Networked control systems are increasingly ubiquitous today, with
applications ranging from vehicle communication and adaptive power
grids to space exploration and economics. The optimal design of
such systems presents major challenges, requiring tools from
various disciplines within applied mathematics such as
decentralized control, stochastic control, information theory, and
quantization. A thorough, self-contained book, Stochastic Networked
Control Systems: Stabilization and Optimization under Information
Constraints aims to connect these diverse disciplines with
precision and rigor, while conveying design guidelines to
controller architects. Unique in the literature, it lays a
comprehensive theoretical foundation for the study of networked
control systems, and introduces an array of concrete tools for work
in the field. Salient features included: * Characterization,
comparison and optimal design of information structures in static
and dynamic teams. Operational, structural and topological
properties of information structures in optimal decision making,
with a systematic program for generating optimal encoding and
control policies. The notion of signaling, and its utilization in
stabilization and optimization of decentralized control systems. *
Presentation of mathematical methods for stochastic stability of
networked control systems using random-time, state-dependent drift
conditions and martingale methods. * Characterization and study of
information channels leading to various forms of stochastic
stability such as stationarity, ergodicity, and quadratic
stability; and connections with information and quantization
theories. Analysis of various classes of centralized and
decentralized control systems. * Jointly optimal design of encoding
and control policies over various information channels and under
general optimization criteria, including a detailed coverage of
linear-quadratic-Gaussian models. * Decentralized agreement and
dynamic optimization under information constraints. This monograph
is geared toward a broad audience of academic and industrial
researchers interested in control theory, information theory,
optimization, economics, and applied mathematics. It could likewise
serve as a supplemental graduate text. The reader is expected to
have some familiarity with linear systems, stochastic processes,
and Markov chains, but the necessary background can also be
acquired in part through the four appendices included at the end. *
Characterization, comparison and optimal design of information
structures in static and dynamic teams. Operational, structural and
topological properties of information structures in optimal
decision making, with a systematic program for generating optimal
encoding and control policies. The notion of signaling, and its
utilization in stabilization and optimization of decentralized
control systems. * Presentation of mathematical methods for
stochastic stability of networked control systems using
random-time, state-dependent drift conditions and martingale
methods. * Characterization and study of information channels
leading to various forms of stochastic stability such as
stationarity, ergodicity, and quadratic stability; and connections
with information and quantization theories. Analysis of various
classes of centralized and decentralized control systems. * Jointly
optimal design of encoding and control policies over various
information channels and under general optimization criteria,
including a detailed coverage of linear-quadratic-Gaussian models.
* Decentralized agreement and dynamic optimization under
information constraints. This monograph is geared toward a broad
audience of academic and industrial researchers interested in
control theory, information theory, optimization, economics, and
applied mathematics. It could likewise serve as a supplemental
graduate text. The reader is expected to have some familiarity with
linear systems, stochastic processes, and Markov chains, but the
necessary background can also be acquired in part through the four
appendices included at the end.
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