IEEE Technology and Society Magazine - September 2016 - 24

topic that is relatively unexplored in
the classical control community.
Second, in classical control, the
system to be controlled does not
change its mathematical description
in response to control signals. In
Smart City applications this funda-
mental paradigm is hard to realize.
In general, models can only provide
approximations of the dynamics of
real processes. This is not problem-
atic as long as there is an under-
standing of the possible deviation
of reality and model. Models in the
area of Smart Cities, however, can-
not readily be derived from first
principles but are empirical, i.e.,
based on data obtained through
measurements of established pro-
cesses. In addition, the empirical
data cannot be obtained from con-
trolled experiments over a range of
operating points but only from the
system as is. An attempt to improve
the processes in question, e.g., by
providing information to the agents
involved, creates a feedback loop
that was not previously present. This
change in the underlying process
may invalidate the empirical model;
in the derivation of the model there
simply was no data available in
order to capture the dynamic effect
of such a feedback. Very often the
proposed solutions do not take this
feedback loop into account. This lat-
ter consideration gives rise for the
need to study prediction and opti-
mization under feedback in a much
more detailed manner than has hith-
erto been the case. In this context,
the topic of signaling to alleviate this
feedback effect is of considerable
cu r rent interest and is an inter-
esting evolution of classical pric-
ing theory. The effect of transport
delays and the fact that all agents
are informed of signals in an instan-
taneous manner pose further inter-
esting problems in this respect.
Third, data sets in the city are
often obtained in "closed loop."

College Dublin, Dublin, Ireland, and
also with IBM Research, Dublin, Ire-
land. Email: robert.shorten@ucd.ie
Fabian Wirth is with the Faculty
of Computer Science and Mathe-
matics, University of Passau, Passau,
Germany.

That is, decision maker information
is often included in available data
sets. The need for building models
of large-scale systems under feed-
back is a significant impediment to
progress in applying some control
techniques in Smart City applica-
tions. Here Smart City research may
have much to learn from both eco-
nomic and control theory in dealing
with such effects.
Finally, a fundamental difference
between classical control and Smart
City control is the need to study the
effect of the control signals on the
statistical properties of the popu-
lations being controlled. Since in
many cases we are dealing with the
delivery of services, these statistical
properties should be stationary and
predictable - giving rise to the need
for "ergodic" control design.
Speaking from a broad perspec-
tive, Smart City applications are
about inducing behavioral change
to make better use of available re-
sources. Control theory has much
to offer in this domain. A theory of
feedback has been well tuned over
the last century and applications
of this theory can provide not only
valuable insights and tools for the
design of Smart City applications,
but also inspiration for the next
generation of control pioneers to
push back the existing boundaries
of control. Embracing this vision
could indeed open the door to a
"Smart City inspired golden age of
control," where control theory can
inspire and contribute to the growth
of smart cities.

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Author Information
Emanuele Crisostomi is with the
Department of Energy, Systems, Ter-
ritory, and Constructions Engineer-
ing, University of Pisa, Pisa, Italy.
Email: emanuele.crisostomi@unipi.it.
Robert Shorten is with the School
of Electrical, Electronic, and Com-
munications Engineering, University
IEEE Technology and Society Magazine

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september 2016

Table of Contents for the Digital Edition of IEEE Technology and Society Magazine - September 2016