Neural network based adaptive dynamic surface control for flight path angle

@article{Guo2012NeuralNB,
  title={Neural network based adaptive dynamic surface control for flight path angle},
  author={Yi Guo and Jinkun Liu},
  journal={2012 IEEE 51st IEEE Conference on Decision and Control (CDC)},
  year={2012},
  pages={5374-5379}
}
A neural network based adaptive dynamic surface control is proposed for the aircraft longitudinal flight path angle. The dynamic surface control method eliminates the problem of “explosion of complexity” existing in traditional backstepping approach with the introduction of low pass filters. Radial basis function (RBF) neural networks are used to approximate the unknown nonlinearities of the model online. Adaptive laws are designed to estimate the weight values of the neural networks and… CONTINUE READING

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SHOWING 1-10 OF 16 REFERENCES

Robust minimax optimal control of nonlinear uncertain systems using feedback linearization with application to hypersonic flight vehicles

  • Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference
  • 2009
VIEW 1 EXCERPT

Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strictfeedback form

D. Wang, J. Huang
  • IEEE Trans. Neural Networks, Vol. 16, No. 1, pp. 195-202, 2005.
  • 2005
VIEW 2 EXCERPTS

Development of a reconfigurable flight control law for the X-36 tailless fighter aircraft

A. J. Calise, S. Lee, M. Sharama
  • Journal of Guidance, Control, and Dynamics, Vol. 24, No. 5, pp. 896-902, 2001.
  • 2001
VIEW 1 EXCERPT

A backstepping design for flight path angle control

  • Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187)
  • 2000
VIEW 2 EXCERPTS