Login | Register

Time-Domain System Identification for Long-EZ Fixed-Wing Aircraft Based on Flight Test Data

Title:

Time-Domain System Identification for Long-EZ Fixed-Wing Aircraft Based on Flight Test Data

Xu, Danyang (2019) Time-Domain System Identification for Long-EZ Fixed-Wing Aircraft Based on Flight Test Data. Masters thesis, Concordia University.

[thumbnail of Xu_MASc_S2019.pdf]
Preview
Text (application/pdf)
Xu_MASc_S2019.pdf - Accepted Version
Available under License Spectrum Terms of Access.
10MB

Abstract

System identification using flight test data based on time-domain method is an accurate way of getting a reliable mathematical aircraft model. This thesis provides a system identification procedure on a canard configured fixed-wing aircraft Long-EZ, which is the early and critical stage of providing accurate aircraft models for designing an effective autopilot in the future.
Flight test designed for Long-EZ aircraft has been carried out by International Test Pilot School (ITPS Canada Ltd). The real flight test data recorded from the testbed has been utilized for the identification and verification of a linear transfer function model, a nonlinear neural network model, and a block-oriented model consisting of linear and nonlinear parts. The linear transfer function structure has been determined with aircraft’s physical dynamics, and the model parameters have been identified using MATLAB System Identification toolbox. The nonlinearity of the aircraft dynamics has been treated with a Multilayer Perceptron (MLP) neural network structure, which has been developed with a set of Python codes. Flight data has been utilized to train this MLP structure.
The results demonstrate different predicting capabilities of the developed linear, nonlinear, and combined linear and nonlinear structure, which is also known as the neural network Wiener model. The developed Wiener model in general shows satisfactory predicting capability for the testbed Long-EZ aircraft.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Mechanical, Industrial and Aerospace Engineering
Item Type:Thesis (Masters)
Authors:Xu, Danyang
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Mechanical Engineering
Date:30 January 2019
Thesis Supervisor(s):Zhang, Youmin
Keywords:System Identification, Flight Test, Fixed-Wing, Least-Squares, Neural Networks
ID Code:985010
Deposited By: DANYANG XU
Deposited On:27 Oct 2022 13:48
Last Modified:27 Oct 2022 13:48

References:

