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Optimal and Robust Scheduling for Networked Control Systems tackles
the problem of integrating system components-controllers, sensors,
and actuators-in a networked control system. It is common practice
in industry to solve such problems heuristically, because the few
theoretical results available are not comprehensive and cannot be
readily applied by practitioners. This book offers a solution to
the deterministic scheduling problem that is based on rigorous
control theoretical tools but also addresses practical
implementation issues. Helping to bridge the gap between control
theory and computer science, it suggests that the consideration of
communication constraints at the design stage will significantly
improve the performance of the control system. Technical Results,
Design Techniques, and Practical Applications The book brings
together well-known measures for robust performance as well as fast
stochastic algorithms to assist designers in selecting the best
network configuration and guaranteeing the speed of offline
optimization. The authors propose a unifying framework for
modelling NCSs with time-triggered communication and present
technical results. They also introduce design techniques, including
for the codesign of a controller and communication sequence and for
the robust design of a communication sequence for a given
controller. Case studies explore the use of the FlexRay TDMA and
time-triggered control area network (CAN) protocols in an
automotive control system. Practical Solutions to Your
Time-Triggered Communication Problems This unique book develops
ready-to-use engineering tools for large-scale control system
integration with a focus on robustness and performance. It
emphasizes techniques that are directly applicable to
time-triggered communication problems in the automotive industry
and in avionics, robotics, and automated manufacturing.
Optimal and Robust Scheduling for Networked Control Systems tackles
the problem of integrating system components-controllers, sensors,
and actuators-in a networked control system. It is common practice
in industry to solve such problems heuristically, because the few
theoretical results available are not comprehensive and cannot be
readily applied by practitioners. This book offers a solution to
the deterministic scheduling problem that is based on rigorous
control theoretical tools but also addresses practical
implementation issues. Helping to bridge the gap between control
theory and computer science, it suggests that the consideration of
communication constraints at the design stage will significantly
improve the performance of the control system. Technical Results,
Design Techniques, and Practical Applications The book brings
together well-known measures for robust performance as well as fast
stochastic algorithms to assist designers in selecting the best
network configuration and guaranteeing the speed of offline
optimization. The authors propose a unifying framework for
modelling NCSs with time-triggered communication and present
technical results. They also introduce design techniques, including
for the codesign of a controller and communication sequence and for
the robust design of a communication sequence for a given
controller. Case studies explore the use of the FlexRay TDMA and
time-triggered control area network (CAN) protocols in an
automotive control system. Practical Solutions to Your
Time-Triggered Communication Problems This unique book develops
ready-to-use engineering tools for large-scale control system
integration with a focus on robustness and performance. It
emphasizes techniques that are directly applicable to
time-triggered communication problems in the automotive industry
and in avionics, robotics, and automated manufacturing.
The next generation of autonomous vehicles will provide major
improvements in traffic flow, fuel efficiency, and vehicle safety.
Several challenges currently prevent the deployment of autonomous
vehicles, one aspect of which is robust and adaptable vehicle
control. Designing a controller for autonomous vehicles capable of
providing adequate performance in all driving scenarios is
challenging due to the highly complex environment and inability to
test the system in the wide variety of scenarios which it may
encounter after deployment. However, deep learning methods have
shown great promise in not only providing excellent performance for
complex and non-linear control problems, but also in generalizing
previously learned rules to new scenarios. For these reasons, the
use of deep neural networks for vehicle control has gained
significant interest. In this book, we introduce relevant deep
learning techniques, discuss recent algorithms applied to
autonomous vehicle control, identify strengths and limitations of
available methods, discuss research challenges in the field, and
provide insights into the future trends in this rapidly evolving
field.
When his father denies paternity, the infant Phil is left with his
poverty stricken mother in a 1950s West Yorkshire mill town. Burned
by loss and desertion Annie's mental state frequently breaks apart.
Before his third birthday she releases Phil to a distant orphanage.
A series of subsequent separations take their toll on this
spirited, imaginative boy who - at every turn - feels violently
cast off and set adrift. In this memoir of life under the roof of a
1960s orphanage Phil Barber has travelled with love and a wry
humour into the dark earth of his tangled roots. Fatherless is an
account of a displaced boy reaching through a lonely childhood and
desperate adolescence to forge his way to manhood. Phil's story is
a winding river of struggles for identity, and a search for beauty
and belonging among the oceanic pain of feeling exiled from
society.
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