Invertibility and Input -to -State Stability of Switched Systems and Applications in Adaptive Control
Vu, Linh Hoang
This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.
Permalink
https://hdl.handle.net/2142/81070
Description
Title
Invertibility and Input -to -State Stability of Switched Systems and Applications in Adaptive Control
Author(s)
Vu, Linh Hoang
Issue Date
2007
Doctoral Committee Chair(s)
Daniel Liberzon
Department of Study
Electrical and Computer Engineering
Discipline
Electrical and Computer Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Engineering, Electronics and Electrical
Language
eng
Abstract
As control applications of switched systems, we apply our stability, results for switched systems to the problem of adaptively controlling uncertain nonlinear plants and linear time-varying plants. For uncertain nonlinear plants with hounded noise and disturbances, we show that using supervisory control, all the closed-loop signals can be kept bounded for arbitrary initial conditions when the controllers provide the ISS property with respect to the estimation errors. We also show that supervisory control is capable of stabilizing uncertain linear plants with large parameter variation in the presence of unmodeled dynamics and hounded noise and disturbances, provided that the unmodeled dynamics are small enough and the parameters vary slowly enough as described by switching profiles.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.