CN112068582B - Method for identifying transition mode model of tilt rotor unmanned aerial vehicle - Google Patents
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Abstract
The invention discloses a method for identifying a transition mode model of a tilt rotor unmanned aerial vehicle, which is characterized in that unknown parameters are determined by a frequency domain identification method, and the unmanned aerial vehicle under the conditions of different airspeeds and different nacelle inclination angles is identified through a test, so that the transition mode model of the tilt rotor unmanned aerial vehicle is obtained. According to the invention, the output quantity of the actuator is used as the input quantity of the model, and the control derivative of each actuator can be finally obtained through tests under any control distribution strategy of any controller, so that the established model not only can truly reflect the transition mode flight characteristics of the tilt rotor unmanned aerial vehicle, but also can provide convenience for the design of a control system.
Description
Technical Field
The invention belongs to the technical field of flight control and modeling of unmanned aerial vehicles, and particularly relates to a method for identifying a transition model of an unmanned aerial vehicle with a tilt rotor wing.
Background
The unmanned plane with the tilting rotor wing is a novel vertical take-off and landing aircraft with the characteristics of a fixed-wing aircraft and a helicopter, and is a hotspot of research at home and abroad since birth. Engine nacelles are respectively arranged on two sides of the wings of the unmanned aerial vehicle, and the taking-off and landing are realized similarly to a double-rotor helicopter; when flying forward, the nacelle tilts forward, and the rotor generates forward thrust, similar to a propeller-driven aircraft. The tilt rotor unmanned aerial vehicle has the vertical take-off and landing and hovering capabilities of a conventional helicopter, has the advantages of high speed, large load capacity, long range and the like of a fixed-wing aircraft, and has high application value for military use and civil use.
The tilt rotor unmanned aerial vehicle has three flight modes, namely a helicopter mode, a transition mode and a fixed wing mode. In which the transition mode is controlled by both the rotor and the rudder, the derivative of the rotor and rudder control is not only related to the nacelle pitch and airspeed, but also changes as the controller or control distribution method changes, and both sets of control systems are more complex than if only one set of control systems were used. There is therefore a need for an efficient method of modeling, through reasonable experimentation, the actuator control derivatives required for control system design under any controller-any control distribution strategy.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a method for identifying a transition mode model of a tilt rotor unmanned aerial vehicle, which utilizes CIFER software to identify a multi-input multi-output unmanned aerial vehicle system. The specific technical scheme of the invention is as follows:
the method for identifying the transition mode model of the tilt rotor unmanned aerial vehicle is characterized by comprising the following steps of:
s1: establishing a nonlinear model of the tilt rotor unmanned aerial vehicle according to the principles of flight mechanics, aerodynamic mechanics and kinematics;
s2: designing a basic control law, a control distribution strategy and an identification scheme aiming at the transition mode flight characteristics of the tilt rotor unmanned aerial vehicle;
s3: testing and collecting test data, selecting data related to the parameters to be identified from the test data, preprocessing the data, and converting the preprocessed data into a data type which can be processed by CIFER software;
s4: performing small-disturbance linearization on the nonlinear model obtained in the step S1, establishing a transfer function and a state equation, and determining a linear model for model parameter identification;
s5: the data obtained in the step S3 is processed by FRESPID, MISOSA and COMPOSITE parts of CIFER software to generate a MIMO frequency response database, and coherence values are reserved
And the data not oscillating with the coherence value curve is used for identification, if the frequency domain data in the identification frequency band exceeds two thirds and does not accord with the coherence value standard set by the CIFER software, the step S2 is returned to redesign the identification scheme;
s6: performing frequency domain identification through the parameterized model identification functions of the CIFER software, namely NAVFIT and DERIVD, and meeting JaveThe parameter to be identified is obtained according to the criterion of less than or equal to 100, JaveIn order to identify the cost function, the linear model obtained in the step S4 is subjected to time domain verification through VERIFY, and if rms fitting error J occursrmsAnd (3) the time domain verification of the identified model is represented when the coefficient TIC of the Theil inequality is less than or equal to 1.0-2.0 and less than or equal to 0.25-0.3, otherwise, the step S4 is returned, the linear model is determined again, and the complete model of the transition mode of the tilt rotor unmanned aerial vehicle is obtained through identification of each channel.
