Event-triggered adaptive neural command-filter-based dynamic surface control for state constrained nonlinear systems

Yu Hua, Tianping Zhang and Xiaonan Xia

Applied Mathematics and Computation, 2022, vol. 434, issue C

Abstract: In this article, an event-triggered adaptive neural command-filter-based dynamic surface control (CFDSC) is discussed for states constrained nonstrict-feedback nonlinear systems with dynamic uncertainties. The order-reduced compensation signals are designed to compensate the tracking error of the filter in original dynamic surface control (DSC). The hyperbolic tangent function is adopted as the invertible mapping to deal with the full state constraints. An auxiliary signal is employed to estimate the dynamical uncertainties produced by the unmodeled dynamics. The unknown smooth nonlinear terms are approximated by radial basis function neural networks (RBFNNs) at recursive each step. An event-triggered input is constructed in this novel CFDSC framework. With the help of the defined compact set in the stability analysis, the semi-global uniform ultimate boundedness (SGUUB) of all the signals in the adaptive closed-loop system is proved. Moreover, each state can be strictly limited within the time-varying state conditions. Two simulation verifications are employed to verify and clarify the theoretical findings.

Keywords: Nonlinear mapping; Command filter; Dynamic surface control; Time-varying constraint; Unmodeled dynamics; Event-triggered input (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:434:y:2022:i:c:s0096300322005148

DOI: 10.1016/j.amc.2022.127440

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