WO2021018050A1 - 风速测算方法、风速估算器及无人机 - Google Patents

风速测算方法、风速估算器及无人机 Download PDF

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Publication number
WO2021018050A1
WO2021018050A1 PCT/CN2020/104585 CN2020104585W WO2021018050A1 WO 2021018050 A1 WO2021018050 A1 WO 2021018050A1 CN 2020104585 W CN2020104585 W CN 2020104585W WO 2021018050 A1 WO2021018050 A1 WO 2021018050A1
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Prior art keywords
drone
wind speed
wind
flying
flight
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PCT/CN2020/104585
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English (en)
French (fr)
Inventor
张添保
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深圳市道通智能航空技术有限公司
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Priority to EP20846707.6A priority Critical patent/EP4006683A4/en
Publication of WO2021018050A1 publication Critical patent/WO2021018050A1/zh
Priority to US17/582,216 priority patent/US20220146546A1/en

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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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
    • G01P5/00—Measuring speed of fluids, e.g. of air stream; Measuring speed of bodies relative to fluids, e.g. of ship, of aircraft
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
    • G01P5/00—Measuring speed of fluids, e.g. of air stream; Measuring speed of bodies relative to fluids, e.g. of ship, of aircraft
    • G01P5/02—Measuring speed of fluids, e.g. of air stream; Measuring speed of bodies relative to fluids, e.g. of ship, of aircraft by measuring forces exerted by the fluid on solid bodies, e.g. anemometer
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64C—AEROPLANES; HELICOPTERS
    • B64C39/00—Aircraft not otherwise provided for
    • B64C39/02—Aircraft not otherwise provided for characterised by special use
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
    • G01P7/00—Measuring speed by integrating acceleration
    • 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
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/0202—Control of position or course in two dimensions specially adapted to aircraft
    • G05D1/0204—Control of position or course in two dimensions specially adapted to aircraft to counteract a sudden perturbation, e.g. cross-wind, gust
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U10/00—Type of UAV
    • B64U10/10—Rotorcrafts
    • B64U10/13—Flying platforms
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U2101/00—UAVs specially adapted for particular uses or applications
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U2201/00—UAVs characterised by their flight controls

Definitions

  • This application relates to the technical field of unmanned aerial vehicles, and in particular to a wind speed measurement method, a wind speed estimator, and an unmanned aerial vehicle.
  • UAVs As a hovering aerial vehicle with strong adaptability, low cost of use, and quick and convenient release, UAVs are widely used in many different occasions. It can play an important role by carrying different types of functional components.
  • the drone During the flight, the drone will be disturbed by the wind.
  • the robustness of the flight control system can resist wind interference and ensure the smooth flight of the UAV.
  • the range of wind that can be adjusted or resisted by the flight control system has a specific limit.
  • the wind speed detection function is a very important function. Based on UAV wind speed detection and estimation, it can provide UAV users with better early warning and effectively avoid accidents.
  • the current wind speed detection or estimation methods can be roughly divided into two methods: using a wind speed sensor to directly measure the air speed, using a method of establishing a database in advance, or using a method of big data to estimate the wind speed.
  • the method of using wind speed or wind sensors to directly measure the airflow speed requires additional sensors on the drone, resulting in an increase in the production cost of the drone.
  • the method of establishing a database or big data calculation requires more computing power, which increases the computational burden of the flight control system.
  • loading the database on an airplane will greatly occupy memory and consume a lot of time, which has a relatively large impact on the real-time performance of wind speed detection.
  • embodiments of the present invention provide a wind speed measurement method, a wind speed estimator, and an unmanned aerial vehicle that do not rely on a database and a newly added wind speed sensor.
  • the wind speed measurement method includes:
  • the current wind resistance interference of the drone is determined through system identification;
  • the flight data includes: the attitude angle, flight speed, acceleration, and flight height of the drone;
  • the attribute data includes: the quality of the drone, the inherent drag coefficient and the nonlinear function used to calculate the windward area;
  • the wind speed of the flying environment of the drone is calculated.
  • the determination of the current wind resistance interference of the drone through system identification based on the flight data and attribute data of the drone includes:
  • Constructing a system identification model of the drone, and the parameter to be identified of the system identification model is the current equivalent drag coefficient of the drone;
  • the calculating the wind speed of the flying environment of the drone according to the wind resistance interference and the inherent wind resistance of the drone includes:
  • the wind speed of the flying environment of the UAV is calculated.
  • the online identification method to obtain the equivalent drag coefficient corresponding to the current flight data and attribute data specifically includes:
  • the current attitude angle, flight speed, and acceleration of the drone recursively calculate the equivalent wind resistance experienced by the drone
  • the windward area is calculated from the current attitude angle of the drone and a non-linear function used to calculate the windward area, and the air density is calculated from the current flying height of the drone.
  • the equivalent wind resistance coefficient is represented by the equivalent wind resistance coefficient component in the x direction and the equivalent wind resistance coefficient component in the y direction
  • the wind speed is represented by the wind speed component in the x direction and the wind speed component in the y direction.
  • the wind speed component represents; the x direction and the y direction are perpendicular to each other and are in the same plane as the drone.
  • the calculating the wind speed of the flying environment of the drone according to the wind resistance interference and the inherent wind resistance of the drone specifically includes:
  • V wx is the wind speed component in the x direction of the wind speed of the flying environment where the drone is located
  • V wy is the wind speed component in the y direction of the wind speed of the flying environment where the drone is located
  • V x is no
  • V y is the speed of the drone in the y direction
  • ⁇ is the air density at the flying altitude
  • S fb is the windward area when the drone is flying in the x direction
  • S rl is none
  • C x is the equivalent drag coefficient component in the x direction
  • Cy is the equivalent drag coefficient component in the y direction
  • C dx is the drone in the x direction
  • the intrinsic drag coefficient, C dy is the intrinsic drag coefficient of the UAV in the y direction.
  • the inherent drag coefficient of the drone in the x direction and the inherent drag coefficient of the drone in the y direction are determined according to the flight data of the drone in the airless chamber by least squares fitting.
  • system identification model is expressed by the following formula:
  • V x is the speed of the drone in the x direction
  • V y is the speed of the drone in the y direction
  • T is the propeller pulling force
  • ⁇ is the pitch angle
  • ⁇ is the roll angle
  • ⁇ is the air density at the flight altitude
  • S fb is the windward area when the UAV is flying in the x direction
  • S rl is when the UAV is flying in the y direction
  • C x is the equivalent drag coefficient component in the x direction
  • Cy is the equivalent drag coefficient component in the y direction
  • m is the mass of the drone
  • w x is the model in the x direction.
  • windward area is calculated and determined by the following formula:
  • S fb is the windward area when the drone is flying in the x direction
  • S rl is the windward area when the drone is flying in the x direction
  • S fb0 is when the attitude angle is 0, when the drone is flying in the x direction Windward area
  • S rl0 is the windward area when the UAV is flying in the y direction when the attitude angle is 0
  • f fb ( ⁇ , ⁇ ) and f rl ( ⁇ , ⁇ ) are nonlinear functions
  • ⁇ is the pitch angle
  • the propeller pulling force is calculated by the following formula:
  • a z is the acceleration of the drone in the z direction
  • g is the acceleration of gravity
  • the z direction is perpendicular to the plane formed by the x direction and the y direction
  • ⁇ is the pitch angle
  • ⁇ is the roll angle
  • m Is the quality of the drone.
  • the method further includes:
  • the wind direction is calculated by the following formula:
  • ⁇ is the yaw angle of the drone
  • ⁇ is the wind direction
  • V wx is the wind speed component in the x direction
  • V wv is the wind speed component in the y direction.
  • the method further includes: sending a warning signal when the wind speed of the flying environment in which the drone is located meets a preset warning condition.
  • the sending a warning signal when the wind speed of the flying environment in which the drone is located meets a preset warning condition includes:
  • V wx is the wind speed component in the x direction
  • V wv is the wind speed component in the y direction
  • V thr is the safe wind speed threshold
  • the wind speed estimator includes:
  • a system identification unit which is used to receive flight data and attribute data of the drone, and identify and determine the current wind resistance interference of the drone according to the flight data and attribute data;
  • the flight data includes: attitude angle, flight speed, acceleration, and flight height of the drone;
  • the attribute data includes: the drone's mass, inherent drag coefficient, and a nonlinear function used to calculate the windward area;
  • a wind speed estimation unit which is connected to the system identification unit, and is configured to calculate the wind speed of the flying environment of the drone based on the wind resistance interference and the inherent wind resistance of the drone.
  • a preset system identification model is stored in the system identification unit, and the parameter to be identified of the system identification model is an equivalent drag coefficient;
  • the system identification unit is used to obtain the corresponding equivalent drag coefficient according to the current flight data and the attribute data through an online identification method.
  • system identification unit is further configured to:
  • the current attitude angle, flight speed, and acceleration of the drone recursively calculate the equivalent wind resistance experienced by the drone
  • the windward area is calculated from the current attitude angle of the drone and a non-linear function used to calculate the windward area, and the air density is calculated from the current flying height of the drone.
  • the equivalent wind resistance coefficient is represented by the equivalent wind resistance coefficient component in the x direction and the equivalent wind resistance coefficient component in the y direction
  • the wind speed is represented by the wind speed component in the x direction and the wind speed component in the y direction.
  • the wind speed component represents; the x direction and the y direction are perpendicular to each other and are in the same plane as the drone.
  • system identification model is expressed by the following formula:
  • V x is the speed of the drone in the x direction
  • V y is the speed of the drone in the y direction
  • T is the propeller pulling force
  • ⁇ is the pitch angle
  • ⁇ is the roll angle
  • ⁇ is the air density at the flight altitude
  • S fb is the windward area when the drone is flying in the x direction
  • S rl is when the drone is flying in the y direction
  • C x is the equivalent drag coefficient component in the x direction
  • Cy is the equivalent drag coefficient component in the y direction
  • m is the mass of the drone
  • w x is the model in the x direction.
  • the wind speed estimation unit is further configured to receive the current attitude angle, flight speed, flight height, inherent drag coefficient of the UAV, and a nonlinear function used to calculate the windward area, and calculate the wind speed by the following formula Wind speed of the flying environment of the drone:
  • V wx is the wind speed component in the x direction of the wind speed of the flying environment where the drone is located
  • V wy is the wind speed component in the y direction of the wind speed of the flying environment where the drone is located
  • V x is no
  • V y is the speed of the drone in the y direction
  • ⁇ is the air density at the flying altitude
  • S fb is the windward area when the drone is flying in the x direction
  • S rl is none
  • C x is the equivalent drag coefficient component in the x direction
  • Cy is the equivalent drag coefficient component in the y direction
  • C dx is the drone in the x direction
  • the intrinsic drag coefficient, C dy is the intrinsic drag coefficient of the UAV in the y direction.
  • the wind speed estimator further includes: an early warning unit;
  • the early warning unit is used to send a warning signal when the wind speed of the flying environment of the drone meets a preset warning condition.
  • the early warning unit is further used for:
  • V wx is the wind speed component in the x direction
  • V wv is the wind speed component in the y direction
  • V thr is the safe wind speed threshold
  • the drone includes a fuselage body, one or more sensors arranged on the fuselage body, a memory, and a flight control system; the memory stores computer executable program instructions, and the computer can When the execution program instruction is called by the flight control system, the flight control system obtains the flight data from the sensor and the attribute data from the memory to execute the wind speed measurement method described above.
