Title
Direct adaptive regulation of unknown nonlinear dynamical systems via dynamic neural networks
Abstract
A direct nonlinear adaptive state regulator, for unknown dynamical systems that are modeled by dynamic neural networks is discussed. In the ideal case of complete model matching, convergence of the state to zero plus boundedness of all signals in the closed loop is ensured. Moreover, the behavior of the closed loop system is analyzed for cases in which the true plant differs from the dynamic neural network model in the sence that it is of higher order, or due to the presence of a modeling error term. In both cases, modifications of the original control and update laws are provided, so that at least uniform ultimate boundedness is guaranteed, even though in some cases the stability results obtained for the ideal case are retained.
Year
DOI
Venue
1995
10.1109/21.478446
IEEE Transactions on Systems, Man and Cybernetics
Keywords
Field
DocType
adaptive control,control system analysis,data structures,fuzzy control,identification,model reference adaptive control systems,stability,Stone Weierstrass theorem,convergence,data representation,fuzzy basis function expansion,fuzzy model-reference adaptive control,fuzzy-MRAC,identification,prediction error,stability,tracking error
Convergence (routing),Regulator,Mathematical optimization,Nonlinear system,Control theory,Computer science,Nonlinear dynamical systems,Dynamical systems theory,Adaptive neuro fuzzy inference system,Adaptive control,Artificial neural network
Journal
Volume
Issue
ISSN
25
12
0018-9472
Citations 
PageRank 
References 
66
5.00
12
Authors
2
Name
Order
Citations
PageRank
George A. Rovithakis174945.73
manolis a christodoulou246159.94