Adaptive neural tracking control for nonstrict-feedback stochastic nonlinear time-delay systems with full-state constraints

Wenjie Si, Xunde Dong and Feifei Yang

International Journal of Systems Science, 2017, vol. 48, issue 14, 3018-3031

Abstract: An adaptive neural tracking control is investigated for a class of nonstrict-feedback stochastic nonlinear time-delay systems with full-state constraints and saturation input. First, the continuous differentiable saturation model is employed to ensure the input constraint, and a barrier Lyapunov function is designed to achieve the full-state constraint. Second, the appropriate Lyapunov–Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown time-delay terms, and neural networks are employed to approximate the unknown nonlinearities. Finally, based on Lyapunov stability theory, an adaptive controller is proposed to guarantee that all the signals in the closed-loop system are 4-Moment (or 2-Moment) semi-globally uniformly ultimately bounded and the tracking error converges to a small neighbourhood of the origin. Two examples are shown to further demonstrate the effectiveness of the proposed control scheme.

Date: 2017
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DOI: 10.1080/00207721.2017.1367049

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