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.

/pdf/evolutionary-algorithms-for-solving-multi-objective-problems-2txxk4mqci.pdf

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 goal of fault detection and isolation (FDI) is to decide whether and where a fault in the system under consideration has occurred avoiding wrong decisions that cause false alarms. To achieve a fault detection scheme which is robust in the sense of false alarms a combined quantitative/qualitative supervision system is used to detect and isolate faults. The quantitative part is used to generate fault symptoms (residuals) using a quantitative (mathematical) model of the process. These residuals contain the information about whether a fault has occurred or not. The next step in the FDI process is the residual evaluation. There exists a number of different residual evaluation techniques, for example simple threshold logic tests, statistical decision making, pattern recognition and decision making based on fuzzy logic or neural networks. The fundamental difficulty with residual evaluation is that residuals are normally uncertain, corrupted by noise, disturbances and, if the residuals are generated by model-based techniques, by modelling uncertainties. In order to select from the given residual data the important fault information a human support tool for the generation of a knowledge base for fault diagnosis is presented in the paper.

http://ieeexplore.ieee.org/iel3/4827/13354/00609659.pdf

FDI with computer-assisted human intelligence

Modelling and identification for highly nonlinear processes

In the paper the framework of generalized semi-Markov processes is applied to represent a continuous-time continuous variable dynamic system with inaccurate parameters. In this model framework the characteristics of state abstraction in qualitative reasoning is retained and a probability measure for depicting the uncertain system is provided. The emphasis is put on the construction of Markov chains to approximately describe the system.

http://ieeexplore.ieee.org/iel4/5651/15195/00703577.pdf

A GSMP model-framework for continuous-time dynamic systems

Studies problems related to modelling, control and monitoring of circulatory systems with an artificial heart. First, presents a pulsatile model of cardiovascular systems. This model has a well-grounded physiological basis and includes several reflex control systems, such as baroreceptor feedback loops, and nonlinearities of vessel compliance, which are inherent in the body. In addition, develops a model of an artificial heart. Based on these models, proposes a pulse frequency modulated (PFM) control scheme for the circulatory systems with an artificial heart. This scheme enables us to regulate the blood pressure and volume of the circulatory systems properly. To monitor the behaviour of the circulatory systems with an artificial heart an observation scheme is finally presented, with which the blood pressure and stroke volumes of the circulatory system can be estimated by using only the measurements of the driving pressure of the artificial heart.

Modelling, Control and Monitoring of Circulatory Systems with an Artificial Heart

Most results on fault detection and isolation have been developed using linear models. This chapter deals with fault detection in nonlinear systems. Particular attention is given to generation of residuals. Both algebraic and geometric methods are used.

Paul M. Frank

Papers

FDI with computer-assisted human intelligence

Modelling and identification for highly nonlinear processes

A GSMP model-framework for continuous-time dynamic systems

Modelling, Control and Monitoring of Circulatory Systems with an Artificial Heart

Residual Generation for FDI in Non-linear Systems