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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 11/2018

18.03.2017 | Original Article

Observer-based adaptive fuzzy quantized tracking DSC design for MIMO nonstrict-feedback nonlinear systems

verfasst von: Shuai Sui, Shaocheng Tong

Erschienen in: Neural Computing and Applications | Ausgabe 11/2018

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Abstract

This paper concerned with the problem of observer-based adaptive fuzzy quantized tracking dynamic surface control (DSC) is investigated for the uncertain multi-input and multi-output (MIMO) nonstrict-feedback nonlinear systems, which contain unknown nonlinear functions, input quantization, and unmeasured states. By using fuzzy logic systems to identify the uncertain MIMO nonstrict-feedback nonlinear systems, a fuzzy state observer is introduced to estimate the immeasurable states. By transforming the hysteretic quantized input into a new nonlinear decomposition, and utilizing the DSC backstepping design method, a novel and less conservative fuzzy adaptive quantized tracking control approach is developed. It is shown that the proposed control scheme can guarantee the stability of the closed-loop system, and also that the system outputs can track the given desired trajectories. The simulation results are provided to verify the effectiveness of the proposed control strategy.
Vorheriger Artikel A recurrent neural network with finite-time convergence for convex quadratic bilevel programming problems
Nächster Artikel A parsimonious SVM model selection criterion for classification of real-world data sets via an adaptive population-based algorithm

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Metadaten
Titel
Observer-based adaptive fuzzy quantized tracking DSC design for MIMO nonstrict-feedback nonlinear systems
verfasst von
Shuai Sui
Shaocheng Tong
Publikationsdatum
18.03.2017
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 11/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-2929-4

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