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This book introduces several observer-based methods, including: *
the sliding-mode observer * the adaptive observer * the
unknown-input observer and * the descriptor observer method for the
problem of fault detection, isolation and estimation, allowing
readers to compare and contrast the different approaches. The
authors present basic material on Lyapunov stability theory, HY
control theory, sliding-mode control theory and linear matrix
inequality problems in a self-contained and step-by-step manner.
Detailed and rigorous mathematical proofs are provided for all the
results developed in the text so that readers can quickly gain a
good understanding of the material. MATLAB (R) and Simulink (R)
codes for all the examples, which can be downloaded from
http://extras.springer.com, enable students to follow the methods
and illustrative examples easily. The systems used in the examples
make the book highly relevant to real-world problems in industrial
control engineering and include a seventh-order aircraft model, a
single-link flexible joint robot arm and a satellite controller. To
help readers quickly find the information they need and to improve
readability, the individual chapters are written so as to be
semi-independent of each other. Robust Oberserver-Based Fault
Diagnosis for Nonlinear Systems Using MATLAB (R) is of interest to
process, aerospace, robotics and control engineers, engineering
students and researchers with a control engineering background.
Mostindustrialbiotechnologicalprocessesareoperatedempirically.Oneofthe
major di?culties of applying advanced control theories is the
highly nonlinear nature of the processes. This book examines
approaches based on arti?cial intelligencemethods, inparticular,
geneticalgorithmsandneuralnetworks, for monitoring, modelling and
optimization of fed-batch fermentation processes. The main aim of a
process control is to maximize the ?nal product with minimum
development and production costs. This book is interdisciplinary in
nature, combining topics from biotechn- ogy, arti?cial
intelligence, system identi?cation, process monitoring, process
modelling and optimal control. Both simulation and experimental
validation are performed in this study to demonstrate the
suitability and feasibility of proposed methodologies. An online
biomass sensor is constructed using a - current neural network for
predicting the biomass concentration online with only three
measurements (dissolved oxygen, volume and feed rate). Results show
that the proposed sensor is comparable or even superior to other
sensors proposed in the literature that use more than three
measurements. Biote- nological processes are modelled by cascading
two recurrent neural networks. It is found that neural models are
able to describe the processes with high accuracy. Optimization of
the ?nal product is achieved using modi?ed genetic algorithms to
determine optimal feed rate pro?les. Experimental results of the
corresponding production yields demonstrate that genetic algorithms
are powerful tools for optimization of highly nonlinear systems.
Moreover, a c- bination of recurrentneural networks and genetic
algorithms provides a useful and cost-e?ective methodology for
optimizing biotechnological process
"Robust Control for Uncertain Networked Control Systems with
Random Delays" addresses the problem of analysis and design of
networked control systems when the communication delays are varying
in a random fashion. The random nature of the time delays is
typical for commercially used networks, such as a DeviceNet (which
is a controller area network) and Ethernet network.
The main technique used in this book is based on the
Lyapunov-Razumikhin method, which results in delay-dependent
controllers. The existence of such controllers and fault estimators
are given in terms of the solvability of bilinear matrix
inequalities. Iterative algorithms are proposed to change this
non-convex problem into quasi-convex optimization problems, which
can be solved effectively by available mathematical tools. Finally,
to demonstrate the effectiveness and advantages of the proposed
design method in the book, numerical examples are given in each
designed control system.
Mostindustrialbiotechnologicalprocessesareoperatedempirically.Oneofthe
major di?culties of applying advanced control theories is the
highly nonlinear nature of the processes. This book examines
approaches based on arti?cial intelligencemethods, inparticular,
geneticalgorithmsandneuralnetworks, for monitoring, modelling and
optimization of fed-batch fermentation processes. The main aim of a
process control is to maximize the ?nal product with minimum
development and production costs. This book is interdisciplinary in
nature, combining topics from biotechn- ogy, arti?cial
intelligence, system identi?cation, process monitoring, process
modelling and optimal control. Both simulation and experimental
validation are performed in this study to demonstrate the
suitability and feasibility of proposed methodologies. An online
biomass sensor is constructed using a - current neural network for
predicting the biomass concentration online with only three
measurements (dissolved oxygen, volume and feed rate). Results show
that the proposed sensor is comparable or even superior to other
sensors proposed in the literature that use more than three
measurements. Biote- nological processes are modelled by cascading
two recurrent neural networks. It is found that neural models are
able to describe the processes with high accuracy. Optimization of
the ?nal product is achieved using modi?ed genetic algorithms to
determine optimal feed rate pro?les. Experimental results of the
corresponding production yields demonstrate that genetic algorithms
are powerful tools for optimization of highly nonlinear systems.
Moreover, a c- bination of recurrentneural networks and genetic
algorithms provides a useful and cost-e?ective methodology for
optimizing biotechnological process
This book introduces several observer-based methods, including: *
the sliding-mode observer * the adaptive observer * the
unknown-input observer and * the descriptor observer method for the
problem of fault detection, isolation and estimation, allowing
readers to compare and contrast the different approaches. The
authors present basic material on Lyapunov stability theory, HY
control theory, sliding-mode control theory and linear matrix
inequality problems in a self-contained and step-by-step manner.
