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Title: An integrated architecture of adaptive neural network control for dynamic systems

Abstract

In this study, an integrated neural network control architecture for nonlinear dynamic systems is presented. Most of the recent emphasis in the neural network control field has no error feedback as the control input which rises the adaptation problem. The integrated architecture in this paper combines feed forward control and error feedback adaptive control using neural networks. The paper reveals the different internal functionality of these two kinds of neural network controllers for certain input styles, e.g., state feedback and error feedback. Feed forward neural network controllers with state feedback establish fixed control mappings which can not adapt when model uncertainties present. With error feedbacks, neural network controllers learn the slopes or the gains respecting to the error feedbacks, which are error driven adaptive control systems. The results demonstrate that the two kinds of control scheme can be combined to realize their individual advantages. Testing with disturbances added to the plant shows good tracking and adaptation.

Authors:
; ;
Publication Date:
Research Org.:
Los Alamos National Lab., NM (United States)
Sponsoring Org.:
USDOE, Washington, DC (United States)
OSTI Identifier:
10164183
Report Number(s):
LA-UR-94-1705; CONF-941190-1
ON: DE94014813
DOE Contract Number:  
W-7405-ENG-36
Resource Type:
Conference
Resource Relation:
Conference: Neural information processing systems,Denver, CO (United States),29 Nov - 3 Dec 1994; Other Information: PBD: [1994]
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; COMPUTERIZED CONTROL SYSTEMS; NEURAL NETWORKS; NONLINEAR PROBLEMS; FEEDBACK; 990200; MATHEMATICS AND COMPUTERS

Citation Formats

Ke, Liu, Tokar, R, and Mcvey, B. An integrated architecture of adaptive neural network control for dynamic systems. United States: N. p., 1994. Web.
Ke, Liu, Tokar, R, & Mcvey, B. An integrated architecture of adaptive neural network control for dynamic systems. United States.
Ke, Liu, Tokar, R, and Mcvey, B. 1994. "An integrated architecture of adaptive neural network control for dynamic systems". United States. https://www.osti.gov/servlets/purl/10164183.
@article{osti_10164183,
title = {An integrated architecture of adaptive neural network control for dynamic systems},
author = {Ke, Liu and Tokar, R and Mcvey, B},
abstractNote = {In this study, an integrated neural network control architecture for nonlinear dynamic systems is presented. Most of the recent emphasis in the neural network control field has no error feedback as the control input which rises the adaptation problem. The integrated architecture in this paper combines feed forward control and error feedback adaptive control using neural networks. The paper reveals the different internal functionality of these two kinds of neural network controllers for certain input styles, e.g., state feedback and error feedback. Feed forward neural network controllers with state feedback establish fixed control mappings which can not adapt when model uncertainties present. With error feedbacks, neural network controllers learn the slopes or the gains respecting to the error feedbacks, which are error driven adaptive control systems. The results demonstrate that the two kinds of control scheme can be combined to realize their individual advantages. Testing with disturbances added to the plant shows good tracking and adaptation.},
doi = {},
url = {https://www.osti.gov/biblio/10164183}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Jul 01 00:00:00 EDT 1994},
month = {Fri Jul 01 00:00:00 EDT 1994}
}

Conference:
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