Please use this identifier to cite or link to this item: https://doi.org/10.1109/ISIC.2006.285583
Title: Robust adaptive neural control of SISO nonlinear systems with unknown dead-zone and completely unknown control gain
Authors: Zhang, T.
Ge, S.S. 
Issue Date: 2006
Source: Zhang, T.,Ge, S.S. (2006). Robust adaptive neural control of SISO nonlinear systems with unknown dead-zone and completely unknown control gain. IEEE International Symposium on Intelligent Control - Proceedings : 88-93. ScholarBank@NUS Repository. https://doi.org/10.1109/ISIC.2006.285583
Abstract: In this paper, robust adaptive neural tracking control is developed for a class of uncertain SISO nonlinear systems in a Brunovsky form with unknown nonlinear deadzone and unknown control gain & its sign. The design is based on the principle of sliding mode control and the use of Nussbaum-type function in solving the problem of the completely unknown function control gain. A novel description of general nonlinear dead-zone, which makes the control system design possible, is introduced by using the mean value theorem. The approach removes the condition of the equal slope with defined region for the dead-zone. By utilizing the integral-type Lyapunov function and introducing an adaptive compensation for the upper bound of the optimal approximation error and the dead-zone disturbance, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded. © 2006 IEEE.
Source Title: IEEE International Symposium on Intelligent Control - Proceedings
URI: http://scholarbank.nus.edu.sg/handle/10635/71653
ISBN: 0780397983
DOI: 10.1109/ISIC.2006.285583
Appears in Collections:Staff Publications

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