CN211685678U - Simulation analysis system of real-time trail of multi-rotor unmanned aerial vehicle - Google Patents
Simulation analysis system of real-time trail of multi-rotor unmanned aerial vehicle Download PDFInfo
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Abstract
The utility model provides a simulation analysis system of real-time trail of many rotor unmanned aerial vehicle. And the control input module reads the input instruction of the control lever and converts the input instruction into corresponding integral motor throttle quantity and flight target quantity. The flight control module receives attitude, position and speed data of the multi-rotor unmanned aerial vehicle, compares the attitude, position and speed data with preset flight attitude data, and outputs the rotating speed distribution of each motor accelerator. The propeller module calculates the segmental lift, the total thrust and the total thrust moment of each rotor blade. The flow field module is used to calculate the average induced velocity generated by the rotor disk. The flight dynamics module obtains the attitude and the speed data of the multi-rotor unmanned aerial vehicle according to the total thrust, the total thrust moment and the motor torque. Compared with the prior art, the utility model discloses can simulate inflow speed effectively and increase the influence to rotor thrust average value and fluctuation volume, improve the flight simulation precision under the high-speed inflow, let the emulation flight state that obtains more be close to actual motion state.
Description
Technical Field
The utility model relates to a people especially relates to a simulation analysis system of real-time wake of many rotor unmanned aerial vehicle in return circuit emulation field.
Background
Simulation techniques are an emerging method of a class of experimental studies that have evolved with the development of computer technology. The simulation technology has the characteristics of safety, high efficiency, controllability, no destructiveness, economy, no environmental climate limitation, repeated operation and the like, and is widely applied. Firstly, due to the safety of the simulation technology in application, aerospace, aviation and the like are always the main fields of the simulation technology application. Secondly, the economy of the simulation technique in application is a very important factor in its widespread use, since the application of the simulation technique can be traded for a substantial reduction in risk with a smaller investment. In the field of engineering application, flight simulation can be roughly divided into three modes, namely digital simulation, semi-physical simulation and human-in-loop simulation according to the characteristics of simulation models and different implementation modes. The mathematical simulation is a method for realizing simulation operation on a host machine by pure software through establishing a simulation model, a simulation algorithm, linear fitting, interpolation and other methods, and is particularly suitable for research and development, scheme demonstration and design stages. The simulation can evaluate partial real objects of the loop without a whole system prototype, and a real-object simulation test must run in real time. Human-in-loop simulation is a simulation test in which an operator, a pilot and an astronaut operate in a system loop. Such simulation tests enable evaluation of aircraft performance, operator skill and quality in the circuit, or the entire human-machine system. Since the operator is in the loop, the human in-loop simulation system must run in real time.
In the prior art, the thrust calculation of each rotor of the multi-rotor unmanned aerial vehicle is one of the important links of flight simulation, and the thrust of the rotor is also the power source of the unmanned aerial vehicle. Conventionally, the thrust of each rotor is generally calculated by a method using a thrust coefficient, which is generally set to a constant value and obtained by a thrust test of the rotor in a hovering state. Due to the complexity of the flight state of the unmanned aerial vehicle, such as the increase of the flight speed, the rise of the external wind speed and the like, the thrust of each rotor wing is not equal any more and changes along with the change of the wind speed, and the fluctuation amount of each thrust is also in direct proportion to the square of the incoming flow speed. Therefore, under special flight conditions, the traditional multi-rotor unmanned aerial vehicle simulation method can not truly reflect the flight condition of the unmanned aerial vehicle any more. The main manifestations are the following two aspects:
1. increase in inflow velocity
Due to the improvement of the flying speed or the external wind speed, the inflow speed of each rotor of the multi-rotor unmanned aerial vehicle is improved. At the same time, an increase in the inflow velocity will cause the thrust of each rotor to increase, and the amount of thrust fluctuation will increase quadratically with the increase in velocity. In addition, although thrust can be increased through increase of the rotating speed of the rotor wings, the average induced speed of the rotor wings is increased at the same time, the change of the average induced speed is nonlinear, and the increase of the induced speed offsets a part of the increase of the thrust, so that the accuracy of flight simulation of the multi-rotor unmanned aerial vehicle is improved by adopting the inflow model to calculate the thrust of each rotor wing.