  • the flight control system is also used to convert the wind speed of the flying environment of the drone into a wind direction, and display the wind speed and wind direction on a remote control device corresponding to the drone.
  • the wind speed measurement method utilizes the principle of system identification, does not rely on newly added wind speed sensors and external databases, and realizes the wind speed calculation process in the form of identification parameters, which saves money. It reduces the cost of hardware equipment without causing additional computing power and real-time problems.
  • the method is simple and low in cost.
  • the result of wind speed calculation can also be applied to the early warning function to prompt or alarm the user, effectively reducing the probability of flight accidents.
  • Fig. 1 is a schematic diagram of an application environment of an embodiment of the present invention
  • Figure 2 is a functional block diagram of a drone provided by an embodiment of the present invention.
  • FIG. 3 is a schematic diagram of a display interface of an RC remote control provided by an embodiment of the present invention.
  • FIG. 4 is a schematic diagram of a display interface of a smart terminal provided by an embodiment of the present invention.
  • Figure 5 is a functional block diagram of a wind speed estimator provided by an embodiment of the present invention.
  • Fig. 6 is a method flowchart of a wind speed measurement method provided by an embodiment of the present invention.
  • FIG. 7 is a method flowchart of a method for recursive calculation of identification parameters provided by an embodiment of the present invention.
  • FIG. 8 is a method flowchart of a wind speed measurement method provided by another embodiment of the present invention.
  • FIG. 9 is a method flowchart of a calculation process executed by a flight control system according to an embodiment of the present invention.
  • FIG. 10 is a graph of changes in wind speed with time according to an embodiment of the present invention.
  • Fig. 11 is a graph of wind direction changing with time according to an embodiment of the present invention.
  • System identification is one of the system control methods, which relies on the input and output time functions of the system to determine and describe the data model of the system behavior, so as to realize the prediction of the system behavior.
  • the specific data model will be determined based on the prior knowledge and the process of parameter identification.
  • Using some calculated parameters to be identified can realize the estimation of the external interference of the entire system, and then obtain the required parameters through a series of suitable conversion methods.
  • the external interference may be the interference imposed on a certain motion system, for example, the impact of wind interference on the drone during the flight of the drone.
  • Fig. 1 is an application environment provided by an embodiment of the present invention.
  • the application environment takes an unmanned aerial vehicle system as an example, including an unmanned aerial vehicle 10, a remote control device 20 and a wireless network 30.
  • the drone 10 may be an unmanned aerial vehicle driven by any type of power (such as electricity), including but not limited to a four-axis drone, a fixed-wing aircraft, and a helicopter model. In this embodiment, a four-axis drone is taken as an example for presentation.
  • the main body of the drone 10 may be equipped with a number of different functional modules. These functional modules may be software modules, hardware modules, or a combination of software and hardware, and are modular devices used to implement one or more functions.
  • the remote control device 20 can be of any type, and is used to establish a communication connection with the drone and control the drone, such as an RC remote control.
  • the RC remote controller may be equipped with one or more different user interaction devices, based on these user interaction devices to collect user instructions or display and feedback information to the user, so as to realize the interaction between the user and the drone.
  • the remote control device 20 may be equipped with a display screen, through which the user's remote control instructions on the drone are received and the aerial images are displayed to the user through the display screen, or the corresponding simulated driving interface is presented to the user on the simulated driving interface. Show one or more flight parameters, such as flight speed, heading, or remaining power.
  • the remote control device 20 may also be implemented by a smart terminal.
  • the smart terminal includes, but is not limited to, smart phones, tablet computers, laptop computers, and wearable devices.
  • the smart terminal establishes a communication connection with the drone by running a specially set APP client or web page to realize data transmission and reception with the drone, thereby being used as a remote control device 20.
  • the wireless network 30 may be a wireless communication network based on any type of data transmission principle for establishing a data transmission channel between two nodes. For example, Bluetooth networks, WiFi networks, wireless cellular networks or their combination located in different signal frequency bands.
  • the frequency band or network form used by the wireless network 30 is related to the communication device used by the drone 10 and the remote control device 20.
  • FIG. 2 is a functional block diagram of the drone 10 provided by an embodiment of the present invention.
  • the functional modules carried by the drone 10 at least include: a sensor 11, a memory 12 and a flight control system 13.
  • the sensor 11 is a sensor arranged in the body of the fuselage and used to detect the motion state parameters of the drone during flight, such as a six-axis gyroscope, an accelerometer, etc.
  • These sensors 11 are basic sensors that must be provided in the design and manufacture of the UAV 10 to monitor the current movement state of the UAV 10 to achieve effective control of the flight of the UAV 10.
  • the memory 12 is a non-volatile computer-readable storage medium, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices. It has a program storage area and a data storage area, which are respectively used to store corresponding data information, such as non-volatile software programs, non-volatile computer executable programs and modules stored in the program storage area, or stored in the data storage area. Area calculation processing results and captured image information.
  • the flight control system 13 is the core of the flight control of the unmanned aerial vehicle. Specifically, any type of processor can be used as the core of logic processing and calculation.
  • the flight control system 13 is used to obtain data, perform logical operation functions, and issue calculation processing results, change the flight state of the drone 10 according to user instructions, and ensure that the drone 10 is in a safe and controllable flight state.
  • the flight control system 13 can obtain one or more types of collected data from the sensor 11, and analyze and determine several items of data related to the UAV (such as attitude angle, attitude angle, etc.) through a set data fusion or analysis method. Acceleration and flight speed, etc.), as the basis for controlling the motion state of the drone.
  • the flight control system 13 is also connected to the memory 12, and the corresponding software program or computer executable program is called in the memory 12 to execute the corresponding logical operation function, and perform the corresponding calculation and judgment.
  • the flight control system 13 can read the data information related to the drone, and use the principle of system identification to realize the current experience of the drone.
  • the corresponding wind speed estimated value is output, and then the output wind speed estimated value is compared with the preset warning condition to determine whether a warning signal needs to be triggered at this time.
  • the drone 10 After triggering the warning signal, the drone 10 will feed back to the remote control device 20 via the wireless network 30. After receiving the warning signal, the remote control device 20 can display corresponding warning prompt information through the interactive device to remind the operator to pay attention to flight safety and land to a suitable place in time.
  • a display interface as shown in FIG. 3 can be used to prompt the user in the center of the simulated driving interface—"the wind speed is high".
  • prompt information may be displayed in a partial area of the display screen of the smart terminal.
  • a special warning tone is played through the speaker of the remote control device 20 to prompt a warning that the current wind speed is too high.
  • the flight control system 13 may also convert it into wind direction data and provide it to the remote control device 20.
  • Interactive devices such as display screens display the wind speed and direction, so that the operator can know the current wind situation in the flight airspace in time.
  • Fig. 5 is a functional block diagram of a wind speed estimator provided by an embodiment of the present invention. As shown in FIG. 5, the wind speed estimator includes a system identification unit 1311 and a wind speed estimation unit 1312.
  • system identification unit 1311 is used to receive flight data and attribute data with the drone, and determine the current wind resistance interference of the drone through system identification.
  • the system identification unit 1311 can be implemented by a processor (such as a flight control system) that can execute logic judgment steps by calling computer software program instructions pre-stored in the memory and related to system identification.
  • the flight data includes: attitude angle, flight speed, acceleration, and flight height of the UAV;
  • the attribute data includes: the UAV's mass, inherent drag coefficient, and a nonlinear function for calculating the windward area.
  • the system identification unit 1311 adopts a system identification method to quantitatively determine the interference suffered by the movement of the drone.
  • the disturbance to the movement of the UAV mainly comes from the wind in the flight airspace. Therefore, the received interference can be equivalently regarded as the resistance caused by the wind, so as to calculate the wind resistance interference.
  • the system identification unit 1311 uses a system identification model constructed by some prior knowledge (such as the speed dynamic change equation of the drone, etc.).
  • the parameter to be identified can be the equivalent drag coefficient.
  • the equivalent wind resistance coefficient is specifically a parameter related to wind resistance interference, which is used to characterize the relationship between the UAV and the wind resistance it receives. That is, after the equivalent wind resistance coefficient is known, multiple data related to the attributes of the drone (ie, attribute data) and motion state (ie, flight data) can be obtained by combining the collection of drone sensors. Wind resistance.
  • the wind speed estimation unit 1312 is connected to the system identification unit 1311, which receives the wind resistance interference and calculates and determines the wind speed of the flight environment in which the drone is located according to the change of its inherent wind resistance relative to the drone.
  • the specific wind speed calculation process can be determined according to the input wind resistance interference form. It can be done using any type of conversion method.
  • the wind speed estimation unit 1312 can be implemented by a processor (such as a flight control system) that can execute logical judgment steps by calling computer software program instructions pre-stored in the memory and related to wind speed calculation.
  • a processor such as a flight control system
  • two mutually perpendicular x and y directions can be constructed on the plane where the drone is located, and the equivalent drag coefficient components in these two directions can be calculated respectively And wind speed component to complete the process of wind speed estimation.
  • V x is the speed of the drone in the x direction
  • V y is the speed of the drone in the y direction
  • T is the propeller pull
  • ⁇ is the pitch angle
  • ⁇ is the air density at the flight altitude
  • S fb is the windward area when the drone is flying in the x direction
  • S rl is the windward area when the drone is flying in the y direction
  • C x is in the x direction
  • Cy is the equivalent drag coefficient component in the y direction
  • m is the mass of the drone
  • w x is the model uncertainty in the x direction
  • w y is the y direction Model uncertainty.
  • Model uncertainty is the adjustment part used to compensate for the inconsistency between the established system model and the actual motion. As an empirical value or function, it can be determined and adjusted through experiments or data analysis and other appropriate statistical methods.
  • the windward area is a parameter that changes with the flight attitude of the UAV. In some embodiments, it can be approximated as a nonlinear function related to the attitude angle.
  • the nonlinear function can be written as shown in the following equations (2) and (3):
  • S fb0 is the windward area when the UAV is flying in the x direction when the attitude angle is 0;
  • S rl0 is the windward area when the UAV is flying in the y direction when the attitude angle is 0.
  • the propeller pull is related to the output power of the motor, which is externally expressed as the acceleration of the UAV. Generally, greater acceleration also means higher propeller pull.
  • the propeller pulling force can be calculated by the following formula (4):
  • a z is the acceleration of the drone in the z direction
  • g is the acceleration of gravity
  • the z direction is perpendicular to the plane formed by the x direction and the y direction.
  • the system identification unit 1311 can complete the parameter identification process based on the changes in the attitude angle, flight speed and acceleration of the drone based on the input and output of the entire drone motion system , Obtain the current equivalent wind resistance coefficient to reflect the wind resistance interference of the flying environment of the drone.
  • the inherent wind resistance of the drone is expressed by the inherent wind resistance coefficient of the drone.
  • the wind speed estimation unit 1312 may calculate the wind speed of the flying environment in which the drone is located by the following formula (5):
  • V wx is the wind speed component in the x direction of the wind speed of the flying environment where the drone is located
  • V wy is the wind speed component in the y direction of the wind speed of the flying environment where the drone is located
  • V x is no
  • V y is the speed of the drone in the y direction
  • ⁇ is the air density at the flying altitude
  • S fb is the windward area when the drone is flying in the x direction
  • S rl is none
  • C x is the equivalent drag coefficient component in the x direction
  • Cy is the equivalent drag coefficient component in the y direction
  • C dx is the drone in the x direction
  • the intrinsic drag coefficient, C dy is the intrinsic drag coefficient of the UAV in the y direction.
