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Browse Journals & Magazines > Neural Networks, IEEE Transac ...> Volume:21 Issue:2 Help

Adaptive Tracking for Periodically Time-Varying and Nonlinearly Parameterized Systems Using Multilayer Neural Networks

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2 Author(s)
Weisheng Chen ; Dept. of Appl. Math., Xidian Univ., Xi''an, China ; Licheng Jiao

This brief addresses the problem of designing adaptive neural network tracking control for a class of strict-feedback systems with unknown time-varying disturbances of known periods which nonlinearly appear in unknown functions. Multilayer neural network (MNN) and Fourier series expansion (FSE) are combined into a novel approximator to model each uncertainty in systems. Dynamic surface control (DSC) approach and integral-type Lyapunov function (ILF) technique are combined to design the control algorithm. The ultimate uniform boundedness of all closed-loop signals is guaranteed. The tracking error is proved to converge to a small residual set around the origin. Two simulation examples are provided to illustrate the feasibility of control scheme proposed in this brief.

Published in:
Neural Networks, IEEE Transactions on  (Volume:21 ,  Issue: 2 )

Date of Publication: Feb. 2010

Page(s):
345 - 351
ISSN :
1045-9227
INSPEC Accession Number:
11136027
Digital Object Identifier :
10.1109/TNN.2009.2038999
Product Type:
Journals & Magazines
Date of Publication :
12 January 2010
Date of Current Version :
05 February 2010
Issue Date :
Feb. 2010
Sponsored by :
IEEE Computational Intelligence Society
PubMed ID :
20071256

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