Please use this identifier to cite or link to this item: https://doi.org/10.1109/TNN.2009.2016959
Title: Adaptive neural control for a class of nonlinear systems with uncertain hysteresis inputs and time-varying state delays
Authors: Ren, B. 
Ge, S.S. 
Lee, T.H. 
Su, C.-Y.
Keywords: Neural networks (NNs)
Prandtl-Ishlinskii (PI) hysteresis model
Time-varying delays
Variable structure control
Issue Date: 2009
Source: Ren, B., Ge, S.S., Lee, T.H., Su, C.-Y. (2009). Adaptive neural control for a class of nonlinear systems with uncertain hysteresis inputs and time-varying state delays. IEEE Transactions on Neural Networks 20 (7) : 1148-1164. ScholarBank@NUS Repository. https://doi.org/10.1109/TNN.2009.2016959
Abstract: In this paper, adaptive variable structure neural control is investigated for a class of nonlinear systems under the effects of time-varying state delays and uncertain hysteresis inputs. The unknown time-varying delay uncertainties are compensated for using appropriate Lyapunov-Krasovskii functionals in the design, and the effect of the uncertain hysteresis with the Prandtl-Ishlinskii (PI) model representation is also mitigated using the proposed control. By utilizing the integral-type Lyapunov function, the closed-loop control system is proved to be semiglobally uniformly ultimately bounded (SGUUB). Extensive simulation results demonstrate the effectiveness of the proposed approach. © 2009 IEEE.
Source Title: IEEE Transactions on Neural Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/54907
ISSN: 10459227
DOI: 10.1109/TNN.2009.2016959
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