  • the inherent drag coefficient is a mathematical parameter determined by the shape and structure of the UAV in a calm environment. Specifically, it can be determined by fitting and determining multiple sets of experimental data collected through a windless indoor flight or flying in other ideal experimental environments before the unmanned aerial vehicle leaves the factory for sale.
  • the above method of calculating the intrinsic drag coefficient is an offline calculation process, which can be completed in advance and recorded and stored in the memory of the drone, called by the wind speed estimation unit 1312, and does not need to be performed on each drone.
  • the drone-related data information that needs to be used includes at least: none The attitude angle, flight speed, acceleration, flying height of the man-machine, the mass of the drone, the inherent drag coefficient, and the nonlinear function used to calculate the windward area.
  • the attitude angle, flight speed, acceleration, and flight height of the UAV are all parameters that change with the motion state of the UAV. It can be obtained through one or more methods such as data fusion based on the sampling data collected by a series of basic sensors set on the drone.
  • the quality, inherent drag coefficient and the nonlinear function used to calculate the windward area of the drone are parameters determined by the inherent properties of the drone. It can be pre-determined by offline calculation through experiments and other methods, and the stored records can be recalled in the memory.
  • the wind speed estimator may further include an early warning unit 1313.
  • the early warning unit 1313 is connected to the wind speed estimation unit 1312, and is configured to receive the wind speed of the current flight environment provided by the wind speed estimation unit 1312, and when the wind speed of the flight environment in which the drone is located meets a preset warning condition, A warning signal is issued to realize the wind speed warning function.
  • the early warning unit 1313 can be implemented by a processor (such as a flight control system) that can execute logical judgment steps by calling computer software program instructions stored in the memory in advance and related to the early warning conditions.
  • a processor such as a flight control system
  • the system identification unit, the wind speed estimation unit, and the early warning unit can all be implemented by the flight control system of the embodiment of the present invention by calling computer software program instructions corresponding to the functional steps.
  • FIG. 5 uses a functional block diagram as an example to describe in detail the structure of the wind speed estimator provided by the embodiment of the present invention.
  • Those skilled in the art can choose to use software and hardware based on the inventive ideas disclosed in the specification, the steps to be performed and the functions to be implemented, and the actual requirements (such as chip power consumption, heating limit, silicon cost or chip volume, etc.) Or a combination of software and hardware implements the function of the above-mentioned wind speed estimator. For example, using more software parts can reduce the cost of the chip and the circuit area occupied, and facilitate modification. The use of more hardware circuits can improve reliability and computing speed.
  • the embodiment of the present invention also provides a complete wind speed measurement method used by the wind speed estimator.
  • the wind speed estimator and wind speed measurement method provided by the embodiments of the present invention are implemented based on the same inventive concept. Therefore, one or more specific steps in the embodiment of the wind speed calculation method can also be applied to the wind speed estimator, which is implemented by the corresponding functional module. For simplicity of presentation, the description is not repeated here.
  • Fig. 6 is a method flowchart of a method for measuring wind speed provided by an embodiment of the present invention.
  • the wind speed measurement method can be executed by the drone shown in FIG. 1 to obtain wind speed information in the current flight environment of the drone. Specifically, it can be implemented by calling the data information provided by the memory and sensors in the flight control system shown in FIG. 2.
  • the method includes the following steps:
  • System identification estimates the interference of the entire motion system (all equivalent to wind resistance interference) based on the changes in the input and output data of the UAV motion system over time.
  • the flight data is real-time detection, data that changes following the flight state of the drone (for example, the attitude angle, flight speed, flight height, and acceleration of the drone).
  • the attribute data is data set in advance and determined by the inherent attributes of the drone (for example, the quality of the drone, the inherent drag coefficient, and the nonlinear function used to calculate the windward area).
  • the wind resistance interference estimated by the system identification is actually constant, and the UAV itself has resistance in the no-wind state and the wind resistance interference imposed by the external wind in the flying airspace.
  • the corresponding conversion calculation can be performed to determine the wind speed of the current flying environment of the UAV.
  • the wind speed measurement method further includes:
  • the x direction and the y direction are two mutually perpendicular directions, which are located in the plane where the drone is located.
  • the process of calculating the wind direction according to the wind speed component is completed by the following formula (6):
  • ⁇ is the yaw angle of the drone
  • ⁇ is the wind direction
  • V wx is the wind speed component in the x direction
  • V wv is the wind speed component in the y direction.
  • the system identification model used for system identification is the current equivalent drag coefficient of the UAV (the construction of the system identification model is an offline construction process).
  • the equivalent drag coefficient corresponding to the current flight data and attribute data is solved through an online identification method.
  • the equivalent wind resistance coefficient is a mathematical parameter determined by taking all the interference received by the UAV in motion as the wind interference. It represents the relationship between the current operating state of the drone and the wind resistance experienced by the drone.
  • FIG. 7 is a method flowchart of an online identification method provided by an embodiment of the present invention. As shown in Figure 7, the online identification method includes the following steps:
  • the current attitude angle, flight speed, and acceleration of the drone recursively calculate the equivalent wind resistance experienced by the drone.
  • Recursive calculation is a way often used in the process of mathematical operations, which can obtain the target result through multiple recursive calculations when the initial value and the recurrence relationship between two items are given.
  • Equivalent wind resistance refers to the sum of all the resistance that the UAV receives during the flight, as determined by system identification and estimation. Since UAV drag mainly comes from wind, it can all be equivalent to wind drag.
  • the equivalent wind resistance coefficient is a mathematical parameter related to wind resistance. Therefore, it can be determined through appropriate conversion calculation steps on the premise that the corresponding flight data and attribute data of the drone are known.
  • the windward area is calculated from the current attitude angle of the drone and a non-linear function used to calculate the windward area, and the air density is calculated from the current flying height of the drone.
  • x V x or V y
  • P(0) and c(0) are both initial values, which are set to 1 and c 0 respectively in this embodiment.
  • Technicians can also set and use appropriate values as initial values to solve the calculation parameter c(k) according to actual needs.
  • the inherent wind resistance is correspondingly expressed by the inherent wind resistance coefficient of the drone.
  • the wind speed of the flying environment in which the drone is located can be specifically calculated and determined by formula (5).
  • the method of wind speed measurement in addition to the flight data obtained by real-time detection and online identification, in addition to the steps that need to be performed online by the drone during operation, it also includes some offline steps, such as determining the drone in the x direction The intrinsic drag coefficient on the above and the intrinsic drag coefficient in the y direction, the nonlinear function required for fitting and calculating the windward area, and the determination of the quality of the UAV.
  • the wind speed measurement method provided by the embodiment of the present invention adopts the parameter identification process using system identification to estimate the current wind resistance interference of the drone and then calculate the wind speed of the flying environment.
  • This method does not require the use of additional wind speed or wind sensors, nor does it rely on a huge database. Its implementation cost is low, real-time performance is good, and it can be widely used in UAV systems.
  • the flight control system can periodically run the wind speed measurement method provided in the embodiment of the present invention according to a set period to obtain the current wind speed and/or wind direction estimate. Since the flying speed of the UAV is mainly affected by the wind interference during flight, the impact caused by other interference is relatively small. Therefore, the wind speed and/or the estimated value of the wind force calculated and determined under the above-mentioned equivalent setting premise can be basically considered to be relatively accurate, and can basically meet the use needs of early warning.
  • FIG. 8 is a method flowchart of a wind speed measurement method provided by another embodiment of the present invention.
  • the method includes:
  • the flight data and attribute data that need to be obtained depend on the variables that need to be input when calculating the theoretical flight speed of the UAV. Those skilled in the art can adjust or change these data information according to actual needs, preference settings, or accuracy requirements.
  • the flight data will change with the movement state of the drone, and it can be obtained by calculating the data fusion algorithm from the sampling data of the sensors of the drone itself.
  • the attribute data is an inherent attribute of the UAV, which is determined by the structure of the UAV, etc., and will not change with the motion state. It can be pre-set and recorded in the memory through experiments, etc., and read from the memory when needed.
  • the wind speed of the flying environment of the UAV can be continuously updated to ensure timely warning.
  • the specific update period is an empirical value, which can be adjusted or set according to the actual situation, such as a period of 1 min or longer.
  • step 803. Determine whether the wind speed meets a preset warning condition. If yes, go to step 804, if no, go back to step 802 to update the wind speed.
  • the preset warning condition is a predetermined judgment standard based on experience or the actual situation of the drone (for example, the drone's ability to withstand wind speed). It can be composed of one or more conditions, which are used to measure the probability of an uncontrolled accident. That is, when the preset warning conditions are met, it indicates that the flight control system of the UAV has basically reached the design upper limit under wind interference, and the possibility of abnormalities or accidents is very high.
  • the warning condition may be a preset warning threshold.
  • the monitor 132 can continuously monitor whether the wind speed has reached the alarm threshold, and when the wind speed reaches the alarm threshold, send a warning signal to the remote control device 20.
  • the alarm threshold is also an empirical value, which can be determined or set by a technician according to the specific operating state of the drone through experimental tests and other methods.
  • the warning signal can be represented by any suitable form or type of identification, for example, a warning flag simply represented by 1 and 0. When the value of the warning flag is 1, it indicates that a warning signal is issued, and when the value of the warning flag is 0, it indicates that there is no warning signal.
  • the logic for the monitor 132 to trigger the warning signal can be expressed by the following formula (11):
  • V wx is the wind speed in the x direction
  • V wv is the wind speed in the y direction
  • flag is the value of the warning signal flag. That is, when the sum of the squares of the wind speeds in the x direction and the y direction is greater than or equal to the square of the preset alarm threshold, the monitor 132 will determine that the wind speed meets the preset warning condition and issue a warning signal.
  • equation (11) is only used for exemplification and is not used to limit the working steps of the monitor 132 to send the warning signal.
  • Those skilled in the art can also use other different warning conditions to measure whether the UAV has excessive wind speed and the aircraft control system cannot effectively control the situation according to actual needs.
  • the remote control device 20 may feed back corresponding warning prompt information to the user through the display screen or other interactive devices, so as to remind the user to stop the drone flight in time and land to a safe and controllable position.
  • the specific warning message can be set according to the actual situation, including but not limited to text or pictures. For example, you can highlight the words—the current wind speed is too high—on the display interface of the remote control, or display the current wind speed exceeding the limit with a specific color icon. Further, voice prompts can also be broadcast through the speaker.
  • the wind speed detection method provided by the embodiment of the present invention is applied to drones, which can effectively solve the problem that the existing ordinary drones cannot make predictions, which causes users/operators to be too late to operate, and the problem of explosions due to excessive wind speeds can be detected at wind speeds. When it is too large, the user is prompted to fly carefully or choose a safe place to land.
  • the flight control system can specifically execute the steps shown in FIG. 9 to realize the wind speed measurement and early warning of the drone without relying on the wind speed sensor and the database.
  • the drone has one or more basic sensors, which can at least collect flight data such as attitude angle (including pitch angle, roll angle, and heading angle), acceleration, and flight speed of the drone in real time.
  • attitude angle including pitch angle, roll angle, and heading angle
  • acceleration and flight speed of the drone in real time.
  • the x direction and y direction are two perpendicular vectors in the plane where the drone is located. Among them, the x direction is the moving direction of the drone forward and backward, and the y direction is the moving direction of the drone flying to the left and to the right.
