CN106844887B - Dynamics modeling method and device for rotor unmanned aerial vehicle - Google Patents
Dynamics modeling method and device for rotor unmanned aerial vehicle Download PDFInfo
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Abstract
The invention relates to the technical field of unmanned aerial vehicles, in particular to a dynamics modeling method and a dynamics modeling device for a rotor unmanned aerial vehicle. The invention provides a dynamics modeling method of a rotor unmanned aerial vehicle, which comprises the following steps: acquiring flight data of the rotor unmanned aerial vehicle; performing parameter estimation according to flight data of the rotor unmanned aerial vehicle to obtain a corrected parameter estimation value of the rotor unmanned aerial vehicle in a six-degree-of-freedom model constructed by Newton's law; and obtaining a dynamic model of the rotor unmanned aerial vehicle from the six-degree-of-freedom model through the corrected parameter estimation value and the flight data. According to the invention, the rotor unmanned aerial vehicle is simulated through Newton's law, and relevant parameters can be corrected by adjusting the dynamic characteristic expression parameters of any one of six degrees of freedom, so that the simulation of the dynamic characteristics of the unmanned aerial vehicle is more accurate on the basis of simplifying the dynamic characteristics.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to a dynamics modeling method and a dynamics modeling device for a rotor unmanned aerial vehicle.
Background
The rotor unmanned aerial vehicle belongs to one of microminiature unmanned helicopters, has advantages such as small, light in weight, flight height is low and mobility is strong, has wide application prospect.
Through the dynamics modeling to rotor unmanned aerial vehicle, can further revise rotor unmanned aerial vehicle's simulation flight data to make it identical with actual flight parameter. In a traditional dynamics modeling model, frequency domain modeling or time domain modeling is generally adopted, the dynamics characteristics of the rotor wing unmanned aerial vehicle cannot be accurately expressed through pure frequency domain modeling or time domain modeling, and the simulation accuracy is low.
Disclosure of Invention
The invention provides a dynamics modeling method and device for a rotor unmanned aerial vehicle, and aims to solve the problems that the dynamics characteristics of the rotor unmanned aerial vehicle cannot be accurately expressed by the existing dynamics modeling data and the simulation accuracy is low.
The invention provides a dynamics modeling method of a rotor unmanned aerial vehicle, which comprises the following steps:
acquiring flight data of the rotor unmanned aerial vehicle;
performing parameter estimation according to flight data of the rotor unmanned aerial vehicle to obtain a corrected parameter estimation value of the rotor unmanned aerial vehicle in a six-degree-of-freedom model constructed by Newton's law;
and obtaining a dynamic modeling of the rotary wing unmanned aerial vehicle from the six-degree-of-freedom model through the corrected parameter estimation value and the flight data.
Further, flight data include the pulse width modulation numerical value of rotor unmanned aerial vehicle flight output and the total lift of the screw of treating the inquiry, acquire rotor unmanned aerial vehicle's flight data, include:
and obtaining corresponding pulse width modulation values and the total lift force of the propeller under different voltages according to a pre-constructed corresponding relationship between the pulse width modulation values and the total lift force of the propeller and a pre-constructed corresponding relationship between the voltage and the pulse width modulation values.
Further, the corrected parameter estimation value includes a power effective coefficient, and the parameter estimation is performed according to the flight data of the rotor unmanned aerial vehicle to obtain the corrected parameter estimation value of the rotor unmanned aerial vehicle in a six-degree-of-freedom model constructed by newton's law, including:
and performing parameter estimation in the six-degree-of-freedom model constructed by Newton's law according to flight data of the rotor unmanned aerial vehicle to obtain a power effective coefficient by taking the total lift of a propeller of the rotor unmanned aerial vehicle as a reference.
Further, the method further comprises:
acquiring wind field data corresponding to the rotor unmanned aerial vehicle for dynamic modeling;
the corrected parameter estimation value further includes a wind field resistance coefficient, and the parameter estimation is performed in the six-degree-of-freedom model constructed by newton's law according to the flight data of the rotor unmanned aerial vehicle with the total lift of the propeller of the rotor unmanned aerial vehicle as a reference to obtain a power effective coefficient, including:
with Euler angle, the total lift of screw in the flight data the rotor unmanned aerial vehicle's quality rotor unmanned aerial vehicle's linear displacement and wind field data are the input adopt the least square method on rotor unmanned aerial vehicle's the position degree of freedom, obtain power effective coefficient and wind field resistance coefficient under the condition that the energy function reaches the minimum.
