CN103760769A - Small unmanned aerial vehicle control object modeling method based on test data - Google Patents

Abstract

The invention discloses a small unmanned aerial vehicle control object modeling method based on test data. According to the method, through transformation of control signals, an established control object model can be applied to the process with small variation and can also be applied to the process with great variation, and an established model system is high in identification precision and good in model prediction capacity. According to the method, by the utilization of test flight data, high-cost wind tunnel tests can be avoided, and therefore early-stage development cost is lowered. According to the method, through changing of mathematical features of input signals (namely the control signals) in the test flight test data, the difference between the different input signals is amplified, therefore, the error range of an estimation model and a true model is narrowed down, and the accuracy and the prediction capacity of the estimation model are improved.

Description

A kind of small-sized unmanned aircraft control object modeling method based on test figure

Technical field

The present invention relates to control object modeling field, be specifically related to a kind of small-sized unmanned aircraft control object model modelling approach based on test figure.

Background technology

For the foundation of aircraft control object model, modeling method mainly contains two classes: the one, and utilize wind tunnel test data to set up unmanned vehicle control object model; The 2nd, utilize test test flight data by Model Distinguish, to set up the control object model of unmanned vehicle.For small-sized unmanned aircraft, wind tunnel test cost huge and when low speed precision not high, therefore in multiplex second method of development stage in early stage, carry out modeling.But, because flight test itself has a lot of restrictions (flight safety and control signal self problem), the small aircraft that general test is novel, test figure generally only has the one piece of data that variation range is little, can not reflect well aircraft self performance, be unfavorable for using the method for System Discrimination to carry out the foundation of control object model, the model finally obtaining is not suitable for one section of process that variation is large conventionally, and precision is not high, estimated performance is poor.

Summary of the invention

In view of this, the invention provides a kind of small-sized unmanned aircraft control object model modelling approach based on test figure, by structural control signal, make the control object model of setting up not only can be suitable for the little process that changes, also can be applicable to change large process, and the model system identification precision of setting up is high, model prediction ability is good.

Small-sized unmanned aircraft control object model modelling approach based on test figure of the present invention, first takes a flight test to unmanned vehicle, gathers control signal and flight state data; Then calculate the difference of each point and control signal minimum value in control signal; Structural control signal is recurrent pulse form, and the described difference corresponding with this cycle of the pulsewidth in each cycle is directly proportional; Finally utilize described flight state data and improved control signal to carry out modeling and the parameter identification of unmanned vehicle control object model.

Specifically can comprise the steps:

Step 1, manual remote control small-sized unmanned aircraft carries out exercises section object under several typical rate takes a flight test, and records control signal and the flight state data of small-sized unmanned aircraft in the process of taking a flight test; Described control signal comprises the control signal of pitching rudder, driftage rudder, aileron rudder and throttle, and described flight state data comprise the aircraft angle of pitch, crab angle, roll angle and flying speed data;

Step 2, the control object model of small-sized unmanned aircraft is divided into and take pitching rudder control signal and be the control object model of the pitch channel of output as input, the angle of pitch, the control object model that the aileron rudder control signal of take is the roll channel of output as input, roll angle, the driftage rudder control signal of take is the control object model of the jaw channel of output as input, crab angle, and take throttle control signal as input, aircraft speed be the control object model of the speed channels of output;

Step 3, is handled as follows the control signal of each passage obtaining in step 1:

$l=\frac{{\left(n_K\right)}_{\max }-{\left(n_K\right)}_{\min }}{N}$

Wherein, u (k) is improved control signal, and a, b are two positive counts, a>b; K _maxfor data total length in original control signal, n _kk data of control signal before representative transformation, n _kk data of control signal after representative transformation, (n _k) _max(n _k) _minrepresent maximum number and the minimum number of original control signal, N is the recurrence interval of control signal after given transformation;

Step 4, sets up the control object model of each passage, and the status data that adopts the improved control signal of step 3 and step 1 to obtain carries out parameter identification to the control object model of each passage.

Wherein, in described step 4, the control object model of each passage adopts linear transfer function model.

