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This book presents the novel state estimation methods for several
classes of networked multi-rate systems including state estimation
methods for networked multi-rate systems with various complex
networked-induced phenomena and communication protocols. The
systems investigated include the stochastic nonlinear systems, the
time-delay systems, the linear repetitive processes, and the
artificial neural networks. The techniques used are mainly the
Lyapunov stability theory, the optimal estimation theory, the
lifting technique, and certain convex optimization method.
Features- •Gives a systematic investigation of the state
estimation of multi-rate systems •Discusses results on state
estimation problems under network-induced complexities • Studies
different kinds of multi-rate systems including multi-rate
nonlinear systems, multi-rate neural networks, and multi-rate
linear repetitive processes •Explores network-enhanced
complexities and communication protocols • Includes case studies
to show the applicability of the developed estimation algorithms
including practical examples like DC servo system and continuous
stirred tank reactor system This book is aimed at graduate students
and researchers in signal processing, control systems, and
electrical engineering.
This book unifies existing and emerging concepts concerning state
estimation, fault detection, fault isolation and fault estimation
on industrial systems with an emphasis on a variety of
network-induced phenomena, fault diagnosis and remaining useful
life for industrial equipment. It covers state estimation/monitor,
fault diagnosis and remaining useful life prediction by drawing on
the conventional theories of systems science, signal processing and
machine learning. Features: Unifies existing and emerging concepts
concerning robust filtering and fault diagnosis with an emphasis on
a variety of network-induced complexities. Explains theories,
techniques, and applications of state estimation as well as fault
diagnosis from an engineering-oriented perspective. Provides a
series of latest results in robust/stochastic filtering, multidate
sample, and time-varying system. Captures diagnosis (fault
detection, fault isolation and fault estimation) for time-varying
multi-rate systems. Includes simulation examples in each chapter to
reflect the engineering practice. This book aims at graduate
students, professionals and researchers in control science and
application, system analysis, artificial intelligence, and fault
diagnosis.
This book discusses the Sliding Mode Control (SMC) problems of
networked control systems (NCSs) under various communication
protocols including static/dynamic/periodic event-triggered
mechanism, and stochastic communication, Round-Robin, weighted
try-once-discard, multiple-packet transmission, and the redundant
channel transmission protocol. The super-twisting algorithm and the
extended-state-observer-based SMC scheme are described in this book
for suppressing chattering. Besides, the SMC designs for
two-dimensional (1-D) and two-dimensional (2-D) NCSs are
illustrated as well. Features: Captures recent advances of
theories, techniques, and applications of networked sliding mode
control from an engineering-oriented perspective. Includes new
design ideas and optimization techniques of networked sliding mode
control theory. Provides advanced tools to apply networked sliding
mode control techniques in the practical applications. Discusses
some new tools to the engineering applications while dealing with
the model uncertainties and external disturbances. This book aims
at Researchers and professionals in Control Systems, Computer
Networks, Internet of Things, and Communication Systems.
This book focuses on the control and state estimation problems for
dynamical network systems with complex samplings subject to various
network-induced phenomena. It includes a series of control and
state estimation problems tackled under the passive sampling
fashion. Further, it explains the effects from the active sampling
fashion, i.e., event-based sampling is examined on the
control/estimation performance, and novel design technologies are
proposed for controllers/estimators. Simulation results are
provided for better understanding of the proposed control/filtering
methods. By drawing on a variety of theories and methodologies such
as Lyapunov function, linear matrix inequalities, and Kalman
theory, sufficient conditions are derived for guaranteeing the
existence of the desired controllers and estimators, which are
parameterized according to certain matrix inequalities or recursive
matrix equations. Covers recent advances of control and state
estimation for dynamical network systems with complex samplings
from the engineering perspective Systematically introduces the
complex sampling concept, methods, and application for the control
and state estimation Presents unified framework for control and
state estimation problems of dynamical network systems with complex
samplings Exploits a set of the latest techniques such as linear
matrix inequality approach, Vandermonde matrix approach, and trace
derivation approach Explains event-triggered multi-rate fusion
estimator, resilient distributed sampled-data estimator with
predetermined specifications This book is aimed at researchers,
professionals, and graduate students in control engineering and
signal processing.
