CN102507131A - Method for acquiring unknown pneumatic parameters in UAV (Unmanned Aerial Vehicle) mathematical model - Google Patents

Method for acquiring unknown pneumatic parameters in UAV (Unmanned Aerial Vehicle) mathematical model Download PDF

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CN102507131A
CN102507131A CN2011102944451A CN201110294445A CN102507131A CN 102507131 A CN102507131 A CN 102507131A CN 2011102944451 A CN2011102944451 A CN 2011102944451A CN 201110294445 A CN201110294445 A CN 201110294445A CN 102507131 A CN102507131 A CN 102507131A
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unmanned plane
control command
model
test
parameter
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CN102507131B (en
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周海军
雷志荣
唐强
樊峪
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No 618 Research Institute of China Aviation Industry
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Abstract

The invention relates to an improved method for acquiring unknown pneumatic parameters in a UAV (Unmanned Aerial Vehicle) mathematical model, belonging to a UAV model identification technology. The improved method comprises the following steps of: determining a model structure of a UAV according to theoretical derivation; acquiring test flight data by a test flight step; and acquiring the unknown parameters in a parameterized model by utilizing a method combining a predication error algorithm with an improved particle swarm optimization algorithm so as to realize identification work of the model. The improved method disclosed by the invention has the advantages of no need of a wind tunnel test, convenience for a test flight test, simple identification process and high model precision. According to the improved method disclosed by the invention, dependence on test equipment and test conditions is greatly reduced, and convenience and generality of model parameter acquisition are improved.

