Adaptive Neural Network Control of Helicopters

  • Shuzhi Sam Ge
  • Keng-Peng Tee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


In this paper, we propose robust adaptive neural network (NN) control for helicopter systems by using the Implicit Function Theorem and the Mean Value Theorem, which are useful tools for handling nonlinear nonaffine systems. We focus on single-input single-output (SISO) helicopter systems, which are exemplified by certain single-channel modes of operation, such as vertical flight and pitch regulation, and also by special conditions under which the multiple channels become decoupled. It is shown that under the proposed NN control, the output tracking error converges to a small neighbourhood of the origin, while all closed loop signals are Semi-Globally Uniformly Ultimately Bounded (SGUUB).


Unmanned Helicopter Pitch Tracking Adaptive Output Feedback Adaptive Neural Network Control Output Tracking Error 
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  1. 1.
    Sira-Ramirez, H., Zribi, M., Ahmad, S.: Dynamical Sliding Mode Control Approach for Vertical Flight Regulation in Helicopters. IEE Proc. - Control Theory Appl. 141, 19–24 (1994)MATHCrossRefGoogle Scholar
  2. 2.
    Vilchis, J.A., Brogliato, B., Dzul, A., Lozano, R.: Nonlinear Modelling and Control of Helicopters. Automatica 39, 1583–1596 (2003)MATHCrossRefGoogle Scholar
  3. 3.
    Koo, T.J., Sastry, S.: Output Tracking Control Design of a Helicopter Model Based on Approximate Linearization. In: Proc. 37th IEEE Conf. Decision & Control, Tampa, Florida, USA, pp. 3635–3640 (1998)Google Scholar
  4. 4.
    Isidori, A., Marconi, L., Serrani, A.: Robust Nonlinear Motion Control of a Helicopter. IEEE Trans. Automatic Control 48, 413–426 (2003)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Kim, N., Calise, A., Corban, J., Prasad, J.: Adaptive Output Feedback for Altitude Control of an Unmanned Helicopter Using Rotor RPM. In: Proc. AIAA Guidance, Navigation and Control Conference, pp. 3635–3640 (2004)Google Scholar
  6. 6.
    Hovakimyan, N., Nardi, F., Calise, A., Kim, N.: Adaptive Output Feedback Control of Uncertain Nonlinear Systems Using Single-Hidden-Layer Neural Networks. IEEE Trans. Neural Networks 13, 1420–1431 (2002)CrossRefGoogle Scholar
  7. 7.
    Enns, R., Si, J.: Helicopter Trimming and Tracking Control Using Direct Neural Dynamic Programming. IEEE Trans. Neural Networks 14, 929–939 (2003)CrossRefGoogle Scholar
  8. 8.
    Ge, S.S., Wang, C.: Adaptive NN Control of Uncertain Nonlinear Pure-Feedback System. Automatica 38, 671–682 (2002)MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Ge, S.S., Zhang, J.: Neural Network Control of Nonaffine Nonlinear System with Zero Dynamics by State and Output Feedback. IEEE Trans. Neural Networks 14, 900–918 (2003)CrossRefGoogle Scholar
  10. 10.
    Ge, S.S., Wang, C.: Adaptive Neural Control of Uncertain MIMO Nonlinear Systems. IEEE Trans. Neural Networks 15, 674–692 (2004)CrossRefGoogle Scholar
  11. 11.
    Apostol, T.M.: Mathematical Analysis, 2nd edn. Addison-Wesley, Reading (1974)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shuzhi Sam Ge
    • 1
  • Keng-Peng Tee
    • 1
  1. 1.Department of Electrical & Computer EngineeringNational University of SingaporeSingapore

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