Modeling the yaw dynamics of an unmanned helicopter through modes partition method
Source abstract
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.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Structural Health Monitoring Techniques · Vehicle Noise and Vibration Control · Hydraulic and Pneumatic Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (39)
Show source-provided references
1. Romero H, Salazar S, Lozano R. Real-Time stabilization of an eight-rotor UAV using optical flow. IEEE Trans Robot, 2009, 25(4): 809–81710.1109/TRO.2009.2018972 ↗
2. 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. 2022–202610.1109/CEC.2009.4983189 ↗
3. 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
4. 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): 3426–343110.1109/TIE.2008.926780 ↗
5. 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. 2–1810.2514/6.2008-133 ↗
6. 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. 4–1710.2514/6.2008-6523 ↗
7. 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. 3–1110.2514/6.2005-6421 ↗
8. Schafroth D, Bermes C, Bouabdallah S, et al. Modeling, system identification and robust control of a coaxial micro helicopter. Control Eng Pract, 2010, 18: 700–70410.1016/j.conengprac.2010.02.004 ↗
9. 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): 534–54310.1007/s12555-010-0306-5 ↗
10. 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
11. 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. 7421–742610.3182/20080706-5-KR-1001.01254 ↗
12. Chen H S, Chen D R. Identification of a model helicopter’s yaw dynamics. J Dyn Syst-T ASME, 2005, 127: 140–14310.1115/1.1849243 ↗
13. 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): 43–5610.1007/s10846-010-9487-7 ↗
14. Kumar R, Ganguli R, Omkar S N. Rotorcraft parameter identification from real time flight data. J Aircraft, 2008, 45: 335–340
15. Verscheure D, Sharf I, Bruyninckx H, et al, Identification of contact dynamics parameters for stiff robotic payloads. IEEE T Robot, 2009, 25(2): 240–250310.1109/TRO.2009.2014126 ↗
16. 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
17. 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
18. 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): 1149–115910.1080/0020717042000274527 ↗
19. 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: 347–36010.1007/s10846-009-9320-3 ↗
20. 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
21. 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
22. 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
23. 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): 142–14910.1016/S1672-6529(09)60200-6 ↗
24. 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): 439–44210.3724/SP.J.1218.2010.00439 ↗
25. Tischler M B, Remple R K. Aircraft and Rotorcraft System Identification. Reston: American Institute of Aeronautics and Astronautics, 2006. 83–450
26. Ivler C M, Tischler M B. System identification modeling for flight control design. In: RAES Rotorcraft Handling-Qualities Conference. Liverpool, UK, 2008. 4–19
27. 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
28. 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. 1–1810.2514/6.2008-6897 ↗
29. 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: 237–23910.1016/S1672-6529(07)60037-7 ↗
30. 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: 223–22710.1007/s10846-008-9295-5 ↗
31. 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. 1720–172510.1109/ISIE.2009.5213061 ↗
32. Leishman J G. Principles of Helicopter Aerodynamics II. Cambridge: Cambridge University Press, 2006. 257–684
33. 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. 1–2410.2514/6.2008-7414 ↗
34. Seddon J, Newman S. Basic Helicopter Aerodynamics II. Reston: American Institute of Aeronautics and Astronautics, 2001. 35–92
35. 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
36. 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
37. 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
38. Alba E, Luna F, Nebro A J, et al. Parallel heterogeneous genetic algorithms for continuous optimization. Parallel Comput, 2004, 30: 699–71910.1016/j.parco.2003.12.011 ↗
39. Wang G L. Flight video. http://u.youku.com/user_show/uid_space-agle