The Minimum-degree Pole Placement algorithm for
Self-tuning Regulator design and the Recursive
Least-square method and the projection algorithm
for plant estimation are studied first in this
thesis. Simulation studies for the estimator and
controller algorithms are mainly undertaken after
describing how to use MATLAB S-function in detail.
Not only do Simulation experiments illustrates
how and how well the MDPP and RLS algorithms work,
but also show how to write and debug MATLAB codes
for S-function programs. The robustness of the
adaptive control system is intensively discussed
subsequently. By using an estimator resistant to
the noise contamination, the adaptive control
system can not be destablized by the introduced
noise at the input of the plant or the estimator.
However, the adaptive control system is subject
to the unmodeled dynamics that have a magnitude
response like an impulse at the crossover
frequency of the system. Simulation results also
show that a classic feedback controller has a
better performance, compared with the adaptive
controller.
| Identifer | oai:union.ndltd.org:LSU/oai:etd.lsu.edu:etd-1107101-134928 |
| Date | 15 November 2001 |
| Creators | Wu, Zhongshan |
| Contributors | Guoxiang Gu, Kemin Zhou, Peter Wolenski |
| Publisher | LSU |
| Source Sets | Louisiana State University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lsu.edu/docs/available/etd-1107101-134928/ |
| Rights | unrestricted, I hereby grant to LSU or its agents the right to archive and to make available my thesis or dissertation in whole or in part in the University Libraries in all forms of media, now or hereafter known. I retain all proprietary rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertation. |
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