Can be for a plurality of linear transfer function models of each Path Setup, the molecule in delivery type, denominator exponent number are less than or equal to all situations on 5 rank, proceed to step 5;

Step 5, re-start Spacecraft Flight Test, by in all control object models of the control signal difference substitution respective channel of each passage in flight test, the actual output of respective channel in the output of all control object models of each passage and flight test process is contrasted, for each passage, choose the final control object model that the control object model the highest with actual Output rusults degree of fitting is this passage.

In described step 5, the computing formula of degree of fitting BF (q) is:

$\mathrm{BF}\left(q\right)=(1-\frac{1}{k_{\max }}\left(\underset{k=1}{\overset{k\max }{\mathrm{\Σ}}}\right|\frac{o_q\left(k\right)-o_s\left(k\right)}{o_s\left(k\right)}|+\underset{k=1}{\overset{k\max }{\mathrm{\Σ}}}|\frac{d^2\left(o_q\left(k\right)\right)-d^2\left(o_s\left(k\right)\right)}{d^2\left(o_s\left(k\right)\right)}\left|\right))\×100$

Wherein, o represents the curve of output of each passage control object model; k _maxfor transforming the data total length of rear control signal; d ²for asking second derivative; Q is control object pattern number, is positive integer; S represents the actual output of taking a flight test.

Wherein, in described step 1, the remote control thereof of small-sized unmanned aircraft comprises the steps:

Step 101: remotely pilotless aircraft enters the flat state that flies, and increases throttle, in order to speed channels Identification Data to be provided;

Step 102: only handle pitching rudder, unmanned vehicle is entered and climb, now as speed is inadequate, refuels and continue to climb after attitude stabilization, in order to pitch channel Identification Data to be provided;

Step 103: make the unmanned vehicle former height that declines back, in order to pitch channel verification msg to be provided;

Step 104: only handle aileron, make unmanned vehicle enter roll mode, in order to aileron passage Identification Data to be provided;

Step 105: only handle driftage rudder, make unmanned vehicle enter driftage state, in order to jaw channel Identification Data to be provided;

Data 106: further, remotely pilotless aircraft coordinate turn, in order to provide driftage, roll channel verification msg;

Step 107: the landing of remotely pilotless aircraft, data acquisition test.

Wherein, during the rear control signal of transformation, can make a=1, b=0.

Beneficial effect:

The present invention utilizes test flight data, can avoid expensive wind tunnel test, has reduced development cost in early stage.The present invention is by changing the mathematical feature of input signal (being control signal) in Flight Test data, amplified the difference between varying input signal, thereby dwindled the error range of estimation model and true model, and then improved precision and the predictive ability of estimation model.The model estimating like this, although be to take original, to be subject to experimental enviroment impact data as basis above, still can estimate exceeding the state of trial stretch, accurately for CONTROLLER DESIGN provides effective help.

Control object model of the present invention adopts simple linear transfer function model, has not only simplified the computing of modeling process, and provides effective help to the design of next step controller.

Accompanying drawing explanation

Fig. 1 is process flow diagram of the present invention.

Fig. 2 is the data that in the process of taking a flight test, pitch channel gathers.

Fig. 3 is the improved effects of data.

Each model Output rusults of Fig. 4 pitch control subsystem and the angle of pitch output contrast of taking a flight test.

Fig. 5 (a) is the autocorrelation function of conversion front signal; Fig. 5 (b) is the autocorrelation function of the rear signal of transformation.

Embodiment

Below in conjunction with the accompanying drawing embodiment that develops simultaneously, describe the present invention.

The invention provides a kind of small-sized unmanned aircraft modeling method based on test figure, first unmanned vehicle is taken a flight test, gather control signal and flight state data; Then calculate the difference of each point and control signal minimum value in control signal; Structural control signal is recurrent pulse form, and the described difference corresponding with this cycle of the pulsewidth in each cycle is directly proportional; Finally utilize described flight state data and improved control signal to carry out modeling and the parameter identification of unmanned vehicle control object model.The present invention has overcome in prior art the low and poor technical matters of predictive ability of model accuracy.Idiographic flow as shown in Figure 1, comprises the steps:

Step 1, the exercises section objects such as manual remote control small-sized unmanned aircraft climbs, declines under several typical rate, turning, rolling, driftage are taken a flight test, and record small-sized unmanned aircraft pitching rudder, the driftage control signal data of rudder, aileron rudder and throttle and flight state data of the aircraft angle of pitch, crab angle, roll angle and flying speed in the process of taking a flight test.