Further, the basic control law and the control distribution strategy designed in step S2 are implemented by matching an INDI controller with a pseudo-inverse method to control an attitude angle, and are specifically implemented as follows:
the nonlinear dynamical system equation of the unmanned aerial vehicle is as follows:
wherein x is an n-dimensional state quantity,
is the derivative of x, u is the p-dimensional control input; y is an m-dimensional output, f (x) is a non-linear function of x, h (x) is a linear function of x, and g (x, u) is a linear function of x and u;
definition of
Wherein A is partial derivative of f (x) + g (x, u) to x, B is partial derivative of g (x, u) to u, and the state quantity and the input quantity at a certain moment are (x)0,u0) The desired closed loop dynamics are
When the calculation frequency of the control law is high enough and the step length is small enough, the incremental dynamic inverse control law is designed as follows:
where Δ u is the control increment, hxIs the partial derivative of g (x, u) with respect to x.
Further, the identification scheme designed in step S2 includes test subjects, test times, input signals and identification methods; the identification scheme specifically comprises the following steps:
in each test, the identification tests of the pitching channel, the rolling channel and the yawing channel are respectively carried out two to three times under the condition that the airspeed and the nacelle inclination angle are controlled to be kept unchanged as much as possible, the channel to be identified is called a main channel, and the rest channels are called auxiliary channels. Because the transition mode has two sets of control systems, the hybrid control mode can only identify the input-output relationship between the hybrid control quantity and the response of the unmanned aerial vehicle, and in order to obtain the control derivative of the actuator, a test only with the action of a rotor wing or only with the action of a control plane is required. Limited by test conditions and control system design, test data of the unmanned aerial vehicle under the conditions of rotor operation and control surface operation are not easy to obtain at the same time, at the moment, a mixed operation test is firstly carried out, and then a single operation mode capable of obtaining high-quality response is selected for testing.
The sweep frequency signal is used as the input signal of the main channel, the aircraft angle response caused by the sweep frequency amplitude is +/-5 degrees to +/-15 degrees, and the frequency range is 0.5 omegaBW≤ω≤2.5ω180，ωBWIs the bandwidth frequency, omega180The frequency corresponding to the phase angle of-180 degrees. The frequency sweep experiment is started after the tilt rotor unmanned aerial vehicle is in a balancing state for 3-5 seconds, the experiment is ended after the frequency sweep is ended and the tilt rotor unmanned aerial vehicle stays in the balancing state for at least 3 seconds, and the time of each experiment is recommended
ωminIs the minimum frequency, ωmin＝0.5ωBW。
And inputting small-amplitude white noise into the secondary channel to avoid the coupling of the input quantity, and adopting a direct identification method to identify, namely directly measuring the output quantity of the actuator and the output quantity of the model to identify.
Further, in the step S3, if the test is limited by the condition, only one single maneuver test is completed, and the other single maneuver response data is obtained by subtracting the completed single maneuver response data from the hybrid maneuver response data under the same condition.
Further, the longitudinal and lateral state equations established in step S4 are:
u, w, q, theta, v, p, r and phi are state quantities, u, v and w are disturbance speeds in the directions of x, y and z axes under a body axis system respectively, p, q and r are disturbance rolling angle speed, pitching angle speed and yaw angle speed respectively, and theta and phi are disturbance pitch angle and rolling angle respectively;
δe,δa,δrthe control variables are respectively a rotor wing longitudinal periodic variable pitch, a rotor wing total pitch differential, a rotor wing longitudinal periodic variable pitch differential, an elevator deflection angle, an aileron deflection angle and a rudder deflection angle; xu、Zu、MuPartial derivatives of the longitudinal force, lateral force and pitching moment, respectively, on u, Xw、Zw、MwPartial derivatives of the longitudinal force, lateral force and pitching moment, respectively, with respect to w, Xq、Zq、MqThe partial derivatives of the longitudinal force, lateral force and pitching moment on q,
respectively longitudinal force, lateral force and pitching moment
The partial derivative of (a) of (b),
longitudinal force, lateral force and pitching moment pairs delta respectivelyePartial derivatives of (d); y isv、Lv、NvPartial derivatives of the transverse force, the roll moment and the yaw moment, respectively, with respect to v, Yp、Lp、NpPartial derivatives of p, Y, of the transverse force, roll moment and yaw moment, respectivelyr、Lr、NrRespectively the partial derivatives of the lateral force, the roll moment and the yaw moment to r,
respectively, transverse force, rolling moment and yawing moment
The partial derivative of (a) of (b),
respectively, transverse force, rolling moment and yawing moment
The partial derivative of (a) of (b),
transverse force, roll moment and yaw moment respectivelyaThe partial derivative of (a) of (b),
transverse force, roll moment and yaw moment respectivelyrPartial derivatives of (d); u shape0、W0、Θ0Respectively the speed of the lower body axis in the x-axis direction in the trim state,The speed in the z-axis direction and the pitch angle under the body axis system; g represents the gravitational acceleration.