Further, the corrected parameter estimation value includes a total moment coefficient, and the parameter estimation is performed according to the flight data of the rotor unmanned aerial vehicle to obtain the corrected parameter estimation value of the rotor unmanned aerial vehicle in a six-degree-of-freedom model constructed by newton's law, including:
and taking the lift force generated by each propeller of the rotor wing unmanned aerial vehicle as a reference, and performing parameter estimation in the six-degree-of-freedom model constructed by the Newton's law according to the flight data of the rotor wing unmanned aerial vehicle to obtain a total moment coefficient.
Further, the method for estimating parameters in the six-degree-of-freedom model constructed by newton's law based on lift force generated by each propeller of the rotor unmanned aerial vehicle to obtain a total moment coefficient includes:
the method comprises the steps of taking angular velocity, each propeller lift force, rotational inertia, the distance from a motor rotating shaft to an original point of a coordinate axis of an engine body and the distance from the motor rotating shaft to the center of gravity of the engine body in flight data as input, and obtaining a total moment coefficient under the condition that an energy function reaches the minimum value by adopting a least square method on the attitude freedom degree of the rotor unmanned aerial vehicle.
The invention also provides a dynamics modeling device of the rotor unmanned aerial vehicle, which comprises:
the flight data acquisition module is used for acquiring flight data of the rotor unmanned aerial vehicle;
the correction parameter estimation module is used for performing parameter estimation according to flight data of the rotor unmanned aerial vehicle to obtain a correction parameter estimation value of the rotor unmanned aerial vehicle in a six-degree-of-freedom model constructed by Newton's law;
and the dynamics modeling module is used for obtaining a dynamics model of the rotor unmanned aerial vehicle from the six-degree-of-freedom model through the corrected parameter estimation value and the flight data.
Further, the corrected parameter estimation value comprises a power effective coefficient, and the corrected parameter estimation module is further used for performing parameter estimation in the six-degree-of-freedom model constructed by the newton's law according to flight data of the rotor unmanned aerial vehicle on the basis of the total lift of a propeller of the rotor unmanned aerial vehicle to obtain the power effective coefficient.
Further, the method also comprises the following steps:
the wind field data acquisition module is used for acquiring wind field data corresponding to the rotor unmanned aerial vehicle for dynamic modeling; the corrected parameter estimation value further comprises a wind field resistance coefficient, the corrected parameter estimation value module is further used for inputting Euler angle in flight data, total lift of a propeller, the mass of the rotor unmanned aerial vehicle, linear displacement of the rotor unmanned aerial vehicle and the wind field data, and a least square method is adopted on the position freedom degree of the rotor unmanned aerial vehicle to obtain a dynamic effective coefficient and a wind field resistance coefficient under the condition that an energy function reaches the minimum value.
Further, the corrected parameter estimation value comprises a total moment coefficient, and the corrected parameter estimation value module is further used for performing parameter estimation in the six-degree-of-freedom model constructed by the newton's law according to flight data of the rotor unmanned aerial vehicle to obtain the total moment coefficient by taking lift force generated by each propeller of the rotor unmanned aerial vehicle as a reference.
According to the invention, the rotor unmanned aerial vehicle is modeled through Newton's law, a six-degree-of-freedom model expressing dynamic characteristics of the rotor unmanned aerial vehicle is constructed, the model simplifies the number of dynamic characteristic expression parameters of the rotor unmanned aerial vehicle, and relevant parameters can be corrected by adjusting the dynamic characteristic expression parameters of any one degree of freedom in six degrees of freedom, so that the simulation of the dynamic characteristics of the unmanned aerial vehicle is more accurate on the basis of simplifying the dynamic characteristics; the simulation is based on flight data, the parameter estimation values of all the degrees of freedom are corrected independently, and the relevance of the parameter estimation values of all the degrees of freedom is reduced, so that the simulation precision of six degrees of freedom including front and back, left and right, up and down, pitching, rolling and yawing is further improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a schematic flow chart of a dynamics modeling method of a rotary wing drone in embodiment 1;
fig. 2 is a schematic of a motor setting for a quad-rotor drone;
fig. 3 is a schematic flow chart of a dynamics modeling method of a rotary wing drone in embodiment 2;
fig. 4 is a schematic structural diagram of a dynamics modeling apparatus of a rotorcraft;
FIG. 5 is a schematic diagram of the results of a conventional kinetic modeling simulation;
FIG. 6 is a schematic diagram of the results of the kinetic modeling simulation of the present invention.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
Example 1:
as shown in fig. 1, the present embodiment provides a method for modeling dynamics of a rotorcraft, the method comprising:
Flight data are various parameters of the rotorcraft during flight including, but not limited to, the mass, moment of inertia, center of gravity, propeller lift, euler angle, angular velocity, etc. of the rotorcraft. The flight data mainly comprises a direct acquisition mode and an off-line acquisition mode. Direct acquisition refers to data that can be directly read during flight; the off-line acquisition includes relationship query by pre-constructing relationship correspondence, and also includes some measurement of basic physical data, such as distance, quality, and other data. In a particular embodiment, flight data may include, but is not limited to, rotorcraft's linear displacement, rotorcraft's mass, total lift of the propellers, lift of each propeller, euler's angle, angular velocity, moment of inertia about the body coordinate axis, distance of the motor shaft to the origin of the body coordinate axis, distance of the motor shaft to the center of gravity, and the like.
