research-article

Broad-learning recurrent Hermite neural control for unknown nonlinear systems

Authors:

Bing-Fei WuAuthors Info & Claims

Volume 242, Issue C

https://doi.org/10.1016/j.knosys.2022.108263

Published: 22 April 2022 Publication History

Abstract

The broad-learning systems (BLS) with advance control theories have been studied, but found to have two disadvantages: one is that the calculations are too complicated and the other is that the convergence time cannot be guaranteed. In order to mitigate the high computational loading, this study proposes a broad-learning Hermite neural network (BHNN), which has the capability of dynamic mapping and reduces the structural complexity of neural network. Meanwhile, a broad-learning recurrent Hermite neural control (BRHNC) system is proposed while maintaining finite-time stability to speed up the tracking error convergence. The proposed BRHNC system comprises two controllers: a recurrent broad controller that utilizes a BHNN to approximate on-line an ideal finite-time controller and a robust exponential controller that ensures system stability through a Lyapunov function. Meanwhile, the BHNN’s full-tuned parameter learning laws are developed to increase the approximating capacity, learning capacity and accuracy using gradient descent method. Finally, simulation and experimental results show that the BRHNC system has good control, tracking and disturbance rejection properties, while the BRHNC system requires no prior knowledge about the system dynamics.

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Cited By

Li LZhou SChao FChang XYang LYu XShang CShen Q(2023)Model compression optimized neural network controller for nonlinear systemsKnowledge-Based Systems10.1016/j.knosys.2023.110311265:COnline publication date: 8-Apr-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110311
Wang ZLi LChao FLin CYang LZhou CChang XShang CShen Q(2022)A Type 2 wavelet brain emotional learning network with double recurrent loops based controller for nonlinear systemsKnowledge-Based Systems10.1016/j.knosys.2022.109274251:COnline publication date: 5-Sep-2022
https://dl.acm.org/doi/10.1016/j.knosys.2022.109274
Jia HLuo DWang JShen H(2022)Fixed-time synchronization for inertial Cohen–Grossberg delayed neural networksKnowledge-Based Systems10.1016/j.knosys.2022.109104250:COnline publication date: 22-Jun-2022
https://dl.acm.org/doi/10.1016/j.knosys.2022.109104

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Broad-learning recurrent Hermite neural control for unknown nonlinear systems

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cover image Knowledge-Based Systems

Knowledge-Based Systems Volume 242, Issue C

Apr 2022

848 pages

ISSN:0950-7051

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Copyright © 2022.

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Elsevier Science Publishers B. V.

Netherlands

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Published: 22 April 2022

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Cited By

Li LZhou SChao FChang XYang LYu XShang CShen Q(2023)Model compression optimized neural network controller for nonlinear systemsKnowledge-Based Systems10.1016/j.knosys.2023.110311265:COnline publication date: 8-Apr-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110311
Wang ZLi LChao FLin CYang LZhou CChang XShang CShen Q(2022)A Type 2 wavelet brain emotional learning network with double recurrent loops based controller for nonlinear systemsKnowledge-Based Systems10.1016/j.knosys.2022.109274251:COnline publication date: 5-Sep-2022
https://dl.acm.org/doi/10.1016/j.knosys.2022.109274
Jia HLuo DWang JShen H(2022)Fixed-time synchronization for inertial Cohen–Grossberg delayed neural networksKnowledge-Based Systems10.1016/j.knosys.2022.109104250:COnline publication date: 22-Jun-2022
https://dl.acm.org/doi/10.1016/j.knosys.2022.109104

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