2. Rotor trail effect
Unlike low speed or low wind speed flight, the rotor inflow velocity increases to a certain stage, the influence of the wake on the downstream flow field is obviously increased, and the wake of the front rotor has an additional effect on the inflow velocity of the rear rotor. And the additional effect is a change in the forward rotor inflow speed, the process being non-linear. According to the description, the inflow change of the rear rotor causes the change of the thrust of the rear rotor and the thrust fluctuation amount, and the simulation reliability of the multi-rotor unmanned aerial vehicle in the complex flight state can be greatly improved by accurately predicting the change.
SUMMERY OF THE UTILITY MODEL
The utility model provides a many rotor unmanned aerial vehicle simulation method to the tradition among the prior art can not truly reflect this defect of unmanned aerial vehicle's flight situation, the utility model provides a simulation analysis system of many rotor unmanned aerial vehicle real-time wake.
According to an aspect of this application, a simulation analysis system of real-time trail of many rotor unmanned aerial vehicle is provided, including manipulating input module, flight control module, screw module, flow field module, motor module and flight dynamics module, wherein:
the control input module is used for reading an input instruction of the control lever and converting the input instruction into a corresponding integral motor throttle amount and a corresponding flight target amount;
the flight control module is connected with the control input module, the motor module and the flight dynamics module, and is used for receiving attitude, position and speed data of the multi-rotor unmanned aerial vehicle from the flight dynamics module, receiving the whole motor throttle amount and flight target amount from the control input module, comparing the whole motor throttle amount and flight target amount with preset flight attitude data, and outputting the rotation speed distribution of each motor throttle;
the propeller module is connected with the motor module, the flow field module and the flight dynamics module and used for calculating the segmental lift force of each rotor blade and the total thrust and total thrust moment of the multi-rotor unmanned aerial vehicle according to the average induced speed of the rotor disk, the attitude and speed data of the multi-rotor unmanned aerial vehicle and the rotating speed of the motor;
the flow field module is connected with the propeller module and the flight dynamics module and used for calculating the average induced speed generated by the rotor disk according to the attitude and speed data of the multi-rotor unmanned aerial vehicle and the segmented lift force of each rotor blade;
the motor module is connected with the propeller module and the motor module and used for calculating the motor rotating speed and the motor torque of each rotor wing according to the integral motor throttle amount from the flight control module and the motor throttle distribution instruction, outputting the motor rotating speed to the propeller module and outputting the motor torque to the flight dynamics module; and
and the flight dynamics module is used for obtaining the attitude and the speed data of the multi-rotor unmanned aerial vehicle according to the total thrust and the total thrust moment output by the propeller module and the motor torque output by the motor module.
In a specific embodiment, the flight dynamics module calculates the current flight state data of the multi-rotor unmanned aerial vehicle according to the received total thrust and the thrust moment, the parameters of the fuselage, the attitude of the last time step and the motion state of the mass center of the fuselage.
In a specific embodiment, the flight attitude data of the multi-rotor drone includes forward flight speed, lift speed, lateral flight speed, fly height, pitch angle, roll angle, and yaw angle.
In one embodiment, the multi-rotor drone comprises a front rotor and a rear rotor, and during high-speed flight, when the free inflow speed of the front rotor increases, the inclination angle of the wake of the front rotor also increases, and the influence on the inflow speed of the rear rotor is intensified.
In one embodiment, the inflow velocity delta of the aft rotor is non-linear and is associated with the pitch angle of the forward rotor wake, the induced velocity with time lag in the forward rotor disk.
In a specific embodiment, many rotor unmanned aerial vehicle are evenly distributed's eight rotor structures.
According to another aspect of the application, a simulation analysis method of real-time trail of a multi-rotor unmanned aerial vehicle is provided, which comprises the following steps:
establishing a rotor blade inflow model of the multi-rotor unmanned aerial vehicle;
according to the established rotor blade inflow model, calculating the inflow influence of a front rotor flying at high speed in the multi-rotor unmanned aerial vehicle on a rear rotor; and
under the condition of considering inflow influence, the aerodynamic force and aerodynamic moment variation of the rotor wing are input into a flight dynamics equation set for resolving, and the flight attitude data of the multi-rotor wing unmanned aerial vehicle is obtained.