The objective of this book is to present the up-to-date research
developments and novel methodologies on state estimation and fault
diagnosis (FD) techniques for a class of complex systems subject to
closed-loop control, nonlinearities, and stochastic phenomena. It
covers state estimation design methodologies and FD unit design
methodologies including framework of optimal filter and FD unit
design, robust filter and FD unit design, stability, and
performance analysis for the considered systems subject to various
kinds of complex factors. Features: Reviews latest research results
on the state estimation and fault diagnosis issues. Presents
comprehensive framework constituted for systems under imperfect
measurements. Includes quantitative performance analyses to solve
problems in practical situations. Provides simulation examples
extracted from practical engineering scenarios. Discusses proper
and novel techniques such as the Carleman approximation and
completing the square method is employed to solve the mathematical
problems. This book aims at Graduate students, Professionals and
Researchers in Control Science and Application, Stochastic Process,
Fault Diagnosis, and Instrumentation and Measurement.
Networked Non-linear Stochastic Time-Varying Systems: Analysis and
Synthesis copes with the filter design, fault estimation and
reliable control problems for different classes of nonlinear
stochastic time-varying systems with network-enhanced complexities.
Divided into three parts, the book discusses the finite-horizon
filtering, fault estimation and reliable control, and randomly
occurring nonlinearities/uncertainties followed by designing of
distributed state and fault estimators, and distributed filters.
The third part includes problems of variance-constrained H state
estimation, partial-nodes-based state estimation and recursive
filtering for nonlinear time-varying complex networks with randomly
varying topologies, and random coupling strengths. Offers a
comprehensive treatment of the topics related to Networked
Nonlinear Stochastic Time-Varying Systems with rigorous math
foundation and derivation Unifies existing and emerging concepts
concerning control/filtering/estimation and distributed filtering
Provides a series of latest results by drawing on the conventional
theories of systems science, control engineering and signal
processing Deal with practical engineering problems such as event
triggered H filtering, non-fragile distributed estimation,
recursive filtering, set-membership filtering Demonstrates
illustrative examples in each chapter to verify the correctness of
the proposed results This book is aimed at engineers,
mathematicians, scientists, and upper-level students in the fields
of control engineering, signal processing, networked control
systems, robotics, data analysis, and automation.
In this book, the stability analysis and estimator design problems
are discussed for delayed discrete-time memristive neural networks.
In each chapter, the analysis problems are firstly considered,
where the stability, synchronization and other performances (e.g.,
robustness, disturbances attenuation level) are investigated within
a unified theoretical framework. In this stage, some novel notions
are put forward to reflect the engineering practice. Then, the
estimator design issues are discussed where sufficient conditions
are derived to ensure the existence of the desired estimators with
guaranteed performances. Finally, the theories and techniques
developed in previous parts are applied to deal with some issues in
several emerging research areas. The book Unifies existing and
emerging concepts concerning delayed discrete memristive neural
networks with an emphasis on a variety of network-induced phenomena
Captures recent advances of theories, techniques, and applications
of delayed discrete memristive neural networks from a
network-oriented perspective Provides a series of latest results in
two popular yet interrelated areas, stability analysis and state
estimation of neural networks Exploits a unified framework for
analysis and synthesis by designing new tools and techniques in
combination with conventional theories of systems science, control
engineering and signal processing Gives simulation examples in each
chapter to reflect the engineering practice
In this book, control and filtering problems for several classes of
stochastic networked systems are discussed. In each chapter, the
stability, robustness, reliability, consensus performance, and/or
disturbance attenuation levels are investigated within a unified
theoretical framework. The aim is to derive the sufficient
conditions such that the resulting systems achieve the prescribed
design requirements despite all the network-induced phenomena.
