Chapter 2
Overview of the Field
Control is a field with broad relevance to a number of engineering applications.
Its impact on modern society is both profound and often poorly understood. In
this chapter, we provide an overview of the field, illustrated with examples and
vignettes, and describe the new environment for control.
2.1 What is Control?
The term “control” has many meanings and often varies between communities. In
this report, we define control to be the use of algorithms and feedback in engineered
systems. Thus, control includes such examples as feedback loops in electronic am-
plifiers, set point controllers in chemical and materials processing, “fly-by-wire”
systems on aircraft, and even router protocols that control traffic flow on the Inter-
net. Emerging applications include high confidence software systems, autonomous
vehicles and robots, battlefield management systems, and biologically engineered
systems. At its core, control is an
information
science, and includes the use of
information in both analog and digital representations.
A modern controller senses the operation of a system, compares that against
the desired behavior, computes corrective actions based on a model of the system’s
response to external inputs, and actuates the system to effect the desired change.
This basic
feedback loop
of sensing, computation, and actuation is the central con-
cept in control. The key issues in designing control logic are ensuring that the
dynamics of the closed loop system are stable (bounded disturbances give bounded
errors) and that dynamics have the desired behavior (good disturbance rejection,
fast responsiveness to changes in operating point, etc). These properties are estab-
lished using a variety of modeling and analysis techniques that capture the essential
physics of the system and permit the exploration of possible behaviors in the pres-
ence of uncertainty, noise, and component failures.
A typical example of a modern control system is shown in Figure 2.1. The
basic elements of of sensing, computation, and actuation are clearly seen. In mod-
ern control systems, computation is typically implemented on a digital computer,
7
8
Chapter 2. Overview of the Field
Actuators
System
Sensors
D/A
Computer
A/D
operator input
noise
external disturbances
noise
Output
Controller
Plant
Σ
Σ
Figure 2.1.
Components of a modern control system.
requiring the use of analog-to-digital (A/D) and digital-to-analog (D/A) converters.
Uncertainty enters the system through noise in sensing and actuation subsystems,
external disturbances that affect the underlying system physics, and uncertain dy-
namics in the physical system (parameter errors, unmodeled effects, etc).
The basic feedback loop of control is often combined with
feedforward
control,
where a commanded actuator input is computed to achieve a desired action based
on a model of the system. While feedback operates in a closed loop, with actions
based on the deviation between measured and desired performance, feedforward
operates in open loop, with actions taken based on plans. It is often advantageous
to use feedback with feedforward to achieve both high performance and robustness.
It is important to note that while feedback is a central element of control, feed-
back as a phenomenon is ubiquitous in science and nature. Homeostasis in biological
systems maintains thermal, chemical, and biological conditions through feedback.
Global climate dynamics depend on the feedback interactions between the atmo-
sphere, oceans, land, and the sun. Ecologies are filled with examples of feedback,
resulting in complex interactions between animal and plant life. The dynamics of
economies are based on the feedback between individuals and corporations through
markets and the exchange of goods and services.
While ideas and tools from control can be applied to these systems, we focus
our attention in this report on the application of feedback to engineering systems.
We also limit ourselves to a small subset of the many aspects of control, choosing
to focus on those that are undergoing the most change and are most in need of new
ideas and techniques.
Control Theory
Control
theory
refers to the mathematical framework used to analyze and synthesize
control systems. Over the last 50 years, there has been careful attention by control
2.1. What is Control?
9
theorists to the issues of completeness and correctness. This includes substantial
efforts by mathematicians and engineers to develop a solid foundation for proving
stability and robustness of feedback controlled systems, and the development of
computational tools that provide guaranteed performance in the presence of un-
certainty. This rigor in approach is a hallmark of modern control and is largely
responsible for the success it has enjoyed across a variety of disciplines.
It is useful in this context to provide a brief history of the development of
modern control theory.
Automatic control traces its roots to the beginning of the industrial revolution,
when simple governors were used to automatically maintain steam engine speed de-
spite changes in loads, steam supply, and equipment. In the early 20th Century,
the same principles were applied in the emerging field of electronics, yielding feed-
back amplifiers that automatically maintained constant performance despite large
variations in vacuum tube devices.
The foundations of the theory of control are rooted in the 1940s, with the
development of methods for single-input, single-output feedback loops, including
transfer functions and Bode plots for modeling and analyzing frequency response
and stability, and Nyquist plots and gain/phase margin for studying stability of
feedback systems [9]. By designing feedback loops to avoid positive reinforcement
of disturbances around a closed loop system, one can ensure that the system is
stable and disturbances are attenuated. This first generation of techniques is known
collectively as “classical control” and is still the standard introduction to controls
for engineering students.
In the 1960s, the second generation of control theory, known as “modern
control,” was developed to provide methods for multi-variable systems where many
strongly coupled loops must be designed simultaneously. These tools made use of
state space representations of control systems and were coupled with advances in
numerical optimization and optimal control. These early state space methods made
use of linear ordinary differential equations to study the response of systems, and
control was achieved by placing the eigenvalues of the closed loop system to ensure
stability.
