Learning control for a class of discrete-time, nonlinear systems
Name
44616043-MIT.pdf
Description
Full printable version
Size
4.31 MB
Format
Adobe PDF
Checksum (MD5)
c38044197200426a2d718fb478b4e58c
Author(s)
Perel, Ron Yitzhak, 1975-
Advisor(s)
Anuradha Annaswarmy.
Date Issued
1999
Publisher
Massachusetts Institute of Technology
Abstract
Over the last few decades, control theory has developed to the level where reliable methods exist to achieve satisfactory performance on even the largest and most complex of dynamical systems. The application of these control methods, though, often require extensive modelling and design effort. Recent techniques to alleviate the strain on modellers use various schemes which allow a particular system to learn about itself by measuring and storing a large, arbitrary collection of data in compact structures such as neural networks, and then using the data to augment a controller. Although many such techniques have demonstrated their capabilities in simulation, performance guarantees are rare. This thesis proposes an alternate learning technique, where a controller, based on minimal initial knowledge of system dynamics, acquires a prescribed data set on which a new controller, with guaranteed performance improvements, is based.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1999.
Includes bibliographical references (p. 132-134).
Subjects
Aeronautics and Astronautics.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
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