[1] L. Zadeh, "From Circuit Theory to System Theory," Proceedings of the IRE, vol. 50, no. 5, pp. 856-865, May 1962.
[2] V. Klein and E. A. Morelli, Aircraft System Identification: Theory and Practice, Reston: AIAA, 2006.
[3] R. V. Jategaonkar, Flight Vehicle System Identification: A Time-Domain Methodology, Reston: AIAA, 2006.
[4] M. B. Tischler and R. K. Remple, Aircraft and Rotorcraft System Identification, Virginia: AIAA, 2006.
[5] P. Hamel and R. Jategaonkar, "Evaluation of Flight Vehicle System Identification," Journal of Aircraft, vol. 33, pp. 9-28, Jan.-Feb., 1996.
[6] W. Milliken and Jr., "Progress in Stability and Control Research," Journal of the Aeronautic Science, vol. 14, pp. 494-519, 1947.
[7] L. Ljung, System Identification, Theory for the User, 2nd ed, Upper Saddle River, NJ: Prentice-Hall, 1999.
[8] V. Klein, "Application of System Identification to High Performance Aircraft," in Proceedings of the 32nd IEEE Conference on Decision and Control, San Antonio, TX, 1993.
[9] V. Klein and P. Murphy, "Aerodynamic Parameters of High Performance Aircraft Estimated from Wind Tunnel and Flight Test Data," in System Identification for Integrated Aircraft Development and Flight Testing, May 1999.
[10] J. Shen, Y. Su, Q. Liang and X. Zhu, "Calculation and Identification of the Aerodynamic Parameters for Small-Scaled Fixed-Wing UAVs," Sensors, vol. 18, no. 1, p. 206, Jan 2018.
[11] W. Wei, M. B. Tischler, N. Schwartz and K. Cohen, "System Identification and Flight Control of an Unmanned Quadrotor," in Advanced UAV Aerodynamics, Flight Stability and Control: Novel Concepts, Theory and Applications, New York, John Wiley & Sons Ltd., 2017, p. 695.
[12] M. H. Mohajerani, H. Bolandhemmat, Y. M. Zhang and H. W. Loewen, "Identification of Low Order Equivalent Transfer Function Model of Trex-700E Helicopter from Flight Test Data," in AIAA Science and Technology Forum and Exposition, National Harbor, MD, 2014.
[13] Z. Liu, Y. R. Zhou and G. D. Wang, "Online Parameter Identification Study on a Small Fixed-Wing UAV," in International Conference on Unmanned Aircraft Systems, Arlington, VA, 2016.
[14] R. Abdulhamid, M. H. Santos, N. Oliveira and B. Maciel, "Initial Experimental Procedures for Modeling and Identification of a Fixed Wing Aerial Vehicle," in International Conference on Unmanned Aircraft Systems, Arlington, VA, 2016.
[15] V. Klein, "Estimation of Aircraft Aerodynamic Parameters from Flight Data," in Progress in Aerospace Sciences, 1989.
[16] D. Stepner and R. Mehra, "Maximum Likelihood Identification and Optimal Input Design for Identifying Aircraft Staiblity and Control Derivatives," NASA CR-2200, 1973.
[17] R. Mehra, "Maximum Likelihood Identification of Aircraft Parameters," in Proceedings of the Joint Automatic Control Conference, Atlanta, GA, June 1970.
[18] N. Kumar and N. Rao, "Estimation of Stability and Control Derivatives of Light Canard Research Aircraft from Flight Data," Defense Science Journal, vol. 54, no. 3, pp. 277-292, 2014.
[19] R. Jategaonkar, D. Fischenberg and W. Von Gruenhagen, "Aerodynamic Modeling and System Identification from Flight Data -- Recent Applications at DLR," Journal of Aircraft, vol. 41, no. 4, pp. 681-691, 2004.
[20] F. Nicolosi, A. De Marco and P. Della Vecchia, "Stability, Flying Qualities and Longitudinal Parameter Estimation of a Twin-Engine CS-23 Certified Light Aircraft," Aerospace Science and Technology, pp. 226-240, 2013.
[21] G. Chowdhary and R. Jategaonkar, "Aerodynamic Parameter Estimation from Flight Data Applying Extended and Unscented Kalman Filter," Aerospace Science and Technology, vol. 14, pp. 106-117, 2010.
[22] J. Garcia-Velo and B. Walker, "Aerodynamic Parameter Estimation for High-Performance Aircraft Using Extended Kalman Filtering," Journal of Guidance, Control and Dynamics, vol. 20, no. 6, pp. 1257-1259, November-December 1997.
[23] A. Kokolios, "Use of a Kalman Filter for the Determination of Aircraft Aerodynamic Characteristics from Flight Test Data," in AIAA Aerospace Sciences Meeting & Exhibit, Reno, NV, 1994.
[24] J. Bauer and D. Andrisani, "Estimating Short-Period Dynamics Using an Extended Kalman Filter," in Fifth Biannual Flight Test Conference, Ontario, CA, May 1990.
[25] M. Shinbrot, "A Least Squares Curve Fitting Method with Application of the Calculation of Stability Coefficients from Transient-Response Data," NACA TN 2341, 1951.
[26] A. Janczak, Identification of Nonlinear Systems Using Neural Networks and Polynomial Models, Switzerland: Springer, 2005.
[27] S. Shanmuganathan and S. Samarasinghe, Artificial Neural Network Modelling, Springer International Publishinig, 2016.
[28] M. Gupta, L. Jin and N. Homma, Neural Networks: From Fundamentals to Advanced Theory, Hoboken: John Wiley and Sons, 2003.
[29] O. Nelles, Nonlinear System Identification. From Classical Approaches to Neural Networks and Fuzzy Models, New York, Berlin, Heidelberg: Springer, 2001.
