Multiple partitioning of flight data for manoeuvrable aircraft aerodynamic modeling and parameter estimation

Μέγεθος: px

Εμφάνιση ξεκινά από τη σελίδα:

Download "Multiple partitioning of flight data for manoeuvrable aircraft aerodynamic modeling and parameter estimation"

Σοφοκλής Μέλιοι
6 χρόνια πριν
Προβολές:

1 DOI: /CTA Control Theory & Applications Vol. 33 No. 10 Oct. 2016, (, ) :,,..,,.,,,.,.. : ; ; ; : V212.1 : A Multiple partitioning of flight data for manoeuvrable aircraft aerodynamic modeling and parameter estimation HU Shuang, ZHU Ji-hong (Department of Computer Science and Technology, Tsinghua University, Beijing , China) Abstract: Under the quasi-steady assumption, aerodynamic characteristics show marked nonlinearity in high angle of attack or large amplitude maneuvers of manoeuvrable aircraft. A linear aerodynamic model generally identified from small amplitude maneuvers about trim conditions is no longer suitable for this case. In order to solve this problem, a method of multiple partitioning of flight data is proposed, which can characterize aerodynamic global nonlinearity by partitioned linearity. In each partitioned subset, a general aerodynamic model is formed by the Taylor s series expansion of static terms, dynamic stability derivatives and control derivatives of aerodynamic force and moment coefficients. The parameters in this aerodynamic model are estimated by using the class of least squares methods. According to the simulation flight test data of a modern fighter aircraft, the aerodynamic parameters are estimated which fit well with the true values. The test results verify the proposed multiple partitioning of flight data and the general aerodynamic model. Key words: aerodynamics; aerodynamic modeling; parameter estimation; multiple partitioning 1 (Introduction).,,,, [1 2].,, [3 4].,. [5],,,.,.,,.,,, [6].,,. [7],.,,. [8 9],,, 2., : ; : jhzhu@tsinghua.edu.cn; Tel.: :. 973 ( ). Supported by National 973 Program ( ).

1290 33., [10].,.,,,.,.,,,.,.,,.,.,,,,,.,.,.,,,. 2 (Multiple partitioning of flight data) 2.1 (Multiple partitioning procedure), [8 9], 1,. ( 2 ),,. 5, 1 6,.,,.,,,,. 1 3, 2., 2, 3 6. 2., 1,. 2 3 Fig.

2 , [10].,.,,,.,.,,,.,.,,.,.,,,,,.,.,.,,,. 2 (Multiple partitioning of flight data) 2.1 (Multiple partitioning procedure), [8 9], 1,. ( 2 ),,. 5, 1 6,.,,.,,,,. 1 3, 2., 2, , 1,. 2 3 Fig. 2 Subset 3 partitioning by sideslip angle, 1, 3,.,. 5, 2, 3,. 3 Fig. 3 Flight data multiple partitioning 1 Fig. 1 Flight data partitioning by angle of attack 2.2 (Multiple partitioning principle),.,,

3 10 : ,,.,,.. 4 C nr, C nr., 10 30, , 5.,.,, ±10.,,,., 2. 4 C nr Fig. 4 Damping-in-yaw derivate C nr wind-tunnel measurement 3 (Aerodynamic modeling and parameter estimation) 3.1 (Aerodynamic modeling), 3 C X,C Y,C Z 3 C l,c m,c n.,,., C a (α, β, V, q, δ lon ),a= X, Z, m, (1) C a (α, β, V, p, r, δ lat ),a= Y,l,n, (2) : α, β, V, p, q r, δ lon ( ), δ lat ( )., 3, : C a0 (α, β)+c aq (α, β) q c C aδlon (α, β)δ lon,a= X, Z, m, (3) C a0 (α, β)+c ap (α, β) pb C ar(α, β) rb C aδlat (α, β)δ lat,a= Y,l,n, (4) : c, b, C a0, C ap, C aq C ar, C aδlon C aδlat. (3) (4),..,, ᾱ β,, C a0 (α, β) = C a0 (ᾱ, β)+c aα (ᾱ, β)δα + C aβ (ᾱ, β)δβ, C aq (α, β) = C aq (ᾱ, β)+c aqα (ᾱ, β)δα + C aqβ (ᾱ, β)δβ, C aδlon (α, β) = C aδlon (ᾱ, β)+c aδlon α(ᾱ, β)δα+ C aδlon β(ᾱ, β)δβ, a = X, Z, m, (5) C a0 (α, β) = C a0 (ᾱ, β)+c aα (ᾱ, β)δα + C aβ (ᾱ, β)δβ, C ap (α, β) = C ap (ᾱ, β)+c apα (ᾱ, β)δα + C apβ (ᾱ, β)δβ, C ar (α, β) = C ar (ᾱ, β)+c arα (ᾱ, β)δα + C arβ (ᾱ, β)δβ, C aδlat (α, β) = C aδlat (ᾱ, β)+c aδlat α(ᾱ, β)δα+ C aδlat β(ᾱ, β)δβ, a = Y,l,n, (6) : Δα = α ᾱ, Δβ = β β. (5) (6) (3) (4) C a0 (ᾱ, β)+c aα (ᾱ, β)δα + C aβ (ᾱ, β)δβ +[C aq (ᾱ, β)+ C aqα (ᾱ, β)δα q c + C aqβ (ᾱ, β)δβ] [C aδlon (ᾱ, β)+c aδlon α(ᾱ, β)δα + C aδlon β(ᾱ, β)δβ]δ lon,a= X, Z, m, (7)

