A. Hussain, N. Essounbouli, A. Hamzaoui, J. Zaytoon



This paper deals with the synthesis of a Wavelet Neural Network adaptive controller for a class of second order systems. Due to its fast convergence, the wavelet neural network is used to approximate the unknown system dynamics. The proposed approximator will be on-line adjusted according to the adaptation laws deduced from the stability analysis. To ensure the robustness of the closed loop system, a modified sliding mode control signal is used. In this work, variable sliding surface is considered to reduce the starting energy without deteriorating the tracking performances. Furthermore, the knowledge of the upper bounds of both the external disturbances and the approximation errors is not needed. The global stability of the closed loop system is guaranteed in the sense of Lyapunov. Finally, a simulation example is presented to illustrate the efficiency of the developed approach.


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Paper Citation

in Harvard Style

Hussain A., Essounbouli N., Hamzaoui A. and Zaytoon J. (2007). ROBUST ADAPTIVE WAVELET NEURAL NETWORK TO CONTROL A CLASS OF NONLINEAR SYSTEMS . In Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-972-8865-82-5, pages 60-67. DOI: 10.5220/0001640400600067

in Bibtex Style

author={A. Hussain and N. Essounbouli and A. Hamzaoui and J. Zaytoon},
booktitle={Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},

in EndNote Style

JO - Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
SN - 978-972-8865-82-5
AU - Hussain A.
AU - Essounbouli N.
AU - Hamzaoui A.
AU - Zaytoon J.
PY - 2007
SP - 60
EP - 67
DO - 10.5220/0001640400600067