JRM Vol.23 No.6 pp. 1091-1099
doi: 10.20965/jrm.2011.p1091


Improved Stability Using Environmental Adaptive Yaw Control for Autonomous Unmanned Helicopter and Bifurcation of Maneuvering in Turning

Hiroaki Nakanishi*, Sayaka Kanata**, and Tetsuo Sawaragi*

*Department of Mechanical Engineering and Science, Graduate School of Engineering, Kyoto University, Yoshidahonmachi, Sakyo-ku, Kyoto 606-8501, Japan

**Department of Aerospace Engineering, Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, Sakai, Osaka 599-8531, Japan

October 8, 2010
July 7, 2011
December 20, 2011
unmanned helicopter, autonomous flight control, adaptive yaw control, bifurcation of turning maneuver

Adaptation to environmental changes, such as wind, plays a very important role in improving the reliability of autonomous unmanned helicopters. Adaptive yaw (heading) control for an autonomous helicopter is discussed in this paper. The control structure is based on a hierarchal scheme that utilizes an inner yaw feedback control loop plus an outer feedback loop. The outer loop estimates the direction of the airspeed using roll angle and roll angular rate. Stable coupling in yaw and roll motion is induced by the proposed controller to improve the stability of the helicopter’s flight. Turning utilizing the proposed adaptive control system is discussed in particular. Results of flight experiments show that bifurcation of the helicopter’smaneuvering in turning occurs depending on airspeed. The results indicate that the autonomous unmanned helicopter can select a turning maneuver that is suitable for the environmental conditions, thus stabilizing its flight.

Cite this article as:
Hiroaki Nakanishi, Sayaka Kanata, and Tetsuo Sawaragi, “Improved Stability Using Environmental Adaptive Yaw Control for Autonomous Unmanned Helicopter and Bifurcation of Maneuvering in Turning,” J. Robot. Mechatron., Vol.23, No.6, pp. 1091-1099, 2011.
Data files:
  1. [1] S. Tadokoro et al., “Rescue Robotics: DDT Project on Robots and Systems for Urban Search and Rescue,” Springer, 2009.
  2. [2] H. Nakanishi, H. Hashimoto, N. Hosokawa, K. Inoue, and A. Sato, “Autonomous Flight Control System for Intelligent Aero-Robot for Disaster Prevention,” J. of Robotics and Mechatronics, Vol.15, No.5, pp. 489-497, 2003.
  3. [3] J. Leitner, A. Calise, and J. V. R. Prased, “Analysis of Adaptive Neural Networks for Helicopter Flight Control,” J. of Guidance, Control, and Dynamics, Vol.20, No.5, pp. 972-979, 1997.
  4. [4] H. Nakanishi, S. Kanata, T. Sawaragi, and Y. Horiguchi, “Environment Adaptive Heading Control for an Autonomous Unmanned Helicopter,” Trans. of the Society of Instrument and Control Engineers, Vol.46, No.1, pp. 8-15, 2010.
  5. [5] B. Mettler, “Identification Modeling and Characteristics of Miniature Rotorcraft,” Kluwer, 2003.
  6. [6] A. Sato, “Research, Development and Civil Application of an Autonomous Unmanned Helicopter,” Proc. of AHS-International Forum, Vol.57, 2001.
  7. [7] O. Amidi, “An Autonomous Vision-Guided Helicopter,” Ph.D. Thesis, Department of Electrical and Computer Engineering Carnegie Mellon University, August, 1996.
  8. [8] A. Ollero et al, “Multiple Eyes in the Skies - Architecture and Perception Issues in the COMETS Unmanned Air Vehicles Project,” IEEE Robotics and Automation Magazine, June, pp. 46-57, 2005.
  9. [9] B. Etkin and L. D. Reid, “Dynamics of Flight - Stability and Control - 3rd. Edition,” John Wiley&Sons Ltd., 1996.

  10. Supporting Online Materials:
  11. [a]

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Mar. 05, 2021