Further, the specific process of step S6 is as follows:
s6-1: fitting the frequency response pair in the MIMO frequency domain database obtained in the step S5 into a SISO transfer function model form through a NAVFIT part of CIFER software, and converting the fitting result of the transfer function into a state equation form to obtain an initial value of a state equation parameter;
s6-2: fitting the MIMO frequency domain data into a state equation through a DERIVD part of the CIFER software;
if the fitting condition of the identification result and the frequency response curve is not good, returning to the step S2 to re-determine the linear model identified by the model parameters, performing time domain verification on the linear model identified by the model parameters with determined parameters in a VERIFY part by adopting bipolar square waves, if the verification fails, proving that the identified model does not have model prediction capability, and then returning to the step S4 to re-determine the linear model identified by the model parameters.
And repeating the step S6 until all the channel actuator control derivatives are determined, so as to obtain a complete transition mode model of the tilt rotor unmanned aerial vehicle.
The invention has the beneficial effects that:
1. the method obtains the transition mode model of the tilt rotor unmanned aerial vehicle through the CIFER software identification result based on the test data, and is different from other models in that the output quantity of an actuator is used as the input quantity of the model instead of the mixed operation quantity.
2. The identification method can finally obtain the control derivative of each actuator through a test under any control distribution strategy of any controller, so that the established model not only can truly reflect the transition mode flight characteristics of the tilt rotor unmanned aerial vehicle, but also can provide convenience for the design of a control system.
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In order to illustrate embodiments of the present invention or technical solutions in the prior art more clearly, the drawings which are needed in the embodiments will be briefly described below, so that the features and advantages of the present invention can be understood more clearly by referring to the drawings, which are schematic and should not be construed as limiting the present invention in any way, and for a person skilled in the art, other drawings can be obtained on the basis of these drawings without any inventive effort. Wherein:
fig. 1 is a flow chart of a method for identifying a transition mode model of a tilt rotor unmanned aerial vehicle according to the present invention;
FIG. 2 is a schematic diagram of a basic controller according to an embodiment of the present invention;
FIGS. 3(a) - (f) are graphs of the roll channel frequency response fit results of the present invention;
FIGS. 4(a) - (f) are graphs of collective differential steering response fit results of the present invention;
FIGS. 5(a) - (b) are graphs of the rolling channel time domain verification results of the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments of the present invention and features of the embodiments may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
As shown in fig. 1, the present invention provides a method for identifying the steering derivative of any actuator under any control allocation strategy of any controller, and particularly provides a method for identifying a transition model of a tilt rotor unmanned aerial vehicle. According to the method, unknown parameters are determined by a frequency domain identification method, and unmanned aerial vehicles with different airspeeds and under different nacelle inclination angles are identified through tests, so that a transition mode model of the tilt rotor unmanned aerial vehicle is obtained.
For the convenience of understanding the above technical aspects of the present invention, the following detailed description will be given of the above technical aspects of the present invention by way of specific examples.
Example 1
A method for identifying a transition mode model of a tilt rotor unmanned aerial vehicle comprises the following steps:
s1: according to the principles of flight mechanics, aerodynamic mechanics and kinematics, the Simulink is utilized to adopt a block combination mode and consider pneumatic coupling with large influence, mechanism modeling is carried out on the tilt rotor unmanned aerial vehicle, and a nonlinear model of the tilt rotor unmanned aerial vehicle is obtained.
The mechanism modeling adopts a modeling method which is a combination of block modeling and combined modeling, namely, the researched tilt rotor unmanned aerial vehicle is divided into a plurality of parts such as a fuselage, a wing, a stabilizing surface, an engine and a rotor wing, the aerodynamic force and the moment of each part are respectively calculated and superposed, and then the movement is calculated to obtain the movement state of the aircraft. And (3) in consideration of mutual coupling influence and model complexity among all the components, carrying out principle-based modeling on some coupling pneumatic effects which have the largest influence, and neglecting factors with smaller influence.
