siam.dvi - Control02.pdf

Control in an Information Rich World

Report of the Panel on Future Directions in

Control, Dynamics, and Systems

30 June 2002

Abstract

The field of

control

provides the principles and methods used to design engineering

systems that maintain desirable performance by automatically adapting to changes

in the environment. Over the last forty years the field has seen huge advances, lever-

aging technology improvements in sensing and computation with breakthroughs in

the underlying principles and mathematics. Control systems now play critical roles

in many fields, including manufacturing, electronics, communications, transporta-

tion, computers and networks, and many military systems.

As we begin the 21st Century, the opportunities to apply control principles and

methods are exploding. Computation, communication and sensing are becoming

increasingly inexpensive and ubiquitous, with more and more devices including

embedded processors, sensors, and networking hardware. This will make possible

the development of machines with a degree of intelligence and reactivity that will

influence nearly every aspect of life on this planet, including not just the products

available, but the very environment in which we live.

New developments in this increasingly information rich world will require a

significant expansion of the basic tool sets of control. The complexity of the control

ideas involved in the operation of the Internet, semi-autonomous command and

control systems, and enterprise-wide supply chain management, for example, are

on the boundary of what can be done with available methods. Future applications

in aerospace and transportation, information and networks, robotics and intelligent

machines, biology and medicine, and materials and processing will create systems

that are well beyond our current levels of complexity, and new research is required

to enable such developments.

The purpose of this report is to spell out some of the prospects for control

in the current and future technological environment, to describe the role the field

will play in military, commercial, and scientific applications over the next decade,

and to recommend actions required to enable new breakthroughs in engineering and

technology through application of control research.

iii

Panel Membership

Richard M. Murray (chair)

California Institute of Technology

Karl J.

Astr ̈

Lund Institute of Technology

Pramod P. Khargonekar

University of Florida

Stephen P. Boyd

Stanford University

P. R. Kumar

University of Illinois

Siva S. Banda

Air Force Research Laboratory

P. S. Krishnaprasad

University of Maryland

Roger W. Brockett

Harvard University

Greg J. McRae

Massachusetts Institute of Technology

John A. Burns

Virginia Tech

Jerrold E. Marsden

California Institute of Technology

Munzer A. Dahleh

Massachusetts Institute of Technology

George Meyer

NASA Ames Research Center

John C. Doyle

California Institute of Technology

William F. Powers

Ford Motor Company

John Guckenheimer

Cornell University

Gunter Stein

Honeywell International

Charles J. Holland

Department of Defense

Pravin Varaiya

University of California, Berkeley

Additional Contributors

Richard Albanese, Jim Batterson, Richard Braatz, Dennis Bernstein, Joel

Burdick, Raffaello D’Andrea, Michael Dickinson, Frank Doyle, Martha Gallivan,

Jonathan How, Marc Jacobs, Jared Leadbetter, Jesse Leitner, Steven Low, Hideo

Mabuchi, Dianne Newman, Shankar Sastry, John Seinfeld, Eduardo Sontag, Anna

Stefanopoulou, Allen Tannenbaum, Claire Tomlin, Kevin Wise

Contents

Preface

vii

Executive Summary

Overview of the Field

2.1

WhatisControl? .......................... 7

2.2

ControlSystemExamples ..................... 13

2.3

The Increasing Role of Information-Based Systems

....... 18

2.4

Opportunities and Challenges Facing the Field

.......... 20

Applications, Opportunities, and Challenges

3.1

Aerospace and Transportation

................... 29

3.2

Information and Networks

..................... 39

3.3

Robotics and Intelligent Machines . .

............... 49

3.4

Biology and Medicine

........................ 58

3.5

Materials and Processing

...................... 65

3.6

Other Applications

......................... 72

Education and Outreach

4.1

The New Environment for Control Education

.......... 79

4.2

MakingControlMoreAccessible.................. 81

4.3

Broadening Control Education

................... 83

4.4

The Opportunities in K-12 Math and Science Education

.... 84

4.5

Other Opportunities and Trends . . .

