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From household appliances to applications in robotics, engineered
systems involving complex dynamics can only be as effective as the
algorithms that control them. While Dynamic Programming (DP) has
provided researchers with a way to optimally solve decision and
control problems involving complex dynamic systems, its practical
value was limited by algorithms that lacked the capacity to scale
up to realistic problems. However, in recent years, dramatic
developments in Reinforcement Learning (RL), the model-free
counterpart of DP, changed our understanding of what is possible.
Those developments led to the creation of reliable methods that can
be applied even when a mathematical model of the system is
unavailable, allowing researchers to solve challenging control
problems in engineering, as well as in a variety of other
disciplines, including economics, medicine, and artificial
intelligence. Reinforcement Learning and Dynamic Programming Using
Function Approximators provides a comprehensive and unparalleled
exploration of the field of RL and DP. With a focus on
continuous-variable problems, this seminal text details essential
developments that have substantially altered the field over the
past decade. In its pages, pioneering experts provide a concise
introduction to classical RL and DP, followed by an extensive
presentation of the state-of-the-art and novel methods in RL and DP
with approximation. Combining algorithm development with
theoretical guarantees, they elaborate on their work with
illustrative examples and insightful comparisons. Three individual
chapters are dedicated to representative algorithms from each of
the major classes of techniques: value iteration, policy iteration,
and policy search. The features and performance of these algorithms
are highlighted in extensive experimental studies on a range of
control applications. The recent development of applications
involving complex systems has led to a surge of interest in RL and
DP methods and the subsequent need for a quality resource on the
subject. For graduate students and others new to the field, this
book offers a thorough introduction to both the basics and emerging
methods. And for those researchers and practitioners working in the
fields of optimal and adaptive control, machine learning,
artificial intelligence, and operations research, this resource
offers a combination of practical algorithms, theoretical analysis,
and comprehensive examples that they will be able to adapt and
apply to their own work. Access the authors' website at
www.dcsc.tudelft.nl/rlbook/ for additional material, including
computer code used in the studies and information concerning new
developments.
Fuzzy Algorithms for Control gives an overview of the research
results of a number of European research groups that are active and
play a leading role in the field of fuzzy modeling and control. It
contains 12 chapters divided into three parts. Chapters in the
first part address the position of fuzzy systems in control
engineering and in the AI community. State-of-the-art surveys on
fuzzy modeling and control are presented along with a critical
assessment of the role of these methodologists in control
engineering. The second part is concerned with several analysis and
design issues in fuzzy control systems. The analytical issues
addressed include the algebraic representation of fuzzy models of
different types, their approximation properties, and stability
analysis of fuzzy control systems. Several design aspects are
addressed, including performance specification for control systems
in a fuzzy decision-making framework and complexity reduction in
multivariable fuzzy systems. In the third part of the book, a
number of applications of fuzzy control are presented. It is shown
that fuzzy control in combination with other techniques such as
fuzzy data analysis is an effective approach to the control of
modern processes which present many challenges for the design of
control systems. One has to cope with problems such as process
nonlinearity, time-varying characteristics for incomplete process
knowledge. Examples of real-world industrial applications presented
in this book are a blast furnace, a lime kiln and a solar plant.
Other examples of challenging problems in which fuzzy logic plays
an important role and which are included in this book are mobile
robotics and aircraft control. The aim of this book is to address
both theoretical and practical subjects in a balanced way. It will
therefore be useful for readers from the academic world and also
from industry who want to apply fuzzy control in practice.