At around this same time, optimal control theory also made great advances,
with the establishment of the maximum principle of Pontryagin and the dynamic
programming results of Bellman. Optimal control theory gave precise conditions
under which a controller minimized a given cost function, either as an open loop
input (such as computing the thrust for optimal trajectory generation) or as a
closed loop feedback law. Estimation theory also benefited from results in optimal
control, and the Kalman filter was developed and quickly became a standard tool
used in many fields to estimate the internal states of a system given a (small) set
of measured signals.
Finally, in the 1980s the third generation of control theory, known as “robust
multi-variable control,” added powerful formal methods to guarantee desired closed
loop properties in the face of uncertainties. In many ways, robust control brought
back some of the key ideas from the early theory of control, where uncertainty was a
dominant factor in the design methodology. Techniques from operator theory were
extremely useful here and there was stronger interaction with mathematics, both
10
Chapter 2. Overview of the Field
in terms of using existing techniques and developing new mathematics.
Over the past two decades, many other branches of control have appeared,
including adaptive, nonlinear, geometric, hybrid, fuzzy, and neural control frame-
works. All of these have built on the tradition of linking applications, theory, and
computation to develop practical techniques with rigorous mathematics. Control
also built on other disciplines, especially applied mathematics, physics, and opera-
tions research.
Today, control theory provides a rich methodology and a supporting set of
mathematical principles and tools for analysis and design of feedback systems. It
links four important concepts that are central to both engineered and natural sys-
tems: dynamics, modeling, interconnection, and uncertainty.
The role of dynamics is central to all control systems and control theory has
developed a strong set of tools for analyzing stability and performance of dynamical
systems. Through feedback, we can alter the behavior of a system to meet the needs
of an application: systems that are unstable can be stabilized, systems that are
sluggish can be made responsive, and systems that have drifting operating points
can be held constant. Control theory provides a rich collection of techniques to
analyze the stability and dynamic response of complex systems and to place bounds
on the behavior of such systems by analyzing the gains of linear and nonlinear
operators that describe their components. These techniques are particularly useful
in the presence of disturbances, parametric uncertainty, and unmodeled dynamics—
concepts that are often not treated in detail in traditional dynamics and dynamical
systems courses.
Control theory also provides new techniques for (control-oriented) system
modeling and identification. Since models play an essential role in analysis and
design of feedback systems, sophisticated tools have been developed to build such
models. These include input/output representations of systems (how disturbances
propagate through the system) and data-driven system identification techniques.
The use of “forced response” experiments to build models of systems is well de-
veloped in the control field and these tools find application in many disciplines,
independent of the use of feedback. A strong theory of modeling has also been
developed, allowing rigorous definitions of model fidelity and comparisons to exper-
imental data.
A third key concept in control theory is the role of interconnection between
subsystems. Input/output representations of systems allow one to build models
of very complex systems by linking component behaviors. The dynamics of the
resulting system is determined not only by the dynamics of the components, but
by the interconnection structure between these components. The tools of control
provide a methodology for studying the characteristics of these interconnections and
when they lead to stability, robustness, and desired performance.
Finally, one of the powerful features of modern control theory is that it pro-
vides an
explicit
framework for representing uncertainty. Thus, we can describe a
“set” of systems that represent the possible instantiations of a system or the pos-
sible descriptions of the system as it changes over time. While this framework is
important for all of engineering, the control community has developed one of the
most powerful collection of tools for dealing with uncertainty. This was necessary
2.1. What is Control?
11
Actuators
Sensors
Functions
Projecte
d
(a) Engine Control Electronics
0
10
20
30
40
50
1986 1989
1995 1998
1980 1983
Year
1992
Computation
Sensing
Communications
Actuation
(b) Control Technology Trends
Complexity
Cost/Performance
Year
2006
2002
1998
Figure 2.2.
Trends in control technology: (a) the number of sen-
sors, actuators and control functions in engine controls [6] and (b) illustration of
cost/performance trends for component technologies.
because the use of feedback is not entirely benign. In fact, it can lead to catastrophic
failure if the uncertainty is not properly managed (through positive feedback, for
example).
Control Technology
Control
technology
includes sensing, actuation and computation, used together to
produce a working system. Figure 2.2a shows some of the trends in sensing, ac-
tuation, and computation in automotive applications.
As in many other
application areas, the number of sensors, actuators, and microprocessors is increas-
ing dramatically, as new features such as antilock brakes, adaptive cruise control,
active restraint systems, and enhanced engine controls are brought to market. The
cost/performance curves for these technologies, as illustrated in Figure 2.2b, is also
insightful. The costs of electronics technologies, such as sensing, computation, and
communications, is decreasing dramatically, enabling more information processing.
Perhaps the most important is the role of communications, which is now inexpensive
enough to offer many new possibilities.
Control is also closely related to the integration of software into physical sys-
tems. Virtually all modern control systems are implemented using digital comput-
ers. Often they are just a small part of much larger computing systems performing
various other system management tasks. Because of this, control software becomes
an integral part of the system design and is an enabler for many new features in
products and processes. Online reconfiguration is a fundamental feature of com-
puter controlled systems and this is, at its heart, a control issue.