Detailed and rigorous mathematical proofs are provided for all the
results developed in the text so that readers can quickly gain a
good understanding of the material. MATLAB (R) and Simulink (R)
codes for all the examples, which can be downloaded from
http://extras.springer.com, enable students to follow the methods
and illustrative examples easily. The systems used in the examples
make the book highly relevant to real-world problems in industrial
control engineering and include a seventh-order aircraft model, a
single-link flexible joint robot arm and a satellite controller. To
help readers quickly find the information they need and to improve
readability, the individual chapters are written so as to be
semi-independent of each other. Robust Oberserver-Based Fault
Diagnosis for Nonlinear Systems Using MATLAB (R) is of interest to
process, aerospace, robotics and control engineers, engineering
students and researchers with a control engineering background.
Most real physical systems are nonlinear in nature. Control and
?ltering of nonlinear systems are still open problems due to their
complexity natures. These problem becomes more complex when the
system's parameters are - certain. A common approach to designing a
controller/?lter for an uncertain nonlinear system is to linearize
the system about an operating point, and uses linear control theory
to design a controller/?lter. This approach is successful when the
operating point of the system is restricted to a certain region. H-
ever, when a wide range operation of the system is required, this
method may fail.
ThisbookpresentsnewnovelmethodologiesfordesigningrobustH fuzzy ?
controllers and robustH fuzzy ?lters for a class of uncertain fuzzy
systems ? (UFSs), uncertain fuzzy Markovian jump systems (UFMJSs),
uncertain fuzzy singularly perturbed systems (UFSPSs) and uncertain
fuzzy singularly p- turbed systems with Markovian jumps
(UFSPS-MJs). These new meth- ologies provide a framework for
designing robustH fuzzy controllers and ? robustH fuzzy ?lters for
these classes of systems based on a Tagaki-Sugeno ? (TS) fuzzy
model. Solutions to the design problems are presented in terms of
linear matrix inequalities (LMIs). To investigate the design
problems, we ?rst describe a class of uncertain nonlinear systems
(UNSs), uncertain nonlinear Markovianjumpsystems(UNMJSs),
uncertainnonlinearsingularlyperturbed
systems(UNSPSs)anduncertainnonlinearsingularlyperturbedsystemswith
Markovian jumps (UNSPS-MJs) by a TS fuzzy system with parametric -
certainties and with/without Markovian jumps. Then, based on an LMI
- proach, we develop a technique for designing robustH fuzzy
controllers and ? robustH fuzzy ?lters such that a given prescribed
performance index is ? guaranteed.
Consensus Tracking of Multi-agent Systems with Switching Topologies
takes an advanced look at the development of multi-agent systems
with continuously switching topologies and relay tracking systems
with switching of agents. Research problems addressed are well
defined and numerical examples and simulation results are given to
demonstrate the engineering potential. The book is aimed at
advanced graduate students in control engineering, signal
processing, nonlinear systems, switched systems and applied
mathematics. It will also be a core reference for control engineers
working on nonlinear control and switched control, as well as
mathematicians and biomedical engineering researchers working on
complex systems.
Non-monotonic Approach to Robust H8 Control of Multi-model Systems
focuses on robust analysis and synthesis problems for multi-model
systems based on the non-monotonic Lyapunov Functionals (LFs)
approach that enlarges the stability region and improves control
performance. By fully considering the diversity of switching laws,
the multi-step time difference, the multi-step prediction, and the
expansion of system dimension, the non-monotonic LF can be properly
constructed. The focus of this book is placed on the H8 state
feedback control, H8 filtering and H8 output feedback control for
multi-model systems via a non-monotonic LF approach. The book's
authors provide illustrative examples to show the feasibility and
efficiency of the proposed methods, along with practical examples
that demonstrate the effectiveness and potential of theoretical
results.
Analysis and Synthesis of Polynomial Discrete-time Systems: An SOS
Approach addresses the analysis and design of polynomial
discrete-time control systems. The book deals with the application
of Sum of Squares techniques in solving specific control and
filtering problems that can be useful to solve advanced control
problems, both on the theoretical side and on the practical side.
Two types of controllers, state feedback controller and output
feedback controller, along with topics surrounding the nonlinear
filter and the H-infinity performance criteria are explored. The
book also proposes a solution to global stabilization of
discrete-time systems.
Marine vessels, including wind-powered yachts, are continually
required to be able to operate with properties of being more
reliable, comfortable and economical. Recently, the global economic
integration has intensified world-wide competition and increased
the demand for sea freights. This demand has stimulated the
development of marine vessels which are larger, faster and safer.
The crisis of non-renewable energy and its steady increase in price
leads to higher requirements of energy efficiency by marine
vessels. This increased awareness of protecting the environment has
ensured that there are now stricter standards in controlling ocean
pollutions hence it leads to more demands on marine vessel control.
These increases in performance and fuel saving can be achieved
through incorporating advanced control technologies. Adaptive and
artificial intelligent control strategies, by connecting to the
dynamics of a marine vessel, enable the vessel to follow an optimal
course or track with minimum rudder action, resulting in collision
avoidance and energy saving. Smaller marine vessels such as yachts
are often powerless against sea conditions, hence advanced control
algorithms combined with modern guidance technology such as global
positioning system (GPS), increase the safety of the navigation. As
a consequence, the autopilot system which integrates the electronic
hardware and the control algorithms has become standard use for
commercial and military marine vessels, and is becoming essential
equipment for smaller marine vessels such as leisure boats and
yachts.
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