Performance and Robustness of Adaptive Controllers for Linear Stochastic Systems
Lin, Sheng-Fuu
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/2142/69414
Description
Title
Performance and Robustness of Adaptive Controllers for Linear Stochastic Systems
Author(s)
Lin, Sheng-Fuu
Issue Date
1988
Doctoral Committee Chair(s)
Kumar, P.R.
Department of Study
Electrical Engineering
Discipline
Electrical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Engineering, Electronics and Electrical
Abstract
In this thesis we address the twin questions of performance as well as robustness of an adaptive controller for linear stochastic systems. Regarding the performance problem, we consider the issue of convergence of the parameter estimates with respect to the stochastic gradient algorithm and the modified least squares algorithm. As to the linear model following problem, we have shown that under certain conditions, both algorithms are strongly consistent. For the general tracking problem, if the reference trajectory is sufficiently rich of order greater than or equal to a certain positive number, both algorithms are strongly consistent. As for the regulation problem, if the controller utilizes the stochastic gradient algorithm, the parameter estimates converge to a random scalar multiple of the true parameter vector. For the robustness problem, we have presented a robust adaptive controller. Its mean square stabilizes the ideal system optimally if the noise signal satisfies a positive real condition. It can also stabilize a non-ideal system if the system is in a certain graph topological neighborhood of an ideal system.
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.
