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

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

1999

Author dc:creator

• Perel, Ron Yitzhak, 1975-

Advisor dc:contributor.advisor

• Anuradha Annaswarmy.

• Aeronautics and Astronautics.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/46684

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/46684