This trend toward increased use of software in systems is both an opportunity
and a challenge for control. As embedded systems become ubiquitous and com-
munication between these systems becomes commonplace, it is possible to design
systems that are not only reconfigurable, but also aware of their condition and
environment, and interactive with owners, users, and maintainers. These “smart
12
Chapter 2. Overview of the Field
systems” provide improved performance, reduced downtime, and new functionality
that was unimaginable before the advent of inexpensive computation, communica-
tions, and sensing. However, they also require increasingly sophisticated algorithms
to guarantee performance in the face of uncertainty and component failures, and
require new paradigms for verifying the software in a timely fashion. Our everyday
experience with commercial word processors shows the difficulty involved in getting
this right.
One of the emerging areas in control technology is the generation of such
real-time embedded software [32]. While often considered within the domain of
computer science, the role of dynamics, modeling, interconnection, and uncertainty
is increasingly making embedded systems synonymous with control systems. Thus
control must embrace software as a key element of control technology and integrate
computer science principles and paradigms into the discipline. This has already
started in many areas, such as hybrid systems and robotics, where the continuous
mathematics of dynamics and control are intersecting with the discrete mathematics
of logic and computer science.
Comparison with Other Disciplines
Control engineering relies on and shares tools from physics (dynamics and mod-
eling), computer science (information and software) and operations research (op-
timization and game theory), but it is also different from these subjects, in both
insights and approach.
A key difference with many scientific disciplines is that control is fundamen-
tally an engineering science. Unlike natural science, whose goal is to understand
nature, the goal of engineering science is to understand and develop new systems
that can benefit mankind. Typical examples are systems for transportation, elec-
tricity, communication and entertainment that have contributed dramatically to the
comfort of life. While engineering originally emerged as traditional disciplines such
as mining, civil, mechanical, electrical and computing, control emerged as a
systems
discipline around 1950 and cut across these traditional disciplines. The pinnacle of
achievement in engineering science is to find new systems principles that are essen-
tial for dealing with complex man-made systems. Feedback is such a principle and
it has had a profound impact on engineering systems.
Perhaps the strongest area of overlap between control and other disciplines is
in modeling of physical systems, which is common across all areas of engineering and
science. One of the fundamental differences between control-oriented modeling and
modeling in other disciplines is the way in which interactions between subsystems
(components) are represented. Control relies on input/output modeling that allows
many new insights into the behavior of systems, such as disturbance rejection and
stable interconnection. Model reduction, where a simpler (lower-fidelity) descrip-
tion of the dynamics is derived from a high fidelity model, is also very naturally
described in an input/output framework. Perhaps most importantly, modeling in a
control context allows the design of
robust
interconnections between subsystems, a
feature that is crucial in the operation of all large, engineered systems.
Control share many tools with the field of operations research. Optimization
2.2. Control System Examples
13
and differential games play central roles in each, and both solve problems of asset
allocation in the face of uncertainty. The role of dynamics and interconnection
(feedback) is much more ingrained within control, as well as the concepts of stability
and dynamic performance.
Control is also closely associated with computer science, since virtually all
modern control algorithms are implemented in software. However, control algo-
rithms and software are very different from traditional computer software. The
physics (dynamics) of the system are paramount in analyzing and designing them
and their (hard) real-time nature dominates issues of their implementation. From
a software-centric perspective, an F-16 is simply another peripheral, while from a
control-centric perspective, the computer is just another implementation medium
for the feedback law. Neither of these are adequate abstractions, and this is one of
the key areas identified in this report as both an opportunity and a need.
2.2 Control System Examples
Control systems are all around us in the modern technological world. They maintain
the environment, lighting, and power in our buildings and factories, they regulate
the operation of our cars, consumer electronics, and manufacturing processes, they
enable our transportation and communications systems, and they are critical ele-
ments in our military and space systems. For the most part, they are hidden from
view, buried within the code of processors, executing their functions accurately
and reliably. Nevertheless, their existence is a major intellectual and engineering
accomplishment that is still evolving and growing, promising ever more important
consequences to society.
Early Examples
The proliferation of control in engineered systems has occurred primarily in the
latter half of the 20th Century. There are some familiar exceptions, such as the
Watt governor described earlier and the thermostat (Figure 2.3a), designed at the
turn of the century to regulate temperature of buildings.
The thermostat, in particular, is often cited as a simple example of feedback
control that everyone can understand. Namely, the device measures the tempera-
ture in a building, compares that temperature to a desired set point, and uses the
“feedback error” between these two to operate the heating plant, e.g., to turn heat-
ing on when the temperature is too low and to turn if off when temperature is too
high. This explanation captures the essence of feedback, but it is a bit too simple
even for a basic device such as the thermostat. Actually, because lags and delays
exist in the heating plant and sensor, a good thermostat does a bit of anticipation,
turning the plant off before the error actually changes sign. This avoids excessive
temperature swings and cycling of the heating plant.
This modification illustrates that, even in simple cases, good control system
design it not entirely trivial. It must take into account the dynamic behavior of
the object being controlled in order to do a good job. The more complex the
dynamic behavior, the more elaborate the modifications. In fact, the development of
14
Chapter 2. Overview of the Field
(a)
(b)
Figure 2.3.