In a specific embodiment, the step of establishing the rotor blade inflow model further comprises:
according to the basic fluid dynamics equation of the rotor wing potential flow field and the principle
Obtaining a conservation of mass equation and a conservation of momentum equation, wherein vtIs the total speed of the rotor, V∞In order to be the inflow velocity,
is the inflow unit vector, v is the perturbation velocity,
conservation of mass equation:
conservation of momentum equation:
where V is the normalized velocity and V is V/V∞(ii) a τ is normalized time, τ ═ tV∞R; p is the normalized pressure of the gas,
the boundary condition is that the flow field upstream of the rotor approaches zero at infinity, set as represented by the velocity potential ΨA normalized velocity of
Substituting the mass conservation equation (1) to obtain:
by substituting the above-mentioned momentum conservation equation (2) and setting the pressure potential P to Φ, the following can be obtained:
the pressure potential is further represented by a first type of Legendre function and a second type of Legendre function
Wherein v, eta, psi is unmanned aerial vehicle rotor ellipsoid coordinate system, expands pressure into:
to satisfy the above boundary conditions, the velocity potential is redefined as:
from the above, the flow field velocity can be expressed as follows:
and finally substituting the speed expression and the pressure expression into a momentum conservation equation (2), and obtaining a final rotor blade inflow model through Galerkin transformation, wherein the final rotor blade inflow model is as follows:
wherein
[M]＝[L]|Tail inclination angle of 0
if the flapping of the rotor blades is not considered and the blades are fixed distances, the segmental lift force of each rotor is as follows:
Lkqi＝0.5ρaci[(Ωkri+μkΩkR sinψq)2θi-(Ωkri+μkΩkR sinψq)(ηckΩkR+vzk)]；
the formula k above is the number of the rotor, q is the blade number of the rotor, i is the blade segment number, e.g. L125Denotes the ith section, Ω, of the 2 nd blade of the 1 st rotorkIs the rotational speed of rotor k, R is the rotor radius, mukAnd ηckThe advance ratio and climb rate, v, of rotor kzkIs the average axial induced velocity, θ, of rotor k in the rotor diskiIs the pitch of the i-th blade element, riFor the length of the infinitesimal section i to the centre of the rotor in the direction of the blades, psiqIs the azimuth angle of the qth blade.
Determining an equality right load function of the rotor blade inflow model (9), then:
when m is 0, n is m +1, m +3,. the term,
when m > 0, n ═ m +1, m +3,. the results obtained are compared,
others are
J in formulae (10) and (11)0(x) Is a Bessel function of the first kind.
In a specific embodiment, the step of calculating the inflow influence of the front rotor on the rear rotor during high-speed flight in the multi-rotor drone further includes:
1) the accompanying model for determining rotor k inflow is:
thus the accompanying velocity is
2) Determining the trail tilt angle of rotor k
3) Determining the position x of a point in the rotor disk of a rear rotor w in the rotor k coordinate systemk，yk0, a unit circle is established in the rotor disk with the center of rotor k as the origin of coordinates, and points { x }k，yk0 to the k coordinate system and the y coordinate axis of the rotor wing are divided into two parts, namely a circle inner section and a circle outer section, wherein the length of the circle inner section is
The length of the outer circle segment is σ ═ xk-s0|；
4) Determine the full edge flow velocity as:
vDS＝v(-s0，yk，0，t-σsinχk)+v*(s0，-yk，0，t-σsinχk)；
5) determining the velocity increment of rotor k wake to rotor w inflow as:
v＝v[1-f(σ，χk)]+vDSf(σ，χk)
wherein the function f (sigma, chi) in the above formulak) Is composed of
In the formula, g (χ)k)＝1.84cos1/2χk-4.06cosχk+11.84cos3/2χk。
In a specific embodiment, the step of obtaining flight attitude data of the multi-rotor drone further includes:
set for many rotor unmanned aerial vehicle body respectively for the attitude angle of ground coordinate system do: yaw angle psi1Angle of pitch theta1Angle of roll
The position coordinate of the center of mass of the machine body relative to the ground coordinate system is xb、yb、zb(ii) a Then there are:
in the formula, TtotalFor combined thrust of all rotors, ζx、ζyAnd ζzThe damping coefficients of the horizontal, lateral and vertical directions of the multi-rotor unmanned plane body are respectively specified, wherein the plane body is defined to be bent positively, clockwise rolling is changed into positive, and anticlockwise yawing is defined as positive;
for the attitude angle of the multi-rotor unmanned body, the kinetic equation is as follows:
in the formula, τxAnd τyThe total thrust moment, tau, of each rotor thrust to the x-direction and the y-direction of the body coordinate systemzFor total torque of each rotor motor, Ix、IyAnd IzRespectively are inertia moments of the unmanned plane body with multiple rotor wings in three directions,
ζθand ζψThree-way rotation damping coefficients are respectively.