Rule-based fuzzy modeling has been recognised as a powerful
technique for the modeling of partly-known nonlinear systems. Fuzzy
models can effectively integrate information from different
sources, such as physical laws, empirical models, measurements and
heuristics. Application areas of fuzzy models include prediction,
decision support, system analysis, control design, etc. Fuzzy
Modeling for Control addresses fuzzy modeling from the systems and
control engineering points of view. It focuses on the selection of
appropriate model structures, on the acquisition of dynamic fuzzy
models from process measurements (fuzzy identification), and on the
design of nonlinear controllers based on fuzzy models. To
automatically generate fuzzy models from measurements, a
comprehensive methodology is developed which employs fuzzy
clustering techniques to partition the available data into subsets
characterized by locally linear behaviour. The relationships
between the presented identification method and linear regression
are exploited, allowing for the combination of fuzzy logic
techniques with standard system identification tools. Attention is
paid to the trade-off between the accuracy and transparency of the
obtained fuzzy models. Control design based on a fuzzy model of a
nonlinear dynamic process is addressed, using the concepts of
model-based predictive control and internal model control with an
inverted fuzzy model. To this end, methods to exactly invert
specific types of fuzzy models are presented. In the context of
predictive control, branch-and-bound optimization is applied. The
main features of the presented techniques are illustrated by means
of simple examples. In addition, three real-world applications are
described. Finally, software tools for building fuzzy models from
measurements are available from the author.
The increasing complexity of our world demands new perspectives on
the role of technology in decision making. Human decision making
has its li- tations in terms of information-processing capacity. We
need new technology to cope with the increasingly complex and
information-rich nature of our modern society. This is particularly
true for critical environments such as crisis management and tra?c
management, where humans need to engage in close collaborations
with arti?cial systems to observe and understand the situation and
respond in a sensible way. We believe that close collaborations
between humans and arti?cial systems will become essential and that
the importance of research into Interactive Collaborative
Information Systems (ICIS) is self-evident. Developments in
information and communication technology have ra- cally changed our
working environments. The vast amount of information available
nowadays and the wirelessly networked nature of our modern so- ety
open up new opportunities to handle di?cult decision-making
situations such as computer-supported situation assessment and
distributed decision making. To make good use of these new
possibilities, we need to update our traditional views on the role
and capabilities of information systems. The aim of the Interactive
Collaborative Information Systems project is to develop techniques
that support humans in complex information en- ronments and that
facilitate distributed decision-making capabilities. ICIS
emphasizes the importance of building actor-agent communities:
close c- laborations between human and arti?cial actors that
highlight their comp- mentary capabilities, and in which task
distribution is ?exible and adaptive.
Many problems in decision making, monitoring, fault detection, and
control require the knowledge of state variables and time-varying
parameters that are not directly measured by sensors. In such
situations, observers, or estimators, can be employed that use the
measured input and output signals along with a dynamic model of the
system in order to estimate the unknown states or parameters. An
essential requirement in designing an observer is to guarantee the
convergence of the estimates to the true values or at least to a
small neighborhood around the true values. However, for nonlinear,
large-scale, or time-varying systems, the design and tuning of an
observer is generally complicated and involves large computational
costs. This book provides a range of methods and tools to design
observers for nonlinear systems represented by a special type of a
dynamic nonlinear model -- the Takagi--Sugeno (TS) fuzzy model. The
TS model is a convex combination of affine linear models, which
facilitates its stability analysis and observer design by using
effective algorithms based on Lyapunov functions and linear matrix
inequalities. Takagi--Sugeno models are known to be universal
approximators and, in addition, a broad class of nonlinear systems
can be exactly represented as a TS system. Three particular
structures of large-scale TS models are considered: cascaded
systems, distributed systems, and systems affected by unknown
disturbances. The reader will find in-depth theoretic analysis
accompanied by illustrative examples and simulations of real-world
systems. Stability analysis of TS fuzzy systems is addressed in
detail. The intended audience are graduate students and researchers
both from academia and industry. For newcomers to the field, the
book provides a concise introduction dynamic TS fuzzy models along
with two methods to construct TS models for a given nonlinear
system
The increasing complexity of our world demands new perspectives on
the role of technology in decision making. Human decision making
has its li- tations in terms of information-processing capacity. We
need new technology to cope with the increasingly complex and
information-rich nature of our modern society. This is particularly
true for critical environments such as crisis management and tra?c
management, where humans need to engage in close collaborations
with arti?cial systems to observe and understand the situation and
respond in a sensible way. We believe that close collaborations
between humans and arti?cial systems will become essential and that
the importance of research into Interactive Collaborative
Information Systems (ICIS) is self-evident. Developments in
information and communication technology have ra- cally changed our
working environments. The vast amount of information available
nowadays and the wirelessly networked nature of our modern so- ety
open up new opportunities to handle di?cult decision-making
situations such as computer-supported situation assessment and
distributed decision making. To make good use of these new
possibilities, we need to update our traditional views on the role
and capabilities of information systems. The aim of the Interactive
Collaborative Information Systems project is to develop techniques
that support humans in complex information en- ronments and that
facilitate distributed decision-making capabilities. ICIS
emphasizes the importance of building actor-agent communities:
close c- laborations between human and arti?cial actors that
highlight their comp- mentary capabilities, and in which task
distribution is ?exible and adaptive.
