation", "connection" and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the invention can be understood according to specific circumstances.

[0018] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments.

[0019] like Figure 1 to Figure 2 As shown, the method for estimating wind speed of a drone described in the embodiment of the present invention includes: S1: Establish a wind speed model to calculate the wind speed during the flight of the UAV. The wind speed model contains the induced drag parameters of the UAV.

[0020] The sources of resistance when a drone is flying include induced resistance and body resistance, namely ; in, Indicates the resistance of the drone during flight. Represents the body resistance, Indicates induced drag. It is expressed as: ; in, K It represents the induced drag parameter, which is related to the propeller size, propeller pitch, and flight speed of the UAV; Indicates airspeed, including ground speed and wind speed ,Right now; .

[0021] Body resistance for: ; in, ρ Indicates the air density. Further obtain the resistance of the drone for: .

[0022] When the drone automatically brakes, it continues to fly due to inertia. At this time, the acceleration generated by the drone is all due to the resistance. Produced, we can further obtain: ; in, m Indicates the quality of the drone, a Indicates the acceleration of the drone in the horizontal direction, ρ represents the air density, S represents the windward area, C D Represents the drag coefficient of the aircraft.

[0023] In some embodiments, based on the above principle, the wind speed model is obtained as: .

[0024] S2: Obtain a first relationship between the windward area of ​​the UAV and the horizontal attitude angle of the UAV, and a second relationship between the body drag coefficient of the UAV and the horizontal attitude angle.

[0025] In some embodiments, a first relationship between the frontal area and the horizontal attitude angle is as follows: S=f 1( β ) =a 1 ×β 2 +b 1 ×β+c 1; in, f 1( · ) represents the first relation, β represents the horizontal attitude angle, a 1. b 1 and c 1 represents the weight in the first relationship; The second relationship between the body drag coefficient and the horizontal attitude angle is as follows: C D =f 2( β ) =a 2 ×β 2 +b 2 ×β+c 2; in, f 2( · ) represents the second relation, a 2. b 2 and c 2 represents the weight in the second relationship.

[0026] Frontal area S, body drag coefficient C D It can be obtained through CFD (Computational Fluid Dynamics) calculation or wind tunnel test. In one embodiment, the windward area S and the body drag coefficient are obtained through CFD calculation. C D , and the specific first relationship is obtained through the analysis software that can perform CFD calculations: ; The specific second relationship is: .

[0027] It should be noted that the windward area mentioned in the present invention is S It is the projection area of ​​the windward surface of the drone projected to the horizontal direction during the flight of the drone.

[0028] S3: Control the UAV to fly at different initial speeds, and determine a third relationship between the induced drag parameter and the ground speed of the UAV according to the wind speed model obtained in step S1.

[0029] In some embodiments, based on the principle of the wind speed model, step S3 includes: S31: After the drone is controlled to fly a certain distance against the wind at an arbitrary initial speed, the target speed of the drone in the horizontal direction is canceled until the drone is blown back to the origin by the wind in the horizontal direction; S32: Record the acceleration, wind speed and ground speed of the drone during the process of being blown back, and bring them into the wind speed model to obtain the induced drag parameter corresponding to the current ground speed; S33: changing the flight speed of the UAV, and repeating steps S31 to S32 to obtain a plurality of ground speeds and an induced drag parameter corresponding to each ground speed; S34: Fitting the ground speed and induced drag parameters obtained in step S33 to obtain a third relationship.

[0030] In one embodiment, step S3 is specifically: S31: Control the drone with mass m=2.7kg in air density ρ =1.225kg / m 3 In windy weather, after flying against the wind at a constant speed of 2m / s for a certain distance, the target speed of the drone in the horizontal direction is cancelled until the drone is blown back to the origin in the horizontal direction.

[0031] S32: Record the acceleration, wind speed and ground speed of the drone during the braking process, and bring them into the wind speed model to obtain the induced drag parameter corresponding to the current ground speed; S33: Change the initial speed of the drone to 4m / s, 6m / s and 8m / s, repeat steps S31-S32, and obtain four ground speeds and the induced drag parameters corresponding to each ground speed. Figure 3 As shown, the wind speed of the drone during this flight is obtained 2.6m / s, ground speed 5.3m / s, acceleration a 0.9m / s 2 , the parameters of the UAV in this flight process are obtained through the above formula , and then get the corresponding induced drag parameter K It is 0.248.

