Model Predictive Control Approaches Based on the Extended State Spac
This monograph introduces the authors’ work on model predictive control system design using extended state space and extended non-minimal state space approaches. It systematically describes model predictive control design for chemical processes, including
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Predictive Control Approaches Based on the Extended State Space Model and Extended Non-minimal State Space Model
Model Predictive Control
Ridong Zhang Anke Xue Furong Gao •
Model Predictive Control Approaches Based on the Extended State Space Model and Extended Non-minimal State Space Model
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Ridong Zhang Institute of Information and Control Hangzhou Dianzi University Hangzhou, Zhejiang, China Anke Xue Key Lab for IOT and Information Fusion Technology of Zhejiang, Institute of Information and Control Hangzhou Dianzi University Hangzhou, Zhejiang, China
Furong Gao Department of Chemical and Biomolecular Engineering Hong Kong University of Science and Technology Hong Kong, China
ISBN 978-981-13-0082-0 ISBN 978-981-13-0083-7 https://doi.org/10.1007/978-981-13-0083-7
(eBook)
Library of Congress Control Number: 2018949340 © Springer Nature Singapore Pte Ltd. 2019 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. 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. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore
Preface
Aims of the Book The aim of this book is (1) to present the novel extended state space model and extended non-minimal state space model-based model predictive control (MPC), predictive functional control (PFC), PID control optimization, and the relevant system performance analysis; (2) to introduce the constraint handling in MPC and the relaxed constrained optimization approach; (3) to address the improved genetic algorithm (GA) based MPC; and (4) to illustrate the corresponding industrial applications. As a promising control algorithm, MPC plays a vital role in the control of industrial processes. During the past decades, great progress in both theory and application has been obtained. Driven by the rapid development of economy and stricter re
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