Fuzzy Algorithms for Control gives an overview of the research
results of a number of European research groups that are active and
play a leading role in the field of fuzzy modeling and control. It
contains 12 chapters divided into three parts. Chapters in the
first part address the position of fuzzy systems in control
engineering and in the AI community. State-of-the-art surveys on
fuzzy modeling and control are presented along with a critical
assessment of the role of these methodologists in control
engineering. The second part is concerned with several analysis and
design issues in fuzzy control systems. The analytical issues
addressed include the algebraic representation of fuzzy models of
different types, their approximation properties, and stability
analysis of fuzzy control systems. Several design aspects are
addressed, including performance specification for control systems
in a fuzzy decision-making framework and complexity reduction in
multivariable fuzzy systems. In the third part of the book, a
number of applications of fuzzy control are presented. It is shown
that fuzzy control in combination with other techniques such as
fuzzy data analysis is an effective approach to the control of
modern processes which present many challenges for the design of
control systems. One has to cope with problems such as process
nonlinearity, time-varying characteristics for incomplete process
knowledge. Examples of real-world industrial applications presented
in this book are a blast furnace, a lime kiln and a solar plant.
Other examples of challenging problems in which fuzzy logic plays
an important role and which are included in this book are mobile
robotics and aircraft control. The aim of this book is to address
both theoretical and practical subjects in a balanced way. It will
therefore be useful for readers from the academic world and also
from industry who want to apply fuzzy control in practice.
Rule-based fuzzy modeling has been recognised as a powerful
technique for the modeling of partly-known nonlinear systems. Fuzzy
models can effectively integrate information from different
sources, such as physical laws, empirical models, measurements and
heuristics. Application areas of fuzzy models include prediction,
decision support, system analysis, control design, etc. Fuzzy
Modeling for Control addresses fuzzy modeling from the systems and
control engineering points of view. It focuses on the selection of
appropriate model structures, on the acquisition of dynamic fuzzy
models from process measurements (fuzzy identification), and on the
design of nonlinear controllers based on fuzzy models. To
automatically generate fuzzy models from measurements, a
comprehensive methodology is developed which employs fuzzy
clustering techniques to partition the available data into subsets
characterized by locally linear behaviour. The relationships
between the presented identification method and linear regression
are exploited, allowing for the combination of fuzzy logic
techniques with standard system identification tools. Attention is
paid to the trade-off between the accuracy and transparency of the
obtained fuzzy models. Control design based on a fuzzy model of a
nonlinear dynamic process is addressed, using the concepts of
model-based predictive control and internal model control with an
inverted fuzzy model. To this end, methods to exactly invert
specific types of fuzzy models are presented. In the context of
predictive control, branch-and-bound optimization is applied. The
main features of the presented techniques are illustrated by means
of simple examples. In addition, three real-world applications are
described. Finally, software tools for building fuzzy models from
measurements are available from the author.
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