This paper presents the identification of a miniature coaxial helicopter system. First, the helicopter flying principles are described and the hardware setup of the developed platform is presented. Further, linear models are developed for the movements of the helicopter using prediction error identification methods. The results in this case are accurate and can be used for performant controller design in some operating points. But in order to model the complete dynamics of the helicopter, nonlinear models are developed using recurrent dynamic neural networks. In this case the models obtained present a higher accuracy compared with the linear case and also with the results published until now. In the end, the advantages of nonlinear modeling based on neural networks is emphasized and some conclusions are drawn.
BudiyonoA.Recent advances in control and instrumentation of unmanned aerial vehicles [lecture]. Conference on Instrumentation and Control, Bandung, Indonesia, 19–20 February 2007.
2.
MeyerJde PlessisFClarkeW.Design considerations for long endurance unmanned aerial vehicles. In: LamThanh Mung (ed) Aerial Vehicles. New York: Intech Publishing, 2009, pp.443–497.
3.
KumarMVSureshSOmkarSNGanguliRSampathP.A direct adaptive neural command controller design for an unstable helicopter. Engineering Applications of Artificial Intelligence2009; 22: 181–191.
4.
WangHMianAAWangDDuanH.Robust multi-mode flight control design for an unmanned helicopter based on multi-loop structure. International Journal of Control, Automation, and Systems2009; 7(5): 723–730.
5.
VerhoevenGJJLoendersJVermeulenFDocterR.Helikite aerial photography—A versatile means of unmanned, radio controlled, low-altitude aerial archaeology. Archaeological Prospection2009; 16(2): 125–138.
6.
TorresGMuellerT. Micro air vehicle development: Design, components, fabrication, and flight-testing. Presented at: AUVSI Unmanned Systems 2000 Symposium and Exhibition, Orlando, FL, 11–13 July 2000. Available at: http://www3.nd.edu/~mav/design.htm
7.
WuHYSunDZhouZY.Micro air vehicle: Configuration, analysis, fabrication and test. IEEE/ASMETrans Mechatronics2004; 9(1): 108–117.
8.
LeiXDuY.A linear domain system identification for small unmanned aerial rotorcraft based on adaptive genetic algorithm. Journal of Bionic Engineering2010; 7: 142–149.
SchafrothDBouabdallahSBermesCSiegwartR.From the test benches to the first prototype of the muFly micro helicopter. Journal of Intelligent and Robotic Systems2008; 54: 245–260.
11.
EscareñoJSanchezAGarciaOLozanoR.Modeling and global control of the longitudinal dynamics of a coaxial convertible mini-UAV in hover mode. Journal of Intelligent and Robotic Systems2009; 54: 261–273.
12.
CaliseAJRysdykRT.Nonlinear adaptive flight control using neural networks. IEEE Controls Systems Magazine1998; 18(6): 14–25.
13.
SavranATasaltinRBecerikliY.Intelligent adaptive nonlinear flight control for a high performance aircraft with neural networks. ISA Transactions2006; 45(2): 225–247.
14.
SchafrothDBouabdallahSBermesCSiegwartR.Modeling and system identification of the muFly helicopter. Journal of Intelligent and Robotic Systems2009; 57: 27–47.
15.
SchafrothDBermesCBouabdallahSSiegwartR.Modeling, system identification and robust control of a coaxial microhelicopter. Control Engineering Practice2010; 18: 700–711.
16.
MettlerBTischlerMBKanadeT. System identification of small-size unmanned helicopter dynamics. In: American Helicopter Society 55th Annual Forum Proceedings, Montreal, Quebec, 25–27 May 1999, Vol. 2, pp.1706–1717.
17.
ParkDParkMSHongSK.A study on the 3-DOF attitude control of free-flying vehicle. In: IEEE international symposium on industrial electronics, Pusan, South Korea, 12–16 June 2001, Vol. 2, pp.1260–1265.
18.
FoleaSGhercioiuM.Tag4M, a Wi-Fi RFIDActive tag optimized for sensor measurements. In: TurcuCristina (ed) Radio Frequency Identification Fundamentals and Applications Design Methods and Solutions. New York: InTech Education and Publishing, 2010, pp.287–310.
19.
KumarRGanguliROmkarSN.Rotorcraft parameter estimation using radial basis function neural network. Applied Mathematics and Computation2010; 216(2): 584–597.
20.
San MartinRBarrientosAGutierrezPdel CerroJ. Unmanned aerial vehicle (UAV) modelling based on supervised neural networks. In: IEEE international conference on robotics and automation, Orlando, FL, 15–19 May 2006, pp.2497–2502.
21.
MettlerBKanadeTTischlerMB.System identification modeling of a model-scale helicopter. Working paper CMU-RI-TR-00-03, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, 2000.
22.
AdiprawitaWAhmadASSembiringJ.Automated flight test and system identification for rotary wing small aerial platform using frequency responses analysis. Journal of Bionic Engineering2007; 4: 237–244.
23.
VelagiJOsminN. Identification and control of 2DOF nonlinear helicopter model using intelligent methods. In: IEEE International conference on systems man and cybernetics (SMC), Istanbul, Turkey, 10–13 October 2010, pp.2267–2275.
24.
GerigM.Modeling, guidance, and control of aerobatics maneuvers of an autonomous helicopter. PhD Diss, Eidgenössische Technische Hochschule ETH Zürich, 2008.
25.
LjungL.System identification: Theory for the user. Upper Saddle River, NJ: Prentice-Hall, 1987.
26.
McKelveyT.Relations between time domain and frequency domain prediction error methods. In: Encyclopedia of Life Support Systems (EOLSS) (Vol. V, Control systems, robotics, and automation), 2004. Available at: http://www.eolss.net. Accessed August 2010.
27.
HornikKStincombeMWhiteH.Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural Networks1990; 3: 211–223.
28.
NarendraKSParthasarathyK.Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks1990; 1(1): 4–27.
29.
SureshSKumarMVOmkarSNManiVSampathP. Neural networks based identification of helicopter dynamics using flight data. In: Proceedings of the 9th international conference on neural information processing, Singapore, 18–22 November 2002, Vol. 1, pp.10–14.
