Abstract
The main difficulties in modeling yaw dynamics of a helicopter arise from the high nonlinearities, cross-couplings and dynamic uncertainties of these aerocraft. This paper proposes a new identification approach for yaw dynamics modeling through modes partition method (MPM) with a concentrated search space limited by implicit human factors. Working from first principles and basic aerodynamics, the nonlinear equations of motion for yaw dynamics is derived. The model is linearized and transformed into a combination of dynamic modes, whose coefficients are identified from real-flight data through distributed genetic algorithm (DGA). The effectiveness of the approach is validated in terms of the identified model which can accurately capture the dynamic characters of the helicopter. Time- and frequency-domain results clearly demonstrate the potential of MPM in modeling such complex systems.
Similar content being viewed by others
References
Romero H, Salazar S, Lozano R. Real-Time stabilization of an eight-rotor UAV using optical flow. IEEE Trans Robot, 2009, 25(4): 809–817
Toha S F, Tokhi M O. Real-coded genetic algorithm for parametric modeling of a TRMS. In: IEEE Congress on Evolutionary Computation. Trondheim, Norway, 2009. 2022–2026
Mettler B, Tischler M B, Kanade T. System identification of small-size unmanned helicopter dynamics. In: American Helicopter Society 55th Forum. Quebec, Canada, 1999. 4–7
Cai G W, Chen B M, Peng K M, et al. Modeling and control of the yaw channel of a UAV helicopter. IEEE T Ind Electron, 2008, 55(9): 3426–3431
Saffarian M, Fahini F. A. Comprehensive kinematic analysis of a model helicopter’s actuating mechanism. In: 46th AIAA Aerospace Sciences Meeting and Exhibit. Reno, USA, 2008. 2–18
Bhandari S, Colgren R. 14-DoF linear parameter varying model of a UAV helicopter using analytical techniques. In: AIAA Modeling and Simulation Technologies Conference and Exhibit. Honolulu, USA, 2008. 4–17
Pinnamaneni M, Frye M T, Qian C J, et al. Nonlinear equations of motion in the simulation of the raptor 50 V2 remote controlled helicopter with optimal controller design. In: AIAA Modeling and Simulation Technologies Conference and Exhibit. San Francisco, USA, 2005. 3–11
Schafroth D, Bermes C, Bouabdallah S, et al. Modeling, system identification and robust control of a coaxial micro helicopter. Control Eng Pract, 2010, 18: 700–704
Song B Q, Mills J K, Liu Y H, et al. Nonlinear dynamic modeling and control of a small-scale helicopter. Int J Control Autom Syst, 2010, 8(3): 534–543
Alam M S, Tokhi M O. Modelling of a twin rotor system: a practical warm optimization approach. J Aerosp Eng, 2007, 221(1): 353–356
Samal M K, Anavatti S, Garratt M. Neural network based system identification for autonomous flight of an eagle helicopter. In: Proc of the 17th World Congress/The International Federation of Automatic Control. Seoul, Korea, 2008. 7421–7426
Chen H S, Chen D R. Identification of a model helicopter’s yaw dynamics. J Dyn Syst-T ASME, 2005, 127: 140–143
Ahmed B, Pota H R. Dynamic compensation for control of a rotary wing UAV using positive position feedback. J Intell Robot Syst, 2011 61(1): 43–56
Kumar R, Ganguli R, Omkar S N. Rotorcraft parameter identification from real time flight data. J Aircraft, 2008, 45: 335–340
Verscheure D, Sharf I, Bruyninckx H, et al, Identification of contact dynamics parameters for stiff robotic payloads. IEEE T Robot, 2009, 25(2): 240–2503
Fang Z, Li P. Grey-box modeling of a small-scale helicopter using physical knowledge and Bayesian techniques. In: 2008 10th Intentional Conference on Control, Automation, Robotics and Vision. Hanoi, Vietnam, 2008. 2096–2100
Sun T, Song Y G, Zhang C L. Subspace based system identification of small-scale helicopter flight dynamics (in Chinese). J Nnanjing Univ Aero Astro, 2008, 40(5): 589–593