............... 85

Recommendations

5.1

Integrated Control, Computation, Communications

....... 89

5.2

Control of Complex Decision Systems

............... 90

5.3

High-Risk, Long-Range Applications of Control

......... 91

5.4

Support for Theory and Interaction with Mathematics

..... 92

5.5

New Approaches to Education and Outreach

........... 93

5.6

ConcludingRemarks ........................ 94

NSF/CSS Workshop on Education

Contents

Bibliography

101

Index

105

Preface

This report documents the findings and recommendations of the Panel on

Future Directions in Control, Dynamics, and Systems. This committee was formed

in April 2000 under initial sponsorship of the Air Force Office of Scientific Research

(AFOSR) to provide a renewed vision of future challenges and opportunities in

the field, along with recommendations to government agencies, universities, and

research organizations to ensure continued progress in areas of importance to the

industrial and defense base. The intent of this report is to raise the overall visibility

of research in control, highlight its importance in applications of national interest,

and indicate some of the key trends that are important for continued vitality of the

field.

The Panel was chaired by Professor Richard Murray (Caltech) and was formed

with the help of an organizing committee consisting of Professor Roger Brock-

ett (Harvard), Professor John Burns (VPI), Professor John Doyle (Caltech) and

Dr. Gunter Stein (Honeywell). The remaining Panel members are Karl

Astr ̈

(Lund Institute of Technology), Siva Banda (Air Force Research Lab), Stephen

Boyd (Stanford), Munzer Dahleh (MIT), John Guckenheimer (Cornell), Charles

Holland (DDR&E), Pramod Khargonekar (University of Florida), P. R. Kumar

(University of Illinois), P. S. Krishnaprasad (University of Maryland), Greg McRae

(MIT), Jerrold Marsden (Caltech), George Meyer (NASA), William Powers (Ford),

and Pravin Varaiya (UC Berkeley). A writing subcommittee consisting of Karl

Astr ̈

om, Stephen Boyd, Roger Brockett, John Doyle, Richard Murray and Gunter

Stein helped coordinate the generation of the report.

The Panel held a meeting on 16-17 July 2000 at the University of Maryland,

College Park to discuss the state of the field and its future opportunities. The

meeting was attended by members of the Panel and invited participants from the

academia, industry, and government. Additional meetings and discussions were held

over the next 15 months, including presentations at DARPA and AFOSR sponsored

workshops, meetings with government program managers, and writing committee

meetings. The results of these meetings, combined with discussions among Panel

members and within the community at workshops and conferences, form the main

basis for the findings and recommendations of this Panel.

A web site has been established to provide a central repository for materials

generated by the Panel:

http://www.cds.caltech.edu/~murray/cdspanel/

vii

viii

Preface

Copies of this report, links to other sources of information, and presentation mate-

rials from the Panel workshop and other meetings can be found there.

Several similar reports and papers highlighting future directions in control

came to the Panel’s attention during the development of this report. Many mem-

bers of the Panel and participants in the June 2000 workshop were involved in

the generation of the 1988 Fleming report [15] and a 1987

IEEE Transactions on

Automatic Control

article [25], both of which provided a roadmap for many of the

activities of the last decade and continue to be relevant. More recently, the Euro-

pean Commission sponsored a workshop on future control systems [14] and several

other more focused workshops have been held over the last several years [1, 2, 33, 34].

Several recent papers and reports highlighted successes of control [35] and new vis-

tas in control [11, 23]. The Panel also made extensive use of a recent NSF/CSS

report on future directions in control engineering education [1], which provided a

partial basis for Chapter 4 of the present report.

The bulk of this report was written before the tragic events of September 11,

2001, but control will clearly play a major role in the world’s effort to combat terror-

ism. From new methods for command and control of unmanned vehicles, to robust

networks linking businesses, transportation systems, and energy infrastructure, to

improved techniques for sensing and detection of biological and chemical agents, the

techniques and insights from control will enable new methods for protecting human

life and safeguarding our society.

The Panel would like to thank the control community for its support of this

report and the many contributions, comments, and discussions that help form the

context and content for the report. We are particularly indebted to Dr. Marc Q.

Jacobs for his initiative in the formation of the Panel and for his support of the

project through AFOSR.

Richard M. Murray

Pasadena, June 2002

Chapter 1

Executive Summary

Rapid advances in computing, communications, and sensing technology offer un-

precedented opportunities for the field of control to expand its contributions to the

economic and defense needs of the nation. This report presents the findings and

recommendations of a panel of experts chartered to examine these opportunities.

We present an overview of the field, review its successes and impact, and describe

the new challenges ahead. We do not attempt to cover the entire field. Rather, we

focus on those areas that are undergoing the most rapid change and that require

new approaches to meet the challenges and opportunities that face the community.