The euler angles and angular velocities in the flight data can be directly obtained. Specifically, the euler angles are obtained by gyroscope measurement; angular velocity is obtained by measuring components on the coordinate axes of the body.
Rotor unmanned aerial vehicle's in the flight data quality, motor shaft to the distance of organism coordinate axis initial point, motor shaft to the centrobaric distance of organism, all adopt the off-line measuring mode to acquire around the inertia of organism coordinate axis and the lift of each screw. Specifically, the mass of the unmanned aerial vehicle is obtained by off-line weighing; the distance from the motor rotating shaft to the coordinate axis of the machine body is obtained in an off-line direct measurement mode; the distance from the motor rotating shaft to the center of gravity of the machine body can also be obtained in an off-line direct measurement mode, the center of gravity can be determined by adopting a balance determination method, and can also be determined in a mode of supposing that the center of gravity is overlapped with the origin of the coordinate axes of the machine body, so that the measurement is carried out; the moment of inertia around the coordinate axis of the machine body is obtained by calculating after offline measurement of the mass obtained by offline measurement and the vertical distance from the rotating point to the rotating shaft; the lift force of each propeller is obtained by adjusting an empirical coefficient after offline measuring the length of the propeller blade, the pitch, the width of the propeller blade, the rotating speed and the atmospheric pressure.
Further elaborating on the way in which the total lift of the propeller is obtained.
Firstly, the corresponding relation between the total lift force of the propeller and the pulse width modulation value is constructed. The total lift force of the propeller corresponding to different PWM (Pulse Width Modulation) values is measured in an off-line measurement mode and recorded, so that the corresponding relation between the total lift force of the propeller and the Pulse Width Modulation values is obtained.
Different PWM values at different voltages are then measured. Specifically, the voltage interval is divided into 10 equal parts or 20 parts according to a certain rule, and the PWM interval is divided into 10 equal parts or 20 parts according to a certain rule, so that different PWM values (200 is 10 × 20 groups of data) under different voltages can be measured and recorded, thereby obtaining the corresponding relationship between the voltage and the PWM value.
And obtaining the total lift force of the propellers under different voltages through the corresponding relation between the total lift force of the propellers and the pulse width modulation value and the corresponding relation between the voltage and the PWM value.
And 102, performing parameter estimation according to the flight data of the rotor unmanned aerial vehicle to obtain a correction parameter estimation value of the rotor unmanned aerial vehicle in a six-degree-of-freedom model constructed by Newton's law.
The method comprises the following steps of firstly simplifying a flight model of the rotor unmanned aerial vehicle so that the flight model can be more easily expressed by constructing a six-degree-of-freedom model through Newton's law, and specifically comprises the following steps:
(1) neglecting the deformation of the structure, and regarding the rotor unmanned aerial vehicle as a rigid body;
(2) the structure of the rotor unmanned aerial vehicle body is completely symmetrical;
(3) neglecting the deformation of the blade, and regarding the blade as a rigid body (compensated by model parameters);
(4) a ground coordinate system is superposed with a body coordinate system on a flying point of the rotor unmanned aerial vehicle;
(5) regardless of rotor flapping, lift and reaction torque are proportional to the square of rotor speed (compensated by model parameters);
(6) the effect of the ground effect is not considered;
(7) the lift coefficient and the drag coefficient are constants;
(8) the euler angular rate is equal to the angular rate in the body coordinate system.
After the simplification, the motion of the unmanned rotorcraft in the space can be considered to be composed of spatial translation (motion along the line of three axes) and spatial rotation (rotation around the three axes), namely, the unmanned rotorcraft can be considered as a rigid body with six degrees of freedom (front and back, left and right, up and down, pitching, rolling and yawing).
The six degrees of freedom refer to position and attitude, wherein the position includes height direction, north-south direction and east-west direction; the attitude comprises three angles of pitch, roll and yaw.
The flight data comprises the position, speed information and attitude information of the unmanned aerial vehicle measured by the sensor, and pulse width modulation signals in the actual flight process. The position information is the position of the unmanned aerial vehicle in the air, the height (up and down), the east and west (left and right), and the north and south (front and back). The speed information includes information on how fast the change is in the direction of three positions. Attitude information includes pitch angle, roll angle, yaw angle and pitch angular velocity, roll angular velocity, and yaw angular velocity.