4 C a0 (ᾱ, β)+c aα (ᾱ, β)δα + C aβ (ᾱ, β)δβ + [C ap (ᾱ, β)+c apα (ᾱ, β)δα + pb C apβ (ᾱ, β)δβ] [C ar(ᾱ, β)+ C arα (ᾱ, β)δα rb + C arβ (ᾱ, β)δβ] [C aδlat (ᾱ, β)+c aδlat α(ᾱ, β)δα + C aδlat β(ᾱ, β)δβ]δ lat,a= Y,l,n. (8) (7) (8). 3.2 (Parameter estimation), 3 C X,C Y,C Z 3 C l,c m,c n.,, : qsc X + X T = mgn x, qsc Y + Y T = mgn y, qsc Z + Z T = mgn z, qsbc l + L T = I x ṗ I xz (pq+ṙ)+(i z I y ) qr, qs cc m + M T = I y q+i xz (p 2 r 2 )+(I x I z ) pr, qsbc n + N T = I z ṙ I xz (ṗ qr)+(i y I x ) pq, (9) : q, S, m, g, I x,i y,i z,i xz, n x,n y,n z 3, X T,Y T,Z T,L T,M T,N T 3.,,.,,., (9). (7) (8), ᾱ β,. N, (7) (8) N, [11],., : 1),,. 2),. [12] 30,.,,. 4 (Simulation flight test), [13].,, [14] 16% [15].,.,. ( 0.6),.,,., (7) (8) : C a0 (ᾱ, β)+c aα (ᾱ, β)δα + C aβ (ᾱ, β)δβ +[C aq (ᾱ, β)+ C aqα (ᾱ, β)δα q c + C aqβ (ᾱ, β)δβ] [C aδe (ᾱ, β)+c aδeα(ᾱ, β)δα + C aδe β(ᾱ, β)δβ]δ e,a= X, Z, m, (10) C a0 (ᾱ, β)+c aα (ᾱ, β)δα + C aβ (ᾱ, β)δβ +[C ap (ᾱ, β)+ C apα (ᾱ, β)δα pb + C apβ (ᾱ, β)δβ] [C ar (ᾱ, β)+c arα (ᾱ, β)δα + rb C arβ (ᾱ, β)δβ] [C aδ a (ᾱ, β)+ C aδaα(ᾱ, β)δα + C aδaβ(ᾱ, β)δβ]δ a + [C aδr (ᾱ, β)+c aδr α(ᾱ, β)δα + C aδr β(ᾱ, β)δβ]δ r,a= Y,l,n, (11) : δ e, δ a, δ r.,,,,. 5 7.,., (9) 3 C X,C Y,C Z 3 C l,c m,c n.,.,