  • the quality of the UAV, the non-linear functions f fb ( ⁇ , ⁇ ) and f rl ( ⁇ , ⁇ ) of the windward area and attitude angle in the x and y directions, and the inherent characteristics of the UAV in the x direction The drag coefficient and the inherent drag coefficient in the y direction are measured and determined, and the records are stored in the UAV's memory.
  • the non-linear function of the windward area and the attitude angle and the inherent drag coefficient can be simulated by using the experimental data obtained in an ideal environment (for example, collecting multiple sets of flight data of a drone flying in an indoor windless environment) by the method of least squares. ⁇ determined.
  • the calculation process performed by the flight control system includes:
  • the initial value needed for the recursive calculation can be set or initialized according to the actual situation. For example, you can simply initialize these initial values to zero.
  • the parameter c(k) is updated according to the recurrence formula shown in equation (10).
  • Fig. 10 is a graph of wind speed changing with time according to an embodiment of the present invention. As shown in Figure 10, when the system identification model disclosed in the embodiment of the present invention is used, the wind speed components in the x direction and the y direction can be calculated separately, and the corresponding two can be combined into the current wind speed experienced by the drone to obtain The curve of wind speed over time.
  • the judgment logic uses the alarm threshold value as the judgment condition to determine whether a warning signal is required. Please continue to refer to Figure 8.
  • the alarm threshold is a preset empirical value. When the wind speed is higher than the alarm threshold, an alarm signal is issued, indicating that the wind is relatively high at this time, and the user or operator needs to be reminded.
  • steps 902 and 903 are executed again to calculate and update the wind speed of the flying environment of the drone.
  • Fig. 11 is a graph of wind direction variation with time according to an embodiment of the present invention.
  • Fig. 11 is based on the wind speed curve shown in Fig. 10 and the corresponding wind direction curve obtained through the conversion of formula (6).
  • the calculated wind direction angle can also be transmitted to the remote control device 20, and displayed to the user through an interactive device (such as a display screen) of the remote control device 20.
  • the wind speed measurement method provided by the embodiments of the present invention and the UAV early warning method implemented based on it do not need to use wind speed-related sensors and create a huge database. Based on existing information, The estimation of the wind speed is realized by algorithm, and then the corresponding wind speed and/or wind direction is determined.

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Abstract

一种风速测算方法、风速估算器及无人机（10）。风速测算方法包括：基于无人机（10）的飞行数据和属性数据，通过系统辨识确定无人机（10）当前的风阻干扰（601）；根据风阻干扰与无人机（10）的固有风阻，计算无人机（10）所处飞行环境的风速（602）。该方法在不依赖新增风速传感器和外部数据库的前提下，通过辨识参数的方式实现了风速测算，既节省了硬件设备的成本，又不会带来额外的算力负担和实时性的问题，方法简单且成本低廉。

Description

风速测算方法、风速估算器及无人机
本申请要求于2019年7月26日提交中国专利局、申请号为201910682355.6、申请名称为“风速测算方法、风速估算器及无人机”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及无人机技术领域，尤其涉及一种风速测算方法、风速估算器、及无人机。
背景技术
无人机作为一种适应性强，使用成本低，投放快速方便的悬停式空中载具，在许多不同的场合都得到广泛的应用。其通过搭载不同类型的功能组件，可以发挥重要的作用。
在飞行过程中，无人机会受到风的干扰。当风速或者风力较小时，飞行控制系统自身的鲁棒性能够抵抗风干扰，保证无人机的平稳飞行。但是，飞行控制系统所能够调节或者抵抗的风力范围是有特定的限度范围的。
当风速超过无人机能够承受的上限以后，飞行控制系统的稳定性将难以维持，容易出现无人机无法返航，甚至炸机等事故。尤其是航拍无人机在风速较大时，其航拍质量会严重地受到影响。
因此，风速检测功能是非常重要的功能。基于无人机风速检测和估计可以为无人机使用者提供较好的预警，有效的避免事故发生。
目前的风速检测或者估计方法大致可以分为采用风速传感器直接测量气流速度和采用事先建立数据库的方法或基于大数据方法来进行风速估计两种。但是，采用风速或者风力传感器直接测量气流速度的方法需要在无人机上增加额外的传感器，造成无人机制作成本的上升。而建立数据库或者大数据计算的方法需要消耗较多的算力，加重飞行控制系统的运算负担。而且，数据库加载在飞机上，会极大的占用内存，耗费的时间也比较多，对于风速检测的实时性有比较大的影响。
因此，迫切需要新的低成本风速检测方式。
发明内容
为了解决上述技术问题，本发明实施例提供一种不依赖数据库和新增风速传感器的风速测算方法、风速估算器及无人机。
为解决上述技术问题，本发明实施例提供一种风速测算方法。所述风速测算方法包括：
基于、无人机的飞行数据和属性数据，通过系统辨识确定所述无人机当前的风阻干扰；所述飞行数据包括：无人机的姿态角、飞行速度、加速度以及飞 行高度；
所述属性数据包括：无人机的质量、固有风阻系数以及用于计算迎风面积的非线性函数；
根据所述风阻干扰与所述无人机的固有风阻，计算所述无人机所处飞行环境的风速。
可选地，所述基于无人机的飞行数据和属性数据，通过系统辨识确定所述无人机当前的风阻干扰，包括：
构建无人机的系统辨识模型，所述系统辨识模型的待辨识参数为所述无人机当前的等效风阻系数；
通过在线辨识方法，根据所述无人机当前的飞行数据以及所述属性数据求解相对应的所述等效风阻系数；
所述根据所述风阻干扰与所述无人机的固有风阻，计算所述无人机所处飞行环境的风速，包括：
根据所述等效风阻系数与所述无人机的固有风阻系数之差，计算所述无人机所处飞行环境的风速。
可选地，所述通过在线辨识方法，求解与当前的飞行数据以及属性数据相对应的所述等效风阻系数，具体包括：
离散化所述系统辨识模型，形成对应的离散方程；
根据预设的初始值，所述无人机当前的姿态角、飞行速度以及加速度，递推计算所述无人机受到的等效风阻力；
根据所述无人机当前的迎风面积以及空气密度，将所述等效风阻力转换为等效风阻系数；
所述迎风面积由所述无人机当前的姿态角和用于计算迎风面积的非线性函数计算获得，所述空气密度由所述无人机当前的飞行高度计算获得。
可选地，所述等效风阻系数由在x方向上的等效风阻系数分量和y方向上的等效风阻系数分量表示，所述风速由在x方向上的风速分量和在y方向上的风速分量表示；所述x方向和所述y方向之间相互垂直，与所述无人机处于相同的平面。
可选地，所述根据所述风阻干扰与所述无人机的固有风阻，计算所述无人机所处飞行环境的风速，具体包括：
通过如下算式计算所述无人机所处飞行环境的风速：
Figure PCTCN2020104585-appb-000001
其中，V wx为所述无人机所处飞行环境的风速在x方向上的风速分量，V wy为所述无人机所处飞行环境的风速在y方向上的风速分量，V x为无人机在x方向上的速度，V y为无人机在y方向上的速度，ρ为飞行海拔处的空气密度，S fb为无人机沿x方向飞行时的迎风面积，S rl为无人机沿y方向飞行时的迎风面 积，C x为在x方向上的等效风阻系数分量，C y为在y方向上的等效风阻系数分量，C dx为无人机在x方向上的固有风阻系数，C dy为无人机在y方向上的固有风阻系数。
可选地，所述无人机在x方向上的固有风阻系数以及在y方向上的固有风阻系数根据所述无人机在无风室内的飞行数据，通过最小二乘法拟合确定。