S2: basic control law and control distribution strategy and identification scheme are designed according to the transition mode flight characteristics of the tilt rotor unmanned aerial vehicle. The designed identification scheme comprises test subjects, test times, input signals and an identification method.
Considering the instability of the tilt rotor unmanned aerial vehicle, the controller must be designed to perform a closed loop test on the basis of the relatively coarse model obtained in step S1. Because the tilt rotor model has an unmodeled part and an uncertain part, which causes a large difference between the model and the actual model, the INDI control method has good robustness, has small dependence on the model and can perform nonlinear control, and therefore the INDI controller is adopted to cooperate with the pseudo-inverse method to control the attitude angle in the embodiment, as shown in fig. 2.
The method comprises the steps of carrying out an identification test aiming at a rolling channel under the condition of a given nacelle inclination angle and airspeed, waiting for 3s after balancing, inputting a sweep frequency signal 15s with an amplitude of 15 degrees and a frequency band of 1-30 rad/s to a rolling angle instruction, inputting white noise with a small amplitude to a channel which is not swept to avoid singularity, and waiting for 3s again to finish the test in a balancing state after the sweep frequency is finished. The simulation was performed twice in the hybrid steering state and twice in the rotor-only steering state.
S3: and carrying out simulation test, acquiring test data, selecting data related to the parameter to be identified, preprocessing the data, and converting the preprocessed data into a data type which can be processed by CIFER software.
Simulating and reading data through matlab, and selecting input quantity and observed quantity-related to the parameter to be identified from the data
δaP and phi, intercepting the sweep frequency data of the quantities in a trim state, abandoning experimental data obviously having errors, defects or poor quality, and converting the experimental data into a data type (dat file) capable of being processed by the CIFER software.
If the test is limited by the condition, only one single manipulation test is completed, and the other single manipulation response data is obtained by subtracting the single manipulation response data from the mixed manipulation response data under the same condition, and then the data is converted into the data type which can be processed by the CIFER software.
S4: and (5) carrying out small-disturbance linearization on the nonlinear model obtained in the step (S1), establishing a transfer function and a state equation, and determining a linear model for model parameter identification. Because some of the stable derivatives and the steering derivative lines are unknown or inaccurate after the model is modeled probabilistically, the parameters are the parameters to be identified. The equation of state of the roll channel in the transition mode is as follows:
wherein, p and phi are state quantities and respectively represent the rolling angle and the rolling angular speed;
δathe total distance differential of the rotor wing and the deflection angle of the aileron are respectively expressed as control quantity; l isp、
Respectively represent the roll torque pairs p,
δaPartial derivatives of (a).
S5: the data obtained in step S3 is processed by FRESPID, MISOSA, COMPOSITE and other parts of CIFER software to generate MIMO frequency response database, and coherence value is reserved
And the data with smooth coherence value curve is not oscillated for identification, the identification frequency band is 1.26-30 rad/S in the embodiment, if the coherence value of the data with more than two thirds of frequency domain data in the identification frequency band does not accord with the coherence value standard set by the CIFER software, the step S2 is returned to redesign the identification scheme, otherwise, the step S6 is continued;
if the frequency band identification or the sweep frequency amplitude is not reasonable to select, or the test flight condition is not good, the nonlinear link or the large noise is caused, the analysis is the problem of test flight, the step S3 is returned to perform the test, and if the problem is the problem of scheme design, the step S2 is returned to.