Further, novel notions such as randomly occurring sensor failures
and consensus in probability are discussed. Finally, the
theories/techniques developed are applied to emerging research
areas. Key Features Unifies existing and emerging concepts
concerning stochastic control/filtering and distributed
control/filtering with an emphasis on a variety of network-induced
complexities Includes concepts like randomly occurring sensor
failures and consensus in probability (with respect to time-varying
stochastic multi-agent systems) Exploits the recursive linear
matrix inequality approach, completing the square method,
Hamilton-Jacobi inequality approach, and parameter-dependent matrix
inequality approach to handle the emerging
mathematical/computational challenges Captures recent advances of
theories, techniques, and applications of stochastic control as
well as filtering from an engineering-oriented perspective Gives
simulation examples in each chapter to reflect the engineering
practice
Nonlinear Stochastic Control and Filtering with
Engineering-oriented Complexities presents a series of control and
filtering approaches for stochastic systems with traditional and
emerging engineering-oriented complexities. The book begins with an
overview of the relevant background, motivation, and research
problems, and then: Discusses the robust stability and
stabilization problems for a class of stochastic time-delay
interval systems with nonlinear disturbances Investigates the
robust stabilization and H control problems for a class of
stochastic time-delay uncertain systems with Markovian switching
and nonlinear disturbances Explores the H state estimator and H
output feedback controller design issues for stochastic time-delay
systems with nonlinear disturbances, sensor nonlinearities, and
Markovian jumping parameters Analyzes the H performance for a
general class of nonlinear stochastic systems with time delays,
where the addressed systems are described by general stochastic
functional differential equations Studies the filtering problem for
a class of discrete-time stochastic nonlinear time-delay systems
with missing measurement and stochastic disturbances Uses
gain-scheduling techniques to tackle the probability-dependent
control and filtering problems for time-varying nonlinear systems
with incomplete information Evaluates the filtering problem for a
class of discrete-time stochastic nonlinear networked control
systems with multiple random communication delays and random packet
losses Examines the filtering problem for a class of nonlinear
genetic regulatory networks with state-dependent stochastic
disturbances and state delays Considers the H state estimation
problem for a class of discrete-time complex networks with
probabilistic missing measurements and randomly occurring coupling
delays Addresses the H synchronization control problem for a class
of dynamical networks with randomly varying nonlinearities
Nonlinear Stochastic Control and Filtering with
Engineering-oriented Complexities describes novel methodologies
that can be applied extensively in lab simulations, field
experiments, and real-world engineering practices. Thus, this text
provides a valuable reference for researchers and professionals in
the signal processing and control engineering communities.
This book presents up-to-date research developments and novel
methodologies regarding recursive filtering for 2-D shift-varying
systems with various communication constraints. It investigates
recursive filter/estimator design and performance analysis by a
combination of intensive stochastic analysis, recursive
Riccati-like equations, variance-constrained approach, and
mathematical induction. Each chapter considers dynamics of the
system, subtle design of filter gains, and effects of the
communication constraints on filtering performance. Effectiveness
of the derived theories and applicability of the developed
filtering strategies are illustrated via simulation examples and
practical insight. Features:- Covers recent advances of recursive
filtering for 2-D shift-varying systems subjected to communication
constraints from the engineering perspective. Includes the
recursive filter design, resilience operation and performance
analysis for the considered 2-D shift-varying systems. Captures the
essence of the design for 2-D recursive filters. Develops a series
of latest results about the robust Kalman filtering and
protocol-based filtering. Analyzes recursive filter design and
filtering performance for the considered systems. This book aims at
graduate students and researchers in mechanical engineering,
industrial engineering, communications networks, applied
mathematics, robotics and control systems.
To harness the high-throughput potential of DNA microarray
technology, it is crucial that the analysis stages of the process
are decoupled from the requirements of operator assistance.
Microarray Image Analysis: An Algorithmic Approach presents an
automatic system for microarray image processing to make this
decoupling a reality. The proposed system integrates and extends
traditional analytical-based methods and custom-designed novel
algorithms. The book first explores a new technique that takes
advantage of a multiview approach to image analysis and addresses
the challenges of applying powerful traditional techniques, such as
clustering, to full-scale microarray experiments. It then presents
an effective feature identification approach, an innovative
technique that renders highly detailed surface models, a new
approach to subgrid detection, a novel technique for the background
removal process, and a useful technique for removing "noise." The
authors also develop an expectation-maximization (EM) algorithm for
modeling gene regulatory networks from gene expression time series
data. The final chapter describes the overall benefits of these
techniques in the biological and computer sciences and reviews
future research topics. This book systematically brings together
the fields of image processing, data analysis, and molecular
biology to advance the state of the art in this important area.