Early control devices: (a) Honeywell T86 thermostat, origi-
nally introduced in 1953, (b) Chrysler cruise control system, introduced in the 1958
Chrysler Imperial (note the centrifugal governor) [21].
a thorough theoretical understanding of the relationship between dynamic behavior
and good controllers constitutes the most significant intellectual accomplishment
of the control community, and the codification of this understanding into powerful
computer aided engineering design tools makes all modern control systems possible.
There are many other control system examples, of course, that have developed
over the years with progressively increasing levels of sophistication and impact. An
early system with broad public exposure was the “cruise control” option introduced
on automobiles in 1958 (see Figure 2.3b). With cruise control, ordinary people
experienced the dynamic behavior of closed loop feedback systems in action—the
slowdown error as the system climbs a grade, the gradual reduction of that error
due to integral action in the controller, the small (but unavoidable) overshoot at the
top of the climb, etc. More importantly, by experiencing these systems operating
reliably and robustly, the public learned to trust and accept feedback systems,
permitting their increasing proliferation all around us. Later control systems on
automobiles have had more concrete impact, such as emission controls and fuel
metering systems that have achieved major reductions of pollutants and increases
in fuel economy.
In the industrial world, control systems have been key enabling technologies
for everything from factory automation (starting with numerically controlled ma-
chine tools), to process control in oil refineries and chemical plants, to integrated
circuit manufacturing, to power generation and distribution. They now also play
critical roles in the routing of messages across the Internet (TCP/IP) and in power
management for wireless communication systems.
Aerospace Applications
Similarly, control systems have been critical enablers in the aerospace and military
world. We are familiar, for example, with the saturation bombing campaigns of
2.2. Control System Examples
15
(a)
(b)
Figure 2.4.
Flight systems: (a) 1903 Wright Flyer, (b) X-29 forward swept
wing aircraft, in 1987. X-29 photograph courtesy of NASA Dryden Flight Research
Center.
World War II, which dropped unguided explosives almost indiscriminately on pop-
ulation centers in order to destroy selected industrial or military targets. These
have been replaced with precision guided weapons with uncanny accuracy, a single
round for a single target. This is enabled by advanced control systems, combining
inertial guidance sensors , radar and infrared homing seekers, satellite navigation
updates from the global positioning system, and sophisticated processing of the
“feedback error,” all combined in an affordably disposable package.
We are also familiar with early space launches. Slender rockets balanced pre-
cariously on the launch pad, failing too often in out-of-control tumbles or fireballs
shortly after ignition. Robust, reliable, and well-designed control systems are not
optional here, because boosters themselves are unstable. And control systems have
lived up to this challenge. We now take routine launch operations for granted,
supporting manned space stations, probes to the outer planets, and a host of satel-
lites for communications, navigation, surveillance, and earth observation missions.
Of course, these payloads are themselves critically dependent on robust, reliable
and well-designed control systems for everything from attitude control, to on-orbit
station-keeping, thermal management, momentum management, communications,
etc.
Flight Control
Another notable success story for control in the aerospace world comes from the
control of flight. This example illustrates just how significant the intellectual and
technological accomplishments of control have been and how important their con-
tinued evolution will be in the future.
Control has played a key role in the development of aircraft from the very
beginning. Indeed, the Wright brother’s first powered flight was successful only
because the aircraft included control surfaces (warpable wings and forward-mounted
vertical and horizontal fins) that were adjusted continuously by the pilot to stabilize
16
Chapter 2. Overview of the Field
the flight [19] (see Figure 2.4a). These adjustments were critical because the Wright
Flyer itself was unstable, and could not maintain steady flight on its own.
Because pilot workload is high when flying unstable aircraft, most early air-
craft that followed the Wright Flyer were designed to be statically stable. Still, as
the size and performance capabilities of aircraft grew, their handling characteristics
deteriorated. Designers then installed so-called “stability augmentation systems”—
automatic control systems designed to modify dynamic behavior of aircraft slightly
in order to make them easier to fly. These systems first appeared during the World
War II years. They used early inertial sensors to measure flight motions, analog
electronic systems to construct and process feedback errors, and hydraulic systems
to actuate the linkages of selected control surfaces (vertical and horizontal tails,
ailerons, etc).
Two issues surfaced immediately as these systems were being fielded: (1) how
to design the control logic systematically (early systems were essentially developed
by trial-and-error), and (2) how to build the systems such that they would operate
reliably. Early systems proved to be quite unreliable. Hence, only a small fraction
of the full authority of the control surfaces was typically allocated to the automatic
system, with the bulk of authority reserved for manual control, so the pilot could
always override the automation.
Control theorists provided the solution for the first issue. They developed
modeling and simulation methods (based on differential equations and transfer func-
tions) that accurately describe aircraft dynamics, and they developed increasingly
powerful generations of control analysis and design methods to design control laws.
Classical control methods enabled the systematic design of early stability augmen-
tation systems, while modern control and robust multi-variable control are critical
in all of today’s modern flight systems.