S6: performing frequency domain identification through a parameterized model identification function of the CIFER software, namely NAVFIT and DERIVD, obtaining parameters to be identified, and setting the state quantity of a rolling channel under the control of a rotor wing to be
For roll rate under differential collective pitch steering,
identifying a cost function J for a roll angle under differential collective pitch control, under rotor controlave40.6 < 100. Bipolar square wave frequency 0.5Hz, partial time domain verification TIC 0.068 by VERIFY<0.25，Jrms0.0886 < 1, wherein TIC refers to the value of Theil inequality coefficient, JrmsRepresents the rms fit error and generally reflects an acceptable level of accuracy in the time domain of flight dynamics modeling. Identifying the obtained formThe equation of state is:
the state quantity of the rolling channel under the control of the control surface is set as
Is the roll angular velocity under the control of the control surface,
the rolling angle under the control surface manipulation is the state quantity of a rolling channel under the mixed manipulation, namely x ═ p phi]TThe state quantity caused by the operation of the rotor under the mixed-mode operation is
Because simulation only obtains
And x, so returning to step S3 the control surface manipulation data is calculated by:
sequentially determining a parameter identification model, generating a corresponding frequency response library, and performing hybrid manipulation identification to obtain a state equation as follows:
the roll channel frequency aileron steering response fitting results are shown in FIG. 3, Jave47.7 < 100, the fitting results of the collective differential steering response are shown in fig. 4(a) - (f), wherein fig. 4(a) - (b) represent logarithmic amplitude-frequency curves, fig. 4(c) - (d) represent logarithmic phase-frequency curves, and fig. 4(e) - (f)) For the correlation value curve, the solid line represents the simulated data curve and the dashed line represents the model fitting curve. Bipolar square wave frequency 1Hz, two indices verified by VERIFY partial time domain: j. the design is a squarerms＝0.107＜1，TIC＝0.099<0.25, the time domain verification results all meet the standard are shown in fig. 5, the solid line is a flight data time domain response curve, and the dotted line is a model time domain response curve.
The essence of parameter fitting is the optimization process of the optimization algorithm, so when the number of parameters to be identified is large or the optimization is so large that the range cannot be determined, the initial values of some parameters need to be determined first, and then the parameter identification of the state equation needs to be performed, so the NAVFIT part is performed first, and then the DERIVID part is performed.
And if the precision does not meet the requirement, returning to the step S4, re-determining the linear model, and finally obtaining a complete model of the transition mode of the tilt rotor unmanned aerial vehicle by identifying each channel.
The method obtains the linear model of the transition mode of the tilt rotor unmanned aerial vehicle through the CIFER software identification result based on the unmanned aerial vehicle test data, and is different from the traditional modeling in that the control derivative of each actuator is obtained instead of the control derivative of the mixed control quantity, so that the method is of great significance for understanding the flight characteristics of the transition mode of the tilt rotor unmanned aerial vehicle and further designing a control system. By using the method, the actuator control derivative under any control distribution strategy of any controller can be obtained, and the finally obtained unmanned aerial vehicle linear model can reflect the flight response of the tilt rotor unmanned aerial vehicle under a certain precision requirement.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (4)
1. The method for identifying the transition mode model of the tilt rotor unmanned aerial vehicle is characterized by comprising the following steps of:
s1: establishing a nonlinear model of the tilt rotor unmanned aerial vehicle according to the principles of flight mechanics, aerodynamic mechanics and kinematics;
s2: designing a basic control law, a control distribution strategy and an identification scheme aiming at the transition mode flight characteristics of the tilt rotor unmanned aerial vehicle;
the identification scheme comprises test subjects, test times, input signals and an identification method; the identification scheme specifically comprises the following steps: in each test, the identification tests of pitching, rolling and yawing channels are respectively carried out two to three times under the condition that the airspeed and the nacelle inclination angle are controlled to be kept unchanged as much as possible, the channel to be identified is called a main channel, and the rest channels are called auxiliary channels; because the transition mode has two sets of control systems, the input-output relationship between the hybrid control quantity and the response of the unmanned aerial vehicle can only be identified by adopting a hybrid control mode, and a test only with the action of a rotor wing or the action of a control plane is also needed to obtain the control derivative of the actuator; limited by test conditions and control system design, test data of the unmanned aerial vehicle under the conditions of rotor operation and control surface operation are generally not easy to obtain at the same time, at the moment, a test of hybrid operation is firstly carried out, and then a single operation mode capable of obtaining high-quality response is selected for testing;
