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Springer Quantitative Measure for Discrete Event Supervisory Control

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Description

Supervisory Control Theory (SCT) provides a tool to model and control human-engineered complex systems, such as computer networks, World Wide Web, identification and spread of malicious executables, and command, control, communication, and information systems. Although there are some excellent monographs and books on SCT to control and diagnose discrete-event systems, there is a need for a research monograph that provides a coherent quantitative treatment of SCT theory for decision and control of complex systems. This new monograph will assimilate many new concepts that have been recently reported or are in the process of being reported in open literature. The major objectives here are to present a) a quantitative approach, supported by a formal theory, for discrete-event decision and control of human-engineered complex systems; and b) a set of applications to emerging technological areas such as control of software systems, malicious executables, and complex engineering systems. The monograph will provide the necessary background materials in automata theory and languages for supervisory control. It will introduce a new paradigm of language measure to quantitatively compare the performance of different automata models of a physical system. A novel feature of this approach is to generate discrete-event robust optimal decision and control algorithms for both military and commercial systems. Review From the reviews: "This book presents a novel method for discrete event decision and control of complex systems, e.g. electric power generation, aircraft problems and multi-agent human and robotic systems. … The book would be of interest to research workers and graduate students in engineering robotics, computer science and applied mathematics … a rather original and mind provoking book." (Robotica, Vol. 24, 2006) "This is a monograph that is designed to reach a readership of researchers and graduate students in engineering and science. It will also provide a very useful text for many cyberneticians and systemists who have interests in the areas covered and have a mathematical background to graduate level. … forms a major source for course studies as well as being an excellent reference for readers who wish to learn more about this approach to the study of supervisory decision and control for discrete event systems." (W. R. Howard, Kybernetes, Vol. 35 (7-8), 2006) From the Back Cover Never before has there been a quantitative approach designed to optimize supervisory decision and control for discrete event systems. The text pioneers a formal system for supervision of human-engineered complex systems, to compare different supervisory models, thereby maximizing potential for achieving high performance. It offers exciting implications for both military and commercial engineering systems.  Quantitative Measure for Discrete Event Supervisory Control presents a novel method for discrete-event decision and control of complex systems, and provides applications for burgeoning technological needs in engineering (i.e., multi-agent human and robotic systems, aircraft, and electric power generation systems), as well as control of software systems and malicious executables. Using Supervisory Control Theory (SCT), a tool to model and control human engineered complex systems, this text initiates new concepts in quantitative treatment of SCT, as a much needed augmentation to existing research on the diagnosis and control of SCT. This survey is the first comprehensive treatment of a language-theoretic quantitative approach to discrete event supervisory decision and control. • Summarizes fundamental materials in supervisory decision and control before integrating new method to quantitatively measure performance • Presents formal theory to support the quantitative approach, thus outlining an effective model for discrete event decision and control of human-engineered complex systems • Outlines diverse

Product Specifications

Format
paperback
Domain
Amazon UK
Publication Date
29 September 2011
Listed Since
01 October 2010

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