TY - JOUR
T1 - Quantized Decentralized Adaptive Neural Network PI Tracking Control for Uncertain Interconnected Nonlinear Systems with Dynamic Uncertainties
AU - Sun, Haibin
AU - Zong, Guangdeng
AU - Ahn, Choon Ki
N1 - 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.
PY - 2021/5
Y1 - 2021/5
N2 - 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.
AB - 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.
KW - Decentralized control
KW - input quantization
KW - interconnected system
KW - neural network-based control
KW - nontriangular form
KW - proportional-integral (PI) tracking controller
UR - http://www.scopus.com/inward/record.url?scp=85104409421&partnerID=8YFLogxK
U2 - 10.1109/TSMC.2019.2918142
DO - 10.1109/TSMC.2019.2918142
M3 - Article
AN - SCOPUS:85104409421
SN - 2168-2216
VL - 51
SP - 3111
EP - 3124
JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems
JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems
IS - 5
M1 - 8736513
ER -