Paper Info
Title
Tracking control of multi-input affine nonlinear dynamical systemswith unknown nonlinearities using dynamical neural networks
Abstract
The purpose of this paper is to design and rigorously analyze a tracking controller, based on a dynamic neural network model for unknown but affine in the control, multi input nonlinear dynamical systems, Lyapunov stability theory is used to guarantee a uniform ultimate boundedness property for the tracking error, as well as of all other signals in the closed loop. The controller derived is smooth. No a priori knowledge of an upper bound on the “optimal” weights and modeling errors is required. Simulation studies are used, to illustrate and clarify the theoretical results
Year
DOI
Venue
1999
10.1109/3477.752792
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Keywords
Field
DocType
simulation study,tracking error,tracking controller,dynamical neural network,theoretical result,Lyapunov stability theory,multi input nonlinear dynamical,dynamic neural network model,unknown nonlinearities,uniform ultimate boundedness property,closed loop
Affine transformation,Control theory,Mathematical optimization,Computer science,Control theory,Upper and lower bounds,A priori and a posteriori,Lyapunov stability,Adaptive control,Artificial neural network,Tracking error
Journal
Volume
Issue
ISSN
29
2
1083-4419
Citations 
PageRank 
References 
24
3.07
16
Authors
1
Name
Order
Citations
PageRank
George A. Rovithakis174945.73