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http://dx.doi.org/10.5302/J.ICROS.2007.13.5.406

Self-Recurrent Wavelet Neural Network Based Adaptive Backstepping Control for Steering Control of an Autonomous Underwater Vehicle  

Seo, Kyoung-Cheol (연세대학교 전기전자공학과)
Yoo, Sung-Jin (연세대학교 전기전자공학과)
Park, Jin-Bae (연세대학교 전기전자공학과)
Choi, Yoon-Ho (경기대학교 전자공학부)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.13, no.5, 2007 , pp. 406-413 More about this Journal
Abstract
This paper proposes a self-recurrent wavelet neural network(SRWNN) based adaptive backstepping control technique for the robust steering control of autonomous underwater vehicles(AUVs) with unknown model uncertainties and external disturbance. The SRWNN, which has the properties such as fast convergence and simple structure, is used as the uncertainty observer of the steering model of AUV. The adaptation laws for the weights of SRWNN and reconstruction error compensator are induced from the Lyapunov stability theorem, which are used for the on-line control of AUV. Finally, simulation results for steering control of an AUV with unknown model uncertainties and external disturbance are included to illustrate the effectiveness of the proposed method.
Keywords
Self-Recurrent Wavelet Neural Network(SRWNN); adaptive backstepping control; autonomous underwater vehicle;
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