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|Title:||Adaptive neural control for a class of nonlinear systems with uncertain hysteresis inputs and time-varying state delays||Authors:||Ren, B.
|Keywords:||Neural networks (NNs)
Prandtl-Ishlinskii (PI) hysteresis model
Variable structure control
|Issue Date:||2009||Citation:||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|
|Appears in Collections:||Staff Publications|
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