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- W2021724787 abstract "A robust adaptive neural network controller is proposed for a class of uncertain non-linear time-delay systems in strict feedback form with both completely unknown control gains and unknown non-symmetric dead-zone non-linearity based on backstepping design. The proposed design approach does not require a priori knowledge of the signs of the unknown control gains. The unknown time delays are compensated for constructing appropriate Lyapunov–Krasovskii functionals. By utilising integral Lyapunov design and sliding-mode control strategy, the controller singularity problem and the effect of dead-zone input non-linearity are avoided perfectly. From Lyapunov stability theorem, it is proved that the proposed design approach is able to guarantee semi-globally uniformly ultimately boundedness of all the signals in the closed-loop system, and the tracking error of the system is proven to be converged to a small neighbourhood of the origin. The simulation results demonstrate the effectiveness of the proposed approach." @default.
- W2021724787 created "2016-06-24" @default.
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- W2021724787 date "2011-10-13" @default.
- W2021724787 modified "2023-09-24" @default.
- W2021724787 title "Robust adaptive neural control for a class of uncertain non-linear time-delay systems with unknown dead-zone non-linearity" @default.
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- W2021724787 doi "https://doi.org/10.1049/iet-cta.2010.0633" @default.
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