DocumentCode
1751610
Title
Stable, controllable neural control of affine uncertain systems
Author
Mears, Mark J. ; Polycarpou, Marios M.
Author_Institution
VACA, AFRL, Wright-Patterson AFB, OH, USA
Volume
5
fYear
2001
fDate
2001
Firstpage
3543
Abstract
This paper describes an approach to using neural networks as part of a control architecture that allows tracking performance to improve as the network assimilates the dynamic characteristics of the plant. Stability is guaranteed by using Lyapunov analysis and controllability is insured as the network learns. The results are shown for a scalar, affine plant where uncertainties exist in the functions representing the dynamics of the plant
Keywords
Lyapunov methods; controllability; neurocontrollers; stability; Lyapunov analysis; affine plant; affine uncertain systems; control architecture tracking performance; controllability; controllable neural control; dynamic characteristics; stability; stable control; Adaptive systems; Control nonlinearities; Control systems; Controllability; Function approximation; Neural networks; Sliding mode control; Trajectory; Uncertain systems; Uncertainty;
fLanguage
English
Publisher
ieee
Conference_Titel
American Control Conference, 2001. Proceedings of the 2001
Conference_Location
Arlington, VA
ISSN
0743-1619
Print_ISBN
0-7803-6495-3
Type
conf
DOI
10.1109/ACC.2001.946182
Filename
946182
Link To Document