Adopt the utility model discloses a simulation analysis system and simulation analysis method of real-time wake of many rotor unmanned aerial vehicle, it is including controlling input module, flying to control module, screw module, flow field module, motor module and flight dynamics module. The control input module reads an input instruction of the control lever and converts the input instruction into a corresponding integral motor throttle amount and a corresponding flight target amount. The flight control module receives attitude, position and speed data of the multi-rotor unmanned aerial vehicle, receives the whole motor throttle amount and the flight target amount, compares the whole motor throttle amount and the flight target amount with preset flight attitude data, and outputs the rotating speed distribution of each motor throttle. The propeller module calculates the segmental lift force of each rotor blade and the total thrust force and the total thrust moment of the multi-rotor unmanned aerial vehicle according to the average induced speed of the rotor disk, the postures and the speed data of the multi-rotor unmanned aerial vehicle and the rotating speed of the motor. The flow field module calculates the average induced speed generated by the rotor disk according to the attitude and speed data of the multi-rotor unmanned aerial vehicle and the segmented lift force of each rotor blade. And the motor module calculates the motor rotating speed and the motor torque of each rotor wing according to the integral motor throttle amount and the motor throttle distribution instruction. The flight dynamics module obtains the attitude and the speed data of the multi-rotor unmanned aerial vehicle according to the total thrust and the total thrust moment output by the propeller module and the motor torque output by the motor module.
Compared with the prior art, the simulation analysis system and the simulation analysis method can effectively simulate the influence of inflow speed increase on the average value and the fluctuation quantity of the thrust of the rotor and the influence of the wake of the upstream rotor on the inflow of the downstream rotor, and the variation quantity of aerodynamic force and aerodynamic moment of the rotor under the influence of the inflow is input into the flight dynamics module to be resolved, so that the influence of the middle-high speed inflow state on the flight performance and the flight quality of the multi-rotor unmanned aerial vehicle can be more accurately simulated, the flight simulation precision under the middle-high speed inflow is improved, and the obtained simulated flight state is closer to the actual motion state.
Drawings
The various aspects of the present application will become more apparent to the reader after reading the detailed description of the application with reference to the attached drawings. Wherein,
FIG. 1 illustrates a block diagram of a simulation analysis system for real-time trails of multi-rotor drones, in accordance with an aspect of the present application;
FIG. 2 is a schematic illustration of the effect of an upstream rotor wake on downstream rotor inflow in the simulation analysis system of FIG. 1;
FIG. 3 shows a schematic diagram of the computation of the full side-stream velocity of a downstream rotor from the inflow velocity in an upstream rotor disk in the simulation analysis system of FIG. 1; and
fig. 4 illustrates a block flow diagram of a simulation analysis method for real-time wake for a multi-rotor drone in accordance with another aspect of the present application.
Detailed Description
In order to make the disclosure more complete and complete, reference may be made to the accompanying drawings, in which like references indicate the same or similar elements, and to the various embodiments of the disclosure described below. However, it should be understood by those skilled in the art that the examples provided below are not intended to limit the scope covered by the present application. In addition, the drawings are only for illustrative purposes and are not drawn to scale.
Specific embodiments of various aspects of the present application are described in further detail below with reference to the attached figures.
