Estimation and Inference in Discrete Event Systems: A Model-Based Approach with Finite Automata

ISBN-13: 9783030308230 / Angielski / Miękka / 2020 / 346 str.

Christoforos N. Hadjicostis

Spis treści

Chapter 1. Introduction to Estimation and Inference in Discrete Event Systems.- Chapter 2. Preliminaries and Notation.- Chapter 3. Finite Automata Models.- Chapter 4. State Estimation.- Chapter 5. Verification of State Isolation Properties.- Chapter 6. Detectability.- Chapter 7. Diagnosability.- Chapter 8. Opacity.- Chapter 9. Decentralized State Estimation.- Chapter 10. Distributed State Estimation.- Index.

O autorze

Christoforos Hadjicostis is Professor of Electrical and Computer Engineering (ECE) at the University of Cyprus (UCY). He received S.B. degrees in Electrical Engineering, Computer Science and Engineering, and Mathematics, the M.Eng. degree in Electrical Engineering and Computer Science in 1995, and the PhD degree in Electrical Engineering and Computer Science in 1999, all from Massachusetts Institute of Technology (MIT), Cambridge, MA. He has served as Assistant and then Associate Professor in the ECE Department at the University of Illinois at Urbana-Champaign. He joined UCY in 2007, where he served as Chair of the ECE Department from 2008 to 2010 and as Dean of Engineering from 2014 to 2017. 
Professor Hadjicostis’ research interests span the areas of discrete event systems, fault diagnosis and tolerance, error control coding, distributed algorithms for monitoring and control of large-scale systems and networks, and algebraic system analysis. On these topics, he has authored two books and more than 270 scientific papers in international journals, edited volumes, and conferences. He has coordinated or participated in several research projects funded by the National Science Foundation (NSF), the Air Force Office for Scientific Research (AFOSR), and various US Corporations while in the US, and by the European Commission (EC), the Cyprus Research Promotion Foundation (CRPF), and Qatar Foundation (QF) while in Cyprus.
Dr. Hadjicostis serves or has served as Departmental Editor for the Journal of Discrete Event Dynamic Systems, and as Associate Editor for Automatica, IEEE Transactions on Automatic Control, IEEE Transactions on Automation Science and Engineering, IEEE Transactions on Control Systems Technology, IEEE Transactions on Circuits and Systems I: Regular Papers, and the journal of Nonlinear Analysis: Hybrid Systems. At the University of Illinois, Dr. Hadjicostis received the Faculty Early Development (CAREER) award from the US National Science Foundation, the ECE Faculty Outstanding Teaching Award, and the Willett Faculty Scholar recognition from the College of Engineering; at the University of Cyprus, he received a Marie Curie International Reintegration Fellowship from the European Commission.

Blurb

Estimation and Inference in Discrete Event Systems chooses a popular model for emerging automation systems—finite automata under partial observation—and focuses on a comprehensive study of the key problems of state estimation and event inference. The text includes treatment of current, delayed, and initial state estimation. Related applications for assessing and enforcing resiliency—fault detection and diagnosis—and security—privacy and opacity—properties are discussed, enabling the reader to apply these techniques in a variety of emerging applications, among them automated manufacturing processes, intelligent vehicle/highway systems, and autonomous vehicles.

The book provides a systematic development of recursive algorithms for state estimation and event inference. The author also deals with the verification of pertinent properties such as:

the ability to determine the exact state of a system, “detectability”;

the ability to ensure that certain classes of faults can be detected/identified, “diagnosability”; and

the ability to ensure that certain internal state variables of the system remain “hidden” from the outside world regardless of the type of activity that is taking place, “opacity”.

This book allows students, researchers and practicing engineers alike to grasp basic aspects of state estimation in discrete event systems, aspects like distributivity and probabilistic inference, quickly and without having to master the entire breadth of models that are available in the literature.