可选地，所述系统辨识模型通过如下算式表示：
Figure PCTCN2020104585-appb-000002
其中，
Figure PCTCN2020104585-appb-000003
为无人机在x方向上的加速度，
Figure PCTCN2020104585-appb-000004
为无人机在y方向上的加速度，V x为无人机在x方向上的速度，V y为无人机在y方向上的速度，
T为螺旋桨拉力，ρ为俯仰角，φ为滚转角，ρ为飞行海拔处的空气密度，S fb为无人机沿x方向飞行时的迎风面积，S rl为无人机沿y方向飞行时的迎风面积，C x为在x方向上的等效风阻系数分量，C y为在y方向上的等效风阻系数分量，m为无人机的质量，w x为在x方向上的模型不确定性，w y为在y方向上的模型不确定性。
可选地，所述迎风面积通过如下算式计算确定：
S fb＝S fb0(1+f fb(θ，φ))
S rl＝S rl0(1+f rl(θ，φ))
其中，S fb为无人机沿x方向飞行时的迎风面积，S rl为无人机沿x方向飞行时的迎风面积；S fb0为姿态角为0时，无人机沿x方向飞行时的迎风面积；S rl0为姿态角为0时，无人机沿y方向飞行时的迎风面积；f fb(θ，φ)和f rl(θ，φ)为非线性函数；θ为俯仰角；φ为滚转角。
可选地，所述螺旋桨拉力通过如下算式计算获得：
Figure PCTCN2020104585-appb-000005
其中，a z为无人机在z方向上的加速度，g为重力加速度；所述z方向垂直于所述x方向和所述y方向组成的平面；θ为俯仰角；φ为滚转角；m为无人机的质量。
可选地，所述方法还包括：
根据在x方向和y方向上的风速分量，通过如下算式，计算风向：
β＝ψ+arctan2(-V wx,-V wv)
其中，ψ为无人机的偏航角，β为风向，V wx为在x方向上的风速分量，V wv为在y方向上的风速分量。
可选地，所述方法还包括；在所述无人机所处飞行环境的风速满足预设的 警告条件时，发出警告信号。
可选地，所述在所述无人机所处飞行环境的风速满足预设的警告条件时，发出警告信号，包括：
通过如下算式计算判断是否符合预设的警告条件：
Figure PCTCN2020104585-appb-000006
其中，V wx为在x方向上的风速分量，V wv为在y方向上的风速分量，V thr为安全风速门限值；
在符合预设的警告条件时，发出警告信号；
在不符合所述预设的警告条件时，继续检测所述无人机所处飞行环境的风速。
本发明另一实施例提供了一种风速估算器。其中，所述风速估算器包括：
系统辨识单元，所述系统辨识单元用于接收无人机的飞行数据和属性数据，并根据所述飞行数据和属性数据，辨识确定所述无人机当前的风阻干扰；
所述飞行数据包括：无人机的姿态角、飞行速度、加速度以及飞行高度；所述属性数据包括：无人机的质量、固有风阻系数以及用于计算迎风面积的非线性函数；
风速估计单元，所述风速估计单元与所述系统辨识单元连接，用于根据所述风阻干扰与所述无人机的固有风阻，计算所述无人机所处飞行环境的风速。
可选地，所述系统辨识单元内存储有预先设置的系统辨识模型，所述系统辨识模型的待辨识参数为等效风阻系数；
所述系统辨识单元用于：通过在线辨识方法，根据所述当前的飞行数据以及所述属性数据求解相对应的所述等效风阻系数。
可选地，所述系统辨识单元还用于：
离散化所述系统辨识模型，形成对应的离散方程；
根据预设的初始值，所述无人机当前的姿态角、飞行速度以及加速度，递推计算所述无人机受到的等效风阻力；
根据所述无人机当前的迎风面积以及空气密度，将所述等效风阻力转换为等效风阻系数；
所述迎风面积由所述无人机当前的姿态角和用于计算迎风面积的非线性函数计算获得，所述空气密度由所述无人机当前的飞行高度计算获得。
可选地，所述等效风阻系数由在x方向上的等效风阻系数分量和y方向上的等效风阻系数分量表示，所述风速由在x方向上的风速分量和在y方向上的风速分量表示；所述x方向和所述y方向之间相互垂直，与所述无人机处于相同的平面。
可选地，所述系统辨识模型通过如下算式表示：
Figure PCTCN2020104585-appb-000007
其中，
Figure PCTCN2020104585-appb-000008
为无人机在x方向上的加速度，
Figure PCTCN2020104585-appb-000009
为无人机在y方向上的加速度，V x为无人机在x方向上的速度，V y为无人机在y方向上的速度，
T为螺旋桨拉力，θ为俯仰角，φ为滚转角，ρ为飞行海拔处的空气密度，S fb为无人机沿x方向飞行时的迎风面积，S rl为无人机沿y方向飞行时的迎风面积，C x为在x方向上的等效风阻系数分量，C y为在y方向上的等效风阻系数分量，m为无人机的质量，w x为在x方向上的模型不确定性，w y为在y方向上的模型不确定性。
可选地，所述风速估计单元还用于接收所述无人机当前的姿态角、飞行速度、飞行高度、固有风阻系数以及用于计算迎风面积的非线性函数，并通过如下算式计算所述无人机所处飞行环境的风速：
Figure PCTCN2020104585-appb-000010
其中，V wx为所述无人机所处飞行环境的风速在x方向上的风速分量，V wy为所述无人机所处飞行环境的风速在y方向上的风速分量，V x为无人机在x方向上的速度，V y为无人机在y方向上的速度，ρ为飞行海拔处的空气密度，S fb为无人机沿x方向飞行时的迎风面积，S rl为无人机沿y方向飞行时的迎风面积，C x为在x方向上的等效风阻系数分量，C y为在y方向上的等效风阻系数分量，C dx为无人机在x方向上的固有风阻系数，C dy为无人机在y方向上的固有风阻系数。
可选地，所述风速估算器还包括：预警单元；
所述预警单元用于在所述无人机所处飞行环境的风速满足预设的警告条件时，发出警告信号。
可选地，所述预警单元还用于：
通过如下算式计算判断是否符合所述预设的警告条件：
Figure PCTCN2020104585-appb-000011
其中，V wx为在x方向上的风速分量，V wv为在y方向上的风速分量，V thr为安全风速门限值；
在符合所述预设的警告条件时，发出警告信号；
在不符合所述预设的警告条件时，继续检测所述无人机所处飞行环境的风速。
本发明又一实施例提供了一种无人机。其中，所述无人机包括机身主体、 设置在所述机身主体上的一个或者多个传感器、存储器以及飞行控制系统；所述存储器内存储有计算机可执行程序指令，在所述计算机可执行程序指令被所述飞行控制系统调用时，以使所述飞行控制系统从所述传感器获取的飞行数据，并从所述存储器获取属性数据，执行如上所述的风速测算方法。
可选地，所述飞行控制系统还用于将所述无人机所处飞行环境的风速转换为风向，并在所述无人机对应的遥控设备上显示所述风速和风向。
与现有技术相比较，本发明实施例提供的风速测算方法利用系统辨识的原理，在不依赖新增风速传感器和外部数据库的前提下，通过辨识参数的形式实现了风速测算的过程，既节省了硬件设备的成本，又不会带来额外的算力负担和实时性的问题，方法简单并且成本低廉。
进一步地，还可以将风速测算的结果应用于预警功能，对使用者进行提示或者报警，有效的降低飞行事故发生的概率。
附图说明
一个或多个实施例通过与之对应的附图中的图片进行示例性说明，这些示例性说明并不构成对实施例的限定，附图中具有相同参考数字标号的元件表示为类似的元件，除非有特别申明，附图中的图不构成比例限制。
图1为本发明实施例的应用环境的示意图；
图2为本发明实施例提供的无人机的功能框图；
图3为本发明实施例提供的RC遥控器的显示界面的示意图；
图4为本发明实施例提供的智能终端的显示界面的示意图；
图5为本发明实施例提供的风速估算器的功能框图；
图6为本发明实施例提供的风速测算方法的方法流程图；
图7为本发明实施例提供的辨识参数递推计算方法的方法流程图；
图8为本发明另一实施例提供的风速测算方法的方法流程图；
图9为本发明实施例提供的飞行控制系统执行的计算过程的方法流程图；
图10为本发明实施例提供的风速跟随时间变化的曲线图；
图11为本发明实施例提供的风向跟随时间变化的曲线图。
具体实施方式
为了便于理解本发明，下面结合附图和具体实施例，对本发明进行更详细的说明。需要说明的是，当元件被表述“固定于”另一个元件，它可以直接在另一个元件上、或者其间可以存在一个或多个居中的元件。当一个元件被表述“连接”另一个元件，它可以是直接连接到另一个元件、或者其间可以存在一个或多个居中的元件。本说明书所使用的术语“上”、“下”、“内”、“外”、“底部”等指示的方位或位置关系为基于附图所示的方位或位置关系，仅是为了便于描述本发明和简化描述，而不是指示或暗示所指的装置或元件必须具有特定 的方位、以特定的方位构造和操作，因此不能理解为对本发明的限制。此外，术语“第一”、“第二”“第三”等仅用于描述目的，而不能理解为指示或暗示相对重要性。
除非另有定义，本说明书所使用的所有的技术和科学术语与属于本发明的技术领域的技术人员通常理解的含义相同。本说明书中在本发明的说明书中所使用的术语只是为了描述具体的实施例的目的，不是用于限制本发明。本说明书所使用的术语“和/或”包括一个或多个相关的所列项目的任意的和所有的组合。
此外，下面所描述的本发明不同实施例中所涉及的技术特征只要彼此之间未构成冲突就可以相互结合。
系统辨识是系统控制方法的其中一种，其依赖于系统的输入输出时间函数来确定和描述系统行为的数据模型，从而实现对系统行为的预测。在系统辨识过程中，会根据先验知识并结合参数辨识的过程来确定具体的数据模型。利用一些计算确定的待辨识参数可以实现对整个系统的外部干扰的估计，并进而通过合适的一系列转换方法获得所需要的参数。该外部干扰可以是对于某个运动系统施加的干扰，例如无人机在飞行过程中，受风力干扰而对无人机造成的影响。
图1为本发明实施例提供的应用环境。如图1所示，所述应用环境以无人机系统为例，包括无人机10、遥控设备20以及无线网络30。
无人机10可以是以任何类型的动力驱动(如电力)的无人飞行载具，包括但不限于四轴无人机、固定翼飞行器以及直升机模型等。在本实施例中以四轴无人机为例进行陈述。无人机10的机身主体上可以搭载有若干不同的功能模块，这些功能模块可以是软件模块、硬件模块或者软件和硬件结合的，用于实现某一项或多项功能的模块化装置。
遥控设备20可以是任何类型，用以与无人机建立通信连接，控制无人机的装置，例如RC遥控器。该RC遥控器可以装配有一种或者多种不同的用户交互装置，基于这些用户交互装置来采集用户指令或者向用户展示和反馈信息，实现用户与无人机之间的交互。
这些交互装置包括但不限于：按键、滚轮、显示屏、触摸屏、鼠标、扬声器以及遥杆。例如，遥控设备20可以装配有显示屏，通过该显示屏接收用户对无人机的遥控指令并通过显示屏向用户展示航拍图像，或者是向用户呈现相应的模拟驾驶界面，在模拟驾驶界面上展示一项或者多项飞行参数，如飞行速度、航向或者剩余电量等。
在另一些实施例中，该遥控设备20还可以由智能终端实现。该智能终端包括但不限于智能手机、平板电脑、手提电脑以及可穿戴设备等。该智能终端通过运行特定设置的APP客户端或者网页端与无人机建立通信连接，实现与无人机之间的数据收发，从而作为遥控设备20使用。
无线网络30可以是基于任何类型的数据传输原理，用于建立两个节点之 间的数据传输信道的无线通信网络。例如，位于不同信号频段的蓝牙网络、WiFi网络、无线蜂窝网络或者其结合。无线网络30具体使用的频段或者网络形式由无人机10和遥控设备20采用的通信设备相关。
图2为本发明实施例提供的无人机10的功能框图。在一些实施例中，如图2所示，为了实现无人机10最基础的飞行需求，无人机10搭载的功能模块至少包括：传感器11、存储器12以及飞行控制系统13。
其中，传感器11是设置在机身主体内，用于检测无人机在飞行过程中的运动状态参数的传感器，例如六轴陀螺仪，加速度计等。这些传感器11是无人机10设计制造中必须具备的基础传感器，用以监控无人机10当前的运动状态以实现对无人机10飞行的有效控制。