But analysis and design methods alone could not address the second issue of
early stability augmentation systems, namely the need for highly reliable control
implementations. That issue was resolved with the development of airborne dig-
ital computers and redundant architectures. These are now routinely used on all
commercial and military aircraft. They have become so highly reliable that the old
solution of granting only partial authority to automation has long been abandoned.
In fact, most modern flight control implementations do not even include mechan-
ical linkages between pilots and control surfaces. All sensed signals and control
commands go through the digital implementation (e.g., fly-by-wire).
Today, we even entrust the very survival of aircraft to automation. Examples
include the all weather auto-land functions of commercial transports, in which safe
go-around maneuvers are not available if failures were to occur at certain critical
flight phases. Other examples include the F-16, B-2, and X-29 military aircraft (see
Figure 2.4), whose basic dynamics are unstable like the Wright Flyer, but so much
more violently that manual stabilization is not possible. Finally, in modern flight
systems there is a growing trend to automate more and more functions—all the way
to removing the pilot entirely from the cockpit. This is already commonplace in
certain military reconnaissance and surveillance missions and will soon be extended
to more lethal ones, such as suppressing enemy air defenses with unmanned aerial
vehicles (UAVs).
2.2. Control System Examples
17
The following vignette describes some of these advances, from the perspective
of one of its successful practitioners.
Vignette: Fighter Aircraft and Missiles (Kevin A. Wise, The Boeing
Company)
The 1990s has been a decade of significant accomplishments and change for the
aerospace community. New systems such as unstable, tailless aircraft, propulsion con-
trolled ejection seats, and low-cost, accurate, GPS guided munitions were developed.
Fly-by-wire flight control systems have become the standard, making control system de-
sign and analysis central to military aircraft and missile system development. Improving
pilot safety and reducing costs were key focus areas in industry.
Flight control system design methods using feedback linearization paved the way for
new gain scheduled flight control systems for aircraft. This method, applied to the
X-36 Tailless Agility Research aircraft and the F-15 ACTIVE, uniquely allows engineers
to better design flying qualities into the aircraft, reducing design and development costs
and improving pilot acceptance. Advances in robustness theory improved analysis tools
allowing engineers to accurately predict and thus expand departure boundaries for these
highly unstable aircraft. To further improve safety, these control laws were augmented
with neural networks for reconfigurable and damage adaptive flight control.
Missile systems, such as the Joint Direct Attack Munition (JDAM) and the Miniaturized
Munition Technology Demonstrator (MMTD) developed their flight control designs us-
ing state feedback optimal control, and then projecting out those states not measured
by sensors . This method eliminated sensor hardware, reducing weight and costs, and
proved to be completely automatable. The Fourth Generation Escape System (GEN4)
ejection seat also used this approach for its control laws. In addition to needing optimal
performance, advances in robustness theory were used to characterize the seat’s con-
trol system performance to uncertain crew member size and weight (95% male to 5%
female). Autocode software tools for implementing controls systems also emerged in
the 1990s. These computer aided design tools provide a single environment for control
design and analysis as well as software design and test. They have greatly reduced the
implementation and testing costs of flight control systems.
The new challenge faced by the control community is the development of unmanned
combat systems (munitions as well as aircraft) and concepts of operations for these
systems to address the intelligent, increasingly hostile, rapidly changing environments
faced by our war fighters. These systems must detect, identify, locate, prioritize, and
employ ordinance to achieve permanent destruction of high value targets. New devel-
opments in intelligent control, vision based control, mission planning, path planning,
decision aiding, communication architectures, logistics and support concepts, and last
but not least, software development, validation, and verification are needed to support
these systems and make them affordable.
18
Chapter 2. Overview of the Field
2.3 The Increasing Role of Information-Based
Systems
Early applications of control focused on the physics of the system being controlled,
whether it was the thermal dynamics of buildings, the flight mechanics of an air-
plane, or the tracking properties of a disk drive head. The situation we now face is
one in which pervasive computing, sensing, and communications are common and
the way that we interact with machines and they interact with each other is chang-
ing rapidly. The consequences of this tremendous increase in information are also
manifest in control, where we are now facing the challenges of controlling large-
scale systems and networks that are well beyond the size and complexity of the
traditional applications of control.
One indication of this shift is the role that embedded systems and software play
in modern technology, described briefly above. Modern computer control systems
are capable of enormous amounts of decision making and control logic. Increasingly,
these software systems are interacting with physical processes and introducing feed-
back algorithms to improve performance and robustness. Already, the amount of
logic-based code is overshadowing the traditional control algorithms in many appli-
cations. Much of this logic is interwoven with the closed loop performance of the
system, but systematic methods for analysis, verification, and design have yet to be
developed.
Another area where control of information-based systems will be increasingly
important is in resource allocation systems. In this context, control can be described
as the science and engineering of optimal dynamic resource allocation under uncer-
tainty. We start with a mathematical model, of a system that describes how current
actions or decisions can affect the future behavior of the system, including our un-
certainty in that behavior. “Resource allocation” means that our decisions can be
interpreted as managing a tradeoff between competing goals, or choosing from a
limited set of possible actions. “Uncertainty” is critical: there is some possible vari-
ation in the system’s behavior, so that decisions have to be made taking different
possibilities into account. Sources of uncertainty include incomplete or corrupted
information available to the decision maker, uncertainty in the mathematical model
used to model the system, and unpredictability of commands due to noise and
disturbance signals that affect the system. While often considered an operations
research problem, the role of dynamics and instabilities points to a clear need for
control theory as well.
