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Summary: DISSERTATION
A SYNTHESIS OF REINFORCEMENT LEARNING AND ROBUST CONTROL THEORY
Submitted by
R. Matthew Kretchmar
Department of Computer Science
In partial fulfillment of the requirements
for the Degree of Doctor of Philosophy
Colorado State University
Fort Collins, Colorado
Summer 2000
ABSTRACT OF DISSERTATION
A SYNTHESIS OF REINFORCEMENT LEARNING AND ROBUST CONTROL THEORY
The pursuit of control algorithms with improved performance drives the entire control research
community as well as large parts of the mathematics, engineering, and artificial intelligence research
communities. A fundamental limitation on achieving control performance is the conflicting require-
ment of maintaining system stability. In general, the more aggressive is the controller, the better
the control performance but also the closer to system instability.
Robust control is a collection of theories, techniques, and tools that form one of the leading edge
approaches to control. Most controllers are designed not on the physical plant to be controlled,
but on a mathematical model of the plant; hence, these controllers often do not perform well on
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