存储器12为非易失性计算机可读存储介质，例如至少一个磁盘存储器件、闪存器件或者其他非易失性固态存储器件。其具有程序存储区和数据存储区，分别用于存储对应的数据信息，例如存储在程序存储区的非易失性软件程序、非易失性计算机可执行程序以及模块，或者是存储在数据存储区的运算处理结果以及拍摄图像信息等。
飞行控制系统13是无人机飞行控制的核心，其具体可以采用任何类型的处理器，作为逻辑处理和运算的核心。飞行控制系统13用于获取数据、执行逻辑运算功能并下发运算处理结果，根据用户的指令改变无人机10的飞行状态并确保无人机10处于安全可控的飞行状态。
一方面，飞行控制系统13可以从所述传感器11获取一种或者多种的采集数据，通过设定的数据融合或者分析方法，分析确定与无人机相关的若干项数据信息(如姿态角、加速度以及飞行速度等)，作为控制无人机运动状态的基础。另一方面，飞行控制系统13还与存储器12连接，在存储器12中调用相应的软件程序或者计算机可执行程序，来执行对应的逻辑运算功能，进行相应的运算和判断。
例如，为实现风速预警的功能，在无人机的飞行过程中，该飞行控制系统13可以读取所述无人机相关的数据信息，利用系统辨识的原理实现对无人机当前所受的风力干扰的估测，输出相应的风速估计值，然后，将输出的风速估计值与预设的警告条件比较，确定此时是否需要触发警告信号。
在触发警告信号以后，无人机10会通过无线网络30反馈至遥控设备20。遥控设备20在接收到该警告信号后，可以通过交互装置展示相应的警告提示信息，提醒操作者注意飞行安全，及时降落至合适地点等。
例如，在遥控设备20为RC遥控器时，可以使用如图3所示的显示界面，在模拟驾驶界面的中央提示用户—“风速较大”。而当遥控设备20为智能终端时，如图4所示，可以在智能终端的显示屏的局部区域显示提示信息(Tips)。又或者是，通过遥控设备20的扬声器播放特殊的警报提示音，提示当前的风速过大的警告情况。
基于该风速估算器提供的所述无人机所处飞行环境的风速，在另一些实施 例中，飞行控制系统13还可以将其转换为风向数据并提供给遥控设备20，由遥控设备20的显示屏等交互设备展示所述风速和风向，令操作者可以及时的获知飞行空域当期的风力情况。
在图1所示的应用环境中，仅显示了风速观测和预警功能在无人机系统上的应用。本领域技术人员可以理解的是，实现该风速观测和预警功能的功能模块还可以被搭载在其它类型的移动载具(如遥控车)上，通过接收移动载具相关的数据信息，计算获得对移动载具的运动干扰情况来实现上述相同的或者类似的预警功能。本发明实施例公开的发明思路并不限于在图1所示的无人机系统上应用。
基于本发明实施例公开的利用风速估算器计算无人机所处飞行环境的风速干扰的发明思路，根据实践的需要或者无人机的使用场景等，本领域技术人员容易想到将其中的一个或者多个步骤、参数进行调整、替换或者变更，用以构建其它的替代模型。这些替代模型均是本领域技术人员在本发明的基础上，从无人机不同的侧面进行考虑，通过合理的推导获得的。
例如，可以通过检测悬停时，无人机的姿态角变化来定量观测干扰强度。还可以替换使用力平衡原理、干扰观测原理等方法，确定出风力对无人机运行的干扰量，进而完成对无人机所承受的风速的估计。
以下详细介绍基于系统辨识原理进行风速测算的过程。图5为本发明实施例提供的风速估算器的功能框图。如图5所示，该风速估算器包括系统辨识单元1311以及风速估计单元1312。
其中，所述系统辨识单元1311用于接收与无人机的飞行数据和属性数据，并通过系统辨识确定所述无人机当前的风阻干扰。该系统辨识单元1311可以由可执行逻辑判断步骤的处理器(如飞行控制系统)，通过调用预先存储在存储器中的，与系统辨识相关的计算机软件程序指令来实现。
其中，所述飞行数据包括：无人机的姿态角、飞行速度、加速度以及飞行高度；所述属性数据包括：无人机的质量、固有风阻系数以及用于计算迎风面积的非线性函数。
该系统辨识单元1311采用系统辨识的方式来定量的确定无人机运动所受到的干扰。一般的，基本可以认为无人机运动所受到的干扰主要来自于飞行空域中的风力。因此，可以将所受到的干扰均等效视作为风力形成的阻力，从而计算获得所述风阻干扰。
具体的，所述系统辨识单元1311使用由一些先验知识(如无人机的速度动态变化方程等)构建的系统辨识模型。在该系统辨识模型中，待辨识参数可以为等效风阻系数。
等效风阻系数具体是与风阻干扰相关的一项参数，用于表征无人机与所受到的风阻力之间的关系。亦即，在获知等效风阻系数后，结合无人机传感器采集获得多项与无人机自身属性(即属性数据)和运动状态相关的数据(即飞行数据)以后，即可换算获得此时的风阻力。
该风速估计单元1312与系统辨识单元1311连接，其接收所述风阻干扰并且根据其相对于所述无人机的固有风阻的变化而计算确定所述无人机所处飞行环境的风速。具体的风速计算过程可以根据输入的风阻干扰的形式所决定。其可以采用任何类型的转换方法来完成。
该风速估计单元1312可以由可执行逻辑判断步骤的处理器(如飞行控制系统)，通过调用预先存储在存储器中的，与风速计算相关的计算机软件程序指令来实现。
在一些实施例中，为了便于进行计算和表示，可以在无人机所处的平面上构建两个相互垂直的x方向和y方向，并分别计算在这两个方向上的等效风阻系数分量以及风速分量来完成风速估算的过程。
具体的，基于无人机的受力，速度动态变化等先验知识，可以构建如下算式(1)所示的系统辨识模型：
Figure PCTCN2020104585-appb-000012
其中，
Figure PCTCN2020104585-appb-000013
为无人机在x方向上的速度变化率(即加速度)，
Figure PCTCN2020104585-appb-000014
为无人机在y方向上的速度变化率，V x为无人机在x方向上的速度，V y为无人机在y方向上的速度，T为螺旋桨拉力，θ为俯仰角，φ为滚转角，ρ为飞行海拔处的空气密度，S fb为无人机沿x方向飞行时的迎风面积，S rl为无人机沿y方向飞行时的迎风面积，C x为在x方向上的等效风阻系数分量，C y为在y方向上的等效风阻系数分量，m为无人机的质量，w x为在x方向上的模型不确定性，w y为在y方向上的模型不确定性。
不同高度具有相应的空气密度，无人机的飞行高度处的空气密度通常可以查表获得。当然，在空气密度变化不显著时，也可以直接使用一个固定值以忽略空气密度的微小变化。
模型不确定性是用于补偿已设立的系统模型与实际运动情况不相符的调节部分。其作为一个经验性数值或者函数，可以通过实验或者数据分析等合适的数据统计方式确定和调整。
迎风面积是一个随着无人机的飞行姿态而发生变化的参数。在一些实施例中，其可以近似的被认为是与姿态角相关的非线性函数。例如，该非线性函数可以写为如下算式(2)和(3)所示的形式：
S fb＝S fb0(1+f fb(θ，φ))        (2)
S rl＝S rl0(1+f rl(θ，φ))          (3)
其中，S fb0为姿态角为0时，无人机沿x方向飞行时的迎风面积；S rl0为姿态角为0时，无人机沿y方向飞行时的迎风面积。
螺旋桨拉力与电机的输出功率相关，其对外表现为无人机的加速度。通常的，越大的加速度也意味着输出的螺旋桨拉力更高。在一些实施例中，该螺旋 桨拉力可以由如下算式(4)计算获得：
Figure PCTCN2020104585-appb-000015
其中，a z为无人机在z方向上的加速度，g为重力加速度；所述z方向垂直于所述x方向和所述y方向组成的平面。
在算式(1)所示的系统辨识模型中，基于整个无人机运动系统的输入和输出的无人机的姿态角、飞行速度以及加速度等的变化，系统辨识单元1311可以完成参数辨识的过程，获得当前的等效风阻系数用以反映无人机所处飞行环境的风阻干扰。
与辨识参数相对应的，所述无人机的固有风阻则使用无人机的固有风阻系数表示。具体的，风速估计单元1312可以通过如下算式(5)计算所述无人机所处飞行环境的风速：
Figure PCTCN2020104585-appb-000016
其中，V wx为所述无人机所处飞行环境的风速在x方向上的风速分量，V wy为所述无人机所处飞行环境的风速在y方向上的风速分量，V x为无人机在x方向上的速度，V y为无人机在y方向上的速度，ρ为飞行海拔处的空气密度，S fb为无人机沿x方向飞行时的迎风面积，S rl为无人机沿y方向飞行时的迎风面积，C x为在x方向上的等效风阻系数分量，C y为在y方向上的等效风阻系数分量，C dx为无人机在x方向上的固有风阻系数，C dy为无人机在y方向上的固有风阻系数。
该固有风阻系数是无人机在无风环境下，因自身的形状结构等所确定的一个数学参数。其具体可以在无人机出厂销售前，通过无风的室内飞行或者其它理想的实验环境下飞行所采集获得的多组实验数据，通过最小二乘法等数据统计方式拟合确定。
应当说明的是，上述计算固有风阻系数的方式是离线计算的过程，可以预先完成并记录存储在无人机的存储器中，由风速估计单元1312调用而并不需要在每一个无人机上进行。
根据以上实施例揭露的算式，本领域技术人员可以理解的，系统辨识单元在进行系统辨识，确定无人机当前所受到的风阻干扰时，需要使用的无人机相关的数据信息至少包括：无人机的姿态角、飞行速度、加速度、飞行高度、无人机的质量、固有风阻系数以及用于计算迎风面积的非线性函数。
其中，无人机的姿态角、飞行速度、加速度以及飞行高度都是跟随无人机的运动状态而变化的参数。其可以基于无人机上设置的一系列基础传感器采集获得的采样数据，通过数据融合等一种或者多种方式获得。
而无人机的质量、固有风阻系数以及用于计算迎风面积的非线性函数是由 无人机的固有属性所决定的参数。其可以预先通过实验等方式离线计算确定后，存储记录在存储器中被调用。
请继续参阅图5，在另一些实施例中，所述风速估算器还可以包括预警单元1313。
所述预警单元1313与所述风速估算单元1312连接，用于接收风速估算单元1312提供的当前飞行环境的风速，并且在所述无人机所处飞行环境的风速满足预设的警告条件时，发出警告信号以实现风速预警的功能。
该预警单元1313可以由可执行逻辑判断步骤的处理器(如飞行控制系统)，通过调用预先存储在存储器中的，与预警条件相关的计算机软件程序指令来实现。
亦即，所述系统辨识单元、风速估计单元以及预警单元均可以由本发明实施例的飞行控制系统，分别调用对应功能步骤的计算机软件程序指令来实现。
应当说明的是，图5以功能框图为例，详细的描述了本发明实施例提供的风速估算器的结构。本领域技术人员根据说明书揭露的发明思想、所要执行的步骤和实现的功能，根据实际情况的需求(例如芯片功耗、发热的限制、硅片成本或者芯片的体积等)可以选择使用软件、硬件或者软硬件结合的方式实现上述风速估算器的功能。例如，使用更多的软件部分可以降低芯片的成本和占用的电路面积，并且便于修改。而使用更多的硬件电路实现可以提高可靠性和运算速度。
在图5所示的风速估算器的结构框架的基础上，本发明实施例还提供了风速估算器所使用的完整的风速测算方法。本发明实施例提供的风速估算器和风速测算方法基于相同的发明构思而实现。因此，风速测算方法实施例中的一个或者多个具体步骤同样也可以应用到风速估算器中，由相应的功能模块实现，为陈述简便，在此不作重复叙述。
图6为本发明实施例提供的风速测算方法的方法流程图。在本实施例中，该风速测算方法可以由图1所示的无人机执行，获得无人机当前飞行环境中的风速信息。具体的，可以由图2所示的飞行控制系统，调用存储器和传感器提供的数据信息所实现。
如图6所示，该方法包括如下步骤：
601、基于无人机的飞行数据和属性数据，通过系统辨识确定所述无人机当前的风阻干扰。
系统辨识根据无人机这一运动系统的输入输出数据随时间的变化情况来估算整个运动系统所受到的干扰(全部等效为风阻干扰)。
其中，飞行数据是实时检测，跟随所述无人机的飞行状态而变化的数据(例如无人机的姿态角、飞行速度、飞行高度以及加速度)。属性数据是预先设置，由所述无人机的固有属性决定的数据(例如无人机的质量、固有风阻系数以及用于计算迎风面积的非线性函数)。