Can fly as follows by remote control small-sized unmanned aircraft:

Step 101 remotely pilotless aircraft enters the flat state that flies, and increases throttle, in order to speed channels Identification Data to be provided;

Step 102: only handle pitching rudder, unmanned vehicle is entered and climb, now can suitably refuel and continue to climb after attitude stabilization not as speed, this step mainly provides pitch channel Identification Data;

Step 103: make the unmanned vehicle former height that declines back, prepare next step test, and pitch channel verification msg is provided;

Step 104: only handle aileron, make unmanned vehicle enter roll mode, in order to aileron passage Identification Data to be provided;

Step 105: only handle driftage rudder, make unmanned vehicle enter driftage state, in order to jaw channel Identification Data to be provided;

Data 106: further, remotely pilotless aircraft coordinate turn, in order to provide driftage, roll channel verification msg;

Step 107: the landing of remotely pilotless aircraft, data acquisition test.

So, can obtain respectively Identification Data and the verification msg of speed channels, pitch channel, jaw channel, 4 single-input single-output passages of roll channel.

Step 2, according to aircraft aerodynamic model, the control object model of small-sized unmanned aircraft is divided into and take pitching rudder control signal and be the control object model of the pitch channel of output as input, the angle of pitch, the control object model that the aileron rudder control signal of take is the roll channel of output as input, roll angle, the driftage rudder control signal of take is the control object model of the jaw channel of output as input, crab angle, and take throttle control signal as input, aircraft speed be the control object model of the speed channels of output.

Step 3, the experimental data that general flight test obtains, because the restriction of flight safety can not meet the requirement of describing aircraft thru-flight characteristic, is 90 degree etc. such as the angle of pitch.For this reason, the present invention transforms flight control signal, the core concept of signal transformation is the difference of amplifying between each input quantity, thereby makes originally to approach the input signal of constant, approaches and meets the requirement that in System Discrimination, desirable input signal statistical nature is approached white Gaussian noise.The concrete thought of transformation is: the difference of each point and control signal minimum value in calculating control signal; Structural control signal is recurrent pulse form, and the described difference corresponding with this cycle of the pulsewidth in each cycle is directly proportional.

The present embodiment has provided a kind of concrete employing piecewise function and has expressed reforming mode, is shown below,

$l=\frac{{\left(n_K\right)}_{\max }-{\left(n_K\right)}_{\min }}{N}$

Wherein, u (k) is improved control signal, and a and b are two positive counts, a>b, and the low and high level of the selected pattern of wants pulse of these two numerical value, calculates for convenient, common a=1, b=0.K _maxfor the data total length in original control signal, n _kk data of control signal, for example n before representative transformation ₁₀the 10th data before representative transformation in control signal data; n _kk data of control signal after representative transformation.(n _k) _max(n _k) _minrepresent maximal value and minimum value in original control signal data.N is the recurrence interval of control signal after given transformation, and N is larger represents that improved data are higher to former data description precision.

The implication of above formula is, calculate all K values (K=1,2 ..., K _max) corresponding scope [(K-1) N, for all k(k=1,2 ...), when k value falls into above-mentioned scope, u (k) is a, otherwise u (k) is b, thereby has obtained the recurrent pulse form as described in Fig. 3 (b), the above-mentioned scope difference corresponding to original control signal is relevant, therefore obtains u _kpulsewidth in each cycle of signal the described difference corresponding with this cycle is directly proportional.Based on this thought, can also adopt other formula building modes to obtain u (k).

