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Adaptive neural control for nonstrict-feedback time-delay systems with input and output constraints

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Abstract

An adaptive tracking control is investigated for a class of nonstrict-feedback nonlinear systems with time delays subject to input saturation nonlinearity and output constraint. First, the Gaussian error function is used to express the continuous differentiable asymmetric saturation model, and a barrier Lyapunov function is designed to ensure that the output parameters are restricted. Then, an appropriate Lyapunov–Krasovskii functional is chosen to deal with the unknown time-delay terms, and the neural network is used to model the unknown nonlinearities. Finally, based on Lyapunov stability theory, an adaptive neural controller is designed to establish the closed-loop system stability. The example is provided to further illustrate the effectiveness and applicability of the proposed approach.

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Correspondence to Wenjie Si.

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Si, W., Dong, X. Adaptive neural control for nonstrict-feedback time-delay systems with input and output constraints. Int. J. Mach. Learn. & Cyber. 9, 1533–1540 (2018). https://doi.org/10.1007/s13042-017-0662-z

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  • DOI: https://doi.org/10.1007/s13042-017-0662-z

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