602、根据所述风阻干扰与所述无人机的固有风阻，计算所述无人机所处 飞行环境的风速。
可以理解的，系统辨识估算的风阻干扰实际上由恒定不变的，在无风状态下无人机自身存在阻力以及飞行空域中，外界风力施加的风阻干扰。
由此，根据完整的风阻干扰相对于固有风阻的变化即可相应的转换计算从而确定所述无人机当前所处飞行环境的风速。
基于风速的测算结果，在一些实施例中，请继续参阅图6，所述风速测算方法还包括：
603、根据所述无人机所处飞行环境的风速在x方向和y方向上的风速分量，计算风向。
其中，x方向和y方向是两个相互垂直的方向，位于无人机所在的平面内。根据风速分量计算所述风向的过程通过如下所示的算式(6)完成：
β＝ψ+arctan2(-V wx,-V wv)           (6)
其中，ψ为无人机的偏航角，β为风向，V wx为在x方向上的风速分量，V wv为在y方向上的风速分量。
在一些实施例中，系统辨识所使用的系统辨识模型为所述无人机当前的等效风阻系数(系统辨识模型的构建为离线构建过程)。而在系统辨识时，通过在线辨识方法来求解与当前的飞行数据以及属性数据相对应的所述等效风阻系数。
该等效风阻系数是将无人机运动时，所受到的所有干扰均等效为风力干扰而确定的一个数学参数。其表示了无人机当前运行状态与无人机受到的风阻力之间的关系。
根据实际使用的需要或者模型的构造结构等，技术人员可以采用任何合适的在线辨识方法来完成系统辨识的过程。图7为本发明实施例提供的在线辨识方法的方法流程图。如图7所示，该在线辨识方法包括如下步骤：
701、离散化所述系统辨识模型，形成对应的离散方程。
由于数学模型一般采用方程组的形式来表示，其跟随时间变化的数值总是连续的。因此，若需要计算机等设备对连续时间系统状态方程求解时，需先将其状态方程化为离散方程。
702、根据预设的初始值，所述无人机当前的姿态角、飞行速度以及加速度，递推计算所述无人机受到的等效风阻力。
递推计算是数学运算过程中经常使用的方式，其可以在给定初始值和两项之间的递推关系时，通过多次递推计算而获得目标结果。
等效风阻力是指系统辨识估算确定的，无人机在飞行过程中所受到的所有阻力之和。因无人机阻力主要来自于风力，因此可以全部等效为风阻力。
703、根据所述无人机当前的迎风面积以及空气密度，将所述等效风阻力转换为等效风阻系数。
如上实施例所记载的，等效风阻系数是一个与风阻力相关的数学参数。因此，可以在已知无人机相应的飞行数据和属性数据的前提下，通过合适的转换 计算步骤而确定。
其中，所述迎风面积由所述无人机当前的姿态角和用于计算迎风面积的非线性函数计算获得，所述空气密度由所述无人机当前的飞行高度计算获得。
以下以算式(1)所示的系统辨识方程为例，详细描述其待辨识参数(即等效风阻系数)的具体求解过程：
Figure PCTCN2020104585-appb-000017
其中，根据所要计算的x方向或者y方向的不同，相对应地有：x＝V x或V y，
Figure PCTCN2020104585-appb-000018
或
Figure PCTCN2020104585-appb-000019
c＝-0.5C xρS fb或-0.5C yρS rl以及w＝w x或w y。
2)设采样步长为T(取极小值)，k＝0,1,2….的正整数，T*k＝t，跟随时间变化的无人机速度动态方程f(t)可以写成如下算式(8)所示的离散方程：
x(k+1)-x(k)-Tu(k)＝Tc(k)x 2(k)+Tw(k)            (8)
3)进一步构造参数y(k)＝x(k+1)-x(k)-Tu(k)，h(k)＝Tx 2(k)以及υ(k)＝Tw(k) 2算式(7)可以被进一步简写为如算式(9)所示：
y(k)＝h(k)c(k)+υ(k)             (9)
4)构造如下算式(10)所示的递推公式，通过递推的方式计算参数c(k)
Figure PCTCN2020104585-appb-000020
其中，P(0)和c(0)均为初始值，在本实施例中分别设置为1和c 0。技术人员也可以根据实际情况的需要，设置使用合适的数值作为初始值用以求解计算参数c(k)。
5)由于有c＝-0.5C xρS fb或-0.5C yρS rl。因此，在计算确定参数c(k)的值以后，便可以根据当前时刻t(即T*k)的空气密度、无人机的迎风面积转换计算相应的等效风阻系数C x和C y。
与所述等效风阻系数相适应地，该固有风阻也相应的通过无人机的固有风阻系数来表示。在步骤602中，具体可以通过算式(5)来计算确定所述无人机所处飞行环境的风速。
在进行风速测算的方法中，除了实时检测获得的飞行数据以及在线辨识等，需要无人机在运行过程中在线进行的步骤以外，还包括一些离线完成的步骤，例如确定无人机在x方向上的固有风阻系数以及在y方向上的固有风阻系数、拟合计算迎风面积所需要的非线性函数以及确定无人机的质量等。
应当说明的是，上述离线完成的步骤不需要在每一个无人机上重复执行， 只需要离线实验计算完成以后记录到无人机的存储器中即可。进一步地，具有相同或者相近外形结构的无人机也可以直接使用已有的数据而省略以上的一个或者多个离线完成的步骤。
本发明实施例提供的风速测算方法，采用利用系统辨识的参数辨识过程，估算无人机当前的风阻干扰并进而推算获得所处飞行环境的风速。这样的方法不需要利用额外的风速或者风力传感器，也不依赖于庞大的数据库。其实现成本低，实时性好，可以广泛的在无人机系统上应用。
在无人机飞行时，飞行控制系统可以按照设定的周期，周期性的运行本发明实施例提供的风速测算方法，获得当前的风速和/或风向的估计值。由于无人机的飞行速度受到的影响主要在于飞行时的风力干扰，其它的干扰造成的影响都是比较小的。因此，上述等效设定前提下计算确定的风速和/或风力的估计值基本可以认为是比较准确的，基本可以满足预警的使用需要。
图8为本发明另一实施例提供的风速测算方法的方法流程图。
如图8所示，所述方法包括：
801、获取无人机的飞行数据和属性数据。
需要获取的飞行数据和属性数据取决于计算无人机的理论飞行速度时，所需要输入的变量。本领域技术人员可以根据实际情况的需要、偏好设置或者精度要求等，对这些数据信息进行调整或者变更。
具体的，所述飞行数据会跟随无人机的运动状态而变化，可以由无人机自身具有的传感器的采样数据，通过数据融合算法计算获得。
所述属性数据是无人机的固有属性，由无人机的结构等所决定，不会随运动状态而发生变动。其可以通过实验等方式预先设定并记录在存储器中，在需要时从存储器中读取获得。
802、基于系统辨识原理，计算获得所述无人机所处飞行环境的风速。
通过周期性的执行步骤802，可以不断更新无人机所处飞行环境的风速，以确保及时的发出预警。具体的更新周期是一个经验性数值，其可以根据实际情况进行调整或者设置，例如1min或者更长的周期。
803、判断所述风速是否满足预设的警告条件。若是，执行步骤804，若否，返回步骤802，更新所述风速。
该预设的警告条件是根据经验或者无人机的实际情况(例如无人机对风速的承受能力)而预先设定的判断标准。其可以由一个或者多个条件所组成，用于衡量无人机出现失控事故的概率。亦即，在满足预设的警告条件时，表明无人机的飞行控制系统在风力干扰下基本已经达到设计上限，出现异常或者事故的可能性非常大。
在一些实施例中，该警告条件可以是预设的报警门限值。监控器132可以不断监视风速是否已经达到报警门限值，并且在风速达到报警门限值时，向遥控设备20发出警告信号。该报警门限值也是一个经验数值，可以通过实验测试等方式，由技术人员根据无人机的具体运行状态来确定或者设置。
804、发出警告信号。
该警告信号具体可以采用任何合适形式或者类型的标识进行表示，例如简单用1和0表示的警告标志位。当警告标志位的取值为1时，表明发出警告信号，而警告标志位当取值为0时，表明没有警告信号。
具体的，在预设的报警门限值下，监视器132触发警告信号的逻辑可以通过如下算式(11)表示：
Figure PCTCN2020104585-appb-000021
其中，V wx为x方向上的风速，V wv为y方向上的风速，flag为警告信号标志位的取值。亦即，在x方向和y方向上的风速的平方和大于等于预设的报警门限值的平方时，监视器132将确定风速满足预设的警告条件，发出警告信号。
当然，算式(11)所示的判断逻辑仅用于举例说明而不用于限制监视器132实现警告信号发送的工作步骤。本领域技术人员也可以根据实际情况的需要，采用其它不同的警告条件来衡量无人机是否存在风速过大、飞行器控制系统无法有效控制的情形。
遥控设备20在接收到警告信号以后，可以通过显示屏或者其它交互设备，向用户反馈对应的警告提示信息，以提醒用户及时的停止无人机飞行，降落到安全可控的位置等。
具体的警告提示信息可以根据实际情况进行设置，包括但不限于文字或者图片的形式。例如，可以在遥控器的显示界面上突出显示—当前风速过大—这样的字样，或者以特定颜色的图标显示当前风速超限。进一步的，还可以通过扬声器播报语音提示。
本发明实施例提供的风速检测方法应用在无人机上可以有效的解决现有普通无人机无法进行预报，导致用户/操作者来不及操作，因风速过大而发生炸机的问题，可以在风速过大时，提示用户谨慎飞行或选择安全地点降落。
基于本发明实施例揭露的递推计算过程，飞行控制系统具体可以执行如图9所示的步骤，在不依赖风速传感器和数据库的前提下，实现无人机风速测算和预警。
在本实施例中，无人机具有一个或者多个基础的传感器，至少可以实时采集无人机的姿态角(包括俯仰角、滚转角以及航向角)、加速度以及飞行速度 等飞行数据。
x方向和y方向是无人机所在的平面内，两个相互垂直的向量。其中，x方向为无人机前进和后退的移动方向，y方向为无人机向左飞和向右飞的移动方向。
另外，无人机的质量、在x方向和y方向上的迎风面积与姿态角的非线性函数f fb(θ，φ)和f rl(θ，φ)以及无人机在x方向上的固有风阻系数以及在y方向上的固有风阻系数均被测量确定，记录存储到无人机的存储器内。
迎风面积与姿态角的非线性函数以及固有风阻系数都可以使用理想环境下的获得的实验数据(例如采集无人机在室内无风环境下飞行的多组飞行数据)通过最小二乘法的方式拟合确定。
如图9所示，飞行控制系统执行的计算的过程包括：
901、给定初始值P(0)和c(0)，并且有k＝1。
应当说明的是，递推计算所需要使用的初始值可以根据实际情况进行设置或者初始化。例如，可以简单的将这些初始值均初始化为0。
902、按照算式(10)所示的递推公式，更新参数P(k)。
903、根据参数P(k)，按照算式(10)所示的递推公式，更新参数c(k)。
904、将参数c(k)换算为等效风阻系数。
具体换算的方式可以根据参数c(k)与等效风阻系数之间的关系所确定(即c＝-0.5C xρS fb或-0.5C yρS rl)。
905、使用算式(5)，计算所述无人机当前所承受的风速。
图10为本发明实施例提供的风速跟随时间变化的曲线图。如图10所示，使用本发明实施例公开的系统辨识模型时，可以分别计算在x方向和y方向上的风速分量，将相对应的两者合成为无人机当前所受的风速，获得风速随时间变化的曲线。
906、通过算式(11)所示的判断逻辑，确定是否发出警告信号。
判断逻辑通过报警门限值作为判断条件来确定是否需要发出警告信号。请继续参阅图8，该报警门限值是一个预先设定的经验性数值。当风速高于报警门限值时，发出警报信号，表明此时的风力较大，需要提醒用户或者操作者注意。
在无人机飞行的过程中，需要周期性的对风速进行更新。在更新风速时，可以令k＝k+1后，重新执行步骤902和903，计算更新所述无人机所处飞行环境的风速。
图11为本发明实施例提供的风向随时间变化的曲线图。图11是以图10所示的风速曲线为基础，通过算式(6)转换获得的对应的风向曲线。计算确 定的风向角也可以传输到遥控设备20中，通过遥控设备20的交互设备(如显示屏)向用户展示。
综上所述，本发明实施例提供的风速测算方法和以此为基础实现的无人机预警方法，不需要使用与风速相关传感器和创建庞大的数据库，在现有信息的基础上，就可以通过算法的方式实现对风速的估算，并进而确定对应的风速和/或风向。
