Nonlinear adaptive control using backpropagating neural networks
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Authors
Menke, Kurt William
Subjects
Neural networks
Backpropagation
Adaptive control system design
Backpropagation
Adaptive control system design
Advisors
Cristi, Roberto
Date of Issue
1992-06
Date
June 1992
Publisher
Monterey, California. Naval Postgraduate School
Language
en_US
Abstract
The objective of this research is to develop a nonlinear regulator for an adaptive control system using backpropagating neural networks (BNN's) in conjunction with a linear quadratic regulator (LQR). the basic concepts of adaptive control and the structure of neural networks are discussed. These concepts are integrated and the nonlinear regulator is derived. Simulation is conducted on a representative nonlinear system with both the LQR and the nonlinear regulator. Training of the regulator and its performance under varying BNN parameter values are examined. The simulation results show that the nonlinear regulator with BNN's exhibits superior performance compared to the LQR when the nonlinearities are large. The optimization of regulator performance with regard to BNN parameter values is discussed. Further research is required in order to determine the general applicability of this regulator and to develop more specific guidelines for BNN parameters.
Type
Thesis
Description
Series/Report No
Department
Department of Electrical and Computer Engineering
Organization
Naval Postgraduate School
Identifiers
NPS Report Number
Sponsors
Funder
Format
52 p.
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.