Description

A kind of method of obtaining unknown aerodynamic parameter in the unmanned plane mathematical model
Technical field
The invention belongs to unmanned plane Model Distinguish technology, relate to improvement unknown aerodynamic parameter acquisition methods in the unmanned plane mathematical model.
Background technology
At present; For the problem of obtaining of unknown aerodynamic parameter in the unmanned plane mathematical model, a kind of method is to adopt wind tunnel technique, obtains the pneumatic mathematical model of unmanned plane through blowing; But its experimental cost is higher, and is difficult to guarantee its model accuracy for rotor class unmanned plane; Another kind is to utilize the CIFER software based on the frequency domain identification technology to obtain unknown parameter, but it has relatively high expectations the realization difficulty to experiment in flight test; And identification process is very complicated, and versatility is relatively poor, referring to " model aircraft frequency domain identification method-CIFER algorithm research "; Zou Yu, Pei Hailong, Liu Xin; Zhou Hongbo etc., electric light with control in May, 2010, the 17th the volume the 5th phase.
Summary of the invention
The objective of the invention is: the unknown aerodynamic parameter acquisition methods of unmanned plane mathematical model that propose a kind ofly to need not wind tunnel experiment, be convenient to carry out experiment in flight test, identification process is simple, model accuracy is high.
Technical scheme of the present invention is: a kind of method of obtaining unknown aerodynamic parameter in the unmanned plane mathematical model is characterized in that the step of obtaining unknown aerodynamic parameter is following:
1, confirm the structure of unmanned plane mathematical model: confirm the linear parameterization model of this unmanned plane to have comprised unknown aerodynamic parameter in the model according to the structure type of unmanned plane, structure type is divided into single rotor depopulated helicopter and fixed-wing unmanned plane;
2, obtain test flight data:
2.1, unmanned plane is taken a flight test, unmanned vehicle is operated under the remote manual control operating pattern, with the aircraft trim;
2.2, obtain the pitch channel test flight data: send the pitch channel control command to unmanned plane; Control command is sinusoidal constant amplitude swept-frequency signal; The amplitude of sinusoidal constant amplitude swept-frequency signal is represented manipulated variable; The frequency range that frequency sweep covers is 1/3 ω c～3 ω c, and ω c is the cutoff frequency of unmanned plane pitch channel, and ω c is provided by unmanned plane designing unit; Unmanned plane responds the control command of receiving, the control command of Airborne Flight Parameter logging modle record is stored as the pitch channel test flight data with corresponding aircraft response;
2.3, obtain course passage test flight data: send course passage control command to unmanned plane; Control command is sinusoidal constant amplitude swept-frequency signal; The amplitude of sinusoidal constant amplitude swept-frequency signal is represented manipulated variable; The frequency range that frequency sweep covers is 1/3 ω c～3 ω c, and ω c is the cutoff frequency of unmanned plane course passage, and ω c is provided by unmanned plane designing unit; Unmanned plane responds the control command of receiving, the control command of Airborne Flight Parameter logging modle record is stored as course passage test flight data with corresponding aircraft response;
2.4, obtain the roll channel test flight data: send the roll channel control command to unmanned plane; Control command is sinusoidal constant amplitude swept-frequency signal; The amplitude of sinusoidal constant amplitude swept-frequency signal is represented manipulated variable; The frequency range that frequency sweep covers is 1/3 ω c～3 ω c, and ω c is the cutoff frequency of unmanned machine rolling passage, and ω c is provided by unmanned plane designing unit; Unmanned plane responds the control command of receiving, the control command of Airborne Flight Parameter logging modle record is stored as the roll channel test flight data with corresponding aircraft response;
3, carrying out aerodynamic parameter estimates: adopt recursive prediction error algorithms that the unknown aerodynamic parameter in the unmanned plane mathematical model is carried out according to a preliminary estimate, obtain the thick value of aerodynamic parameter;
4, obtain aerodynamic parameter: with the initial value that the thick value of aerodynamic parameter is moved as the improvement particle cluster algorithm, employing improves particle cluster algorithm and calculates, and obtains final aerodynamic parameter.
Advantage of the present invention is: need not wind tunnel experiment, be convenient to carry out experiment in flight test, identification process is simple, and model accuracy is high.The present invention has significantly reduced the dependence to experimental facilities and experiment condition, has improved convenience and versatility that model parameter is obtained.
Embodiment
Explain further details in the face of the present invention down.A kind of method of obtaining unknown aerodynamic parameter in the unmanned plane mathematical model is characterized in that, the step of obtaining unknown aerodynamic parameter is following:
1, confirm the structure of unmanned plane mathematical model: confirm the linear parameterization model of this unmanned plane to have comprised unknown aerodynamic parameter in the model according to the structure type of unmanned plane, structure type is divided into single rotor depopulated helicopter and fixed-wing unmanned plane; For single rotor depopulated helicopter; Adopt ten single order parameterized models, referring to " the layer rank flight control system design of depopulated helicopter " (" Hierarchical Flight Control System Synthesis for Rotorcraft-based Unmanned Aerial Vehicles "), Hyunchul Shim; PhD Thesis; University of California, Berkeley, 2000; For the fixed-wing unmanned plane, adopt nine rank parameterized models, referring to " aerodynamics and flight mechanics ", colleague Liu, publishing house of Beijing Aeronaution College, 1987.
2, obtain test flight data:
2.1, unmanned plane is taken a flight test, unmanned vehicle is operated under the remote manual control operating pattern, with the aircraft trim;
