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Observer-Based Fault Estimation Techniques

  • Ke Zhang
  • Bin Jiang
  • Peng Shi
  • Vincent Cocquempot

Part of the Studies in Systems, Decision and Control book series (SSDC, volume 127)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Ke Zhang, Bin Jiang, Peng Shi, Vincent Cocquempot
    Pages 1-7
  3. Ke Zhang, Bin Jiang, Peng Shi, Vincent Cocquempot
    Pages 9-26
  4. Ke Zhang, Bin Jiang, Peng Shi, Vincent Cocquempot
    Pages 27-49
  5. Ke Zhang, Bin Jiang, Peng Shi, Vincent Cocquempot
    Pages 51-86
  6. Ke Zhang, Bin Jiang, Peng Shi, Vincent Cocquempot
    Pages 87-104
  7. Ke Zhang, Bin Jiang, Peng Shi, Vincent Cocquempot
    Pages 105-125
  8. Ke Zhang, Bin Jiang, Peng Shi, Vincent Cocquempot
    Pages 127-142
  9. Ke Zhang, Bin Jiang, Peng Shi, Vincent Cocquempot
    Pages 143-156
  10. Ke Zhang, Bin Jiang, Peng Shi, Vincent Cocquempot
    Pages 157-175
  11. Ke Zhang, Bin Jiang, Peng Shi, Vincent Cocquempot
    Pages 177-178
  12. Back Matter
    Pages 179-187

About this book

Introduction

This book investigates observer-fault estimation techniques in detail, while also highlighting recent research and findings regarding fault estimation. Many practical control systems are subject to possible malfunctions, which may cause significant performance loss or even system instability. To improve the reliability, performance and safety of dynamical systems, fault diagnosis techniques are now receiving considerable attention, both in research and applications, and have been the subject of intensive investigations. Fault detection – the essential first step in fault diagnosis – is a binary decision-making process used to determine whether or not a fault has occurred. In turn, fault isolation is used to identify the location of the faulty component, while fault estimation is used to identify the size of the fault online. Compared with the problems involved in fault detection and isolation, fault estimation is considerably more challenging.

Keywords

Fault Diagnosis Finite-frequency Domain Finite-time Convergence Adaptive Observer Multi-agent Systems Fuzzy Models

Authors and affiliations

  • Ke Zhang
    • 1
  • Bin Jiang
    • 2
  • Peng Shi
    • 3
  • Vincent Cocquempot
    • 4
  1. 1.College of Automation EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.College of Automation EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina
  3. 3.School of Electrical and Electronic EngineeringUniversity of AdelaideAdelaideAustralia
  4. 4.UMR 9189, CRIStAL—Centre de Recherche en Informatique, Signal et Automatique de LilleCNRS, Université de Lille, Centrale LilleLilleFrance

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-67492-6
  • Copyright Information Springer International Publishing AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-67491-9
  • Online ISBN 978-3-319-67492-6
  • Series Print ISSN 2198-4182
  • Series Online ISSN 2198-4190
  • Buy this book on publisher's site