Modeling Building of Miniature Unmanned Helicopter for Hovering Status Based on Local Least Square Support Vector Machine

Abstract:

Miniature unmanned helicopter (MUH) is a controlled member which is very complicated, due to their some characteristics such as highly nonlinear, close coupled, time-variation, open-loop unstable etc. The traditional method of identification is a whole model method. Although those can solve some hard problem, the time-variation is not treated well. The paper introduces a method of model building for miniature unmanned helicopter (MUH), based on local least square support vector machine. Namely the nearest samples to the predicted sample are selected online, and model building is finished by those samples with prediction. The feature of this method is that using the idea of local model building updates the model online, and the global model building brings the low ability of model generalization. In the last, compared with the traditional method of least square support vector machine in the experiment, the results show the algorithm is more effective.

• Periodical: Applied Mechanics and Materials (Volumes 48-49)
• Pages: 705-709
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.48-49.705
• Online since: February 2011
• Authors: De Gang Huang, Jian De Wu, Yu Gang Fan, Ting Feng
• Keywords: Kernel Function, Least Squares Support Vector Machines (Ls-SVM), Local, Miniature Unmanned Helicopter (MUH), Nonlinear

References

[1] USA, Office of the Secretary of Defense. Unmanned aircraft systems roadmap: 2005-2030[R]. (2005).

[2] GAVRILETSV. Autonomous aerobatic maneuvering of miniature helicopter [D]. Cambridge: Massachusetts Institute of Technology, (2003).

[3] Wang Hui, Xu Jin-fa, Gao Zheng. Design of attitude control system based on neural network to unmanned helicopter. Chinese Journal of Aeronautics [J], 2005, 26(6): 670-674.

[4] M. Vijaya Kumar, S. Suresh, S.N. Omkar, Ranjan Ganguli, Prasad Sampath. A direct adaptive neural command controller design for an unstable helicopter. Engineering Applications of Artificial Intelligence [J], 2009, 22: 181-191.

DOI: 10.1016/j.engappai.2008.07.004

[5] Kukolj D, Levi E. Identification of complex systems based on neural and Takagi-Sugeno fuzzy model [J]. IEEE Transactions on Systems, Man, and Cybernetics. Part B: Cybernetics, 2004, 34(1): 272-282.

DOI: 10.1109/tsmcb.2003.811119

[6] Jongho Shin, H. Jin Kim, Sewook Park, Youdan Kim. Model predictive flight control using adaptive support vector regression. Neurocomputing [J]. 2010, 73: 1031-1037.

DOI: 10.1016/j.neucom.2009.10.002

[7] Fang Zhou, Li Ping, Han Bo, Hou Xin, Modeling Hover Dynamics of Small-scale Unmanned Helicopter Based on Modeling Hover Dynamics of Small-scale Unmanned Helicopter Based on Least Square Support Vector Machine, Chinese Journal of Aeronautics[J], 2009, 30(8): 1508-1514.

DOI: 10.1109/chicc.2008.4605784

[8] Schaal, S., Atkeson, CG., Vijayakumar, S. Real-time robot learning with locally weighted statistical learning, International Conference on Robotics and Automation (ICRA2000). San Francisco, April (2000).

DOI: 10.1109/robot.2000.844072

[9] Wu Jiande. Research on modeling and control of mini unmanned helicopter based on frequency identification. HangZhou: ZheJiang university doctoral dissertation[D], (2007).

[10] J.A.K. Suykens, T. Van Gestel, J. De Brabanter, et al. Vandewalle. Least Squares Support Vector Machines. Singapore, World Scientific Publishing Co. Pte. Lte, (2002).

DOI: 10.1142/5089

[11] Atkeson C G, Moore A W, Schaal S. Locally weighted learning. Artificial Intelligence Review, 1997, 11: 11-73.

DOI: 10.1007/978-94-017-2053-3_2