File:Back-propagation neural networks in adaptive control of unknown nonlinear systems (IA backpropagationn1094530830).pdf
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Summary[edit]
Back-propagation neural networks in adaptive control of unknown nonlinear systems ( ) | ||
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Author |
Cakarcan, Alpay |
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Title |
Back-propagation neural networks in adaptive control of unknown nonlinear systems |
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Publisher |
Monterey, California. Naval Postgraduate School |
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Description |
The objective of this thesis research is to develop a Back-Propagation Neural Network (BNN) to control certain classes of unknown nonlinear systems and explore the network's capabilities. The structure of the Direct Model Reference Adaptive Controller (DMRAC) for Linear Time Invariant (LTI) systems with unknown parameters is first analyzed and then is extended to nonlinear systems by using BNN, Nonminimum phase systems, both linear and nonlinear, have also be considered. The analysis of the experiments shows that the BNN DMRAC gives satisfactory results for the representative nonlinear systems considered, while the conventional least-squares estimator DMRAC fails. Based on the analysis and experimental findings, some general conditions are shown to be required to ensure that this technique is satisfactory. These conditions are presented and discussed. It has been found that further research needs to be done for the nonminimum phase case in order to guarantee stability and tracking. Also, to establish this as a more general and significant control technique, further research is required to develop more specific rules and guidelines for the BNN design and training. Subjects: Back-Propagation Neural Network, Direct Model Adapative Control, Nonlinear Systems |
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Language | English | |
Publication date |
June 1994 publication_date QS:P577,+1994-06-00T00:00:00Z/10 |
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Current location |
IA Collections: navalpostgraduateschoollibrary; fedlink |
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Accession number |
backpropagationn1094530830 |
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Source | ||
Permission (Reusing this file) |
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. As such, it is in the public domain, and under the provisions of Title 17, United States Code, Section 105, may not be copyrighted. |
Licensing[edit]
Public domainPublic domainfalsefalse |
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This file has been identified as being free of known restrictions under copyright law, including all related and neighboring rights. |
https://creativecommons.org/publicdomain/mark/1.0/PDMCreative Commons Public Domain Mark 1.0falsefalse
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Short title | Back-propagation neural networks in adaptive control of unknown nonlinear systems |
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Author | Cakarcan, Alpay |
Software used | Cakarcan, Alpay |
Conversion program | Adobe Acrobat 9.51 Paper Capture Plug-in |
Encrypted | no |
Page size |
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Version of PDF format | 1.4 |