Overview of Control

Control as defined in this report refers to the use of algorithms and feedback in

engineered systems. At its simplest, a control system is a device in which a sensed

quantity is used to modify the behavior of a system through computation and

actuation. Control systems engineering traces its roots to the industrial revolution,

to devices such as the centrifugal governor, shown in Figure 1.1. This device used

a flyball mechanism to sense the rotational speed of a steam turbine and adjust

the flow of steam into the machine using a series of linkages. By thus regulating

the turbine’s speed, it provided the safe , reliable , consistent operation that was

required to enable the rapid spread of steam-powered factories.

Control played an essential part in the development of technologies such as

power, communications, transportation, and manufacturing. Examples include au-

topilots in military and commercial aircraft (Figure 1.2a), regulation and control of

the electrical power grid, and high accuracy positioning of read/write heads in disk

drives (Figure 1.2b). Feedback is an enabling technology in a variety of application

areas and has been reinvented and patented many times in different contexts.

A modern view of control sees feedback as a tool for uncertainty management.

By measuring the operation of a system, comparing it to a reference, and adjusting

available control variables, we can cause the system to respond properly even if its

dynamic behavior is not exactly known or if external disturbances tend to cause it

Chapter 1. Executive Summary

(a)

(b)

Figure 1.1.

The centrifugal governor (a), developed in the 1780s, was an

enabler of the successful Watt steam engine (b), which fueled the industrial revolu-

tion. Figures courtesy of Cambridge University.

to respond incorrectly. This is an essential feature in engineering systems since they

must operate reliably and efficiently under a variety of conditions. It is precisely

this aspect of control as a means of ensuring robustness to uncertainty that ex-

plains why feedback control systems are all around us in the modern technological

world. They are in our homes, cars and consumer electronics, in our factories and

communications systems, and in our transportation, military and space systems.

The use of control is extremely broad and encompasses a number of different

applications. These include control of electromechanical systems, where computer-

controlled actuators and sensors regulate the behavior of the system; control of elec-

tronic systems, where feedback is used to compensate for component or parameter

variations and provide reliable, repeatable performance; and control of information

and decision systems, where limited resources are dynamically allocated based on

estimates of future needs. Control principles can also be found in areas such as

biology, medicine, and economics, where feedback mechanisms are ever present. In-

creasingly, control is also a mission critical function in engineering systems: the

systems will fail if the control system does not work.

Contributions to the field of control come from many disciplines, including

pure and applied mathematics; aerospace, chemical, mechanical, and electrical en-

gineering; operations research and economics; and the physical and biological sci-

ences. The interaction with these different fields is an important part of the history

and strength of the field.

Successes and Impact

Over the past 40 years, the advent of analog and digital electronics has allowed

control technology to spread far beyond its initial applications, and has made it an

enabling technology in many applications. Visible successes from past investment

(a)

(b)

Figure 1.2.

Applications of control: (a) the Boeing 777 fly-by-wire aircraft

and (b) the Seagate Barracuda 36ES2 disk drive. Photographs courtesy of the Boeing

Company and Seagate Technology.

in control include:

•

Guidance and control systems for aerospace vehicles, including commercial

aircraft, guided missiles, advanced fighter aircraft, launch vehicles, and satel-

lites. These control systems provide stability and tracking in the presence of

large environmental and system uncertainties.

•

Control systems in the manufacturing industries, from automotive to inte-

grated circuits. Computer controlled machines provide the precise positioning

and assembly required for high quality, high yield fabrication of components

and products.

•

Industrial process control systems, particularly in the hydrocarbon and chemi-

cal processing industries. These maintain high product quality by monitoring

thousands of sensor signals and making corresponding adjustments to hun-

dreds of valves, heaters, pumps, and other actuators.

•

Control of communications systems, including the telephone system, cellular

phones, and the Internet. Control systems regulate the signal power lev-

els in transmitters and repeaters, manage packet buffers in network routing

equipment, and provide adaptive noise cancellation to respond to varying

transmission line characteristics.

These applications have had an enormous impact on the productivity of modern

society.

In addition to its impact on engineering applications, control has also made

significant intellectual contributions. Control theorists and engineers have made

rigorous use of and contributions to mathematics, motivated by the need to develop

provably correct techniques for design of feedback systems. They have been consis-

tent advocates of the “systems perspective,” and have developed reliable techniques

Chapter 1. Executive Summary

(a)

(b)

Figure 1.3.