Although the text focuses on improving the processes involved in
the analysis of microarray image data, the methods discussed can be
applied to a broad range of medical and computer vision analysis
areas.
Nonlinear Stochastic Control and Filtering with
Engineering-oriented Complexities presents a series of control and
filtering approaches for stochastic systems with traditional and
emerging engineering-oriented complexities. The book begins with an
overview of the relevant background, motivation, and research
problems, and then: Discusses the robust stability and
stabilization problems for a class of stochastic time-delay
interval systems with nonlinear disturbances Investigates the
robust stabilization and H control problems for a class of
stochastic time-delay uncertain systems with Markovian switching
and nonlinear disturbances Explores the H state estimator and H
output feedback controller design issues for stochastic time-delay
systems with nonlinear disturbances, sensor nonlinearities, and
Markovian jumping parameters Analyzes the H performance for a
general class of nonlinear stochastic systems with time delays,
where the addressed systems are described by general stochastic
functional differential equations Studies the filtering problem for
a class of discrete-time stochastic nonlinear time-delay systems
with missing measurement and stochastic disturbances Uses
gain-scheduling techniques to tackle the probability-dependent
control and filtering problems for time-varying nonlinear systems
with incomplete information Evaluates the filtering problem for a
class of discrete-time stochastic nonlinear networked control
systems with multiple random communication delays and random packet
losses Examines the filtering problem for a class of nonlinear
genetic regulatory networks with state-dependent stochastic
disturbances and state delays Considers the H state estimation
problem for a class of discrete-time complex networks with
probabilistic missing measurements and randomly occurring coupling
delays Addresses the H synchronization control problem for a class
of dynamical networks with randomly varying nonlinearities
Nonlinear Stochastic Control and Filtering with
Engineering-oriented Complexities describes novel methodologies
that can be applied extensively in lab simulations, field
experiments, and real-world engineering practices. Thus, this text
provides a valuable reference for researchers and professionals in
the signal processing and control engineering communities.
The book addresses the system performance with a focus on the
network-enhanced complexities and developing the
engineering-oriented design framework of controllers and filters
with potential applications in system sciences, control engineering
and signal processing areas. Therefore, it provides a unified
treatment on the analysis and synthesis for discrete-time
stochastic systems with guarantee of certain performances against
network-enhanced complexities with applications in sensor networks
and mobile robotics. Such a result will be of great importance in
the development of novel control and filtering theories including
industrial impact. Key Features Provides original methodologies and
emerging concepts to deal with latest issues in the control and
filtering with an emphasis on a variety of network-enhanced
complexities Gives results of stochastic control and filtering
distributed control and filtering, and security control of complex
networked systems Captures the essence of performance analysis and
synthesis for stochastic control and filtering Concepts and
performance indexes proposed reflect the requirements of
engineering practice Methodologies developed in this book include
backward recursive Riccati difference equation approach and the
discrete-time version of input-to-state stability in probability
This monograph introduces methods for handling filtering and
control problems in nonlinear stochastic systems arising from
network-induced phenomena consequent on limited communication
capacity. Such phenomena include communication delay, packet
dropout, signal quantization or saturation, randomly occurring
nonlinearities and randomly occurring uncertainties. The text is
self-contained, beginning with an introduction to nonlinear
stochastic systems, network-induced phenomena and filtering and
control, moving through a collection of the latest research results
which focuses on the three aspects of: * the state-of-the-art of
nonlinear filtering and control; * recent advances in recursive
filtering and sliding mode control; and * their potential for
application in networked control systems, and concluding with some
ideas for future research work. New concepts such as the randomly
occurring uncertainty and the probability-constrained performance
index are proposed to make the network models as realistic as
possible. The power of combinations of such recent tools as the
completing-the-square and sums-of-squares techniques,
Hamilton-Jacobi-Isaacs matrix inequalities, difference linear
matrix inequalities and parameter-dependent matrix inequalities is
exploited in treating the mathematical and computational challenges
arising from nonlinearity and stochasticity. Nonlinear Stochastic
Systems with Network-Induced Phenomena establishes a unified
framework of control and filtering which will be of value to
academic researchers in bringing structure to problems associated
with an important class of networked system and offering new means
of solving them. The significance of the new concepts, models and
methods presented for practical control engineering and signal
processing will also make it a valuable reference for engineers
dealing with nonlinear control and filtering problems.
