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.