Fig. 1 illustrates a block diagram of a simulation analysis system for real-time trails of a multi-rotor drone, according to one aspect of the present application. FIG. 2 is a schematic illustration of the effect of an upstream rotor wake on downstream rotor inflow in the simulation analysis system of FIG. 1. FIG. 3 shows a schematic diagram of calculating a full edge flow velocity from an in-flow velocity.
Referring to fig. 1, in this embodiment, the simulation analysis system of the present application includes a steering input module, a flight control module, a motor module, a propeller module, a flight dynamics module, and a flow field module. The flight control module is connected to the control input module, the flight dynamics module and the motor module. The propeller module is connected to the motor module, the flight dynamics module and the flow field module. The flight dynamics module is connected to the flight control module, the motor module, the propeller module and the flow field module.
In detail, the control input module is used for reading an input command of the control lever and converting the input command into a corresponding integral motor throttle amount and a corresponding flight target amount. The flight control module is used for receiving attitude, position and speed data of the multi-rotor unmanned aerial vehicle from the flight dynamics module, receiving the whole motor throttle amount and flight target amount from the operation input module, comparing the whole motor throttle amount and the flight target amount with preset flight attitude data, and outputting the rotating speed distribution of each motor throttle. For example, multi-rotor drones are evenly distributed eight-rotor structures. The propeller module is used for calculating the segmental lift force of each rotor blade and the total thrust and total thrust moment of the multi-rotor unmanned aerial vehicle according to the average induced speed of the rotor disk, the postures of the multi-rotor unmanned aerial vehicle, the speed data and the rotating speed of the motor. The flow field module is used for calculating the average induced speed generated by the rotor disk according to the attitude and speed data of the multi-rotor unmanned aerial vehicle and the segmented lift force of each rotor blade. It can be seen that the input parameters for the flow field module need to be provided from the propeller module, which in turn needs to be provided from the flow field module, so that the two modules are calculated in parallel.
The motor module is used for calculating the motor rotating speed and the motor torque of each rotor wing according to the whole motor throttle amount from the flight control module and the motor throttle distribution instruction, and then outputting the motor rotating speed to the propeller module and the motor torque to the flight dynamics module. The flight dynamics module is used for obtaining the attitude and the speed data of the multi-rotor unmanned aerial vehicle according to the total thrust and the total thrust moment output by the propeller module and the motor torque output by the motor module. Preferably, the flight dynamics module calculates current flight state data of the multi-rotor unmanned aerial vehicle according to the received total thrust and the thrust moment, the fuselage parameters, the attitude of the last time step and the motion state of the mass center of the airframe. Here, the flight attitude data of the multi-rotor drone includes forward flight speed, lift speed, side flight speed, flying height, pitch angle, roll angle, and yaw angle.
In one embodiment, the multi-rotor drone includes a front rotor and a rear rotor, and during high-speed flight, when the free inflow speed of the front rotor increases, the inclination angle of the wake of the front rotor also increases, and the influence on the inflow speed of the rear rotor is intensified. Further, the inflow velocity delta of the aft rotor is non-linear and is associated with the tilt angle of the forward rotor wake, the induced velocity with time lag in the forward rotor disk. Knowing easily, the induced flow that the place ahead rotor produced can influence the inflow of rear rotor, and the change of inflow makes rotor thrust change, finally makes the thrust of rear rotor receive the influence of the induced flow of place ahead rotor. During the non-linear change of inflow and rotor thrust, the inflow model of the application needs to be used for real-time calculation. If the rotors basically have no interference during low-speed flight, various parameters of the rotors can be calculated respectively; if the aircraft flies at medium and high speed, the front rotor and the rear rotor interfere with each other. In case of interference, the front rotor acts as a fan blowing air to the rear rotor, thereby generating an additional inflow velocity to the rear rotor. The inflow speed affects the change of aerodynamic force and aerodynamic moment, and further affects the data calculation precision during medium and high speed flight.
Fig. 4 illustrates a block flow diagram of a simulation analysis method for real-time wake for a multi-rotor drone in accordance with another aspect of the present application.
