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Tytuł artykułu

Power spectrum optimization in the design of multisine manoeuvre for identification purposes

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, two sets of multisine signals are designed for system identification purposes. The first one is obtained without any information about system dynamics. In the second case, the a priori information is given in terms of dimensional stability and control derivatives. Magnitude Bode plots are obtained to design the multisine power spectrum that is optimized afterwards. A genetic algorithm with linear ranking, uniform crossover and mutation operator has been employed for that purpose. Both designed manoeuvres are used to excite the aircraft model, and then system identification is performed. The estimated parameters are obtained by applying two methods: Equation Error and Output Error. The comparison of both investigated cases in terms of accuracy and manoeuvre time is presented afterwards.
Rocznik
Strony
1193--1203
Opis fizyczny
Bibliogr. 22 poz., rys., tab.
Twórcy
autor
  • Warsaw University of Technology, Institute of Aeronautics and Applied Mechanics, Poland
autor
  • Warsaw University of Technology, Institute of Aeronautics and Applied Mechanics, Poland
  • Universidad de San Buenaventura, Bogotá, Colombia
  • Universidad de San Buenaventura, Bogotá, Colombia
Bibliografia
  • 1. Albisser M., Dobre S., Berner C., Thomassin M., Garnier H., 2017, Aerodynamic coefficient identification of a space vehicle from multiple free-flight tests, Journal of Spacecraft and Rockets, in press, DOI: 10.2514/1.A33587
  • 2. Fedorov V.V., 1972, Theory of Optimal Experiments, Probability and Mathematical Statistics, Acade, New York, USA
  • 3. Goodwin G.C., Payne R.L., 1977, Dynamic System Identification: Experiment Design and Data Analysis, Academic Press Inc., New York, USA
  • 4. Grauer J.A., 2016, Aircraft fault detection using real-time frequency response estimation, AIAA Guidance, Navigation, and Control Conference, San Diego, USA, AIAA 20160372, DOI: 10.2514/6.2016-0372
  • 5. Jategaonkar R.V., 2015, Flight Vehicle System Identification: A Time Domain Methodology, 2nd ed., Progess in Astronautics and Aeronautics, AIAA, Reston, USA, DOI: 10.2514/4.102790
  • 6. Kalaba R.E., Springarn K., 1982, Control, Identification and Input Optimization, Mathematical Concepts and Methods in Science and Engineering, Plenum Press, New York, USA, DOI: 10.1007/978-1-4684-7662-0
  • 7. Kendall M.G., Stuart A., Ord J.K., 1983, The Advanced Theory of Statistics, 4th ed., Vol. 3, Charles Griffin (Edit.), London, UK, DOI: 10.1002/for.3980040310
  • 8. Lagarias J.C., Reeds J.A., Wright M.H., Wright P.E., 1998, Convergence properties of the Nelder-Mead simplex method in low dimensions, SIAM Journal of Optimization, 9, 1, 112147, DOI: 10.1137/S1052623496303470
  • 9. Levin M.J., 1960, Optimum estimation of impulse response in the presence of noise, IRE Transactions on Circuit Theory, 7, 1, 5056, DOI: 10.1109/TCT.1960.1086622
  • 10. Lichota P., 2016, Inclusion of the D-optimality in multisine manoeuvre design for aircraft parameter estimation, Journal of Theoretical and Applied Mechanics, 54, 1, 8798, DOI: 10.15632/jtam-pl.54.1.87
  • 11. Lichota P., Sibilski K., Ohme P., 2017, D-optimal simultaneous multistep excitations for aircraft parameter estimation, Journal of Aircraft, in press, DOI: 10.2514/1.C033794
  • 12. Marchand M., 1974, Untersuchung der Bestimmbarkeit von Fleugzeugderivativen aus Flugversuchen, DLR, Report DFVLR-IB 154-74/32, Braunschweig, Germany
  • 13. Morelli E.A., 2009, Flight test experiment design for characterizing stability and control of hypersonic vehicles, Journal of Guidance, Control and Dynamics, 32, 3, 949959, DOI: 10.2514/1.37092
  • 14. Morelli E.A., Klein V., 1990, Optimal input design for aircraft parameter estimation using dynamic programming principles, AIAA Atmospheric Flight Mechanics Conference, Portland, USA, DOI: 10.2514/6.1990-2801
  • 15. Nahi N.E., Wallis D.E.J., 1969, Optimal inputs for parameter estimation in dynamic systems with white observation noise, Proceedings of Joint Automatic Control Conference, 506512
  • 16. Schroeder M., 1970, Synthesis of low-peak-factor signals and binary sequences with low autocorrelation, IEEE Transactions on Information Theory, 16, 1, 8589, DOI: 10.1109/tit.1970.1054411
  • 17. Seren C., Bommier F., Verdier L., Bucharles A., Alazard D., 2006, Optimal experiment and input design for flight test protocol optimization, AIAA Athmospheric Flight Mechanics Conference, Keystone, USA, DOI: 10.2514/6.2006-6280
  • 18. Seren C., Hardier G., Roos C., 2013, Swarm intelligence and system identification: a hybrid discrete jumping frogs algorithm for optimal input design, 3rd IFAC International Conference on Intelligent Control, Chengdu, China
  • 19. Tischler M.B., Remple R.K., 2012, Aircraft and Rotorcraft System Identification, 2nd ed., AIAA Education Series, AIAA, Washington D.C., USA, DOI: 10.2514/4.868207
  • 20. Viana M.V.P., 2016, Time-domain system identification of rigid-body multipoint loads model, AIAA Atmospheric Flight Mechanics Conference, Washington D.C., USA, AIAA 20163706, DOI: 10.2514/6.2016-3706
  • 21. Wells W.R., Ramachandran S., 1977, Multiple control input design for identification of light aircraft, IEEE Transactions on Automatic Control, 22, 985987, DOI: 10.1109/TAC.1977.1101653
  • 22. Young P., Patton R.J., 1988, Frequency domain identification of remotely-piloted helicopter dynamics using frequency-sweep and Schroeder-phased test signals, AIAA Atmospheric Flight Mechanics Conference, Minneapolis, USA, AIAA 884349CP, DOI: 10.2514/6.1988-4349
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-76055e01-6361-4f15-9476-93b816efd118
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