Switched systems are embedded devices widespread in industrial
applications such as power electronics and automotive control. They
consist of continuous-time dynamical subsystems and a rule that
controls the switching between them. Under a suitable control rule, the
system can improve its steady-state performance and meet essential
properties, such as safety and stability, in desirable operating zones.
There is an unprecedented enthusiasm for radio frequency
identification (RFID) technologies today. RFID is based on the
exchange of information carried by electromagnetic waves between a
label, or tag, and a reader. This technology is currently in full
economic expansion, which has manifested itself in widely backed
research activities, some of which will be examined in this book.
Radio frequency identification (RFID) and Wireless sensor networks (WSN) are
the two key wireless technologies that have diversified applications in the present
and the upcoming systems in this area. RFID is a wireless automated recognition
technology which is primarily used to recognize objects or to follow their posi-
tion without providing any sign about the physical form of the substance. On the
other hand, WSN not only offers information about the state of the substance
and environment but also enables multi-hop wireless communications.
To a quantum mechanic the whole universe is one godawful big interacting wavefunction ?
but to the rest of us, it’s a world full of separate and distinguishable objects that hurt us
when we kick them. At a few months of age, human children recognize objects, expect
them to be permanent and move continuously, and display surprise when they aren’t or
don’t. We associate visual, tactile, and in some cases audible and olfactory sensations with
identifiable physical things. We’re hardwired to understand our environment as being
composed of separable things with specific properties and locations. We understand the
world in terms of what was where when.
This book introduces students to the theory and practice of control systems engineer-
ing. The text emphasizes the practical application of the subject to the analysis and
design of feedback systems.
The study of control systems engineering is essential for students pursuing
degrees in electrical, mechanical, aerospace, biomedical, or chemical engineering.
Control systems are found in a broad range of applications within these disciplines,
from aircraft and spacecraft to robots and process control systems.
This book is either ambitious, brave, or reckless approaching
a topic as rapidly evolving as industrial control system (ICS)
security. From the advent of ICS-targeted malicious software
such as Stuxnet to the advanced persistent threats posed by
organized crime and state-sponsored entities, ICS is in the
crosshairs and practices and controls considered safe today
may be obsolete tomorrow. Possibly more so than in more
traditional IT security, because of the differences inherent in
ICS.
If you are acquainted with neural networks, automatic control problems
are good industrial applications and have a dynamic or evolutionary nature
lacking in static pattern-recognition; control ideas are also prevalent in the
study of the natural neural networks found in animals and human beings.
If you are interested in the practice and theory of control, artificial neu-
ral networks offer a way to synthesize nonlinear controllers, filters, state
observers and system identifiers using a parallel method of computation.
Control systems are used to regulate an enormous variety of machines, products, and
processes. They control quantities such as motion, temperature, heat flow, fluid flow,
fluid pressure, tension, voltage, and current. Most concepts in control theory are based
on having sensors to measure the quantity under control. In fact, control theory is
often taught assuming the availability of near-perfect feedback signals. Unfortunately,
such an assumption is often invalid. Physical sensors have shortcomings that can
degrade a control system.
In this chapter we give a quick overview of control theory, explaining why
integral feedback control works, describing PID controllers, and summariz-
ing some of the currently available techniques for PID controller design.
This background will serve to motivate our results on PID control, pre-
sented in the subsequent chapters.
Although state of the art in many typical machine learning tasks, deep learning
algorithmsareverycostly interms ofenergyconsumption,duetotheirlargeamount
of required computations and huge model sizes. Because of this, deep learning
applications on battery-constrained wearables have only been possible through
wireless connections with a resourceful cloud. This setup has several drawbacks.
First, there are privacy concerns. Cloud computing requires users to share their raw
data—images, video, locations, speech—with a remote system. Most users are not
willing to do this. Second, the cloud-setup requires users to be connected all the
time, which is unfeasible given current cellular coverage. Furthermore, real-time
applications require low latency connections, which cannot be guaranteed using
the current communication infrastructure. Finally, wireless connections are very
inefficient—requiringtoo much energyper transferredbit for real-time data transfer
on energy-constrained platforms.