Further, in an exemplary embodiment, a quad-rotor drone coordinate axis is set. Setting of coordinate axes: ground coordinate system NED (normal, east, down), unmanned aerial vehicle body coordinate system: use the focus as the original point, the x axle, four motors of four rotor unmanned aerial vehicle are divided in the region of difference to the y axle, and the z axle accords with the right-hand rule. The body center of gravity and the geometric center are assumed to coincide. The geometric center is defined as the center of the plane at the midpoint of the connecting line of the axes of the four motors and is in the same plane with the center of gravity. As shown in fig. 2, the motors are numbered counterclockwise as motor 1, motor 2, motor 3 and motor 4, respectively, and motor 1 is located in the first quadrant constructed by the x and y axes. The motor 1 and the motor 3 rotate counterclockwise, and the motor 2 and the motor 4 rotate clockwise.
According to model simplification and coordinate axis setting, a linear motion equation is respectively established in a ground coordinate system and an equation is respectively established in a machine body coordinate system through a Newton-Euler equation, and the expression of a mathematical model of the six-degree-of-freedom model is obtained as follows:
wherein the content of the first and second substances,
is the second derivative of linear displacement x, y, z of a quad-rotor unmanned aerial vehicle, phi, theta, psi is the Euler angle, T is the total lift of the propeller, Cx，Cy，CzIs a power effective coefficient, and m is the mass of the quad-rotor unmanned aerial vehicle;
This embodiment is exemplified by a quad-rotor drone, wherein the method can be applied to six-rotor, eight-rotor, and other multi-rotor drones. According to the Newton-Euler equation, the front-back, left-right and up-down freedom degrees of six-rotor, eight-rotor and other multi-rotor unmanned aerial vehicles are not directly related to the lift force generated by each propeller. The degrees of freedom directly related to the lift generated by each propeller are pitch, roll and yaw. Therefore, the lift F generated by the quadrotor propeller in the above-mentioned Newton-Euler equation is used1，F2，F3，F4Lift F corresponding to six rotors in six rotors1，F2，F3，F4，F5，F6Or the lift F corresponding to eight of the eight rotors1，F2，F3，F4，F5，F6，F7，F8The superposition relationship between the two is just needed.
The parameters are substituted into the six-degree-of-freedom model, thereby obtaining a corrected parameter estimate in the case of being closest to the actual data.
In an embodiment, when the corrected parameter estimation value includes a dynamic effective coefficient, as can be known from the above mathematical model, the step 102 of performing parameter estimation according to the flight data of the unmanned rotorcraft to obtain the corrected parameter estimation value of the unmanned rotorcraft in the six-degree-of-freedom model constructed by newton's law may be implemented as follows:
and performing parameter estimation in the six-degree-of-freedom model constructed by Newton's law according to flight data of the rotor unmanned aerial vehicle to obtain a power effective coefficient by taking the total lift of a propeller of the rotor unmanned aerial vehicle as a reference.
Specifically, the specific implementation manner of obtaining the effective coefficient of power by using the total lift of the propeller of the rotor unmanned aerial vehicle as a reference and performing parameter estimation according to the flight data of the rotor unmanned aerial vehicle in the six-degree-of-freedom model constructed by newton's law may further be:
with Euler angle, the total lift of screw, rotor unmanned aerial vehicle's quality and rotor unmanned aerial vehicle's linear displacement in the flight data as the input adopt the least square method on rotor unmanned aerial vehicle's the position degree of freedom, obtain the power effective coefficient under the condition that the energy function reaches the minimum.
In an embodiment, when the corrected parameter estimation value includes a total moment coefficient, as can be known from the above mathematical model, the step 102 of performing parameter estimation according to the flight data of the unmanned rotorcraft to obtain the corrected parameter estimation value of the unmanned rotorcraft in the six-degree-of-freedom model constructed by newton's law may be implemented as follows:
and taking the lift force generated by each propeller of the rotor wing unmanned aerial vehicle as a reference, and performing parameter estimation in the six-degree-of-freedom model constructed by the Newton's law according to the flight data of the rotor wing unmanned aerial vehicle to obtain a total moment coefficient.
Specifically, the specific implementation manner of obtaining the total moment coefficient by using the lift force generated by each propeller of the rotor unmanned aerial vehicle as a reference and performing parameter estimation according to the flight data of the rotor unmanned aerial vehicle in the six-degree-of-freedom model constructed by newton's law may further be:
the method comprises the steps of taking angular velocity, each propeller lift force, rotational inertia, the distance from a motor rotating shaft to an original point of a coordinate axis of an engine body and the distance from the motor rotating shaft to the center of gravity of the engine body in flight data as input, and obtaining a total moment coefficient under the condition that an energy function reaches the minimum value by adopting a least square method on the attitude freedom degree of the rotor unmanned aerial vehicle.