2.2, obtain the pitch channel test flight data: send the pitch channel control command to unmanned plane; Control command is sinusoidal constant amplitude swept-frequency signal; The amplitude of sinusoidal constant amplitude swept-frequency signal is represented manipulated variable; The frequency range that frequency sweep covers is 1/3 ω c～3 ω c, and ω c is the cutoff frequency of unmanned plane pitch channel, and ω c is provided by unmanned plane designing unit; Unmanned plane responds the control command of receiving, the control command of Airborne Flight Parameter logging modle record is stored as the pitch channel test flight data with corresponding aircraft response;
2.3, obtain course passage test flight data: send course passage control command to unmanned plane; Control command is sinusoidal constant amplitude swept-frequency signal; The amplitude of sinusoidal constant amplitude swept-frequency signal is represented manipulated variable; The frequency range that frequency sweep covers is 1/3 ω c～3 ω c, and ω c is the cutoff frequency of unmanned plane course passage, and ω c is provided by unmanned plane designing unit; Unmanned plane responds the control command of receiving, the control command of Airborne Flight Parameter logging modle record is stored as course passage test flight data with corresponding aircraft response;
2.4, obtain the roll channel test flight data: send the roll channel control command to unmanned plane; Control command is sinusoidal constant amplitude swept-frequency signal; The amplitude of sinusoidal constant amplitude swept-frequency signal is represented manipulated variable; The frequency range that frequency sweep covers is 1/3 ω c～3 ω c, and ω c is the cutoff frequency of unmanned machine rolling passage, and ω c is provided by unmanned plane designing unit; Unmanned plane responds the control command of receiving, the control command of Airborne Flight Parameter logging modle record is stored as the roll channel test flight data with corresponding aircraft response;
3, carrying out aerodynamic parameter estimates: adopt recursive prediction error algorithms that the unknown aerodynamic parameter in the unmanned plane mathematical model is carried out according to a preliminary estimate; Obtain the thick value of aerodynamic parameter; Referring to " research of small-sized depopulated helicopter physical parameter identification problem "; Yan Chao, South China Science & Engineering University's master thesis, 2004;
4, obtain aerodynamic parameter: with the initial value that the thick value of aerodynamic parameter is moved as the improvement particle cluster algorithm, employing improves particle cluster algorithm and calculates, and obtains final aerodynamic parameter; Referring to " based on the parameter identification and the optimization of APSO algorithm "; Wu Yanxiang, Li Xiaobin, Sun Haiyan etc.; Science and technology and engineering in July, 2008, the 8th the 14th phase of volume.
Principle of work of the present invention is: the model structure of at first confirming no human aircraft according to theoretical derivation; Gather test flight data through the step of taking a flight test then; Utilize recursive prediction error algorithms afterwards and improve the method that particle cluster algorithm combines; Unknown parameter in the getting parms model is realized the identification work to model.
Embodiment 1
Single rotor depopulated helicopter has carried the flight data recording unit, and unknown aerodynamic parameter obtains in the employing said method completion depopulated helicopter mathematical model.
1, the linear parameterization model of confirming single rotor depopulated helicopter is:
Δ x · = AΔx + BΔu
u＝[δ lat?δ lon?δ col?δ ped] T
A = X u 0 0 0 0 - g X als 0 0 0 0 0 Y v 0 0 g 0 0 Y bls 0 0 0 L u L v 0 0 0 0 L als L bls 0 0 0 M u M v 0 0 0 0 M als M bls 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 - 1 0 0 - 1 / τ f 0 0 0 0 0 0 - 1 0 0 0 B als - 1 / τ f 0 0 0 0 0 0 0 0 0 Z als Z bls Z w Z r 0 0 0 N p 0 0 0 0 0 N w N r MN ped 0 0 0 0 0 0 0 0 0 K r MK rfb B = 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 A lat A lon 0 0 B lat B lon 0 0 0 0 Z col 0 0 0 N col N ped 0 0 0 0
Wherein:
X=[u v p q φ θ a 1sb 1sW r r Fb] T, and [u v w] be respectively the axis speed component, and [φ θ ψ] is respectively lift-over, pitching and crab angle, and [p q r] is respectively lift-over, pitching and yawrate, [a 1sb 1s] be flapping angle, be respectively back chamfering and side chamfering, r FbBe the gyrosystem state of feedback, [δ Latδ Lonδ Colδ Ped] TBe successively the horizontal cyclic pitch control amount of main oar, the vertical cyclic pitch control amount of main oar, main oar always apart from manipulated variable and tailrotorpiston manipulated variable, comprised unknown aerodynamic parameter among matrix A, the B;
2, obtain test flight data:
2.1, single rotor depopulated helicopter is taken a flight test, it is operated under the remote manual control operating pattern, and with the aircraft trim;
2.2, the cutoff frequency of the pitching of single rotor depopulated helicopter, course and roll channel is 3～4rad/s; Respectively depopulated helicopter pitching, course and roll channel are sent the roll channel control command; Control command is sinusoidal constant amplitude swept-frequency signal, and the frequency range that frequency sweep covers is 1～12rad/s;
3, adopt recursive prediction error algorithms that the unknown aerodynamic parameter in the unmanned plane mathematical model is carried out according to a preliminary estimate, obtain the thick value of aerodynamic parameter;
4, the initial value that the thick value of aerodynamic parameter is moved as the improvement particle cluster algorithm, employing improves particle cluster algorithm and calculates, and obtains final aerodynamic parameter:
Δ x · = AΔx + BΔu
x＝[u(m/s)v(m/s)p(rad/s)q(rad/s)φ(rad)θ(rad)a 1s(rad)b 1s(rad)w(m/s)r(rad/s)r fb] T
u＝[δ lat(V)δ lon(V)δ col(V)δ ped(V)] T
A = - 0.1778 0 0 0 0 - 9.8 - 9.8 0 0 0 0 0 - 0.3104 0 0 9.8 0 0 9.8 0 0 0 - 0.3326 - 0.5353 0 0 0 0 98.5724 208.4603 0 0 0 0.1903 - 0.2940 0 0 0 0 102.3681 - 18.7227 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 - 1 0 0 - 4.3916 2.5303 0 0 0 0 0 - 1 0 0 0 - 8.0492 - 4.3916 0 0 0 0 0 0 0 0 0 0 0 - 0.2 1.01 0 0 0 0 0 0 0 0 0 - 0.08 0.3616 - 36.79 0 0 0 0 0 0 0 0 0 2.1945 - 11.2 B = 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.6155 - 6.0259 0 0 - 8.0668 - 1.5142 0 0 0 0 20 0 0 0 0.12 66.7919 0 0 0 0
Obtain with regard to the unknown parameter of having accomplished single rotor depopulated helicopter aerodynamic model like this.