One of the consequences of this shift toward information-based systems is that
we are moving from an era where physics was the bottleneck to progress to one in
which complexity is the bottleneck.
There are already many examples of this new class of systems that are being
deployed. Congestion control in routers for the Internet, power control in wireless
communications systems, and real-time use of information in service and supply
chains are a few examples. In all of these systems, it is the interaction of informa-
tion flow with the underlying physics that is responsible for the overall performance.
Another example is the air traffic control network, where the density of flights, de-
mand for efficiency, and intolerance for failure have created a situation that couples
2.3. The Increasing Role of Information-Based Systems
19
Figure 2.5.
San Francisco Bay area aircraft arrival and departure routes.
Figure courtesy of Federal Aviation Authority.
vast amounts of information—everything from the location of the planes to the indi-
vidual customer itineraries—that must be managed to maintain high performance,
robust, and reliable operation at all times. Figure 2.5 shows just one small part of
this problem, the local departure and arrival routes in the San Francisco Bay area.
There is an important role for control in many of these applications. As
in traditional application areas, control serves as a mechanism for getting both
information and, more importantly,
action
out of data. Furthermore, the theory
of control provides insights and tools for analyzing and designing interconnected
systems with desirable stability and robustness properties.
One fundamental change in the use of control is the role of communications
and networking. This will radically change the use of feedback in modern systems,
through increased access to large amounts of information as well as the new envi-
ronment in which control systems will have to operate. Control computations must
increasingly be done in a distributed, partially asynchronous environment where
tight control over the timing of data and computation is not available, due for
example to the existence of packet-based communications networks between sens-
ing, actuation, and computational nodes. Many traditional approaches may no
longer work in this context and we anticipate the need to develop new paradigms
for designing robust, high performance, feedback systems in this information rich
environment.
The role of uncertainty in information rich systems is also critical (and largely
20
Chapter 2. Overview of the Field
unexplored) and concepts from control will play an important role in managing
this uncertainty in the analysis, design, and operation of large-scale, interconnected
systems. Uncertainty must be represented in order to build tractable models for
answering questions that take into account the whole range of possible variations
in the details of components and their interconnections. Control ideas will be in-
creasingly important as a tool for managing both the complexity and uncertainty in
these systems, and must be made available to the designers of such systems, through
education and design software. One aspect of this that is likely to be particularly
important is the exploration of fundamental limits of performance, robustness, and
stability, since tradeoffs between these will be the primary design challenge in this
space.
Examples of the need for increased development in this area can be seen in the
applications discussed in the next chapter. Vehicle, mission, and airspace manage-
ment systems for transportation; source, power, and router control for networks;
and genetic, cellular, and integrative feedback networks in biological systems are
just a few examples. The simplest of these problems lies at the boundaries of
current tools and understanding, and future progress will require a much deeper
understanding of the integration between control, communications, computing, and
networks as well as modeling, analysis, and control of complex decision systems.
2.4 Opportunities and Challenges Facing the Field
Control has developed into a major field in which generations of engineers are able
to solve problems of practical importance and enormous impact. Over the past few
years, the opportunities for control have expanded enormously, but there are many
challenges that must be addressed to realize the potential for impact. In this section
we attempt to characterize some of the overarching themes that describe these
opportunities and challenges, and recommend an approach for moving forward.
Characteristics of the New Environment
The future of control will be driven by a new environment that differs substantially
from that of the past 40 years. Some of the features of this new environment are
already apparent and provide insight into the new research directions that must be
pursued.
Ubiquitous Computation, Communication and Sensing
. The dominant change in
the engineering environment is the presence of ever more powerful computation
and cheaper communication. The new software and storage products that these
developments have spawned have further changed the engineering landscape in many
areas. In addition, microelectronics and MEMS have made available inexpensive
sensors , such as those shown in Figure 2.6, and new actuator concepts that can be
made available via communication networks, allowing increasingly sensor rich and
actuator rich control.
It will require decades to take full advantage of these developments. Some
innovation will involve standalone improvements to individual systems and some
2.4. Opportunities and Challenges Facing the Field
21
(a)
(b)
(c)
Figure 2.6.
Examples of current sensor technology: (a) 1024x1024 CCD
array, (b) MEMS-based microgryoscope, and (c) sensor web pod. All photographs
courtesy of Jet Propulsion Laboratory.
will involve extreme interconnectedness of the type seen in the telephone system,
the power grid, the Internet, and their descendants. Both types may, and probably
will, depend on the use of control. The new ideas required to be successful in
the two cases are, however, likely to be qualitatively different because we do not
yet have a great deal of experience in building and operating safe, reliable, highly
interconnected systems.
New Application Domains
. In addition to the revolutionary changes in information
technology, future control systems will involve interactions between physical, chem-
ical, biological, and information sciences, integrated with algorithms and feedback.
