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http://dx.doi.org/10.5302/J.ICROS.2006.12.3.233

Adaptive Backstepping Control Using Self Recurrent Wavelet Neural Network for Stable Walking of the Biped Robots  

Yoo Sung-Jin (연세대학교 전기전자공학과)
Park Jin-Bae (연세대학교 전기전자공학과)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.12, no.3, 2006 , pp. 233-240 More about this Journal
Abstract
This paper presents the robust control method using a self recurrent wavelet neural network (SRWNN) via adaptive backstepping design technique for stable walking of biped robots with unknown model uncertainties. The SRWNN, which has the properties such as fast convergence and simple structure, is used as the uncertainty observer of the biped robots. The adaptation laws for weights of the SRWNN and reconstruction error compensator are induced from the Lyapunov stability theorem, which are used for on-line controlling biped robots. Computer simulations of a five-link biped robot with unknown model uncertainties verify the validity of the proposed control system.
Keywords
adaptive backstepping control; self recurrent wavelet neural network; biped robot;
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Times Cited By KSCI : 1  (Citation Analysis)
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