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• W3211349701 endingPage "1287" @default.
• W3211349701 startingPage "1274" @default.
• W3211349701 abstract "The adaptive neural tracking control problem of a class of strict-feedback nonlinear systems with unknown control directions (UCD) and full-state constraints is investigated in this paper. The neural network (NN) is adopted to identify the totally unknown nonlinear functions. In the meanwhile, by resorting to the Nussbaum gain technique, the effects caused by the UCD and output dead zone are counteracted. Given physical limits and safety demands, a novel barrier Lyapunov function (BLF)-based adaptive neural control scheme is devised for the strict-feedback nonlinear systems to ensure that the constraints are not violated during operations. Besides, a rigorous theoretical analysis has been given to indicate that all of the closed-loop signals are bounded and the tracking error achieves asymptotic convergence performance. Finally, the effectiveness and flexibility of our proposed scheme are illustrated by two numerical examples." @default.
• W3211349701 created "2021-11-22" @default.
• W3211349701 creator A5011866457 @default.
• W3211349701 creator A5023317630 @default.
• W3211349701 date "2021-11-12" @default.
• W3211349701 modified "2023-09-26" @default.
• W3211349701 title "Nussbaum gain adaptive neural asymptotic tracking of nonlinear systems with full-state constraints" @default.
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• W3211349701 doi "https://doi.org/10.1080/00207721.2021.1998720" @default.
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