Optimization of PID Controllers Using Ant Colony and Genetic Algorithms
Artificial neural networks, genetic algorithms and the ant colony optimization algorithm have become a highly effective tool for solving hard optimization problems. As their popularity has increased, applications of these algorithms have grown in more tha
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Muhammet Ünal, Ayça Ak, Vedat Topuz, and Hasan Erdal
Optimization of PID Controllers Using Ant Colony and Genetic Algorithms
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Authors Muhammet Ünal Technical Education Faculty, D401 Marmara University Goztepe Campus Istanbul Turkey
Vedat Topuz Vocational School of Technical Sciences Marmara University Goztepe Campus Istanbul Turkey
Ayça Ak Vocational School of Technical Sciences Marmara University Goztepe Campus Istanbul Turkey
Hasan Erdal Technology Faculty Marmara University Goztepe Campus Istanbul Turkey
ISSN 1860-949X e-ISSN 1860-9503 ISBN 978-3-642-32899-2 e-ISBN 978-3-642-32900-5 DOI 10.1007/978-3-642-32900-5 Springer Heidelberg New York Dordrecht London Library of Congress Control Number: 2012945298 c Springer-Verlag Berlin Heidelberg 2013 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Foreword
This book describes a real time control algorithm using Genetic Algorithm (GA) and Ant Colony Optimization (ACO) algorithm for optimizing PID controller parameters. Proposed method was tested on GUNT RT 532 Pressure Process Control System. The dynamic model of the process to be controlled was obtained using Artificial Neural Network (ANN). Using the chosen model, the parameters of PID cont
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