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Neural Processing Letters
Erschienen in: Neural Processing Letters 2/2022

28.10.2021

Fixed-Time Fuzzy Adaptive Tracking Control for Output-Constrained Uncertain Nonlinear Systems in Nonstrict-Feedback Form

verfasst von: Qijia Yao

Erschienen in: Neural Processing Letters | Ausgabe 2/2022

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Abstract

In this paper, a novel fixed-time fuzzy adaptive tracking control approach is proposed for a class of output constrained uncertain nonlinear systems in nonstrict-feedback form. The proposed controller is designed by utilizing the barrier Lyapunov function (BLF) and fuzzy logic systems (FLSs) under the framework of backstepping technique. The BLF is introduced to preserve the system output always within the predefined output constraints and the FLSs are adopted to approximate the unknown nonlinear functions. The semi-global fixed-time stability of the resulting closed-loop system is theoretically proved. The proposed controller can guarantee all the closed-loop error signals converge to the adjustable small neighborhoods around zero in fixed time, while ensuring the output constraints can never be exceeded during the whole tracking process. Moreover, the proposed controller is structurally simple, which makes it suitable for practical implementations. At last, two simulation examples are performed to demonstrate the effectiveness and advantages of the proposed control approach.
Vorheriger Artikel A Dynamically Stabilized Recurrent Neural Network
Nächster Artikel Deep Recurrent Neural Network Architecture of High Order Indirect Integration Method

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Metadaten
Titel
Fixed-Time Fuzzy Adaptive Tracking Control for Output-Constrained Uncertain Nonlinear Systems in Nonstrict-Feedback Form
verfasst von
Qijia Yao
Publikationsdatum
28.10.2021
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2022
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-021-10675-8

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