For above formula, it is asked for to autocorrelation function and obtains:

If $\mathrm{\ω}=\frac{n_K-{\left(n_K\right)}_{\min }}{l},$

When ω >=N/2

$R\left(\mathrm{\&tau;}\right)=\left\{\begin{array}{ccc}(\mathrm{\&omega;}-\mathrm{\&tau;})\&CenterDot;{\mathrm{<figure-callout\; id="2"\; label="a"\; filenames="HDA0000451308910000012.png"\; state="\{\{state\}\}">a</figure-callout>}}^2+\mathrm{\&tau;}\&CenterDot;a\&CenterDot;b+(N-\mathrm{\&omega;}-\mathrm{\&tau;})\&CenterDot;{\mathrm{<figure-callout\; id="2"\; label="b"\; filenames="HDA0000451308910000012.png"\; state="\{\{state\}\}">b</figure-callout>}}^2& 0\&le;\mathrm{\&tau;}<N-\mathrm{\&omega;}& \\ (\mathrm{\&omega;}-\mathrm{\&tau;})\&CenterDot;a^2+(N-\mathrm{\&omega;})\&CenterDot;a\&CenterDot;b& N-\mathrm{\&omega;}\&le;\mathrm{\&tau;}\&le;\mathrm{\&omega;}& \\ (N-\mathrm{\&tau;})\&CenterDot;a\&CenterDot;b& \mathrm{\&omega;}<\mathrm{\&tau;}<N& \end{array}\right.$

When ω < N/2

$R\left(\mathrm{\&tau;}\right)=\left\{\begin{array}{ccc}(\mathrm{\&omega;}-\mathrm{\&tau;})\&CenterDot;{\mathrm{<figure-callout\; id="2"\; label="a"\; filenames="HDA0000451308910000012.png"\; state="\{\{state\}\}">a</figure-callout>}}^2+\mathrm{\&tau;}\&CenterDot;a\&CenterDot;b+(N-\mathrm{\&omega;}-\mathrm{\&tau;})\&CenterDot;{\mathrm{<figure-callout\; id="2"\; label="b"\; filenames="HDA0000451308910000012.png"\; state="\{\{state\}\}">b</figure-callout>}}^2& 0\&le;\mathrm{\&tau;}<\mathrm{\&omega;}& \\ \mathrm{\&omega;}\&CenterDot;a\&CenterDot;b+(N-\mathrm{\&omega;}-\mathrm{\&tau;})\&CenterDot;{\mathrm{<figure-callout\; id="2"\; label="b"\; filenames="HDA0000451308910000012.png"\; state="\{\{state\}\}">b</figure-callout>}}^2& \mathrm{\&omega;}\&le;\mathrm{\&tau;}\&le;N-\mathrm{\&omega;}& \\ (N-\mathrm{\&tau;})\&CenterDot;a\&CenterDot;b& N-\mathrm{\&omega;}<\mathrm{\&tau;}<N& \end{array}\right.$

Wherein, the independent variable that τ is autocorrelation function; Identical in a, b definition and data transformation; Like this, the numerical value of adjusting a and b just can change the data statistics characteristic of ordered series of numbers, makes it more to approach the statistical property of white noise.Generalized case, calculates for convenient, can establish a=1, b=0.Before control signal data transformations and improved autocorrelation function as shown in Figure 5, wherein, Fig. 5 (a) is the autocorrelation function of conversion front signal, approaches the statistical property of constant; Fig. 5 (b) is the autocorrelation function of the rear signal of transformation, approaches the statistical property of white Gaussian noise.

Can find out, improved control signal ordered series of numbers autocorrelation function changes and obviously levels off to white Gaussian noise, therefore can dwindle the error range between identification model and real goal model, namely improves precision and the predictability of model.

Step 4, sets up the control object model of each passage, and the status data that adopts the improved control signal of step 3 and step 1 to obtain carries out parameter identification to the control object model of each passage.

For convenience of CONTROLLER DESIGN, the control object model of each passage adopts linear transfer function model, and citation form is:

$G\left(s\right)=\frac{b_1\&CenterDot;s^m+{\mathrm{<figure-callout\; id="2"\; label="b"\; filenames="HDA0000451308910000012.png"\; state="\{\{state\}\}">b</figure-callout>}}_2\&CenterDot;s^{m-1}+...+b_m\&CenterDot;s+b_{m+1}}{a_1\&CenterDot;s^n+a_2\&CenterDot;s^{n-1}+...+a_n\&CenterDot;s+a_{n+1}}$

Wherein, the exponent number that m is molecule, the exponent number that n is denominator, will estimate all situations when molecule, denominator exponent number are less than 5 rank in the present invention, and denominator is 5 rank, and molecule exponent number is respectively 4,3, and 2,1,0; Denominator is 4 rank, and molecule exponent number is respectively 3,2,1,0; Denominator is 3 rank, and molecule exponent number is respectively 2,1,0; Denominator is 2 rank, and molecule exponent number is respectively 1,0.

