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Learning control for a class of discrete-time, nonlinear systems

Author(s)
Perel, Ron Yitzhak, 1975-
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Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics.
Advisor
Anuradha Annaswarmy.
Terms of use
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582
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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).
 
Date issued
1999
URI
http://hdl.handle.net/1721.1/46684
Department
Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics.
Publisher
Massachusetts Institute of Technology
Keywords
Aeronautics and Astronautics.

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  • Aeronautics and Astronautics - Master's degree

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