Abstract
The objective of this paper is to present a system identification method suitable for miniature rotorcrafts under hovering. The proposed model to be identified is a Takagi-Sugeno fuzzy system, representing translational and rotational velocity dynamics. For parameter estimation of the Takagi-Sugeno system a classical Recursive Least Squares (RLS) algorithm is used, which allows identification to take place on-line since parameter updates are produced whenever a new measurement becomes available. The validity of this approach is tested using the X-Plane © flight simulator. Data obtained offer justification for the applicability of the approach in real-time applications.
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References
Angelov, P.P., Filev, D.P.: An approach to online identification of takagi-sugeno fuzzy models. IEEE Trans. Syst. Man Cybern. Part B Cybern. 34, 484–498 (2004)
Calise, A.J., Rysdyk, R.T.: Nonlinear adaptive flight control using neural networks. IEEE Control Syst. Mag. 18, 14–25 (1998)
Castillo, P., Lozano, R., Dzul, A.E.: Modelling and Control of Mini-Flying Machines. Springer, New York (2005)
Ernst, D., Valavanis, K., Craighead, J.: Automated process for unmanned aerial systems controller implementation using matlab. In: 14th Mediterranean Conference on Control and Automation MED ’06, Ancona, June 2006
Gavrilets, V., Mettler, B., Feron, E.: Nonlinear model for a small-size acrobatic helicopter. In: AIAA Guidance, Navigation, and Control Conference and Exhibit, Montreal, August 2001
Hamel, P.G., Kaletka, J.: Advances in rotorcraft system identification. Prog. Aerosp. Sci. 33, 259–284 (1997)
Isidori, A., Marconi, L., Serrani, A.: Robust nonlinear motion control of a helicopter. IEEE Trans. Automat. Contr. 48, 413–426 (2003)
Johnson, W.: Helicopter Theory. Princeton University Press, Princeton (1980)
Kim, S.K., Tilbury, D.M.: Mathematical modeling and experimental identification of a model helicopter. In: AIAA Modeling and Simulation Technologies Conference and Exhibit, Boston, 10–12 August 1998
Klein, V., Moreli, E.A.: Aircraft System Identification Theory and Practice. AIAA Education Series. American Institute of Aeronautics and Astronautics, New York (2006)
Koo, T.J., Sastry, S.: Output tracking control design of a helicopter model based on approximate linearization. In: Proceedings of the 37th IEEE Conference on Decision and Control, vol. 4, pp. 3635–3640. IEEE, Piscataway (1998)
Mahony, R., Hamel, T., Dzul, A.: Hover control via Lyapunov control for an autonomous model helicopter. In: Proceedings of the 38th IEEE Conference on Decision and Control, vol. 4, pp. 3490–3495. IEEE, Piscataway (1999)
Marconi, L., Naldi, R.: Robust full degree-of-freedom tracking control of a helicopter. Automatica 43, 1909–1920 (2007)
McCormick, B.W.: Aerodynamics Aeronautics and Flight Mechanics. Wiley, New York (1995)
Mendel, J.M.: Lessons in Estimation Theory for Signal Processing, Communications, and Control. Prentice Hall PTR, Englewood Cliffs (1995)
Mettler, B.: Identification Modeling and Characteristics of Miniature Rotorcraft. Kluwer Academic, Dordrecht (2003)
Mettler, B., Tischler, M.B., Kanade, T.: System identification of small-size unmanned helicopter dynamics. In: 55th Forum of the American Helicopter Society 55th Forum, Montréal, 25–27 May 1999
Morris, J.C., Van Nieuwstadt, M., Bendotti, P.: Identification and control of a mode helicopter at hover. In: American Control Conferance, vol. 2, pp. 1238–1242 (1994)
Murray, R.M., Zexiang, L., Sastry, S.: A Mathematical Introduction to Robotic Manipulation. CRC, Boca Raton (1994)
Passino, K.M., Yurkovich, S.: Fuzzy Control. Prentice Hall, Englewood Cliffs (1998)
Seckel, E.: Stability and Control of Airplanes and Helicopters. Academic, London (1964)
Spong, M.W., Hutchinson, S., Vidyasagar, M.: Robot Modeling and Control. Wiley, New York (2005)
Spooner, J.T., Passino, K.M.: Stable adaptive control using fuzzy systems and neural networks. IEEE Trans. Fuzzy Syst. 4, 339–359 (1996)
Suresh, S., Kumar, M.V., Omkar, S.N., Mani, V., Sampath, P.: Neural networks based identification of helicopter dynamics using flight data. In: The 9th International Conference on Neural Information Processing ICONIP ’02, Singapore, 18–22 November 2002
Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 15, 116–132 (1985)
Tischler, M.B.: System identification requirements for high-bandwidth rotorcraft flight control system design. J. Guid. Control Dyn. 13, 835–841 (1990)
Tischler, M.B., Cauffman, M.G.: Frequency-response method for rotorcraft system identification: flight applications to BO-105 coupled fuselage/rotor dynamics. J. Am. Helicopter Soc. 3, 3–17 (1992)
Tischler, M.B., Remple, R.K.: Aircraft and Rotorcraft System Identification. AIAA Education Series. American Institute of Aeronautics and Astronautics, New York (2006)
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Raptis, I.A., Valavanis, K.P., Kandel, A. et al. System Identification for a Miniature Helicopter at Hover Using Fuzzy Models. J Intell Robot Syst 56, 345–362 (2009). https://doi.org/10.1007/s10846-009-9320-3
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DOI: https://doi.org/10.1007/s10846-009-9320-3