Step 5, builds control object model to step 4 and carries out verification experimental verification, and choosing the control object model the highest with test findings degree of fitting is final control object model.

For improving the reliability of control object model, re-start small-sized unmanned aircraft flight test, by the flight state data of the actual acquisition of flight test data as a comparison, by all control object models of respective channel in the control signal difference substitution step 4 of each passage in test, the actual output of respective channel in the output of all control object models of each passage and flight test process is contrasted, for each passage, selecting the control object model the highest with test actual result degree of fitting is the control object model of this passage of small-sized unmanned aircraft.

Wherein, each model curve of output carries out degree of fitting calculating according to the following formula:

$\mathrm{BF}\left(q\right)=(1-\frac{1}{k_{\max }}\left(\underset{k=1}{\overset{k\max }{\mathrm{\&Sigma;}}}\right|\frac{o_q\left(k\right)-o_s\left(k\right)}{o_s\left(k\right)}|+\underset{k=1}{\overset{k\max }{\mathrm{\&Sigma;}}}|\frac{d^2\left(o_q\left(k\right)\right)-d^2\left(o_s\left(k\right)\right)}{d^2\left(o_s\left(k\right)\right)}\left|\right))\&times;100$

Wherein, o represents the curve of output of each passage control object model; k _maxfor transforming the data total length of rear control signal; ; d ²for asking second derivative; Q is control object pattern number, and which model representative is, is positive integer; The actual output of s representative test.Degree of fitting scope is-100～100, and wherein numerical value more approaches 100 and shows that degree of fitting is higher, and more approaching-100 explanation degrees of fitting are lower.

Take pitch control subsystem object model as example, step 1 is taken a flight test in process, the small-sized unmanned aircraft angle of pitch and pitching rudder change as shown in Figure 2, utilize step 3 pair pitching rudder to control and carry out difference processing, obtain the changing value of the angle of pitch, as shown in Figure 3, wherein, the data-signal using when Fig. 3 (a) is conventional system identification (modeling), Fig. 3 (b) is the form after signal shown in Fig. 3 (a) changes, and with signal shown in Fig. 3 (b), does identification.Utilize the linear transfer function model in step 4 to set up the pitch control subsystem object model of 5 rank with interior (comprising 5 rank).Again carry out small-sized unmanned aircraft Flight Test, by this pitching rudder change in each pitch control subsystem object model of input, obtain each model the angle of pitch as shown in Figure 4, wherein, solid line is test findings, calculate the degree of fitting of each model curve of output and test findings curve, wherein, dotted line is 86.2, line-21.7, dot-and-dash line 77.4.The final exponent number model of determining that dotted line represents is unmanned plane model, is molecule 2 rank, denominator 3 rank, and final pitch control subsystem object model is:

$G\left(z\right)=\frac{{-0.6899\mathrm{<figure-callout\; id="2"\; label="z"\; filenames="HDA0000451308910000012.png"\; state="\{\{state\}\}">z</figure-callout>}}^2+0.9811z}{z^3-{1.272z}^2-0.4503z+0.727}$

In sum, these are only preferred embodiment of the present invention, be not intended to limit protection scope of the present invention.Within the spirit and principles in the present invention all, any modification of doing, be equal to replacement, improvement etc., within all should being included in protection scope of the present invention.

Claims (7)

1. the small-sized unmanned aircraft control object model modelling approach based on test figure, is characterized in that, unmanned vehicle is taken a flight test, and gathers control signal and flight state data; The difference of each point and control signal minimum value in calculating control signal; Structural control signal is recurrent pulse form, and the described difference corresponding with this cycle of the pulsewidth in each cycle is directly proportional; Utilize described flight state data and improved control signal to carry out modeling and the parameter identification of unmanned vehicle control object model.