And 103, obtaining a dynamic model of the rotor unmanned aerial vehicle from the six-degree-of-freedom model through the corrected parameter estimation value and the flight data.
The modified parameter estimation of the present embodiment has the following advantages: the model simplifies the number of the dynamic characteristic expression parameters of the rotor wing unmanned aerial vehicle, and the correction of related parameters can be carried out by adjusting the dynamic characteristic expression parameters of any one degree of freedom in six degrees of freedom, so that the simulation of the dynamic characteristic of the unmanned aerial vehicle is more accurate on the basis of simplifying the dynamic characteristic; the simulation is based on flight data, the parameter estimation values of all the degrees of freedom are corrected independently, and the relevance of the parameter estimation values of all the degrees of freedom is reduced, so that the simulation precision of six degrees of freedom including front and back, left and right, up and down, pitching, rolling and yawing is further improved. Last term yaw acceleration in a parametric model
In which the roll rate is the lift F generated by four-rotor propellers1，F2，F3，F4Therefore, the parameter complexity in the formula can be reduced, and the uniformity of modeling of the unmanned aerial vehicle system is improved.
Example 2:
the flight of a rotorcraft is simulated under consideration of the effects of disturbances in the wind field, using the same assumptions and coordinate systems as in example 1.
As shown in fig. 3, the present embodiment provides a dynamics modeling method for a rotorcraft, where the simulation method includes:
The flight data were acquired in the same manner as in example 1.
Wind field data refers to the relative speed of the unmanned rotorcraft on the coordinate axis relative to the airflow, centered on the unmanned rotorcraft. Specifically, the speed of the rotorcraft relative to the airflow is obtained in an off-line manner. Wind field data and rotor unmanned aerial vehicle flight's coordinate one-to-one, at arbitrary coordinate position, all have a corresponding wind field data.
It is understood that step 202 may be performed later than step 201, may be performed before step 201, may be performed simultaneously or alternately with step 201, and the embodiment of the present invention is not limited thereto.
And 203, performing parameter estimation in a six-degree-of-freedom model constructed by Newton's law according to flight data of the rotor unmanned aerial vehicle by taking the wind field data as a negative factor to obtain a corrected parameter estimation value.
Wherein the modified parameter estimate comprises a power efficiency coefficient and a wind field resistance coefficient. Due to the consideration of the influence of the wind field disturbance, a six-degree-of-freedom model under the influence of the wind field disturbance can be further constructed according to Newton's law, and parameter estimation is carried out according to flight data of the rotor unmanned aerial vehicle to obtain a dynamic effective coefficient and a wind field resistance coefficient which are included in a corrected parameter estimation value of the rotor unmanned aerial vehicle in the six-degree-of-freedom model constructed by the Newton's law.
According to model simplification and coordinate axis setting, a linear motion equation is respectively established in a ground coordinate system and an equation is respectively established in a machine body coordinate system through a Newton-Euler equation, and wind field disturbance related terms are added to obtain a mathematical model expression of a six-degree-of-freedom model of the quadrotor unmanned aerial vehicle, wherein the mathematical model expression is as follows:
wherein the content of the first and second substances,
is the second derivative of linear displacement x, y, z of a quad-rotor drone, phi, theta, psi is the Euler angle, T is the total lift of the propeller, Cx，Cy，CzIs the effective coefficient of power, vx，vy，vzIs the velocity of the quad-rotor drone relative to the airflow,
is the wind field resistance coefficient, and m is the mass of the quad-rotor unmanned aerial vehicle;
As can be seen from the formulas, the above six equations can divide the dynamics of a quad-rotor drone into six degrees of freedom. As can be seen from the above mathematical model, the total moment coefficient Cp，Cq，CrRegardless of wind field data, it is not affected by wind field effects, and its specific acquisition method may be the same as in embodiment 1. And coefficient of power efficiency Cx，Cy，CzWill be influenced by the wind field effect, the magnitude of the coefficient and the wind field data vx，vy，vzIt is related.
Specifically, as can be seen from the above mathematical model, in step 203, the specific implementation manner of obtaining the corrected parameter estimation value by performing parameter estimation according to the flight data of the rotary-wing drone in the six-degree-of-freedom model constructed by newton's law with the wind field data as the negative factor may be:
with Euler angle, the total lift of screw in the flight data the rotor unmanned aerial vehicle's quality rotor unmanned aerial vehicle's linear displacement and wind field data are the input adopt the least square method on rotor unmanned aerial vehicle's the position degree of freedom, obtain power effective coefficient and wind field resistance coefficient under the condition that the energy function reaches the minimum.