the sweep frequency signal is used as the input signal of the main channel, the aircraft angle response caused by the sweep frequency amplitude is +/-5 degrees to +/-15 degrees, and the frequency range is 0.5 omegaBW≤ω≤2.5ω180，ωBWIs the bandwidth frequency, omega180The frequency corresponding to the phase angle of-180 degrees; the frequency sweep experiment is started after the tilt rotor unmanned aerial vehicle is in a balancing state for 3-5 seconds, the experiment is ended after the frequency sweep is ended and the tilt rotor unmanned aerial vehicle stays in the balancing state for at least 3 seconds, and the time of each experiment is recommended
ωminIs the minimum frequency, ωmin＝0.5ωBW；
Inputting small-amplitude white noise into the secondary channel to avoid the coupling of the input quantity, and adopting a direct identification method to identify, namely directly measuring the output quantity of the actuator and the output quantity of the model to identify;
s3: testing and collecting test data, selecting data related to the parameters to be identified from the test data, preprocessing the data, and converting the preprocessed data into a data type which can be processed by CIFER software;
s4: performing small-disturbance linearization on the nonlinear model obtained in the step S1, establishing a transfer function and a state equation, and determining a linear model for model parameter identification; wherein, the established longitudinal and transverse state equation is as follows:
u, w, q, theta, v, p, r and phi are state quantities, u, v and w are disturbance speeds in the directions of x, y and z axes under a body axis system respectively, p, q and r are disturbance rolling angle speed, pitching angle speed and yaw angle speed respectively, and theta and phi are disturbance pitch angle and rolling angle respectively;
δe,δa,δrthe control variables are respectively a rotor wing longitudinal periodic variable pitch, a rotor wing total pitch differential, a rotor wing longitudinal periodic variable pitch differential, an elevator deflection angle, an aileron deflection angle and a rudder deflection angle; xu、Zu、MuPartial derivatives of the longitudinal force, lateral force and pitching moment, respectively, on u, Xw、Zw、MwPartial derivatives of the longitudinal force, lateral force and pitching moment, respectively, with respect to w, Xq、Zq、MqThe partial derivatives of the longitudinal force, lateral force and pitching moment on q,
respectively longitudinal force, lateral force and pitching moment
The partial derivative of (a) of (b),
longitudinal force, lateral force and pitching moment pairs delta respectivelyePartial derivatives of (d); y isv、Lv、NvPartial derivatives of the transverse force, the roll moment and the yaw moment, respectively, with respect to v, Yp、Lp、NpPartial derivatives of p, Y, of the transverse force, roll moment and yaw moment, respectivelyr、Lr、NrRespectively the partial derivatives of the lateral force, the roll moment and the yaw moment to r,
respectively, transverse force, rolling moment and yawing moment
The partial derivative of (a) of (b),
respectively, transverse force, rolling moment and yawing moment
The partial derivative of (a) of (b),
transverse force, roll moment and yaw moment respectivelyaThe partial derivative of (a) of (b),
transverse force, roll moment and yaw moment respectivelyrPartial derivatives of (d); u shape0、W0、Θ0The speed in the x-axis direction under the body axis system, the speed in the z-axis direction under the body axis system and the pitch angle are respectively in a trim state; g represents the gravitational acceleration;
s5: the data obtained in step S3 is processed by FRESPID, MISOSA and COMPOSITE parts of CIFER software to generate MIMO frequency response database, preserving coherence values
And the data not oscillating with the coherence value curve is used for identification, if the frequency domain data in the identification frequency band exceeds two thirds and does not accord with the coherence value standard set by the CIFER software, the step S2 is returned to redesign the identification scheme;
s6: performing frequency domain identification through the parameterized model identification functions of the CIFER software, namely NAVFIT and DERIVD, and meeting JaveThe parameter to be identified is obtained according to the criterion of less than or equal to 100, JaveIn order to identify the cost function, the linear model obtained in the step S4 is subjected to time domain verification through VERIFY, and if rms fitting error J occursrmsAnd (3) the time domain verification of the identified model is represented when the coefficient TIC of the Theil inequality is less than or equal to 1.0-2.0 and less than or equal to 0.25-0.3, otherwise, the step S4 is returned, the linear model is determined again, and the complete model of the transition mode of the tilt rotor unmanned aerial vehicle is obtained through identification of each channel.
2. The method for identifying the transition mode model of the tilt rotor unmanned aerial vehicle according to claim 1, wherein the basic control law and the control distribution strategy designed in the step S2 are implemented by matching an INDI controller with a pseudo-inverse method to control the attitude angle, and are specifically implemented as follows:
the nonlinear dynamical system equation of the unmanned aerial vehicle is as follows:
wherein x is an n-dimensional state quantity,
is the derivative of x, u is the p-dimensional control input; y is an m-dimensional output, f (x) is a non-linear function of x, h (x) is a linear function of x, and g (x, u) is a linear function of x and u;
definition of
Wherein A is partial derivative of f (x) + g (x, u) to x, B is partial derivative of g (x, u) to u, and the state quantity and the input quantity at a certain moment are (x)0,u0) The desired closed loop dynamics are
When the calculation frequency of the control law is high enough and the step length is small enough, the incremental dynamic inverse control law is designed as follows:
where Δ u is the control increment, hxIs the partial derivative of g (x, u) with respect to x.