2. the small-sized unmanned aircraft control object model modelling approach based on test figure as claimed in claim 1, is characterized in that, comprises the steps:

Step 1, manual remote control small-sized unmanned aircraft carries out exercises section object under several typical rate takes a flight test, and records control signal and the flight state data of small-sized unmanned aircraft in the process of taking a flight test; Described control signal comprises the control signal of pitching rudder, driftage rudder, aileron rudder and throttle, and described flight state data comprise the aircraft angle of pitch, crab angle, roll angle and flying speed data;

Step 2, the control object model of small-sized unmanned aircraft is divided into and take pitching rudder control signal and be the control object model of the pitch channel of output as input, the angle of pitch, the control object model that the aileron rudder control signal of take is the roll channel of output as input, roll angle, the driftage rudder control signal of take is the control object model of the jaw channel of output as input, crab angle, and take throttle control signal as input, aircraft speed be the control object model of the speed channels of output;

Step 3, is handled as follows the control signal of each passage obtaining in step 1:

$l=\frac{{\left(n_K\right)}_{\max }-{\left(n_K\right)}_{\min }}{N}$

Wherein, u (k) is improved control signal, and a, b are two positive counts, a>b; K _maxfor data total length in original control signal, n _kk data of control signal before representative transformation, n _kk data of control signal after representative transformation, (n _k) _max(n _k) _minrepresent maximum number and the minimum number of original control signal, N is the recurrence interval of control signal after given transformation;

Step 4, sets up the control object model of each passage, and the status data that adopts the improved control signal of step 3 and step 1 to obtain carries out parameter identification to the control object model of each passage.

3. the small-sized unmanned aircraft control object model modelling approach based on test figure as claimed in claim 2, is characterized in that, in described step 4, the control object model of each passage adopts linear transfer function model.

4. the small-sized unmanned aircraft control object model modelling approach based on test figure as claimed in claim 3, it is characterized in that, for a plurality of linear transfer function models of each Path Setup, the molecule in delivery type, denominator exponent number are less than or equal to all situations on 5 rank, proceed to step 5;

Step 5, re-start Spacecraft Flight Test, by in all control object models of the control signal difference substitution respective channel of each passage in flight test, the actual output of respective channel in the output of all control object models of each passage and flight test process is contrasted, for each passage, choose the final control object model that the control object model the highest with actual Output rusults degree of fitting is this passage.

5. the small-sized unmanned aircraft control object model modelling approach based on test figure as claimed in claim 4, is characterized in that, in described step 5, the computing formula of degree of fitting BF (q) is:

$\mathrm{BF}\left(q\right)=(1-\frac{1}{k_{\max }}\left(\underset{k=1}{\overset{k\max }{\mathrm{\&Sigma;}}}\right|\frac{o_q\left(k\right)-o_s\left(k\right)}{o_s\left(k\right)}|+\underset{k=1}{\overset{k\max }{\mathrm{\&Sigma;}}}|\frac{d^2\left(o_q\left(k\right)\right)-d^2\left(o_s\left(k\right)\right)}{d^2\left(o_s\left(k\right)\right)}\left|\right))\&times;100$

Wherein, o represents the curve of output of each passage control object model; k _maxfor transforming the data total length of rear control signal; d ²for asking second derivative; Q is control object pattern number, is positive integer; S represents the actual output of taking a flight test.

6. the small-sized unmanned aircraft control object model modelling approach based on test figure as claimed in claim 2, is characterized in that, in described step 1, the remote control thereof of small-sized unmanned aircraft comprises the steps:

Step 101: remotely pilotless aircraft enters the flat state that flies, and increases throttle, in order to speed channels Identification Data to be provided;

Step 102: only handle pitching rudder, unmanned vehicle is entered and climb, now as speed is inadequate, refuels and continue to climb after attitude stabilization, in order to pitch channel Identification Data to be provided;

Step 103: make the unmanned vehicle former height that declines back, in order to pitch channel verification msg to be provided;

Step 104: only handle aileron, make unmanned vehicle enter roll mode, in order to aileron passage Identification Data to be provided;

Step 105: only handle driftage rudder, make unmanned vehicle enter driftage state, in order to jaw channel Identification Data to be provided;

Data 106: further, remotely pilotless aircraft coordinate turn, in order to provide driftage, roll channel verification msg;

Step 107: the landing of remotely pilotless aircraft, data acquisition test.

7. the small-sized unmanned aircraft control object model modelling approach based on test figure as claimed in claim 2, is characterized in that a=1, b=0.

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