Referring to fig. 4 in combination with fig. 2 and 3, in the simulation analysis method, step S101 is first executed to establish a rotor blade inflow model of the multi-rotor drone, step S103 is then executed to calculate an inflow influence of a front rotor flying at a high speed in the multi-rotor drone on a rear rotor according to the established rotor blade inflow model, and step S105 is finally executed to input rotor aerodynamic force and aerodynamic moment variation into a flight dynamics equation set to be resolved in consideration of the inflow influence, so as to obtain flight attitude data of the multi-rotor drone.
In step S101, the step of establishing the rotor blade inflow model further includes:
according to the basic fluid dynamics equation of the rotor wing potential flow field and the principle
Obtaining a conservation of mass equation and a conservation of momentum equation, wherein vtIs the total speed of the rotor, V∞In order to be the inflow velocity,
is the inflow unit vector, v is the perturbation velocity,
conservation of mass equation:
conservation of momentum equation:
where V is the normalized velocity and V is V/V∞(ii) a τ is normalized time, τ ═ tV∞R; p is the normalized pressure of the gas,
the boundary condition is that the flow field upstream of the rotor approaches zero at infinity, and the normalized velocity is set to be represented by the velocity potential Ψ
Substituting the mass conservation equation (1) to obtain:
by substituting the above-mentioned momentum conservation equation (2) and setting the pressure potential P to Φ, the following can be obtained:
the pressure potential is further represented by a first type of Legendre function and a second type of Legendre function
Wherein v, eta, psi is unmanned aerial vehicle rotor ellipsoid coordinate system, expands pressure into:
to satisfy the above boundary conditions, the velocity potential is redefined as:
from the above, the flow field velocity can be expressed as follows:
and finally substituting the speed expression and the pressure expression into a momentum conservation equation (2), and obtaining a final rotor blade inflow model through Galerkin transformation, wherein the final rotor blade inflow model is as follows:
wherein
[M]＝[L]|Tail inclination angle of 0
if the flapping of the rotor blades is not considered and the blades are fixed distances, the segmental lift force of each rotor is as follows:
Lkqi＝0.5ρaci[(Ωkri+μkΩkR sinψq)2θi-(Ωkri+μkΩkR sinψq)(ηckΩkR+vzk)]；
the formula k above is the number of the rotor, q is the blade number of the rotor, i is the blade segment number, e.g. L125Denotes the ith section, Ω, of the 2 nd blade of the 1 st rotorkIs the rotational speed of rotor k, R is the rotor radius, mukAnd ηckThe advance ratio and climb rate, v, of rotor kzkIs the average axial induced velocity, θ, of rotor k in the rotor diskiIs the pitch of the i-th blade element, riFor the length of the infinitesimal section i to the centre of the rotor in the direction of the blades, psiqIs the azimuth angle of the qth blade.
Determining an equality right load function of the rotor blade inflow model (9), then:
when m is 0, n is m +1, m +3,. the term,
when m > 0, n ═ m +1, m +3,. the results obtained are compared,
others are
J in formulae (10) and (11)0(x) Is a Bessel function of the first kind.
In step S103, the step of calculating the inflow influence of the front rotor flying at a high speed in the multi-rotor drone on the rear rotor further includes:
1) the accompanying model for determining rotor k inflow is:
thus the accompanying velocity is
2) Determining the trail tilt angle of rotor k
3) Determining the position x of a point in the rotor disk of a rear rotor w in the rotor k coordinate systemk，yk0, a unit circle is established in the rotor disk with the center of rotor k as the origin of coordinates, and points { x }k，yk0 to the k coordinate system and the y coordinate axis of the rotor wing are divided into two parts, namely a circle inner section and a circle outer section, wherein the length of the circle inner section is
The length of the outer circle segment is σ ═ xk-s0|；
4) The full edge flow velocity (as shown in FIG. 3) is determined as:
vDS＝v(-s0，yk，0，t-σsinχk)+v*(s0，-yk，0，t-σsinχk)；
5) the velocity increment of rotor k wake versus rotor w inflow (as shown in figure 2) is determined as:
v＝v[1-f(σ，χk)]+vDSf(σ，χk)
wherein the function f (sigma, chi) in the above formulak) Is composed of
In the formula, g (χ)k)＝1.84cos1/2χk_4.06cosχk+11.84cos3/2χk。
In step S105, the step of obtaining the flight attitude data of the multi-rotor drone further includes:
set for many rotor unmanned aerial vehicle body respectively for the attitude angle of ground coordinate system do: yaw angle psi1Angle of pitch theta1Angle of roll
The position coordinate of the center of mass of the machine body relative to the ground coordinate system is xb、yb、zb(ii) a Then there are:
in the formula, TtotalFor combined thrust of all rotors, ζx、ζyAnd ζzThe damping coefficients of the horizontal, lateral and vertical directions of the multi-rotor unmanned plane body are respectively specified, wherein the plane body is defined to be bent positively, clockwise rolling is changed into positive, and anticlockwise yawing is defined as positive;
for the attitude angle of the multi-rotor unmanned body, the kinetic equation is as follows:
in the formula, τxAnd τyThe total thrust moment, tau, of each rotor thrust to the x-direction and the y-direction of the body coordinate systemzFor total torque of each rotor motor, Ix、IyAnd IzRespectively are inertia moments of the unmanned plane body with multiple rotor wings in three directions,
ζθand ζψThree-way rotation damping coefficients are respectively.