3. The method for identifying a transitional-mode model of a tilt-rotor drone of claim 1 or 2, wherein in step S3, if the test is limited by the condition, only a single-maneuver test is completed, and the other single-maneuver response data is obtained by subtracting the completed single-maneuver response data from the mixed-maneuver response data under the same condition.
4. The method for identifying the transition mode model of the tilt rotor unmanned aerial vehicle according to claim 1 or 2, wherein the specific process of the step S6 is as follows:
s6-1: fitting the frequency response pair in the MIMO frequency domain database obtained in the step S5 into a SISO transfer function model form through a NAVFIT part of CIFER software, and converting the fitting result of the transfer function into a state equation form to obtain an initial value of a state equation parameter;
s6-2: fitting the MIMO frequency domain data into a state equation through a DERIVD part of the CIFER software;
if the fitting condition of the identification result and the frequency response curve is not good, returning to the step S2 to re-determine the linear model identified by the model parameters, performing time domain verification on the linear model identified by the model parameters with the determined parameters in a VERIFY part by adopting bipolar square waves, and if the verification fails, proving that the identified model does not have model prediction capability, and then returning to the step S4 to re-determine the linear model identified by the model parameters;
and repeating the step S6 until all the channel actuator control derivatives are determined, so as to obtain a complete transition mode model of the tilt rotor unmanned aerial vehicle.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103010485A (en) * | 2012-12-21 | 2013-04-03 | 南京航空航天大学 | Simulation modeling method for tilt-rotor unmanned plane and system thereof |
| CN107992070A (en) * | 2017-12-03 | 2018-05-04 | 中国直升机设计研究所 | A kind of tiltrotor aircraft transition mode Automatic implementation |
| CN111240212A (en) * | 2020-03-25 | 2020-06-05 | 北京航空航天大学 | Tilt rotor unmanned aerial vehicle control distribution method based on optimization prediction |
| CN111538237A (en) * | 2020-03-20 | 2020-08-14 | 北京航空航天大学 | Method for identifying and correcting non-linear light gray model of tilt rotor unmanned aerial vehicle |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8972310B2 (en) * | 2012-03-12 | 2015-03-03 | The Boeing Company | Method for identifying structural deformation |
| CN103144781A (en) * | 2012-12-21 | 2013-06-12 | 南京航空航天大学 | Method for determining transient process switching corridor of tilt rotor unmanned aircraft |
| CN109946971B (en) * | 2019-04-04 | 2021-09-24 | 南京航空航天大学 | Smooth switching control method for transition section of tilt rotor unmanned aerial vehicle |
-
2020
- 2020-09-24 CN CN202011016069.5A patent/CN112068582B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103010485A (en) * | 2012-12-21 | 2013-04-03 | 南京航空航天大学 | Simulation modeling method for tilt-rotor unmanned plane and system thereof |
| CN107992070A (en) * | 2017-12-03 | 2018-05-04 | 中国直升机设计研究所 | A kind of tiltrotor aircraft transition mode Automatic implementation |
| CN111538237A (en) * | 2020-03-20 | 2020-08-14 | 北京航空航天大学 | Method for identifying and correcting non-linear light gray model of tilt rotor unmanned aerial vehicle |
| CN111240212A (en) * | 2020-03-25 | 2020-06-05 | 北京航空航天大学 | Tilt rotor unmanned aerial vehicle control distribution method based on optimization prediction |
Non-Patent Citations (4)
| Title |
|---|
| 基于角加速度估计的非线性增量动态逆控制及试飞;党小为，等;《航空学报》;20200425;第41卷(第4期);第1-9页 * |
| 小型农用无人直升机飞行控制技术研究;朱兖植;《中国优秀硕士学位论文全文数据库》;20140715(第7期);第C031-146页 * |
| 旋翼/固定翼混合模式无人机在悬停状态下的模型辨识;孔维胜;《第29届中国控制与决策会议论文集（3）》;20170531;第1573-1579页 * |
| 无人飞行器飞行控制仿真与软件设计;陈澄;《中国优秀硕士学位论文全文数据库》;20150115(第1期);第C031-139页 * |
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