This will open up new application domains for control, such as biological engineer-
ing and quantum systems. While there are already researchers within the control
community that are attacking problems in these areas, it will be necessary to ed-
ucate new generations of researchers in both control and other disciplines in order
to make advances in these applications. The possibilities for control are potentially
very fundamental in nature, as illustrated in the following vignette.
Vignette: Quantum Measurement and Control (Hideo Mabuchi, Cal-
tech)
To illustrate the applications of control in new domains, consider the research of Hideo
Mabuchi, who is exploring the use of feedback and control in quantum systems and its
implications for unifying quantum and classical physics:
A grand enigma, which is perhaps our primary legacy from 20th Century
physics, is that the states and dynamics we ascribe to microscopic (quan-
tum) systems seem incompatible with macroscopic (classical) phenomenol-
ogy. For example, physical theory claims that it should be illogical simulta-
neously to assign definite values to certain sets of measurable properties of
a quantum system. And yet we want to believe that quantum mechanics
is a correct description of microscopic physics, which evolves robustly into
classical dynamics for systems of sufficiently large size and with a sufficiently
high degree of interconnection among their manifold degrees of freedom.
22
Chapter 2. Overview of the Field
How can we understand the consistency of quantum mechanics, as a mi-
croscopic theory, with classical physics as a manifestly valid description of
macroscopic phenomena?
Control theory provides a new set of tools for understanding quantum systems. One set
of tools is through systematic techniques for model reduction:
Viewed from a “multiscale” perspective, our challenge in explaining the
quantum-classical transition will be to show that classical physics can rig-
orously be obtained as a robust and parsimonious approximation to the
dynamics of certain aggregate degrees of freedom for generic complex quan-
tum systems. In the language of control theory, one would like to derive
classical physics as an optimal model reduction of quantum physics. A
number of fundamental questions arise as soon as the problem is posed this
way. How can this model reduction be so general and robust, depending
only upon the structure of quantum theory and not the details of any par-
ticular dynamical system? What are the general parameters that control
the error bounds on this model reduction? What impact will this program
have, if successful, on our basic interpretation of quantum mechanics?
In addition, control can provide new techniques for doing experiments, allowing us to
better explore physical understanding:
... we hope that feedback control will provide a crucial experimental
methodology for scrutinizing the validity of quantum measurement the-
ory in realistic laboratory scenarios, especially with regard to the equations
for conditional evolution of a system under continuous observation. Such
equations could be used as the starting point for controller synthesis, for ex-
ample, and their validity would be assessed by comparison of experimentally
observed closed-loop behavior with theoretical expectations.
Mabuchi’s work illustrates the potential power of control theory as a disruptive tech-
nology for understanding the world around us.
Reliable Systems with Unreliable Parts
. Most reasonably complex man-made sys-
tems are not rendered inoperable by the failure of any particular component and
biological systems often demonstrate remarkable robustness in this regard. Simple
redundancy, or the spare parts approach to such problems, is of limited effectiveness
because it is uneconomical. Designs that allow the system to reconfigure itself when
a component fails, even if this degrades the performance roughly in proportion to
the magnitude of the failure, are usually preferred. Although computer memory
chips and disk drive controllers often take advantage of strategies of this type, it is
still true that the design of self healing systems is not well studied or analyzed.
This issue takes on considerable significance when dealing with interconnected
systems of the complexity of the Internet. In this case there are billions of compo-
2.4. Opportunities and Challenges Facing the Field
23
nents and yet the system is so essential that little downtime can be tolerated.
Complexity
. Air traffic control systems, power grid control systems and other large-
scale, interconnected systems are typical of a class of problems whose complexity
is fixed not by the designer but rather by economic considerations and the natural
scale of the problem. An acceptable solution in this context must be capable of
dealing with the given complexity. In deciding if a system can be built or not, it is
important to correctly gauge the feasibility because there is no value in a product
that “almost” works.
Every discipline has methods for dealing with some types of complexity. In
the physical sciences, for example, the tools developed for studying statistical me-
chanics have lead to a very substantial body of literature, effective for solving some
problems. However, in discussing complexity it is one thing to find a point of view
from which aspects of the behavior is compressible (e.g., “the entropy of a closed
system can only increase”) but it is another to have a “theory of complex systems”.
The latter is something of an oxymoron, in that it suggests that the system is not
really complex. On the other hand, it does make sense to seek to understand and
organize the methodologies which have proven to be useful in the design of highly
interconnected systems and to study naturally occurring systems with this in mind.
Engineers looking at the immune system may very well be able to suggest new
methods to defeat Internet viruses and ideas from neuroscience may inspire new
developments in building reliable systems using unreliable components.
Vision for the Future
This new environment for control presents many challenges, but also many opportu-
nities for impact across a broad variety of application areas. The future directions
in control, dynamics, and systems must continue to address fundamental issues,
guided by new applications.