Therefore, the simulation analysis method provides a robust multi-rotor induced flow real-time calculation method for the influence of medium-high speed inflow on the thrust of the rotor and the incremental effect of the wake of the front rotor (or called as an upstream rotor) on the inflow of the rear rotor (or called as a downstream rotor), and is used for the multi-rotor unmanned aerial vehicle human-loop flight real-time simulation in more application scenes. As the free inflow velocity of the leading rotor increases, its trailing-edge pitch angle increases, exacerbating the effect on the inflow velocity of the trailing rotor, the velocity increase being non-linear and related to the trailing-edge pitch angle, the induced velocity of the upstream rotor in-disk lag, as shown in fig. 2. In addition, the full side-stream induced velocity downstream of the rotor can be calculated from the induced velocity at two points in the rotor disk with time lag, as shown in FIG. 3. The calculation result shows that the robust simulation model established by the simulation algorithm is closer to the actual characteristic in the high-speed inflow state of the multi-rotor unmanned aerial vehicle. The multi-rotor unmanned aerial vehicle flight control system can be used for control design and flight training of the multi-rotor unmanned aerial vehicle under the condition of executing special tasks or severe flight conditions, and has certain practical value and application prospect.
The simulation analysis system and the simulation analysis method for the real-time trail of the multi-rotor unmanned aerial vehicle comprise a control input module, a flight control module, a propeller module, a flow field module, a motor module and a flight dynamics module. The control input module reads an input instruction of the control lever and converts the input instruction into a corresponding integral motor throttle amount and a corresponding flight target amount. The flight control module receives attitude, position and speed data of the multi-rotor unmanned aerial vehicle, receives the whole motor throttle amount and the flight target amount, compares the whole motor throttle amount and the flight target amount with preset flight attitude data, and outputs the rotating speed distribution of each motor throttle. The propeller module calculates the segmental lift force of each rotor blade and the total thrust force and the total thrust moment of the multi-rotor unmanned aerial vehicle according to the average induced speed of the rotor disk, the postures and the speed data of the multi-rotor unmanned aerial vehicle and the rotating speed of the motor. The flow field module calculates the average induced speed generated by the rotor disk according to the attitude and speed data of the multi-rotor unmanned aerial vehicle and the segmented lift force of each rotor blade. And the motor module calculates the motor rotating speed and the motor torque of each rotor wing according to the integral motor throttle amount and the motor throttle distribution instruction. The flight dynamics module obtains the attitude and the speed data of the multi-rotor unmanned aerial vehicle according to the total thrust and the total thrust moment output by the propeller module and the motor torque output by the motor module.
Compared with the prior art, the simulation analysis system and the simulation analysis method can effectively simulate the influence of inflow speed increase on the average value and the fluctuation quantity of the thrust of the rotor and the influence of the wake of the upstream rotor on the inflow of the downstream rotor, and the variation quantity of aerodynamic force and aerodynamic moment of the rotor under the influence of the inflow is input into the flight dynamics module to be resolved, so that the influence of the middle-high speed inflow state on the flight performance and the flight quality of the multi-rotor unmanned aerial vehicle can be more accurately simulated, the flight simulation precision under the middle-high speed inflow is improved, and the obtained simulated flight state is closer to the actual motion state.
