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.

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Evolutionary algorithms for solving multi-objective problems

http://web.aeromech.usyd.edu.au/AMME4500/mpc_survey.pdf

A survey of industrial model predictive control technology

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

/pdf/stability-of-time-delay-systems-5d2qjgdknf.pdf

Stability of Time-Delay Systems

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.

Detection of abrupt changes: theory and application

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

Robust Model-Based Fault Diagnosis for Dynamic Systems

The simulated laboratory set-up Three-Tank System is investigated from the standpoint of fault-tolerant control. The problem of robust model-based diagnosis is therefore addressed. Dynamic neural networks with mixed structure are used to design different observer-based schemes. Symptom evaluation is based on static neural nets. They are used to classify the obtained residuals. Different classifiers and decision criteria are analysed. Experimental results of simulation are included into a comparative study. This refers to actuator, component and instrument fault detection and isolation.

Neural observer-based approach to fault detection and isolation of a three-tank system

Observer-based fault detection via control system decomposition

Prof. Dr.-Ing. Dr. h.c. mult. Paul Martin Frank ist Leiter des Fachgebiets Meßund Regelungstechnik im Fachbereich Elektrotechnik der Gerhard-McrcatorUniversität Gesamthochschule Duisburg und Sprecher des Deutsch-Französischen Instituts für Automation und Robotik, Sektion Duisburg. Hauptarbeitsgebiete: Analyse und Synthese von Regelkreisen, Fehlerdiagnose, Fuzzy-Logik für Regelung und Überwachung. Adresse: Gerhard-Mercator-Universität Gesamthochschule Duisburg, Fachgebiet Meß und Regelungstechnik, Bismarckstr. 81, D-47057 Duisburg, Tel. 0203379-3386, e-mail: p.m.frank@uni-duisburg.de.

Komplexe systeme : nichtlineare ruckkopplungssysteme jenseits der stabilitat

This paper presents a component failure detection scheme based on analytical redundancy. The redundant signals are generated by state estimation using local second-order observers that are designed for each component of a given nonlinear process. The scheme may be considered as a generalization for a class of nonlinear systems of the method proposed by Jansen and Frank (1984, 1986) for linear or linearized systems. The introduction of the nonlinearities of the process into the structure of the observers contributes to improve the efficiency of the failure location scheme. Some results of a digital simulation illustrate the method in the context of an example.

Component Failure Detection Using Local Second-Order Observers

In previous years several control concepts have been applied to robot control. Based on the computed torque method a predictive control law is considered in the paper. It is shown that the predictive control concept is a powerful method to decrease the tracking error in robot manipulator control. Being aware of appearing model-plant mismatches the controller design-procedure must be modified to guarantee disturbance attenuation. Applying an input-output stability analysis it can be shown that the resulting control law is robust against these model-plant mismatches. >

Paul M. Frank

Papers

Neural observer-based approach to fault detection and isolation of a three-tank system

Observer-based fault detection via control system decomposition

Komplexe systeme : nichtlineare ruckkopplungssysteme jenseits der stabilitat

Component Failure Detection Using Local Second-Order Observers

Robust predictive robot control