Learning control for a class of discrete-time, nonlinear systems
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1999.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2009
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| Online Access: | http://hdl.handle.net/1721.1/46684 |
| _version_ | 1826217490489802752 |
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| author | Perel, Ron Yitzhak, 1975- |
| author2 | Anuradha Annaswarmy. |
| author_facet | Anuradha Annaswarmy. Perel, Ron Yitzhak, 1975- |
| author_sort | Perel, Ron Yitzhak, 1975- |
| collection | MIT |
| description | Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1999. |
| first_indexed | 2024-09-23T17:04:29Z |
| format | Thesis |
| id | mit-1721.1/46684 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T17:04:29Z |
| publishDate | 2009 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/466842019-04-12T21:31:30Z Learning control for a class of discrete-time, nonlinear systems Perel, Ron Yitzhak, 1975- Anuradha Annaswarmy. Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics. Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics. Aeronautics and Astronautics. Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1999. Includes bibliographical references (p. 132-134). 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. by Ron Yitzhak Perel. S.M. 2009-08-26T17:21:22Z 2009-08-26T17:21:22Z 1999 1999 Thesis http://hdl.handle.net/1721.1/46684 44616043 eng 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 134 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Aeronautics and Astronautics. Perel, Ron Yitzhak, 1975- Learning control for a class of discrete-time, nonlinear systems |
| title | Learning control for a class of discrete-time, nonlinear systems |
| title_full | Learning control for a class of discrete-time, nonlinear systems |
| title_fullStr | Learning control for a class of discrete-time, nonlinear systems |
| title_full_unstemmed | Learning control for a class of discrete-time, nonlinear systems |
| title_short | Learning control for a class of discrete-time, nonlinear systems |
| title_sort | learning control for a class of discrete time nonlinear systems |
| topic | Aeronautics and Astronautics. |
| url | http://hdl.handle.net/1721.1/46684 |
| work_keys_str_mv | AT perelronyitzhak1975 learningcontrolforaclassofdiscretetimenonlinearsystems |