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Neural-network-based decentralized adaptive output-feedback control for large-scale stochastic nonlinear systems

Zhou, Qi, Shi, Peng, Liu, Honghai and Xu, Shengyuan (2012) Neural-network-based decentralized adaptive output-feedback control for large-scale stochastic nonlinear systems. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 42 (6). pp. 1608-1619. ISSN 1083-4419 (print) 1941-0492 (online)

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Abstract

This paper focuses on the problem of neural-network-based decentralized adaptive output-feedback control for a class of nonlinear strict-feedback large-scale stochastic systems. The dynamic surface control technique is used to avoid the explosion of computational complexity in the backstepping design process. A novel direct adaptive neural network approximation method is proposed to approximate the unknown and desired control input signals instead of the unknown nonlinear functions. It is shown that the designed controller can guarantee all the signals in the closed-loop system to be semiglobally uniformly ultimately bounded in a mean square. Simulation results are provided to demonstrate the effectiveness of the developed control design approach.

Item Type: Article
Uncontrolled Keywords: ResPubID25834, backstepping design process, closed-loop system, computational complexity, control design approach, control input signals, decentralised adaptive output-feedback control, direct adaptive neural network approximation method, dynamic surface control technique, mean square, nonlinear strict-feedback large-scale stochastic systems, adaptive control, backstepping, stochastic nonlinear systems, approximation methods, distributed control, neural networks
Subjects: Faculty/School/Research Centre/Department > College of Business
Depositing User: Yimin Zeng
Date Deposited: 22 Jul 2014 07:10
Last Modified: 12 Aug 2014 00:51
URI: http://vuir.vu.edu.au/id/eprint/23147
DOI: https://doi.org/10.1109/TSMCB.2012.2196432
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Citations in Scopus: 201 - View on Scopus

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