research-article

Intelligent wavelet fuzzy brain emotional controller using dual function-link network for uncertain nonlinear control systems

Authors:

Nguyen-Quoc-Khanh Le,

Ngoc Phi Nguyen,

Fei ChaoAuthors Info & Claims

Applied Intelligence, Volume 52, Issue 3

Pages 2720 - 2744

https://doi.org/10.1007/s10489-021-02482-4

Published: 01 February 2022 Publication History

Abstract

This study aims to propose a more efficient hybrid algorithm to achieve favorable control performance for uncertain nonlinear systems. The proposed algorithm comprises a dual function-link network-based multilayer wavelet fuzzy brain emotional controller and a sign(.) functional compensator. The proposed algorithm estimates the judgment and emotion of a brain that includes two fuzzy inference systems for the amygdala network and the prefrontal cortex network via using a dual-function-link network and three sub-structures. Three sub-structures are a dual-function-link network, an amygdala network, and a prefrontal cortex network. Particularly, the dual-function-link network is used to adjust the amygdala and orbitofrontal weights separately so that the proposed algorithm can efficiently reduce the tracking error, follow the reference signal well, and achieve good performance. A Lyapunov stability function is used to determine the adaptive laws, which are used to efficiently tune the system parameters online. Simulation and experimental studies for an antilock braking system and a magnetic levitation system are presented to verify the effectiveness and advantage of the proposed algorithm.

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Cited By

Kanungo AChoubey CGupta VKumar PKumar N(2023)Design of an intelligent wavelet-based fuzzy adaptive PID control for brushless motorMultimedia Tools and Applications10.1007/s11042-023-14872-682:21(33203-33223)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1007/s11042-023-14872-6
Pan WLiu S(2023)Deep reinforcement learning for the dynamic and uncertain vehicle routing problemApplied Intelligence10.1007/s10489-022-03456-w53:1(405-422)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1007/s10489-022-03456-w
Hsu CChen BWu B(2022)Broad-learning recurrent Hermite neural control for unknown nonlinear systemsKnowledge-Based Systems10.1016/j.knosys.2022.108263242:COnline publication date: 22-Apr-2022
https://dl.acm.org/doi/10.1016/j.knosys.2022.108263

Index Terms

Intelligent wavelet fuzzy brain emotional controller using dual function-link network for uncertain nonlinear control systems
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
      1. Computational control theory
    2. Knowledge representation and reasoning
      1. Probabilistic reasoning
      2. Vagueness and fuzzy logic
  2. Machine learning
    1. Machine learning approaches
2. Mathematics of computing
  1. Mathematical analysis

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Applied Intelligence

Applied Intelligence Volume 52, Issue 3

Feb 2022

1148 pages

ISSN:0924-669X

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© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 February 2022

Accepted: 28 April 2021

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Cited By

Kanungo AChoubey CGupta VKumar PKumar N(2023)Design of an intelligent wavelet-based fuzzy adaptive PID control for brushless motorMultimedia Tools and Applications10.1007/s11042-023-14872-682:21(33203-33223)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1007/s11042-023-14872-6
Pan WLiu S(2023)Deep reinforcement learning for the dynamic and uncertain vehicle routing problemApplied Intelligence10.1007/s10489-022-03456-w53:1(405-422)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1007/s10489-022-03456-w
Hsu CChen BWu B(2022)Broad-learning recurrent Hermite neural control for unknown nonlinear systemsKnowledge-Based Systems10.1016/j.knosys.2022.108263242:COnline publication date: 22-Apr-2022
https://dl.acm.org/doi/10.1016/j.knosys.2022.108263

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