Verdult V, Lovera M, Verhaegen M. Identification of linear parameter-varying state-space models with application to helicopter rotor dynamics. Int J Control, 2004, 77(13): 1149–1159
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, 2009, 56: 347–360
Wu W, Chen R L. Identification method for helicopter fully coupled flight dynamics model in hover condition (in Chinese). Acta Aeronaut Astronaut Sin, 2011, 32(2): 202–211
Song D L, Qi J T, Han J D. Model identification and active modeling control for small-size unmanned helicopters: Theory and experiment. In: AIAA Guidance, Navigation, and Control Conference. Toronto, Canada, 2010. 1–21
Zhao Z G, Lu T S. GA-based evolutionary identification of model structure for small-scale robot helicopter system. Embedded Systems Modeling—Technology and Applications. Netherlands: Springer, 2006. 151–155
Lei X S, Du Y H. A linear domain system identification for small unmanned aerial rotorcraft based on adaptive genetic algorithm. J Bionic Eng, 2010, 7(2): 142–149
Zhao Z G, Gou X F, Lv T S. GA-based model evolutionary identification for yaw channel of small-scale unmanned helicopter (in Chinese). Robot, 2010, 32(3): 439–442
Tischler M B, Remple R K. Aircraft and Rotorcraft System Identification. Reston: American Institute of Aeronautics and Astronautics, 2006. 83–450
Ivler C M, Tischler M B. System identification modeling for flight control design. In: RAES Rotorcraft Handling-Qualities Conference. Liverpool, UK, 2008. 4–19
Downs J, Prentice R, Dalzell S, et al. Control system development and flight test experience with the MQ-8B fire scout vertical take-off unmanned aerial vehicle (VTUAV). In: American Helicopter Society 63rd Annual Forum. Virginia, USA, 2007. 6
Fletcher J W. Identification of a high-order linear model of the UH-60M helicopter flight dynamics in hover. In AIAA Atmospheric Flight Mechanics Conference and Exhibit. Hawaii, USA, 2008. 1–18
Adiprawita W, Ahmad A S, Sembiring J. Automated flight test and system identification for rotary wing small aerial platform using frequency responses analysis. J Bionic Eng, 2007, 4: 237–239
Raptis I A, Valavanis K P, Moreno W A. System identification and discrete nonlinear control of miniature helicopter using backstepping. J Intell Robot Syst, 2009, 55: 223–227
Khaldi M R, Chamchoum S, El Abiad H H, et al. Plant Identification and Predictive Control of a Scaled-Model Helicopter, In: IEEE International Symposium on Industrial Electronics. Seoul, Korea, 2009. 1720–1725
Leishman J G. Principles of Helicopter Aerodynamics II. Cambridge: Cambridge University Press, 2006. 257–684
Cai G W, Chen B M, Lee T H. Comprehensive nonlinear modeling of an unmanned-aerial-vehicle helicopter. In: AIAA Guidance, Navigation and Control Conference and Exhibit. Honolulu, USA, 2008. 1–24
Seddon J, Newman S. Basic Helicopter Aerodynamics II. Reston: American Institute of Aeronautics and Astronautics, 2001. 35–92
Kim S K, Tilbury D M. Mathematical modeling and experimental identification of an unmanned helicopter robot with flybar dynamics. J Intell Robot Syst, 2004, 21(3): 96–113
Wu J D, Li P, Han B. A modeling method of miniature unmanned helicopter based on parameter identification (in Chinese). Acta Aeronaut Astronaut Sin, 2007, 28(4): 845–850
Wang Z G, Wong Y S, Rahman M. Development of a parallel optimization method based on genetic simulated annealing algorithm. Parallel Comput, 2005, 31: 846–847
Alba E, Luna F, Nebro A J, et al. Parallel heterogeneous genetic algorithms for continuous optimization. Parallel Comput, 2004, 30: 699–719
Wang G L. Flight video. http://u.youku.com/user_show/uid_space-agle
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wang, G., Xia, H., Yuan, X. et al. Modeling the yaw dynamics of an unmanned helicopter through modes partition method. Sci. China Technol. Sci. 55, 182–192 (2012). https://doi.org/10.1007/s11431-011-4576-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11431-011-4576-9