Nonlinear Stochastic Processes addresses the frequently-encountered
problem of incomplete information. The causes of this problem
considered here include: missing measurements; sensor delays and
saturation; quantization effects; and signal sampling. Divided into
three parts, the text begins with a focus on H filtering and
control problems associated with general classes of nonlinear
stochastic discrete-time systems. Filtering problems are considered
in the second part, and in the third the theory and techniques
previously developed are applied to the solution of issues arising
in complex networks with the design of sampled-data-based
controllers and filters. Among its highlights, the text provides: *
a unified framework for filtering and control problems in complex
communication networks with limited bandwidth; * new concepts such
as random sensor and signal saturations for more realistic
modeling; and * demonstration of the use of techniques such as the
Hamilton-Jacobi-Isaacs, difference linear matrix, and
parameter-dependent matrix inequalities and sums of squares to
handle the computational challenges inherent in these systems. The
collection of recent research results presented in Nonlinear
Stochastic Processes will be of interest to academic researchers in
control and signal processing. Graduate students working with
communication networks with lossy information and control of
stochastic systems will also benefit from reading the book.
This book focuses on filtering, control and model-reduction
problems for two-dimensional (2-D) systems with imperfect
information. The time-delayed 2-D systems covered have system
parameters subject to uncertain, stochastic and parameter-varying
changes. After an initial introduction of 2-D systems and the ideas
of linear repetitive processes, the text is divided into two parts
detailing: * General theory and methods of analysis and optimal
synthesis for 2-D systems; and * Application of the general theory
to the particular case of differential/discrete linear repetitive
processes. The methods developed provide a framework for stability
and performance analysis, optimal and robust controller and filter
design and model approximation for the systems considered.
Solutions to the design problems are couched in terms of linear
matrix inequalities. For readers interested in the state of the art
in linear filtering, control and model reduction, Filtering and
Control for Classes of Two-Dimensional Systems will be a useful
reference for exploring the field of 2-D systems either from a
purely theoretical research perspective or from the point of view
of a multitude of potential applications including image
processing, and the study of seismographic data or thermal
processes.
This monograph introduces methods for handling filtering and
control problems in nonlinear stochastic systems arising from
network-induced phenomena consequent on limited communication
capacity. Such phenomena include communication delay, packet
dropout, signal quantization or saturation, randomly occurring
nonlinearities and randomly occurring uncertainties. The text is
self-contained, beginning with an introduction to nonlinear
stochastic systems, network-induced phenomena and filtering and
control, moving through a collection of the latest research results
which focuses on the three aspects of: * the state-of-the-art of
nonlinear filtering and control; * recent advances in recursive
filtering and sliding mode control; and * their potential for
application in networked control systems, and concluding with some
ideas for future research work. New concepts such as the randomly
occurring uncertainty and the probability-constrained performance
index are proposed to make the network models as realistic as
possible. The power of combinations of such recent tools as the
completing-the-square and sums-of-squares techniques,
Hamilton-Jacobi-Isaacs matrix inequalities, difference linear
matrix inequalities and parameter-dependent matrix inequalities is
exploited in treating the mathematical and computational challenges
arising from nonlinearity and stochasticity. Nonlinear Stochastic
Systems with Network-Induced Phenomena establishes a unified
framework of control and filtering which will be of value to
academic researchers in bringing structure to problems associated
with an important class of networked system and offering new means
of solving them. The significance of the new concepts, models and
methods presented for practical control engineering and signal
processing will also make it a valuable reference for engineers
dealing with nonlinear control and filtering problems.
