Accession Number:

ADA282414

Title:

Back-Propagation Neural Networks in Adaptive Control of Unknown Nonlinear Systems

Descriptive Note:

Master's thesis

Corporate Author:

NAVAL POSTGRADUATE SCHOOL MONTEREY CA

Personal Author(s):

Report Date:

1994-06-01

Pagination or Media Count:

84.0

Abstract:

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 networks 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 been 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. Back-Propagation Neural Network, Direct Model Adaptive Control, Nonlinear Systems

Subject Categories:

  • Computer Programming and Software
  • Computer Systems

Distribution Statement:

APPROVED FOR PUBLIC RELEASE