In order to obtain simulation data which is closer to actual flight data in terms of respective degrees of freedom, further, the optimal values of the dynamic effective coefficient and the wind field resistance coefficient and the optimal value of the total moment coefficient under the condition of the minimum error e are obtained by defining an energy density mode. The definition and the acquisition of the optimal value can be performed in a similar manner in the respective degrees of freedom. The method comprises the following specific steps:
the following energy function in x degrees of freedom is defined:
J(e)＝min{eT*e}
f2(a)＝m*a-m*g
e＝f1(φ，θ，ψ)*Cx-kxvx-f2(a)
by means of least squares, c is determined for the case where the energy function reaches a minimum valuex，kxThe estimated values of (a) are:
wherein:
the number of measured values is n, the measured value of acceleration is
Measured value of pitch angle is
The roll angle is measured as
A yaw angle of
Altitude speed measurement of
Further, the following energy function in the y degree of freedom is defined:
J(e)＝min{eT*e}
f2(a)＝m*a-m*g
e＝f1(φ，θ，ψ)*Cy-kyvy-f2(a)
by the least squares method, the estimated value of Cy, ky at which the energy function reaches a minimum is found to be:
wherein:
the number of measured values is n, the measured value of acceleration is
Measured value of pitch angle is
The roll angle is measured as
A yaw angle of
Altitude speed measurement of
Further, the z-degree of freedom is defined as the energy function:
J(e)＝min{eT*e}
f2(a)＝m*a-m*g
e＝f1(φ，θ，ψ)*Cz-kzvz-f2(a)
by means of least square method, finding C when the energy function reaches the minimum valuez，kzThe estimated values of (a) are:
wherein:
the number of measured values is n, the measured value of acceleration is
Measured value of pitch angle is
The roll angle is measured as
A yaw angle of
Altitude speed measurement of
Further, the p degrees of freedom are defined as the following energy function:
J(e)＝min{eT*e}
by means of least squares, finding C when the energy function reaches a minimumpThe estimated values of (a) are:
wherein:
the number of measured values is n and,
by measuring angular acceleration and converting
The lifting force generated by the four propellers is respectively F1，F2，F3And F4，
Further, the q-degree of freedom is defined as the energy function:
J(e)＝min{eT*e}
by means of least squares, finding C when the energy function reaches a minimumqThe estimated values of (a) are:
wherein:
the number of measured values is n and,
by measuring angular acceleration and converting
The lifting force generated by the four propellers is respectively F1，F2，F3And F4，
Further, the r degrees of freedom are defined as the following energy function:
J(e)＝min{eT*e}
by means of least squares, finding C when the energy function reaches a minimumrThe estimated values of (a) are:
wherein:
the number of measured values is n, the measured value of acceleration is
Measured value of pitch angle is
The roll angle is measured as
A yaw angle of
Altitude speed measurement of
And step 204, obtaining a dynamic model of the rotor unmanned aerial vehicle from the six-degree-of-freedom model through the corrected parameter estimation value and the flight data.
From fig. 5, it can be seen that, using a conventional pure theoretical model, the relationship between the model and the measured data, v (z) and accel (z) are velocity and acceleration in the height degree of freedom, respectively. It can be seen that a pure theoretical model can express a certain acceleration accuracy, and the accuracy can only reach 65.38%. The velocity accuracy is-700.2%, in which case the velocity error accumulates and diverges rapidly over time.
From FIG. 6, it can be seen that V (z) and Accel (z) are velocity and acceleration in the height degree of freedom, respectively, using the model of the present invention, as a function of the measured data. It can be seen that the model of the invention can express a certain acceleration accuracy, and the accuracy can reach 88.21%. The velocity accuracy can be 83.54%, and the velocity and acceleration in the high degree of freedom can be largely close to the actual situation.
This embodiment has introduced wind field disturbance item on the basis of four rotor unmanned aerial vehicle kinetic equations of embodiment 1, can be with the accurate reflection of the influence of wind field among the model, compile simulation program according to this model, can improve simulation system's simulation accuracy for simulation system is more close to with actual conditions.
Example 3:
the present embodiment also provides a dynamics modeling apparatus for a rotorcraft, which may be used to perform the method of any of embodiments 1 and 2 above. As shown in fig. 4, the apparatus includes: a flight data acquisition module 301, a modified parameter estimation module 302, and a dynamics modeling module 303.
The flight data acquisition module 301 is used for acquiring flight data of the rotor unmanned aerial vehicle; the correction parameter estimation module 302 is used for performing parameter estimation according to flight data of the rotor unmanned aerial vehicle to obtain a correction parameter estimation value of the rotor unmanned aerial vehicle in a six-degree-of-freedom model constructed by Newton's law; the dynamics modeling module 303 is configured to derive a dynamics model of the rotorcraft from the six-degree-of-freedom model by modifying the parameter estimates and the flight data.
