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 paper considers the problem of decentralised fuzzy control of multivariable systems. Some definitions and theorems with regard to this problem are given. Decentralised fuzzy control algorithms, based on passive decomposition of control laws, are presented and illustrated by numerical examples. The algorithms use local sets of fuzzy relations for the subsystems whose control variables are not affected by the interactional sets of fuzzy relations. It is shown that the number of fuzzy relations is significantly reduced, and thus real-time control implementation is facilitated.

Decentralised fuzzy control of multivariable systems by passive decomposition

Parallel Evolutionary Approach to System Identification for Process Fault Diagnosis

In this contribution a well known method for robust Component Fault Detection and Isolation (CFDI) which was mainly developed for linear dynamic systems is extended to a class of nonlinear dynamic systems. The proposed technique is based on a new concept of Nonlinear Unknown Input Observers. The aim is to develop a CFDI-technique which is robust to model-plant mismatches and external disturbances while being sensitive to faults. The component faults as well as the model-plant mismatches are modelled in form of unknown input signals by a Taylor series expansion of the system model. By a nonlinear transformation, the model is decoupled from the unknown inputs. Subsequently, a Robust Component Fault Detection Observer (RCFDO) based on the decoupled model is designed. The RCFDO is used to generate a residual which is not affected by the unknown inputs representing disturbances and model-plant mismatches but still reflects the occurence of faults. Additionally, it is shown how the robustness concept can be utilized for the purpose of Fault Isolation. Based on the nonlinear model of a synchronous machine, the design of a RCFDO is illustrated and tested by simulations.

Robust component fault detection and isolation in nonlinear dynamic systems using nonlinear unknown input observers

A brief survey on different methods to examine the controllability of bilinear systems is given. Some results, obtained with the help of group theory, on the controllability of strictly bilinear systems and homogeneous-in-the-state bilinear systems are presented in more detail. Finally homogeneous-in-the-state bilinear systems in state spaces of dimensions two and three are discussed. The cases where the system matrices of the homogeneous-in-the-state bilinear system generate a Lie algebra which spans a space with a dimension equal to the dimension of the state space are considered, i.e. the corresponding strictly bilinear system is controllable. For the two-dimensional case it turns out that there are only two situations when the strictly bilinear system is controllable but the homogeneous-in-the-state bilinear system is not. By checking the vector fields on the boundaries of the reachable sets obtained in these two situations, one can easily determine whether the homogeneous-in-the-state bilinear system is controllable or not, provided the Lie algebra of the system matrices spans R2. This approach is extended to the three-dimensional case and thus, previously obtained results can be generalized.

Paul M. Frank

Papers

Decentralised fuzzy control of multivariable systems by passive decomposition

Parallel Evolutionary Approach to System Identification for Process Fault Diagnosis

Robust component fault detection and isolation in nonlinear dynamic systems using nonlinear unknown input observers

Controllability of bilinear systems—a survey and some new results

Fault Diagnosis on a Winding-Machine