Please use this identifier to cite or link to this item: https://doi.org/10.1109/CDC.2008.4739240
Title: Adaptive neural control for uncertain nonlinear systems in pure-feedback form with hysteresis input
Authors: Ren, B. 
Ge, S.S. 
Lee, T.H. 
Su, C.-Y.
Issue Date: 2008
Source: Ren, B., Ge, S.S., Lee, T.H., Su, C.-Y. (2008). Adaptive neural control for uncertain nonlinear systems in pure-feedback form with hysteresis input. Proceedings of the IEEE Conference on Decision and Control : 86-91. ScholarBank@NUS Repository. https://doi.org/10.1109/CDC.2008.4739240
Abstract: In this paper, adaptive neural control is investigated for a class of unknown nonlinear systems in pure-feedback form with the generalized Prandtl-Ishlinskii hysteresis input. The non-affine problem both in the pure-feedback form and in the generalized Prandtl-Ishlinskii hysteresis input function is solved by adopting the Mean Value Theorem. By utilizing Lyapunov synthesis, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded (SGUUB), and the tracking error converges to a small neighborhood of zero. Simulation results are provided to illustrate the performance of the proposed approach. © 2008 IEEE.
Source Title: Proceedings of the IEEE Conference on Decision and Control
URI: http://scholarbank.nus.edu.sg/handle/10635/69191
ISBN: 9781424431243
ISSN: 01912216
DOI: 10.1109/CDC.2008.4739240
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