Paper Info
Title
Prescribed Performance Output Feedback/Observer-Free Robust Adaptive Control of Uncertain Systems Using Neural Networks
Abstract
A neural network output feedback/observer-free continuous controller for multiple-input–multiple-output uncertain nonlinear systems is designed, which is capable of guaranteeing prescribed performance bounds on the system output, as well as the boundedness of all other closed-loop signals, despite the presence of additive external disturbances and unmodeled dynamics. The assumptions that were made concern the satisfaction of an unboundedness observability property and an output Lagrange stability condition of the unmodeled dynamics subsystem and that the nominal system is output feedback equivalent to a strictly passive one. Simulations on an induction motor system illustrate the approach.
Year
DOI
Venue
2011
10.1109/TSMCB.2011.2154328
IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society
Keywords
Field
DocType
multiple-output uncertain nonlinear system,output feedback equivalent,additive external disturbance,observer-free robust adaptive control,induction motor system,nominal system,neural network output feedback,output lagrange stability condition,unmodeled dynamics subsystem,prescribed performance output,neural networks,system output,unmodeled dynamic,uncertain systems,neural network,convergence,observability,robustness,neural nets,feedback,adaptive control,robust control,artificial neural networks,induction motors,artificial neural network
Control theory,Observability,Nonlinear system,Nonlinear control,Computer science,Control theory,Robustness (computer science),Adaptive control,Observer (quantum physics),Robust control
Journal
Volume
Issue
ISSN
41
6
1941-0492
Citations 
PageRank 
References 
18
0.76
22
Authors
2
Name
Order
Citations
PageRank
Artemis K. Kostarigka1532.53
George A. Rovithakis274945.73