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This book explores event-based estimation problems. It shows how
several stochastic approaches are developed to maintain estimation
performance when sensors perform their updates at slower rates only
when needed. The self-contained presentation makes this book
suitable for readers with no more than a basic knowledge of
probability analysis, matrix algebra and linear systems. The
introduction and literature review provide information, while the
main content deals with estimation problems from four distinct
angles in a stochastic setting, using numerous illustrative
examples and comparisons. The text elucidates both theoretical
developments and their applications, and is rounded out by a review
of open problems. This book is a valuable resource for researchers
and students who wish to expand their knowledge and work in the
area of event-triggered systems. At the same time, engineers and
practitioners in industrial process control will benefit from the
event-triggering technique that reduces communication costs and
improves energy efficiency in wireless automation applications.
This book explores event-based estimation problems. It shows how
several stochastic approaches are developed to maintain estimation
performance when sensors perform their updates at slower rates only
when needed. The self-contained presentation makes this book
suitable for readers with no more than a basic knowledge of
probability analysis, matrix algebra and linear systems. The
introduction and literature review provide information, while the
main content deals with estimation problems from four distinct
angles in a stochastic setting, using numerous illustrative
examples and comparisons. The text elucidates both theoretical
developments and their applications, and is rounded out by a review
of open problems. This book is a valuable resource for researchers
and students who wish to expand their knowledge and work in the
area of event-triggered systems. At the same time, engineers and
practitioners in industrial process control will benefit from the
event-triggering technique that reduces communication costs and
improves energy efficiency in wireless automation applications.
Among the many techniques for designing linear multivariable
analogue controllers, the two most popular optimal ones are H2 and
H-infinity optimization. The fact that most new industrial
controllers are digital provides strong motivation for adapting or
extending these techniques to digital control systems. This book,
now available as a corrected reprint, attempts to do so. Part I
presents two indirect methods of sampled-data controller design:
These approaches include approximations to a real problem, which
involves an analogue plant, continuous-time performance
specifications, and a sampled-data controller. Part II proposes a
direct attack in the continuous-time domain, where sampled-data
systems are time-varying. The findings are presented in forms that
can readily be programmed in, e.g., MATLAB.
This brief reviews concepts of inter-relationship in modern
industrial processes, biological and social systems. Specifically
ideas of connectivity and causality within and between elements of
a complex system are treated; these ideas are of great importance
in analysing and influencing mechanisms, structural properties and
their dynamic behaviour, especially for fault diagnosis and hazard
analysis. Fault detection and isolation for industrial processes
being concerned with root causes and fault propagation, the brief
shows that, process connectivity and causality information can be
captured in two ways: * from process knowledge: structural modeling
based on first-principles structural models can be merged with
adjacency/reachability matrices or topology models obtained from
process flow-sheets described in standard formats; and * from
process data: cross-correlation analysis, Granger causality and its
extensions, frequency domain methods, information-theoretical
methods, and Bayesian networks can be used to identify pair-wise
relationships and network topology. These methods rely on the
notion of information fusion whereby process operating data is
combined with qualitative process knowledge, to give a holistic
picture of the system.
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