Author
Paul M. Frank
Other affiliations: University of Duisburg-Essen, Centre national de la recherche scientifique, Control Group ...read more
Bio: Paul M. Frank is an academic researcher from University of Duisburg. The author has contributed to research in topics: Fault detection and isolation & Robustness (computer science). The author has an hindex of 48, co-authored 228 publications receiving 15777 citations. Previous affiliations of Paul M. Frank include University of Duisburg-Essen & Centre national de la recherche scientifique.
Papers published on a yearly basis
Papers
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01 Jan 1978TL;DR: It is the reviewer's personal experience that the existence of this textbook can both facilitate subsequent research in sensitivity and enable it to be conducted in a smoother less disjointed way than in the past.
Abstract: The author stresses that this book is written at a basic introductory level: accordingly, the book neither contains a rigorous treatment of the covered theory, nor is the covered theory chosen at a level other than the fundamental concepts of sensitivity theory. It is the reviewer's opinion that the author has accomplished his aims: thus he has written an introductory textbook on sensitivity theory which both covers the fundamentals of the theory and provides a guided entry to more advanced theory and to research. Indeed, it is the reviewer's personal experience [2] that the existence of this textbook, enfolding a unified set of definitions and a unified set of terminologies, can both facilitate subsequent research in sensitivity and enable it to be conducted in a smoother less disjointed way than in the past. Relative to the importance of sensitivity theory, it is to be recalled that in system identification, and therefore in the control domain, there exists
538 citations
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TL;DR: It is shown that the analysis results provide an efficient technique for the design of fuzzy controllers and a stabilization approach for nonlinear retarded systems through fuzzy state feedback and fuzzy observer-based controller is proposed.
497 citations
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TL;DR: Some schemes extending the well-known diagnosis methods for linear systems to the nonlinear case are considered and the robustness of these schemes in presence of unknown inputs is discussed.
486 citations
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TL;DR: To increase the robustness of residual evaluation a frequency domain residual evaluation index is introduced, and optimal input adaptive fault thresholds are derived with respect to the frequency domain evaluation index.
423 citations
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TL;DR: In this article, problems of optimizing observer-based fault detection (FD) systems in the sense of increasing the robustness to the unknown inputs and simultaneously enhancing the sensitivity to the faults are studied.
Abstract: In this paper, problems of optimizing observer-based fault detection (FD) systems in the sense of increasing the robustness to the unknown inputs and simultaneously enhancing the sensitivity to the faults are studied. The core of the study is the development of an approach that simultaneously solves four optimization problems. Different algorithms are derived for the application of this approach to the optimal selection of post-filters as well as optimization of fault detection filters, and to the systems with and without structure constraints. The achieved results also reveal some interesting relationships among the optimization problems considered. Copyright © 2000 John Wiley & Sons, Ltd.
423 citations
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30 Jun 2002
TL;DR: This paper presents a meta-anatomy of the multi-Criteria Decision Making process, which aims to provide a scaffolding for the future development of multi-criteria decision-making systems.
Abstract: List of Figures. List of Tables. Preface. Foreword. 1. Basic Concepts. 2. Evolutionary Algorithm MOP Approaches. 3. MOEA Test Suites. 4. MOEA Testing and Analysis. 5. MOEA Theory and Issues. 3. MOEA Theoretical Issues. 6. Applications. 7. MOEA Parallelization. 8. Multi-Criteria Decision Making. 9. Special Topics. 10. Epilog. Appendix A: MOEA Classification and Technique Analysis. Appendix B: MOPs in the Literature. Appendix C: Ptrue & PFtrue for Selected Numeric MOPs. Appendix D: Ptrue & PFtrue for Side-Constrained MOPs. Appendix E: MOEA Software Availability. Appendix F: MOEA-Related Information. Index. References.
5,994 citations
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TL;DR: An overview of commercially available model predictive control (MPC) technology, both linear and nonlinear, based primarily on data provided by MPC vendors, is provided in this article, where a brief history of industrial MPC technology is presented first, followed by results of our vendor survey of MPC control and identification technology.
4,819 citations
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26 Jun 2003
TL;DR: Preface, Notations 1.Introduction to Time-Delay Systems I.Robust Stability Analysis II.Input-output stability A.LMI and Quadratic Integral Inequalities Bibliography Index
Abstract: Preface, Notations 1.Introduction to Time-Delay Systems I.Frequency-Domain Approach 2.Systems with Commensurate Delays 3.Systems withIncommensurate Delays 4.Robust Stability Analysis II.Time Domain Approach 5.Systems with Single Delay 6.Robust Stability Analysis 7.Systems with Multiple and Distributed Delays III.Input-Output Approach 8.Input-output stability A.Matrix Facts B.LMI and Quadratic Integral Inequalities Bibliography Index
4,200 citations
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TL;DR: A unified framework for the design and the performance analysis of the algorithms for solving change detection problems and links with the analytical redundancy approach to fault detection in linear systems are established.
Abstract: This book is downloadable from http://www.irisa.fr/sisthem/kniga/. Many monitoring problems can be stated as the problem of detecting a change in the parameters of a static or dynamic stochastic system. The main goal of this book is to describe a unified framework for the design and the performance analysis of the algorithms for solving these change detection problems. Also the book contains the key mathematical background necessary for this purpose. Finally links with the analytical redundancy approach to fault detection in linear systems are established. We call abrupt change any change in the parameters of the system that occurs either instantaneously or at least very fast with respect to the sampling period of the measurements. Abrupt changes by no means refer to changes with large magnitude; on the contrary, in most applications the main problem is to detect small changes. Moreover, in some applications, the early warning of small - and not necessarily fast - changes is of crucial interest in order to avoid the economic or even catastrophic consequences that can result from an accumulation of such small changes. For example, small faults arising in the sensors of a navigation system can result, through the underlying integration, in serious errors in the estimated position of the plane. Another example is the early warning of small deviations from the normal operating conditions of an industrial process. The early detection of slight changes in the state of the process allows to plan in a more adequate manner the periods during which the process should be inspected and possibly repaired, and thus to reduce the exploitation costs.
3,830 citations
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27 Sep 2011TL;DR: Robust Model-Based Fault Diagnosis for Dynamic Systems targets both newcomers who want to get into this subject, and experts who are concerned with fundamental issues and are also looking for inspiration for future research.
Abstract: There is an increasing demand for dynamic systems to become safer and more reliable This requirement extends beyond the normally accepted safety-critical systems such as nuclear reactors and aircraft, where safety is of paramount importance, to systems such as autonomous vehicles and process control systems where the system availability is vital It is clear that fault diagnosis is becoming an important subject in modern control theory and practice Robust Model-Based Fault Diagnosis for Dynamic Systems presents the subject of model-based fault diagnosis in a unified framework It contains many important topics and methods; however, total coverage and completeness is not the primary concern The book focuses on fundamental issues such as basic definitions, residual generation methods and the importance of robustness in model-based fault diagnosis approaches In this book, fault diagnosis concepts and methods are illustrated by either simple academic examples or practical applications The first two chapters are of tutorial value and provide a starting point for newcomers to this field The rest of the book presents the state of the art in model-based fault diagnosis by discussing many important robust approaches and their applications This will certainly appeal to experts in this field Robust Model-Based Fault Diagnosis for Dynamic Systems targets both newcomers who want to get into this subject, and experts who are concerned with fundamental issues and are also looking for inspiration for future research The book is useful for both researchers in academia and professional engineers in industry because both theory and applications are discussed Although this is a research monograph, it will be an important text for postgraduate research students world-wide The largest market, however, will be academics, libraries and practicing engineers and scientists throughout the world
3,826 citations