[0032] S34: Fitting the four ground speeds obtained in step S33 and the four corresponding induced drag parameters to obtain a third relationship.

[0033] S4: Control the flight of the drone and collect the flight parameters of the drone in real time during the flight.

[0034] In some embodiments, the flight parameters in step S4 include horizontal attitude angle, ground speed and acceleration.

[0035] S5: Calculate the wind speed according to the wind speed model obtained in step S1, the first relationship and the second relationship obtained in step S2, the third relationship obtained in step S3, and the flight parameters obtained in step S4.

[0036] In some embodiments, in step S5: Substitute the horizontal attitude angle into the first and second relations to obtain the frontal area and the body drag coefficient; According to the ground speed, the corresponding induced drag parameter is determined in combination with the third relationship; The acceleration, induced drag parameter, frontal area and body drag coefficient are introduced into the wind speed model to obtain the wind speed.

[0037] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the disclosure of the present invention can be performed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and this document does not limit this.

[0038] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for estimating wind speed using an unmanned aerial vehicle, characterized in that: include: S1: Establishing a wind speed model for calculating the wind speed during the flight of the UAV, wherein the wind speed model has the induced drag parameter of the UAV; S2: Acquire a first relationship between the windward area of ​​the UAV and the horizontal attitude angle of the UAV, and a second relationship between the body drag coefficient of the UAV and the horizontal attitude angle; S3: controlling the UAV to fly at different initial speeds, and determining a third relationship between the induced drag parameter and the ground speed of the UAV according to the wind speed model obtained in step S1; S4: Control the flight of the UAV and collect the flight parameters of the UAV in real time during the flight; S5: Calculate the wind speed according to the wind speed model obtained in step S1, the first relationship and the second relationship obtained in step S2, the third relationship obtained in step S3, and the flight parameters obtained in step S4.

2. The method for estimating wind speed using a drone according to claim 1, characterized in that: In step S1, the wind speed model is: ； in, represents the wind speed, represents the ground speed, m represents the mass of the drone, K represents the induced drag parameter, a represents the acceleration of the UAV in the horizontal direction, ρ represents the air density, S represents the frontal area, C D represents the drag coefficient of the aircraft body.

3. The method for estimating wind speed using a drone according to claim 1, characterized in that: In step S1, the first relationship is as follows: S=f 1( β ) =a 1 ×β 2 +b 1 ×β+c 1； in, f 1( · ) represents the first relationship, β represents the horizontal attitude angle, S represents the frontal area, a 1. b 1 and c 1 represents the weight in the first relationship; The second relationship is as follows: C D =f 2( β ) =a 2 ×β 2 +b 2 ×β+c 2； in, f 2(·) represents the second relationship, C D represents the body drag coefficient, a 2. b 2 and c 2 represents the weight in the second relationship.

4. The method for estimating wind speed using a drone according to claim 2, characterized in that: In step S3, it includes: S31: After controlling the drone to fly a certain distance against the wind at an arbitrary initial speed from the origin, canceling the target speed of the drone in the horizontal direction until the drone is blown back to the origin by the wind in the horizontal direction; S32: recording the acceleration, wind speed and ground speed of the UAV during the process of being blown back, and bringing them into the wind speed model to obtain the induced drag parameter corresponding to the current ground speed; S33: changing the flight speed of the UAV, and repeating steps S31 to S32 to obtain a plurality of ground speeds and an induced drag parameter corresponding to each ground speed; S34: Fitting the ground speed and the induced drag parameter obtained in step S33 to obtain the third relationship.

5. The method for estimating wind speed using a drone according to claim 2, characterized in that: In step S4, the flight parameters include the horizontal attitude angle, the ground speed and the acceleration.

6. The method for estimating wind speed using a drone according to claim 2, characterized in that: In step S5: Substituting the horizontal attitude angle into the first relationship and the second relationship to obtain the frontal area and the body drag coefficient; Determining a corresponding induced drag parameter according to the ground speed and in combination with the third relationship; The acceleration, the induced drag parameter, the frontal area and the body drag coefficient are introduced into the wind speed model to obtain the wind speed.

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