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

Full text for this resource is not available from the Research Repository.

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)

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.

Dimensions Badge

Altmetric Badge

Item type Article
URI https://vuir.vu.edu.au/id/eprint/23147
DOI https://doi.org/10.1109/TSMCB.2012.2196432
Official URL http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arn...
Subjects Historical > Faculty/School/Research Centre/Department > College of Business
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
Citations in Scopus 267 - View on Scopus
Download/View statistics View download statistics for this item

Search Google Scholar

Repository staff login