The flight data include the pulse width modulation numerical value of rotor unmanned aerial vehicle flight output and the total lift of the screw that waits to inquire, and flight data acquisition module 301 is specifically used for according to the corresponding relation between the pulse width modulation numerical value of constructing in advance and the total lift of screw to and the corresponding relation of the voltage of constructing in advance and pulse width modulation numerical value, obtain the corresponding pulse width modulation numerical value under different voltages and the total lift of screw.
The revised parameter estimate may include a power effective coefficient, and revised parameter estimate module 302 is further configured to perform parameter estimation based on a total lift of a propeller of the rotary-wing drone in a six-degree-of-freedom model constructed by newton's law according to flight data of the rotary-wing drone to obtain the power effective coefficient.
Specifically, the correction parameter estimation module 302 uses the euler angle in the flight data, the total lift of the propeller, the mass of the unmanned rotorcraft, and the linear displacement of the unmanned rotorcraft as inputs, and obtains the effective power coefficient when the energy function reaches the minimum value by using the least square method in the degree of freedom of the position of the unmanned rotorcraft.
After the flight data of the rotor unmanned aerial vehicle is acquired by the flight data acquisition module 301, parameter estimation is performed by the corrected parameter estimation module 302, and a corrected parameter estimation value is obtained and then applied to the dynamics modeling module 303 to obtain a dynamics modeling model.
In other embodiments, the apparatus may further include a wind field data acquisition module configured to acquire wind field data corresponding to the dynamics modeling of the rotorcraft.
The corrected parameter estimation value further includes a wind field resistance coefficient, and the corrected parameter estimation value module 302 is further configured to obtain a dynamic effective coefficient and a wind field resistance coefficient when the energy function reaches a minimum value, with an euler angle in flight data, a total lift of a propeller, a mass of the unmanned rotorcraft, a linear displacement of the unmanned rotorcraft, and the wind field data as input, by using a least square method on a position degree of freedom of the unmanned rotorcraft.
The corrected parameter estimate comprises a total moment coefficient, and the corrected parameter estimate module 302 is further configured to perform parameter estimation in a six-degree-of-freedom model constructed by newton's law according to flight data of the rotor-wing drone, with a lift force generated by each propeller of the rotor-wing drone serving as a reference, to obtain the total moment coefficient.
Specifically, the correction parameter estimation module 302 takes the angular velocity, the lift force of each propeller, the rotational inertia, the distance from the motor shaft to the origin of the coordinate axis of the airframe, and the distance from the motor shaft to the center of gravity of the airframe in the flight data as inputs, and obtains the total moment coefficient when the energy function reaches the minimum value by using the least square method in the attitude degree of freedom of the unmanned rotorcraft.
Those skilled in the art will appreciate that all or part of the flow of the method implementing the above embodiments may be implemented by a computer program, which is stored in a computer readable storage medium, to instruct related hardware. The computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (8)
1. A method of dynamics modeling of a rotorcraft, the method comprising:
acquiring flight data of the rotor unmanned aerial vehicle;
performing parameter estimation according to flight data of the rotor unmanned aerial vehicle to obtain a corrected parameter estimation value of the rotor unmanned aerial vehicle in a six-degree-of-freedom model constructed by Newton's law;
obtaining a kinetic model of the rotary-wing drone from the six-degree-of-freedom model through the revised parameter estimates and flight data;
wherein, revise the parameter valuation and include power significant coefficient and total moment coefficient, carry out parameter estimation according to rotor unmanned aerial vehicle's flight data, obtain rotor unmanned aerial vehicle is in the revise parameter valuation in the six degrees of freedom model by newton's law construction, include:
performing parameter estimation in the six-degree-of-freedom model constructed by Newton's law according to flight data of the rotor unmanned aerial vehicle by taking the total lift of a propeller of the rotor unmanned aerial vehicle as a reference to obtain a power effective coefficient;
and taking the lift force generated by each propeller of the rotor wing unmanned aerial vehicle as a reference, and performing parameter estimation in the six-degree-of-freedom model constructed by the Newton's law according to the flight data of the rotor wing unmanned aerial vehicle to obtain a total moment coefficient.
2. The method of claim 1, wherein the flight data includes a pulse width modulation value of the drone flight output and a total lift of a propeller to be queried, and the acquiring the flight data for the drone includes:
and obtaining corresponding pulse width modulation values and the total lift force of the propeller under different voltages according to a pre-constructed corresponding relationship between the pulse width modulation values and the total lift force of the propeller and a pre-constructed corresponding relationship between the voltage and the pulse width modulation values.