One of the biggest challenges facing the field is the integration of computa-
tion, communications, and control. As computing, communications, and sensing
become more ubiquitous, the use of control will become increasingly ubiquitous as
well. However, many of the standard paradigms that allow the separation of these
different disciplines will no longer be valid. For example, the ability to separate the
computational architecture from the functions that are being computed is already
beginning to unravel as we look at distributed systems with redundant, intermit-
tent, and sometimes unreliable computational elements. Beyond simply looking at
hybrid systems, a theory must be developed that integrates computer science and
control.
Similarly, the simplification that two nodes that are connected can communi-
cate with sufficient reliability and bandwidth such that the properties of the com-
munications channel can be ignored no longer holds in the highly networked envi-
ronment of the future. Control must become more integrated with the protocols
of communications so that high response feedback loops are able to use the same
channels as high throughput, lower bandwidth information, without interfering with
each other.
24
Chapter 2. Overview of the Field
Another element of the future of control is to begin to understand analysis
and synthesis of control using higher levels of decision making. Traditionally control
has dealt with the problem of keeping a few variables constant (regulation) or
making variables follow specified time functions (tracking). In robotics, control
was faced with more complicated problems such as obstacle avoidance and path
planning (task-based control). Future systems will require that control be applied
to problems that cannot necessarily be expressed in terms of continuous variables,
but rather have symbolic, linguistic, or protocol-based descriptions. This is required
as we move to more sophisticated autonomous and semi-autonomous systems that
require high-level decision making capabilities.
At the same time as control moves to higher levels of decision making, it will
also move to new domains that are only beginning to emerge at the present time.
This includes biological, quantum and environmental systems; software systems;
enterprise level systems; and economic and financial systems. In all of these new
problem domains, it will be necessary to develop a
rigorous
theory of control. This
has been a historical strength of the field and has allowed it to be successful in an
enormous number of systems.
Finally, we envision an increased awareness of control principles in science and
engineering, including much more exposure to feedback systems in math and science
education.
Approach
The opportunities and challenges describe here should be addressed on two fronts.
There is a need for a broadly supported, active research program whose goals are to
explore and further develop methodologies for design and operation of reliable and
robust highly interactive systems, and there is a need to make room in the academic
programs for material specific to this area.
The research program must be better integrated with research activities in
other disciplines and include scientists trained in software engineering, molecular
biology, statistical mechanics, systems engineering and psychology. Control re-
searchers must continue to branch out beyond traditional discipline boundaries and
become participants and contributors in areas such as computer science, biology,
economics, environmental science, materials science and operations research. There
is particular need for increased control research in information-based systems, in-
cluding communications, software, verification and validation, and logistics.
To support this broader research program, a renewed academic program must
also be developed. This program should strengthen the systems view and stretch
across traditional discipline boundaries. To do so, it will be necessary to provide
better dissemination of tools to new communities and provide a broader education
for control engineers and researchers. This will require considerable effort to present
current knowledge in a more compact way and to allow new results in software,
communications, and emerging application domains to be added, while maintaining
the key principles of control on which new results will rest. Simultaneously, the
control community must seek to increase exposure to feedback in math and science
education at all levels, even K-12. Feedback is a fundamental principle that should
2.4. Opportunities and Challenges Facing the Field
25
be part of every technically literate person’s knowledge base.
One of the characteristics of the control field has been an emphasis on theory
and mathematical formulations of the problems being considered. This discipline
has resulted in a body of work that is reliable and unambiguous. Moreover, because
this style appeals to some very able graduate students, it has been an important
factor in maintaining the flow of talent into the field. However, for engineers and
scientists this has been a barrier to entry and can make it difficult for outsiders
to assimilate and use the work in their own field. In addition, it has sometimes
had a chilling effect on the development of ideas that are not easily translated into
mathematical form. The challenge presented by the need to steer a course between
the possible extremes here is not new, it has always been present. What is new
is that the availability of easily used simulation tools has made the use of heuris-
tic reasoning both more appealing and more reliable. In particular, optimization
involving problems that are so large and/or so badly non-convex that rigorous anal-
ysis is infeasible can now be approached using principled heuristics. Because of the
software and computing power now available this may be the most effective way to
proceed. It is important to find a place for effective heuristics in the training of
students and the highest level professional meetings of the field.
Finally, experimentation on representative systems must be an integral part
of the control community’s approach. The continued growth of experiments, both
in education and research, should be supported and new experiments that reflect
the new environment will need to be developed. These experiments are important
for the insight into application domains that they bring, as well as the development
of software and algorithms for applying new theory. But they also form the training
ground for systems engineers, who learn about modeling, robustness, interconnec-
tion, and data analysis through their experiences on real systems.
The recommendations of the Panel, detailed in Chapter 5, provide a high
level plan for implementing this basic approach. The recommendations focus on
the need to pursue vigorously new application domains and, in particular, those
domains in which the principles of control will be essential for future progress.
They also highlight the need to maintain the field’s strong theoretical base and
historical rigor, while at the same time finding new ways to broaden the exposure
and use of control to a broader collection of scientists and engineers.
The new environment that control faces is one with many new challenges and
an enormous array of opportunities. Advancing the state of the art will require that
that the community accelerate its integration across disciplines and look beyond the
current paradigms to tackle the next generation of applications. In the next chapter,
we explore some of the application areas in more detail and identify some of the
specific advancements that will be required.