Nonlinear Stochastic Processes addresses the frequently-encountered
problem of incomplete information. The causes of this problem
considered here include: missing measurements; sensor delays and
saturation; quantization effects; and signal sampling. Divided into
three parts, the text begins with a focus on H filtering and
control problems associated with general classes of nonlinear
stochastic discrete-time systems. Filtering problems are considered
in the second part, and in the third the theory and techniques
previously developed are applied to the solution of issues arising
in complex networks with the design of sampled-data-based
controllers and filters. Among its highlights, the text provides: *
a unified framework for filtering and control problems in complex
communication networks with limited bandwidth; * new concepts such
as random sensor and signal saturations for more realistic
modeling; and * demonstration of the use of techniques such as the
Hamilton-Jacobi-Isaacs, difference linear matrix, and
parameter-dependent matrix inequalities and sums of squares to
handle the computational challenges inherent in these systems. The
collection of recent research results presented in Nonlinear
Stochastic Processes will be of interest to academic researchers in
control and signal processing. Graduate students working with
communication networks with lossy information and control of
stochastic systems will also benefit from reading the book.
In this book, control and filtering problems for several classes of
stochastic networked systems are discussed. In each chapter, the
stability, robustness, reliability, consensus performance, and/or
disturbance attenuation levels are investigated within a unified
theoretical framework. The aim is to derive the sufficient
conditions such that the resulting systems achieve the prescribed
design requirements despite all the network-induced phenomena.
Further, novel notions such as randomly occurring sensor failures
and consensus in probability are discussed. Finally, the
theories/techniques developed are applied to emerging research
areas. Key Features Unifies existing and emerging concepts
concerning stochastic control/filtering and distributed
control/filtering with an emphasis on a variety of network-induced
complexities Includes concepts like randomly occurring sensor
failures and consensus in probability (with respect to time-varying
stochastic multi-agent systems) Exploits the recursive linear
matrix inequality approach, completing the square method,
Hamilton-Jacobi inequality approach, and parameter-dependent matrix
inequality approach to handle the emerging
mathematical/computational challenges Captures recent advances of
theories, techniques, and applications of stochastic control as
well as filtering from an engineering-oriented perspective Gives
simulation examples in each chapter to reflect the engineering
practice
The book addresses the system performance with a focus on the
network-enhanced complexities and developing the
engineering-oriented design framework of controllers and filters
with potential applications in system sciences, control engineering
and signal processing areas. Therefore, it provides a unified
treatment on the analysis and synthesis for discrete-time
stochastic systems with guarantee of certain performances against
network-enhanced complexities with applications in sensor networks
and mobile robotics. Such a result will be of great importance in
the development of novel control and filtering theories including
industrial impact. Key Features Provides original methodologies and
emerging concepts to deal with latest issues in the control and
filtering with an emphasis on a variety of network-enhanced
complexities Gives results of stochastic control and filtering
distributed control and filtering, and security control of complex
networked systems Captures the essence of performance analysis and
synthesis for stochastic control and filtering Concepts and
performance indexes proposed reflect the requirements of
engineering practice Methodologies developed in this book include
backward recursive Riccati difference equation approach and the
discrete-time version of input-to-state stability in probability
Communication-Protocol-Based Filtering and Control of Networked
Systems is a self-contained treatment of the state of the art
in communication-protocol-based filtering and control; recent
advances in networked systems; and the potential for application in
sensor networks. This book provides new concepts, new models and
new methodologies with practical significance in control
engineering and signal processing. The book first establishes
signal-transmission models subject to different communication
protocols and then develops new filter design techniques based on
those models and preset requirements for filtering performance. The
authors then extend this work to finite-horizon H-infinity control,
ultimately bounded control and finite-horizon consensus control.
The focus throughout is on three typical communications protocols:
the round-robin, random-access and try-once-and-discard protocols,
and the systems studied are drawn from a variety of classes, among
them nonlinear systems, time-delayed and time-varying systems,
multi-agent systems and complex networks. Readers are shown the
latest techniques—recursive linear matrix inequalities, backward
recursive difference equations, stochastic analysis and mapping
methods. The unified framework for communication-protocol-based
filtering and control for different networked systems established
in the book will be of interest to academic researchers and
practicing engineers working with communications and other
signal-processing systems. Senior undergraduate and graduate
students looking to increase their knowledge of current methods in
control and signal processing of networked systems will also find
this book valuable.
