Please use this identifier to cite or link to this item: https://doi.org/10.1049/iet-cta.2010.0055
Title: Adaptive neural network output feedback control for a class of non-affine non-linear systems with unmodelled dynamics
Authors: Du, H.
Ge, S.S. 
Liu, J.K.
Issue Date: 17-Feb-2011
Source: Du, H.,Ge, S.S.,Liu, J.K. (2011-02-17). Adaptive neural network output feedback control for a class of non-affine non-linear systems with unmodelled dynamics. IET Control Theory and Applications 5 (3) : 465-477. ScholarBank@NUS Repository. https://doi.org/10.1049/iet-cta.2010.0055
Abstract: In this study, an output feedback-based adaptive neural controller is presented for a class of uncertain non-affine pure-feedback non-linear systems with unmodelled dynamics. Two major technical difficulties for this class of systems lie in: (i) the few choices of mathematical tools in handling the non-affine appearance of control in the systems, and (ii) the unknown control direction embedded in the unknown control gain functions, in great contrast to the standard assumptions of constants or bounded time-varying coefficients. By exploring the new properties of Nussbaum gain functions, stable adaptive neural network control is possible for this class of systems by using a strictly positive-realness-based filter design. The closed-loop system is proven to be semi-globally uniformly ultimately bounded, and the regulation error converges to a small neighbourhood of the origin. The effectiveness of the proposed design is verified by simulations. © 2011 The Institution of Engineering and Technology.
Source Title: IET Control Theory and Applications
URI: http://scholarbank.nus.edu.sg/handle/10635/54920
ISSN: 17518644
DOI: 10.1049/iet-cta.2010.0055
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

32
checked on Dec 13, 2017

WEB OF SCIENCETM
Citations

29
checked on Nov 11, 2017

Page view(s)

30
checked on Dec 9, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.