Claims (1)

1. a method of obtaining unknown aerodynamic parameter in the unmanned plane mathematical model is characterized in that, the step of obtaining unknown aerodynamic parameter is following:
1.1, confirm the structure of unmanned plane mathematical model: confirm the linear parameterization model of this unmanned plane to have comprised unknown aerodynamic parameter in the model according to the structure type of unmanned plane, structure type is divided into single rotor depopulated helicopter and fixed-wing unmanned plane;
1.2, obtain test flight data:
1.2.1, unmanned plane is taken a flight test, unmanned vehicle is operated under the remote manual control operating pattern, with the aircraft trim;
1.2.2, obtain the pitch channel test flight data: send the pitch channel control command to unmanned plane; Control command is sinusoidal constant amplitude swept-frequency signal; The amplitude of sinusoidal constant amplitude swept-frequency signal is represented manipulated variable; The frequency range that frequency sweep covers is 1/3 ω c～3 ω c, and ω c is the cutoff frequency of unmanned plane pitch channel, and ω c is provided by unmanned plane designing unit; Unmanned plane responds the control command of receiving, the control command of Airborne Flight Parameter logging modle record is stored as the pitch channel test flight data with corresponding aircraft response;
1.2.3, obtain course passage test flight data: send course passage control command to unmanned plane; Control command is sinusoidal constant amplitude swept-frequency signal; The amplitude of sinusoidal constant amplitude swept-frequency signal is represented manipulated variable; The frequency range that frequency sweep covers is 1/3 ω c～3 ω c, and ω c is the cutoff frequency of unmanned plane course passage, and ω c is provided by unmanned plane designing unit; Unmanned plane responds the control command of receiving, the control command of Airborne Flight Parameter logging modle record is stored as course passage test flight data with corresponding aircraft response;
1.2.4, obtain the roll channel test flight data: send the roll channel control command to unmanned plane; Control command is sinusoidal constant amplitude swept-frequency signal; The amplitude of sinusoidal constant amplitude swept-frequency signal is represented manipulated variable; The frequency range that frequency sweep covers is 1/3 ω c～3 ω c, and ω c is the cutoff frequency of unmanned machine rolling passage, and ω c is provided by unmanned plane designing unit; Unmanned plane responds the control command of receiving, the control command of Airborne Flight Parameter logging modle record is stored as the roll channel test flight data with corresponding aircraft response;
1.3, carry out aerodynamic parameter and estimate: adopt recursive prediction error algorithms that the unknown aerodynamic parameter in the unmanned plane mathematical model is carried out according to a preliminary estimate, obtain the thick value of aerodynamic parameter;
1.4, obtain aerodynamic parameter: the thick value of aerodynamic parameter as the initial value that improves the particle cluster algorithm operation, is adopted and improves particle cluster algorithm and calculate, obtain final aerodynamic parameter.
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CN102799187A (en) * 2012-06-26 2012-11-28 中国航空工业第六一八研究所 Spraying breakpoint continuing method for unmanned helicopter
CN102789527A (en) * 2012-07-11 2012-11-21 南京航空航天大学 Particle swarm optimization method for airplane trim
CN104102127A (en) * 2014-07-17 2014-10-15 厦门大学 Airborne aerodynamic parameter identification system
CN104102127B (en) * 2014-07-17 2016-06-01 厦门大学 A kind of machine load Aerodynamic Parameter Identification system
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CN106650095A (en) * 2016-12-21 2017-05-10 中国航天空气动力技术研究院 Method for correcting unmanned aerial vehicle control matrix based on wind tunnel test data and CFD calculation
CN106650095B (en) * 2016-12-21 2020-07-14 中国航天空气动力技术研究院 Correction method of unmanned aerial vehicle control matrix based on wind tunnel test data and CFD calculation
CN110704948A (en) * 2019-09-24 2020-01-17 江西慧识智能科技有限公司 Design method of intelligent controller of unmanned aerial vehicle
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
CN111538237B (en) * 2020-03-20 2021-10-29 北京航空航天大学 Method for identifying and correcting non-linear light gray model of tilt rotor unmanned aerial vehicle
CN113029512A (en) * 2021-04-01 2021-06-25 中国空气动力研究与发展中心高速空气动力研究所 Air flow field model correction control method for temporary-impulse transonic wind tunnel
CN113029512B (en) * 2021-04-01 2022-08-05 中国空气动力研究与发展中心高速空气动力研究所 Air flow field model correction control method for temporary transonic wind tunnel

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