由于不依赖风速相关的传感器和数据库。因此，有效的降低了无人机的硬件实现成本，避免了数据库运算量大、对内存需求大以及时间延迟大的缺点，具有良好的应用前景。
最后应说明的是：以上实施例仅用以说明本发明的技术方案，而非对其限制；在本发明的思路下，以上实施例或者不同实施例中的技术特征之间也可以进行组合，步骤可以以任意顺序实现，并存在如上所述的本发明的不同方面的许多其它变化，为了简明，它们没有在细节中提供；尽管参照前述实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的范围。

Claims (22)

  1. 一种风速测算方法，其特征在于，包括：
    基于无人机的飞行数据和属性数据，通过系统辨识确定所述无人机当前的风阻干扰，其中，所述飞行数据包括：无人机的姿态角、飞行速度、加速度以及飞行高度，所述属性数据包括：无人机的质量、固有风阻系数以及用于计算迎风面积的非线性函数；
    根据所述风阻干扰与所述无人机的固有风阻，计算所述无人机所处飞行环境的风速。
  2. 根据权利要求1所述的风速测算方法，其特征在于，所述基于无人机的飞行数据和属性数据，通过系统辨识确定所述无人机当前的风阻干扰，包括：
    构建无人机的系统辨识模型，所述系统辨识模型的待辨识参数为所述无人机当前的等效风阻系数；
    通过在线辨识方法，根据所述无人机当前的飞行数据以及所述属性数据求解相对应的所述等效风阻系数；
    所述根据所述风阻干扰与所述无人机的固有风阻，计算所述无人机所处飞行环境的风速，包括：
    根据所述等效风阻系数与所述无人机的固有风阻系数之差，计算所述无人机所处飞行环境的风速。
  3. 根据权利要求2所述的风速测算方法，其特征在于，所述通过在线辨识方法，根据所述无人机当前的飞行数据以及所述属性数据求解相对应的所述等效风阻系数，包括：
    离散化所述系统辨识模型，形成对应的离散方程；
    根据预设的初始值，所述无人机当前的姿态角、飞行速度以及加速度，递推计算所述无人机受到的等效风阻力；
    根据所述无人机当前的迎风面积以及空气密度，将所述等效风阻力转换为等效风阻系数；
    所述迎风面积由所述无人机当前的姿态角和用于计算迎风面积的非线性函数计算获得，所述空气密度由所述无人机当前的飞行高度计算获得。
  4. 根据权利要求2所述的风速测算方法，其特征在于，所述等效风阻系数由在x方向上的等效风阻系数分量和y方向上的等效风阻系数分量表示，所述风速由在x方向上的风速分量和在y方向上的风速分量表示；所述x方向和所述y方向之间相互垂直，与所述无人机处于相同的平面。
  5. 根据权利要求4所述的风速测算方法，其特征在于，所述根据所述等效风阻系数与所述无人机的固有风阻系数之差，计算所述无人机所处飞行环境 的风速，具体包括：
    通过如下算式计算所述无人机所处飞行环境的风速：
    Figure PCTCN2020104585-appb-100001
    其中，V wx为所述无人机所处飞行环境的风速在x方向上的风速分量，V wy为所述无人机所处飞行环境的风速在y方向上的风速分量，V x为无人机在x方向上的速度，V y为无人机在y方向上的速度，ρ为飞行高度处的空气密度，S fb为无人机沿x方向飞行时的迎风面积，S rl为无人机沿y方向飞行时的迎风面积，C x为在x方向上的等效风阻系数分量，C y为在y方向上的等效风阻系数分量，C dx为无人机在x方向上的固有风阻系数，C dy为无人机在y方向上的固有风阻系数。
  6. 根据权利要求5所述的风速测算方法，其特征在于，所述无人机在x方向上的固有风阻系数以及在y方向上的固有风阻系数根据所述无人机在无风室内的飞行数据，通过最小二乘法拟合确定。
  7. 根据权利要求4所述的风速测算方法，其特征在于，所述系统辨识模型通过如下算式表示：
    Figure PCTCN2020104585-appb-100002
    其中，
    Figure PCTCN2020104585-appb-100003
    为无人机在x方向上的加速度，
    Figure PCTCN2020104585-appb-100004
    为无人机在y方向上的加速度，V x为无人机在x方向上的速度，V y为无人机在y方向上的速度，
    T为螺旋桨拉力，θ为俯仰角，φ为滚转角，ρ为飞行海拔处的空气密度，S fb为无人机沿x方向飞行时的迎风面积，S rl为无人机沿y方向飞行时的迎风面积，C x为在x方向上的等效风阻系数分量，C y为在y方向上的等效风阻系数分量，m为无人机的质量，w x为在x方向上的模型不确定性，w y为在y方向上的模型不确定性。
  8. 根据权利要求5或7所述的风速测算方法，其特征在于，所述迎风面积通过如下算式计算确定：
    S fb＝S fb0(1+f fb(θ，φ））
    S rl＝S rl0(1+f rl(θ，φ))
    其中，S fb为无人机沿x方向飞行时的迎风面积，S rl为无人机沿x方向飞行时的迎风面积；S fb0为姿态角为0时，无人机沿x方向飞行时的迎风面积；S rl0为姿态角为0时，无人机沿y方向飞行时的迎风面积；f fb(θ，φ）和f rl(θ，φ)为非线性函数；θ为俯仰角；φ为滚转角。
  9. 根据权利要求7所述的风速测算方法，其特征在于，所述螺旋桨拉力通过如下算式计算获得：
    Figure PCTCN2020104585-appb-100005
    其中，a z为无人机在z方向上的加速度，g为重力加速度；所述z方向垂直于所述x方向和所述y方向组成的平面；θ为俯仰角；φ为滚转角；m为无人机的质量。
  10. 根据权利要求7所述的风速测算方法，其特征在于，所述方法还包括：
    根据在x方向和y方向上的风速分量，通过如下算式，计算风向：
    β＝ψ+arctan2(-V wx,-V wy)
    其中，ψ为无人机的偏航角，β为风向，V wx为在x方向上的风速分量，V wy为在y方向上的风速分量。
  11. 根据权利要求1-10中任一项所述的风速测算方法，其特征在于，该方法还包括：
    在所述无人机所处飞行环境的风速满足预设的警告条件时，发出警告信号。
  12. 根据权利要求11所述的风速测算方法，其特征在于，所述在所述无人机所处飞行环境的风速满足预设的警告条件时，发出警告信号，包括：
    通过如下算式计算判断是否符合所述预设的警告条件：
    Figure PCTCN2020104585-appb-100006
    其中，V wx为在x方向上的风速分量，V wy为在y方向上的风速分量，V thr为安全风速门限值；
    在符合所述预设的警告条件时，发出警告信号；
    在不符合所述预设的警告条件时，继续检测所述无人机所处飞行环境的风速。
  13. 一种风速估算器，其特征在于，所述风速估算器包括：
    系统辨识单元，所述系统辨识单元用于接收无人机的飞行数据和属性数据，并根据所述飞行数据和属性数据，辨识确定所述无人机当前的风阻干扰；
    所述飞行数据包括：无人机的姿态角、飞行速度、加速度以及飞行高度；所述属性数据包括：无人机的质量、固有风阻系数以及用于计算迎风面积的非线性函数；
    风速估计单元，所述风速估计单元与所述系统辨识单元连接，用于根据所述风阻干扰与所述无人机的固有风阻，计算所述无人机所处飞行环境的风速。
  14. 根据权利要求13所述的风速估算器，其特征在于，所述系统辨识单元内存储有预先设置的系统辨识模型，所述系统辨识模型的待辨识参数为等效风阻系数；
    所述系统辨识单元用于：通过在线辨识方法，根据所述当前的飞行数据以及所述属性数据求解相对应的所述等效风阻系数。
  15. 根据权利要求14所述的风速估算器，其特征在于，所述系统辨识单元还用于：
    离散化所述系统辨识模型，形成对应的离散方程；
    根据预设的初始值，所述无人机当前的姿态角、飞行速度以及加速度，递推计算所述无人机受到的等效风阻力；
    根据所述无人机当前的迎风面积以及空气密度，将所述等效风阻力转换为等效风阻系数；
    所述迎风面积由所述无人机当前的姿态角和用于计算迎风面积的非线性函数计算获得，所述空气密度由所述无人机当前的飞行高度计算获得。
  16. 根据权利要求13所述的风速估算器，其特征在于，所述等效风阻系数由在x方向上的等效风阻系数分量和y方向上的等效风阻系数分量表示，所述风速由在x方向上的风速分量和在y方向上的风速分量表示；所述x方向和所述y方向之间相互垂直，与所述无人机处于相同的平面。
  17. 根据权利要求16所述的风速估算器，其特征在于，所述系统辨识模型通过如下算式表示：
    Figure PCTCN2020104585-appb-100007
    其中，
    Figure PCTCN2020104585-appb-100008
    为无人机在x方向上的加速度，
    Figure PCTCN2020104585-appb-100009
    为无人机在y方向上的加速度，V x为无人机在x方向上的速度，V y为无人机在y方向上的速度，
    T为螺旋桨拉力，θ为俯仰角，φ为滚转角，ρ为飞行海拔处的空气密度，S fb为无人机沿x方向飞行时的迎风面积，S rl为无人机沿y方向飞行时的迎风面积，C x为在x方向上的等效风阻系数分量，C y为在y方向上的等效风阻系数分量，m为无人机的质量，w x为在x方向上的模型不确定性，w y为在y方向上的模型不确定性。
  18. 根据权利要求16所述的风速估算器，其特征在于，所述风速估计单元还用于接收所述无人机当前的姿态角、飞行速度、飞行高度、固有风阻系数以及用于计算迎风面积的非线性函数，并通过如下算式计算所述无人机所处飞行环境的风速：
    Figure PCTCN2020104585-appb-100010
    其中，V wx为所述无人机所处飞行环境的风速在x方向上的风速分量，V wy为所述无人机所处飞行环境的风速在y方向上的风速分量，V x为无人机在x方向上的速度，V y为无人机在y方向上的速度，ρ为飞行高度处的空气密度，S fb为无人机沿x方向飞行时的迎风面积，S rl为无人机沿y方向飞行时的迎风面积，C x为在x方向上的等效风阻系数分量，C y为在y方向上的等效风阻系数分量，C dx为无人机在x方向上的固有风阻系数，C dy为无人机在y方向上的固有风阻系数。
  19. 根据权利要求13-18任一项所述的风速估算器，其特征在于，所述风速估算器还包括：预警单元；
    所述预警单元用于在所述无人机所处飞行环境的风速满足预设的警告条件时，发出警告信号。
  20. 根据权利要求19所述的风速估算器，其特征在于，所述预警单元还用于：
    通过如下算式计算判断是否符合所述预设的警告条件：
    Figure PCTCN2020104585-appb-100011
    其中，V wx为在x方向上的风速分量，V wy为在y方向上的风速分量，V thr为安全风速门限值；
    在符合所述预设的警告条件时，发出警告信号；
    在不符合所述预设的警告条件时，继续检测所述无人机所处飞行环境的风速。
  21. 一种无人机，其特征在于，所述无人机包括机身主体、设置在所述机身主体上的一个或者多个传感器、存储器以及飞行控制系统；所述存储器内存储有计算机可执行程序指令，在所述计算机可执行程序指令被所述飞行控制系统调用时，以使所述飞行控制系统从所述传感器获取的飞行数据，并从所述存储器获取属性数据，执行如权利要求1-12任一项所述的风速测算方法。
  22. 根据权利要求21所述的无人机，其特征在于，所述飞行控制系统还用于将所述无人机所处飞行环境的风速转换为风向，并在所述无人机对应的遥控设备上显示所述风速和风向。
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