Controller decision making using neural networks

Most industrial processes contain nonlinearities, making them difficult to control. To overcome this issue many authors have developed complex nonlinear algorithms and models, most of them being process dependant. However, creating local models to approximate the plant by linear regions is a suita...

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Main Author:	Andres Prado Espinoza
Other Authors:	Mao Kezhi
Format:	Theses and Dissertations
Language:	English
Published:	2010
Subjects:	DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation
Online Access:	http://hdl.handle.net/10356/41420
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Institution:	Nanyang Technological University
Language:	English

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spelling	sg-ntu-dr.10356-414202023-07-04T15:29:25Z Controller decision making using neural networks Andres Prado Espinoza Mao Kezhi School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation Most industrial processes contain nonlinearities, making them difficult to control. To overcome this issue many authors have developed complex nonlinear algorithms and models, most of them being process dependant. However, creating local models to approximate the plant by linear regions is a suitable approach in most cases. This approach lets the engineer create local PID controllers and switch them according to the plant linear regions. Master of Science (Computer Control and Automation) 2010-07-02T08:13:27Z 2010-07-02T08:13:27Z 2008 2008 Thesis http://hdl.handle.net/10356/41420 en 59 p. application/pdf
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation
spellingShingle	DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation Andres Prado Espinoza Controller decision making using neural networks
description	Most industrial processes contain nonlinearities, making them difficult to control. To overcome this issue many authors have developed complex nonlinear algorithms and models, most of them being process dependant. However, creating local models to approximate the plant by linear regions is a suitable approach in most cases. This approach lets the engineer create local PID controllers and switch them according to the plant linear regions.
author2	Mao Kezhi
author_facet	Mao Kezhi Andres Prado Espinoza
format	Theses and Dissertations
author	Andres Prado Espinoza
author_sort	Andres Prado Espinoza
title	Controller decision making using neural networks
title_short	Controller decision making using neural networks
title_full	Controller decision making using neural networks
title_fullStr	Controller decision making using neural networks
title_full_unstemmed	Controller decision making using neural networks
title_sort	controller decision making using neural networks
publishDate	2010
url	http://hdl.handle.net/10356/41420
_version_	1772828614258065408

Controller decision making using neural networks

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