Hereinbefore, specific embodiments of the present application are described with reference to the drawings. However, those skilled in the art will appreciate that various modifications and substitutions can be made to the specific embodiments of the present application without departing from the spirit and scope of the application. Such modifications and substitutions are intended to be included within the scope of the appended claims.
Claims (6)
1. A simulation analysis system of real-time wake of a multi-rotor unmanned aerial vehicle is characterized by comprising a control input module, a flight control module, a propeller module, a flow field module, a motor module and a flight dynamics module, wherein,
the control input module is used for reading an input instruction of the control lever and converting the input instruction into a corresponding integral motor throttle amount and a corresponding flight target amount;
the flight control module is connected with the control input module, the motor module and the flight dynamics module, and is used for receiving attitude, position and speed data of the multi-rotor unmanned aerial vehicle from the flight dynamics module, receiving the whole motor throttle amount and flight target amount from the control input module, comparing the whole motor throttle amount and flight target amount with preset flight attitude data, and outputting the rotation speed distribution of each motor throttle;
the propeller module is connected with the motor module, the flow field module and the flight dynamics module and used for calculating the segmental lift force of each rotor blade and the total thrust and total thrust moment of the multi-rotor unmanned aerial vehicle according to the average induced speed of the rotor disk, the attitude and speed data of the multi-rotor unmanned aerial vehicle and the rotating speed of the motor;
the flow field module is connected with the propeller module and the flight dynamics module and used for calculating the average induced speed generated by the rotor disk according to the attitude and speed data of the multi-rotor unmanned aerial vehicle and the segmented lift force of each rotor blade;
the motor module is connected with the propeller module and the motor module and used for calculating the motor rotating speed of each rotor wing and the motor torque output by the motor module according to the whole motor throttle amount from the flight control module and the distribution instruction of each motor throttle, outputting the motor rotating speed to the propeller module and outputting the motor torque to the flight dynamics module; and
and the flight dynamics module is used for obtaining the attitude and the speed data of the multi-rotor unmanned aerial vehicle according to the total thrust and the total thrust moment output by the propeller module and the motor torque output by the motor module.
2. The system of claim 1, wherein the flight dynamics module calculates current flight status data for the multi-rotor drone based on the received total thrust and thrust moment, and fuselage parameters, attitude at the last time step, and body centroid motion state.
3. The system of claim 2, wherein the flight attitude data of the drone includes forward flight speed, lift speed, side flight speed, altitude, pitch angle, roll angle, and yaw angle.
4. The system of claim 1, wherein the multi-rotor drone includes a front rotor and a rear rotor, and when the free inflow velocity of the front rotor increases during high-speed flight, the tilt angle of the wake increases, which increases the influence on the inflow velocity of the rear rotor.
5. The system of claim 4, wherein the inflow velocity delta of the aft rotor is non-linear and is associated with a tilt angle of the forward rotor wake, an induced velocity with time lag in the forward rotor disk.
6. The system of claim 1, wherein the multi-rotor drone is an evenly distributed eight-rotor structure.
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| CN112441253A (en) * | 2019-09-04 | 2021-03-05 | 苏州翼搏特智能科技有限公司 | Simulation analysis system and method for real-time trail of multi-rotor unmanned aerial vehicle |
| CN112487730A (en) * | 2020-10-30 | 2021-03-12 | 南京航空航天大学 | Phase angle control-based multi-rotor aircraft noise suppression method |
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| CN112441253A (en) * | 2019-09-04 | 2021-03-05 | 苏州翼搏特智能科技有限公司 | Simulation analysis system and method for real-time trail of multi-rotor unmanned aerial vehicle |
| CN112487730A (en) * | 2020-10-30 | 2021-03-12 | 南京航空航天大学 | Phase angle control-based multi-rotor aircraft noise suppression method |
| CN112487730B (en) * | 2020-10-30 | 2024-05-28 | 南京航空航天大学 | Multi-rotor aircraft noise suppression method based on phase angle control |
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