Paper Info
Title
Robust redesign of a neural network controller in the presence of unmodeled dynamics.
Abstract
This paper presents a neural network control redesign, which achieves robust stabilization in the presence of unmodeled dynamics restricted to be input to output practically stable (IOpS), without requiring any prior knowledge on any bounding function. Moreover, the state of the unmodeled dynamics is permitted to go unbounded provided that the nominal system state and/or the control input also go unbounded. The neural network controller is equipped with a resetting strategy to deal with the problem of possible division by zero, which may appear since we consider unknown input vector fields with unknown signs. The uniform ultimate boundedness of the system output to an arbitrarily small set, plus the boundedness of all other signals in the closed-loop is guaranteed.
Year
DOI
Venue
2004
10.1109/TNN.2004.837782
IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council
Keywords
Field
DocType
robust adaptive control,unmodeled dynamics,neurocontrollers,input to output practically stable,robust stabilization,control system synthesis,robust control,nonlinear dynamical systems,nonlinear systems,resetting strategy,neural control,uniform ultimate boundedness,closed loop systems,neural network controller redesign
Division by zero,Nonlinear system,IOPS,Control theory,Computer science,Stochastic process,Artificial neural network,Robust control,Small set,Bounding overwatch
Journal
Volume
Issue
ISSN
15
6
1045-9227
Citations 
PageRank 
References 
15
1.55
22
Authors
1
Name
Order
Citations
PageRank
George A. Rovithakis158152.21