[30] R. J. Williams and D. Zipser, "A Learning Algorithm for Continually Running Fully Recurrent Neural Networks," Neural Computations, vol. 1, pp. 270-280, 1989.
[31] T. Kohonene and G. Deboeck, Visual Explorations in Finance with Self-organizing Maps, London: Springer, 1998.
[32] W. McCulloch and W. Pitts, "A Logical Calculus of the Ideas Immanent in Nervous Activity," Bulletin of Mathematical Biophysics, vol. 5, pp. 115-133, 1943.
[33] D. Hebb, The Organization of Behavior: A Neuropsychological Theory, New York: Wiley, 1949.
[34] F. Rosenblatt, "The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain," Psychological Review, vol. 65, no. 6, pp. 386-408, 1958.
[35] M. Minsky and S. Papert, Perceptrons: An Introduction to Computational Geometry, Cambridge: MIT Press, 1969.
[36] P. Werbos, The Roots of Backpropagation: From Ordered Derivatives to Neural Networks and Political Forecasting, New York: Wiley, 1994.
[37] J. Hopfield, "Neural Networks and Physical Systems with Emergent Collective Computational Abilities," Proceedings of the National Academy of Science, vol. 79, pp. 2554-2558, 1982.
[38] J. Wang and Y. Chen, "A Hammerstein-Wiener Recurrent Neural Network with Universal Approximation Capability," in IEEE International Conference on Systems, Man and Cybernetics, Systems, Man and Cybernetics, 2008.
[39] J. Raskam, Methods for Estimating Stability and Control Derivatives of Conventional Subsonic Airplanes, USA: Roskam Aviation and Engineering Corporation, 1971.
[40] B. L. Stevens, F. L. Lewis and E. N. Johnson, Aircraft Control and Simulation: Dynamics, Controls Design, and Autonomous Systems, Hoboken, New Jersey: John Wiley & Sons, 2016.
[41] "JSBSim," [Online]. Available: http://www.openvsp.org.
[42] D. Y. Xu, Z. X. Liu and Y. M. Zhang, "System Identification of Long-EZ Fixed-Wing Aircraft Using Time-Domain Method," in The 14th International Conference on Intelligent Unmanned System, Jeju, South Korea, 2018.
[43] "OpenVsp," [Online]. Available: http://www.openvsp.org.
[44] D. T. Ward and T. W. Strganac, Introduction to Flight Test Engineering, Dubuque, Iowa: Kendall/Hunt Publising Company, 1998.
[45] Rutan Aircraft Factory Inc., "http://www.ez.org/downloads/longez_poh.pdf," 1980. [Online].
[46] M. Mohajerani, Frequency-Domain System Identification for Unmanned Helicopters from Flight Data, Master's Thesis, Concordia University, 2014.
[47] E. A. Morelli, "Flight Test Validation of Optimal Input Design and Comparison to Conventional Input," NASA Langley Research Center, Hampton, Virginia, USA.
[48] J. M. Ortega, Iterative Solution of Nonlinear Equations in Several Variables, New York: Academic Press, 1970.
[49] A. V. Balakrishnan, Communication Theory, New York: McGraw-Hill, 1968.
[50] K. S. P. Kumar and R. Sridhar, "On the Identification of Control Systems by the Quasi-Linearization Method," IEEE Transactions on Automatic Control, vol. AC-9, pp. 151-154, 1964.
[51] R. L. Kashyap, "A Bayesian Comparison of Different Classes of Dynamic Models Using Empirical Data," IEEE Transaction on Automatic Control, vol. AC-22, no. 5, pp. 715-727, 1977.
[52] Y. M. Zhang and X. R. Li, "A Fast U-D Factorization-Based Learning Algorithm with Applications to Nonlinear System Modeling and Identification," IEEE Transactions on Neural Networks, vol. 10, no. 4, pp. 930-938, 1999.
[53] L. Rutkowski, New Soft Computing Techniques for System Modelling, Pattern Classification and Image Processing, Berlin, Heidelberg: Springer, 2004.
[54] "Keras," [Online]. Available: https://keras.io.
[55] A. P. Sage and J. L. Melsa, System Identification, New York: Academic International Press, 1971.
[56] P. Eykhoff, System Identification, Parameter and State Estimation, New York: Wiley, 1974.
[57] G. Goodwin and R. Payne, Dynamic System Identification: Experiment Design and Data Analysis, New York: Academic International Press, 1977.
[58] H. Greenberg, "A Survey of Methods for Determining Stability Parameters of an Airplane from Dynamic Flight Measurements," 1951.
[59] T. Hsia, System Identification, Lexington, MA: Lexington Books, 1977.
[60] E. A. Morelli, Flight Test Maneuvers for Efficient Aerodynamic Modelling, Hampton: NASA Langley Research Center.
[61] J. P. Norton, An Introduction to Identification, London: Academic International Press, 1986.
[62] F. Schweppe, Uncertain Dynamic Systems, Upper Saddle River, NJ: Prentice-Hall, 1973.
[63] T. Söderström and P. Stoica, System Identification, Upper Saddle River, NJ, Prentice-Hall, 1989.
[64] L. Taylor, K. Iliff and B. Powers, "A Comparison of Newton-Raphson and Other Methods for Determining Stability Derivatives from Flight Data," 3rd Flight Test, Simulation, and Support Conference, pp. 69-315, 1969.
[65] O. Gerlach, "The Determination of Stability Derivatives and Performance Characteristics from Dynamic Maneuvers," in Society of Automotive Engineers, Paper 700236, 1970.
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Research related to the current document (at the CORE website)
- Research related to the current document (at the CORE website)
Back to top Back to top