This book establishes a unified framework for dealing with typical
engineering complications arising in modern, complex, large-scale
networks such as parameter uncertainties, missing measurement and
cyber-attack. Distributed Filtering, Control and
Synchronization is a timely reflection on methods designed to
handle a series of control and signal-processing issues in modern
industrial engineering practice in areas like power grids and
environmental monitoring. It exploits the latest techniques to
handle the emerging mathematical and computational challenges
arising from, among other things, the dynamic topologies of
distributed systems and in the context of sensor networks and
multi-agent systems. These techniques include recursive linear
matrix inequalities, local-performance and stochastic analyses and
techniques based on matrix theory. Readers interested in the theory
and application of control and signal processing will find much to
interest them in the new models and methods presented in this book.
Academic researchers can find ideas for developing their own
research, graduate and advanced undergraduate students will be made
aware of the state of the art, and practicing engineers will find
methods for addressing practical difficulties besetting modern
networked systems
Communication-Protocol-Based Filtering and Control of Networked
Systems is a self-contained treatment of the state of the art in
communication-protocol-based filtering and control; recent advances
in networked systems; and the potential for application in sensor
networks. This book provides new concepts, new models and new
methodologies with practical significance in control engineering
and signal processing. The book first establishes
signal-transmission models subject to different communication
protocols and then develops new filter design techniques based on
those models and preset requirements for filtering performance. The
authors then extend this work to finite-horizon H-infinity control,
ultimately bounded control and finite-horizon consensus control.
The focus throughout is on three typical communications protocols:
the round-robin, random-access and try-once-and-discard protocols,
and the systems studied are drawn from a variety of classes, among
them nonlinear systems, time-delayed and time-varying systems,
multi-agent systems and complex networks. Readers are shown the
latest techniques-recursive linear matrix inequalities, backward
recursive difference equations, stochastic analysis and mapping
methods. The unified framework for communication-protocol-based
filtering and control for different networked systems established
in the book will be of interest to academic researchers and
practicing engineers working with communications and other
signal-processing systems. Senior undergraduate and graduate
students looking to increase their knowledge of current methods in
control and signal processing of networked systems will also find
this book valuable.
This book establishes a unified framework for dealing with typical
engineering complications arising in modern, complex, large-scale
networks such as parameter uncertainties, missing measurement and
cyber-attack. Distributed Filtering, Control and Synchronization is
a timely reflection on methods designed to handle a series of
control and signal-processing issues in modern industrial
engineering practice in areas like power grids and environmental
monitoring. It exploits the latest techniques to handle the
emerging mathematical and computational challenges arising from,
among other things, the dynamic topologies of distributed systems
and in the context of sensor networks and multi-agent systems.
These techniques include recursive linear matrix inequalities,
local-performance and stochastic analyses and techniques based on
matrix theory. Readers interested in the theory and application of
control and signal processing will find much to interest them in
the new models and methods presented in this book. Academic
researchers can find ideas for developing their own research,
graduate and advanced undergraduate students will be made aware of
the state of the art, and practicing engineers will find methods
for addressing practical difficulties besetting modern networked
systems
Stochastic Control and Filtering over Constrained Communication
Networks presents up-to-date research developments and novel
methodologies on stochastic control and filtering for networked
systems under constrained communication networks. It provides a
framework of optimal controller/filter design, resilient filter
design, stability and performance analysis for the systems
considered, subject to various kinds of communication constraints,
including signal-to-noise constraints, bandwidth constraints, and
packet drops. Several techniques are employed to develop the
controllers and filters desired, including: recursive Riccati
equations; matrix decomposition; optimal estimation theory; and
mathematical optimization methods. Readers will benefit from the
book's new concepts, models and methodologies that have practical
significance in control engineering and signal processing.
Stochastic Control and Filtering over Constrained Communication
Networks is a practical research reference for engineers dealing
with networked control and filtering problems. It is also of
interest to academics and students working in control and
communication networks.
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