research-article

Neural network based adaptive prescribed performance control for a class of switched nonlinear systems

Authors:

Jun ZhaoAuthors Info & Claims

Neurocomputing, Volume 230, Issue C

Pages 316 - 321

https://doi.org/10.1016/j.neucom.2016.12.011

Published: 22 March 2017 Publication History

Abstract

In this paper, an adaptive prescribed performance control problem is studied for a class of switched uncertain nonlinear systems under arbitrary switching signals. Radial basis function neural networks (RBFNNs) are employed to approximate the unknown terms. By introducing the transformation errors, the original system with prescribed performance constraints are transformed into a class of new systems without constraints. Then, we design controllers to stabilize the transformed system. In this way, the original system is stabilizable and the prescribed performance is guaranteed. Update laws are designed such that the parameter estimation is fixed until its corresponding subsystem is active. Compared with the existing results, the main contributions of this paper are characterized as follows: (1) a PPC approach is proposed for a class of switched uncertain nonlinear systems in a general form and (2) RBFNNs are used to approximate the unknown terms of each subsystem. In the end, a simulation example is developed to illustrate the effectiveness of the present approach.

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Cited By

He KTang L(2024)Adaptive Event-Triggered Finite-Time Control for Switched Nonlinear SystemsIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2023.330607432:3(745-754)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1109/TFUZZ.2023.3306074
Tang LHe KLiu Y(2023)Adaptive Output Feedback Fuzzy Event-Triggered Control for Fractional-Order Nonlinear Switched SystemsIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2023.325807431:10(3459-3469)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1109/TFUZZ.2023.3258074
Chen L(2018)Asymmetric prescribed performance‐barrier Lyapunov function for the adaptive dynamic surface control of unknown pure‐feedback nonlinear switched systems with output constraintsInternational Journal of Adaptive Control and Signal Processing10.1002/acs.292132:10(1417-1439)Online publication date: 9-Oct-2018
https://dl.acm.org/doi/10.1002/acs.2921
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Neural network based adaptive prescribed performance control for a class of switched nonlinear systems
1. Computing methodologies
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Published In

cover image Neurocomputing

Neurocomputing Volume 230, Issue C

March 2017

434 pages

ISSN:0925-2312

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Copyright © Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 22 March 2017

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Cited By

He KTang L(2024)Adaptive Event-Triggered Finite-Time Control for Switched Nonlinear SystemsIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2023.330607432:3(745-754)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1109/TFUZZ.2023.3306074
Tang LHe KLiu Y(2023)Adaptive Output Feedback Fuzzy Event-Triggered Control for Fractional-Order Nonlinear Switched SystemsIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2023.325807431:10(3459-3469)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1109/TFUZZ.2023.3258074
Chen L(2018)Asymmetric prescribed performance‐barrier Lyapunov function for the adaptive dynamic surface control of unknown pure‐feedback nonlinear switched systems with output constraintsInternational Journal of Adaptive Control and Signal Processing10.1002/acs.292132:10(1417-1439)Online publication date: 9-Oct-2018
https://dl.acm.org/doi/10.1002/acs.2921
Wang JZhu LSi X(2017)Adaptive Neural Tracking Control for Discrete-Time Switched Nonlinear Systems with Dead Zone InputsComplexity10.1155/2017/56289322017Online publication date: 23-Mar-2017
https://dl.acm.org/doi/10.1155/2017/5628932

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