3. The method of claim 1, further comprising:
acquiring wind field data corresponding to the rotor unmanned aerial vehicle for dynamic modeling;
the corrected parameter estimation value further includes a wind field resistance coefficient, the wind field data is used as a negative factor, the total lift force of a propeller of the rotor wing unmanned aerial vehicle is used as a reference, parameter estimation is carried out in a six-degree-of-freedom model constructed by Newton's law according to flight data of the rotor wing unmanned aerial vehicle, and a power effective coefficient is obtained, and the method includes the following steps:
with Euler angle, the total lift of screw in the flight data the rotor unmanned aerial vehicle's quality rotor unmanned aerial vehicle's linear displacement and wind field data are the input adopt the least square method on rotor unmanned aerial vehicle's the position degree of freedom, obtain power effective coefficient and wind field resistance coefficient under the condition that the energy function reaches the minimum.
4. The method of claim 1, wherein the performing a parameter estimation from flight data of the unmanned rotorcraft in the six-degree-of-freedom model constructed from newton's law, based on lift generated by each propeller of the unmanned rotorcraft, to obtain a total moment coefficient, comprises:
the method comprises the steps of taking angular velocity, each propeller lift force, rotational inertia, the distance from a motor rotating shaft to an original point of a coordinate axis of an engine body and the distance from the motor rotating shaft to the center of gravity of the engine body in flight data as input, and obtaining a total moment coefficient under the condition that an energy function reaches the minimum value by adopting a least square method on the attitude freedom degree of the rotor unmanned aerial vehicle.
5. A rotorcraft's dynamics modeling apparatus, comprising:
the flight data acquisition module is used for acquiring flight data of the rotor unmanned aerial vehicle;
the correction parameter estimation module is used for performing parameter estimation according to flight data of the rotor unmanned aerial vehicle to obtain a correction parameter estimation value of the rotor unmanned aerial vehicle in a six-degree-of-freedom model constructed by Newton's law;
a dynamics modeling module for obtaining a dynamics model of the rotary-wing drone from the six-degree-of-freedom model through the revised parameter estimates and flight data;
wherein, revise the parameter valuation and include power significant coefficient and total moment coefficient, carry out parameter estimation according to rotor unmanned aerial vehicle's flight data, obtain rotor unmanned aerial vehicle is in the revise parameter valuation in the six degrees of freedom model by newton's law construction, include:
performing parameter estimation in the six-degree-of-freedom model constructed by Newton's law according to flight data of the rotor unmanned aerial vehicle by taking the total lift of a propeller of the rotor unmanned aerial vehicle as a reference to obtain a power effective coefficient;
and taking the lift force generated by each propeller of the rotor wing unmanned aerial vehicle as a reference, and performing parameter estimation in the six-degree-of-freedom model constructed by the Newton's law according to the flight data of the rotor wing unmanned aerial vehicle to obtain a total moment coefficient.
6. The apparatus of claim 5, wherein the revised parameter estimate comprises a dynamic effective coefficient, and wherein the revised parameter estimate module is further configured to perform a parameter estimation from flight data of the unmanned rotary wing aircraft in the six degree of freedom model constructed from Newton's Law based on a total lift of a propeller of the unmanned rotary wing aircraft to obtain the dynamic effective coefficient.
7. The apparatus of claim 6, further comprising:
the wind field data acquisition module is used for acquiring wind field data corresponding to the rotor unmanned aerial vehicle for dynamic modeling;
the corrected parameter estimation value further comprises a wind field resistance coefficient, the corrected parameter estimation value module is further used for inputting Euler angle in flight data, total lift of a propeller, the mass of the rotor unmanned aerial vehicle, linear displacement of the rotor unmanned aerial vehicle and the wind field data, and a least square method is adopted on the position freedom degree of the rotor unmanned aerial vehicle to obtain a dynamic effective coefficient and a wind field resistance coefficient under the condition that an energy function reaches the minimum value.
8. The apparatus of claim 5, wherein the revised parameter estimates include a total moment coefficient, and wherein the revised parameter estimates module is further configured to perform parameter estimation from flight data of the UAV in the six-DOF model constructed from Newton's Law based on lift generated by each propeller of the UAV to obtain the total moment coefficient.
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| CN109635376B (en) * | 2018-11-26 | 2020-06-12 | 中国科学院长春光学精密机械与物理研究所 | Modeling method and system based on unmanned aerial vehicle |
| CN111008488B (en) * | 2020-01-02 | 2022-03-01 | 西北工业大学 | Propeller unmanned aerial vehicle launching process reaction torque modeling method |
| CN112269031B (en) * | 2020-10-22 | 2022-05-10 | 天津职业技术师范大学(中国职业培训指导教师进修中心) | Neural network-based real-time wind speed estimation method for rotor unmanned aerial vehicle |
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| CN113408646B (en) * | 2021-07-05 | 2022-11-25 | 上海交通大学 | External disturbance classification method and system for unmanned aerial vehicle |
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