Analysis and Synthesis for Interval Type-2 Fuzzy-Model-Based Systems
This book develops a set of reference methods capable of modeling uncertainties existing in membership functions, and analyzing and synthesizing the interval type-2 fuzzy systems with desired performances. It also provides numerous simulation results for
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is and Synthesis for Interval Type-2 Fuzzy-ModelBased Systems
Analysis and Synthesis for Interval Type-2 Fuzzy-Model-Based Systems
Hongyi Li Ligang Wu Hak-Keung Lam Yabin Gao •
•
Analysis and Synthesis for Interval Type-2 Fuzzy-Model-Based Systems
123
Hongyi Li College of Engineering Bohai University Jinzhou China
Hak-Keung Lam Division of Engineering King’s College London London UK
Ligang Wu Space Control and Inertial Technology Research Center Harbin Institute of Technology Harbin China
Yabin Gao Space Control and Inertial Technology Research Center Harbin Institute of Technology Harbin China
ISBN 978-981-10-0592-3 DOI 10.1007/978-981-10-0593-0
ISBN 978-981-10-0593-0
(eBook)
Library of Congress Control Number: 2016932340 © Springer Science+Business Media Singapore 2016 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. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer Science+Business Media Singapore Pte Ltd.
To my family Hongyi Li To my family Ligang Wu To my family Hak-Keung Lam To my family Yabin Gao
Preface
Problem formulations of physical systems and processes can often lead to complex nonlinear systems, which may cause analysis and synthesis difficulties. Study of nonlinear systems is often problematic due to their complexities. One effective way of representing a complex nonlinear dynamic system is the so-called Takagi-Sugeno (T-S) fuzzy model, which is governed by a family of fuzzy IF-THEN rules that represent local linear input–output relations of the system. It incorporates a family of local linear models that smoothly blend together through fuzzy membership functions. This in essence, is a multi-model approach in which simple sub-models (typically linear models) are fuzzily combined to describe the global behavior of a nonlinear system. Based on the fuzzy model, the control design is carried out by using the parallel distributed compensation (PDC) scheme. The strategy is that a linear
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