5 10 : 1293 [11].,,,. 5 Fig. 5 Angle of attack and sideslip angle variation,.,. 5, 2, C l C l0 C lp C lr C lδa C lδr 8 12,. (MSE), MSE = 1 N (C li N Ĉli) 2, (12) i=1 : N, C li, Ĉli. 1,., C lp C lr,.. 8 C l0 Fig. 8 Comparison of estimated results and true values of C l0 6 Fig. 6 Elevator, aileron and rudder variation 9 C lp Fig. 9 Comparison of estimated results and true values of C lp 7 Fig. 7 Roll, pitch and yaw angular velocity variation 5 (Result analysis) 5 7. ( 15) ( 30). (10) (11) 10 C lr Fig. 10 Comparison of estimated results and true values of C lr

6 C lδa Fig. 11 Comparison of estimated results and true values of C lδa 12 C lδr Fig. 12 Comparison of estimated results and true values of C lδr ᾱ/( ) β/( ) MSE Table 1 Comparison of estimated results and true values of roll moments parameters C l0 C lp C lr C lδa C lδr , 2, C lδa 13.,,.., 11, ,,,. 10, 2, C lδa 14.,,.,,, C lδa Fig. 13 Comparison of estimated results and true values of C lδa when 2 angle of attack variation in subsets C lδa Fig. 14 Comparison of estimated results and true values of C lδa when 10 angle of attack variation in subsets

7 10 : (Conclusions),,,,, : 1),. 2),.,,. 3),.,.,,.,,,.,,.,,,,,. (References):(References): [1] KLEIN V, MORELLI E. Aircraft System Identification: Theory and Practice [M]. Reston, VA, USA: American Institute of Aeronautics and Astronautics, [2] CAI Jinshi. Aircraft System Identification [M]. Beijing: National Defend Industry Press, (. [M]. :, 2003.) [3] CHOWDHARY G, JATEGAONKAR R. Aerodynamic parameter estimation from flight data applying extended and unscented Kalman filter [J]. Aerospace Science and Technology, 2010, 14(2): [4] KLEIN V, MURPHY P. Aerodynamic parameters of high performance aircraft estimated from wind tunnel and flight test data [R]. Hampton, VA, USA: NASA, [5] BATTERSON J G. Estimation of airplane stability and control derivatives from large-amplitude longitudinal maneuvers [R]. Hampton, VA, USA: NASA, [6] BESGAS Γ C. Aerodynamics and Flight Dynamics for Supersonic Aircraft [M]. Shanghai: Shanghai Jiao Tong University Press, ( Γ C. [M]. :, 2009.) [7] KLEIN V, NODERER K D. Modeling of aircraft unsteady aerodynamic characteristics, part 3: parameters estimated from flight data [R]. Hampton, VA, USA: NASA, [8] BATTERSON J G, KLEIN V. Partitioning of flight data for aerodynamic modeling of aircraft at high angles of attack [J]. Journal of Aircraft, 1989, 26(4): [9] PARAMESWARAN V, GIRIJA G, RAOL J R. Estimation of parameters from large amplitude maneuvers with partitioned data for aircraft [C] //AIAA Atomospheric Flight Mechanics Conference and Exhibit. Austin, Texas, USA: AIAA, 2003: [10] KLEIN V, BATTERSON J G, MURPHY P C. Determination of airplane model structure from flight data by using modified stepwise regression [R]. Hampton, VA, USA: NASA, [11] XIAO Deyun. Theory of System Identification with Applications [M]. Beijing: Tsinghua University Press, (. [M]. :, 2014.) [12] BELSLEY D A, KUH E, WELSCH R E. Regression Diagnostics: Identifying Influential Data and Sources of Collinearity [M]. New Y- ork, USA: John Wiley & Sons, Inc, [13] SONNEVELDT L. Nonlinear F 16 model description [R]. Netherlands: Delft University of Technology, [14] MORELLI E. Global nonlinear parametric modelling with application to F 16 aerodynamics [C] //American Control Conference. New York, USA: IEEE, 1998: [15] STEVENS B L, LEWIS F L. Aircraft Control and Simulation [M]. 2nd Edition. New Jersey, USA: John Wiley & Sons, Inc, : (1990 ),,,, hus13@mails.tsinghua.edu.cn; (1968 ),,,,, jhzhu@tsinghua.edu.cn.

Σχετικά έγγραφα

Motion analysis and simulation of a stratospheric airship

32 11 Vol 32 11 2011 11 Journal of Harbin Engineering University Nov 2011 doi 10 3969 /j issn 1006-7043 2011 11 019 410073 3 2 V274 A 1006-7043 2011 11-1501-08 Motion analysis and simulation of a stratospheric