Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/77659
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
Type: | Journal article |
Title: | Neural-network-based decentralized adaptive output-feedback control for large-scale stochastic nonlinear systems |
Author: | Zhou, Q. Shi, P. Liu, H. Xu, S. |
Citation: | IEEE Transactions on Cybernetics, 2012; 42(6):1608-1619 |
Publisher: | IEEE-Inst Electrical Electronics Engineers Inc |
Issue Date: | 2012 |
ISSN: | 1083-4419 1941-0492 |
Statement of Responsibility: | Qi Zhou, Peng Shi, Honghai Liu and Shengyuan Xu |
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. |
Keywords: | Adaptive control backstepping decentralized control dynamic surface control neural network (NN) stochastic nonlinear systems |
Rights: | © 2012 IEEE |
DOI: | 10.1109/TSMCB.2012.2196432 |
Published version: | http://dx.doi.org/10.1109/tsmcb.2012.2196432 |
Appears in Collections: | Aurora harvest Electrical and Electronic Engineering publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.