Quantized Decentralized Adaptive Neural Network PI Tracking Control for Uncertain Interconnected Nonlinear Systems with Dynamic Uncertainties

Haibin Sun; Guangdeng Zong; Choon Ki Ahn

doi:10.1109/TSMC.2019.2918142

Quantized Decentralized Adaptive Neural Network PI Tracking Control for Uncertain Interconnected Nonlinear Systems with Dynamic Uncertainties

Haibin Sun, Guangdeng Zong, Choon Ki Ahn

Research output: Contribution to journal › Article › peer-review

27 Citations (Scopus)

Abstract

In this paper, a decentralized adaptive neural network proportional-integral (PI) tracking control scheme is proposed for interconnected nonlinear systems with input quantization and dynamic uncertainties. This algorithm is underpinned by the use of the dynamic signal, graph theory, and function recombination to deal with the difficulties existing in the nontriangular form, unmodeled dynamics, and unknown interconnected terms. Recalling the backstepping method and neural network approximation technology, a new PI tracking controller characterized by simple structure and easy implementation is developed which ensures that all the closed-loop signals are uniformly ultimately bounded. The effectiveness of the obtained controller is exemplified via a numerical example and an application to an inverted pendulum.

Original language	English
Article number	8736513
Pages (from-to)	3111-3124
Number of pages	14
Journal	IEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume	51
Issue number	5
DOIs	https://doi.org/10.1109/TSMC.2019.2918142
Publication status	Published - 2021 May

Bibliographical note

Funding Information:
Manuscript received December 15, 2018; revised March 28, 2019; accepted May 10, 2019. Date of publication June 13, 2019; date of current version April 15, 2021. This work was supported in part by the National Natural Science Foundation of China under Grant 61773236, Grant 61773235, Grant 61873331, Grant 61803225, and Grant 61703233, in part by the Taishan Scholar Project of Shandong Province under Grant TSQN20161033 and Grant ts201712040, in part by the Post-Doctoral Science Foundation of China under Grant 2017M612236, and in part by the National Research Foundation of Korea through the Ministry of Science, ICT and Future Planning under Grant NRF-2017R1A1A1A05001325. This paper was recommended by Associate Editor Z.-G. Wu. (Corresponding author: Guangdeng Zong.) H. Sun and G. Zong are with the School of Engineering, Qufu Normal University, Rizhao 276826, China (e-mail: fengyun198212@163.com).

Publisher Copyright:
© 2013 IEEE.

Keywords

Decentralized control
input quantization
interconnected system
neural network-based control
nontriangular form
proportional-integral (PI) tracking controller

ASJC Scopus subject areas

Software
Control and Systems Engineering
Human-Computer Interaction
Computer Science Applications
Electrical and Electronic Engineering

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10.1109/TSMC.2019.2918142

Cite this

Quantized Decentralized Adaptive Neural Network PI Tracking Control for Uncertain Interconnected Nonlinear Systems with Dynamic Uncertainties. / Sun, Haibin; Zong, Guangdeng; Ahn, Choon Ki.
In: IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 51, No. 5, 8736513, 05.2021, p. 3111-3124.

Research output: Contribution to journal › Article › peer-review

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Quantized Decentralized Adaptive Neural Network PI Tracking Control for Uncertain Interconnected Nonlinear Systems with Dynamic Uncertainties

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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