Modern networked systems: (a) the California power grid and

(b) the NSFNET Internet backbone. Figures courtesy of the state of California and

the National Center for Supercomputer Applications (NCSA).

for modeling, analysis, design, and testing that enable design and implementation

of the wide variety of very complex engineering systems in use today. Moreover,

the control community has been a major source and training ground for people who

embrace this systems perspective and who wish to master the substantial set of

knowledge and skills it entails.

Future Opportunities and Challenges

As we look forward, the opportunities for new applications that will build on ad-

vances in control expand dramatically. The advent of ubiquitous, distributed com-

putation, communication, and sensing systems has begun to create an environment

in which we have access to enormous amounts of data and the ability to process

and communicate that data in ways that were unimagined 20 years ago. This will

have a profound effect on military, commercial and scientific applications, especially

as software systems begin to interact with physical systems in more and more in-

tegrated ways. Figure 1.3 illustrates two systems where these trends are already

evident. Control will be an increasingly essential element of building such intercon-

nected systems, providing high performance, high confidence, and reconfigurable

operation in the presence of uncertainties.

In all of these areas, a common feature is that system level requirements far

exceed the achievable reliability of individual components. This is precisely where

control (in its most general sense) plays a central role, since it allows the system

to ensure that it is achieving its goal through correction of its actions based on

sensing its current state. The challenge to the field is to go from the traditional

view of control systems as a single process with a single controller, to recognizing

control systems as a heterogeneous collection of physical and information systems,

with intricate interconnections and interactions.

In addition to inexpensive and pervasive computation, communication, and

sensing—and the corresponding increased role of information-based systems—an

important trend in control is the move from low-level control to higher levels of de-

cision making. This includes such advances as increased autonomy in flight systems

(all the way to complete unmanned operation), and integration of local feedback

loops into enterprise-wide scheduling and resource allocation systems. Extending

the benefits of control to these non-traditional systems offers enormous opportuni-

ties in improved efficiency, productivity, safety, and reliability.

Control is a critical technology in defense systems and is increasingly impor-

tant in the fight against terrorism and asymmetric threats. Control allows the

operation of autonomous and semi-autonomous unmanned systems for difficult and

dangerous missions, as well as sophisticated command and control systems that

enable robust, reconfigurable decision making systems. The use of control in mi-

crosystems and senosr webs will improve our ability to detect threats before they

cause damage. And new uses of feedback in communications systems will provide

reliable, flexible, and secure networks for operation in dynamic, uncertain, and ad-

versarial environments.

In order to realize the potential of control applied to these emerging appli-

cations, new methods and approaches must be developed. Among the challenges

currently facing the field, a few examples provide insight into the difficulties ahead:

•

Control of systems with both symbolic and continuous dynamics.

Next gener-

ation systems will combine logical operations (such as symbolic reasoning and

decision making) with continuous quantities (such as voltages, positions, and

concentrations). The current theory is not well-tuned for dealing with such

systems, especially as we scale to very large systems.

•

Control in distributed, asynchronous, networked environments.

Control dis-

tributed across multiple computational units, interconnected through packet-

based communications, will require new formalisms for ensuring stability, per-

formance and robustness. This is especially true in applications where one

cannot ignore computational and communications constraints in performing

control operations.

•

High level coordination and autonomy.

Increasingly, feedback is being de-

signed into enterprise-wide decision systems, including supply chain manage-

ment and logistics, airspace management and air traffic control, and C4ISR

systems. The advances of the last few decades in analysis and design of ro-

bust control systems must be extended to these higher level decision making

systems if they are to perform reliably in realistic settings.

•

Automatic synthesis of control algorithms, with integrated verification and val-

idation.

Future engineering systems will require the ability to rapidly de-

sign, redesign and implement control software. Researchers need to develop

much more powerful design tools that automate the entire control design pro-

cess from model development to hardware-in-the-loop simulation, including

system-level software verification and validation.

Chapter 1. Executive Summary

•

Building very reliable systems from unreliable parts.

Most large engineering

systems must continue to operate even when individual components fail. In-

creasingly, this requires designs that allow the system to automatically recon-

figure itself so that its performance degrades gradually rather than abruptly.

Each of these challenges will require many years of effort by the research community

to make the results rigorous, practical, and widely available. They will also require

investments by funding agencies to ensure that current progress is continued and

that forthcoming technologies are realized to their fullest.

Recommendations

To address these challenges and deliver on the promise of the control field, the Panel

recommends that the following actions be undertaken:

1. Substantially increase research aimed at the

integration

of control, computer

science, communications, and networking. This includes principles, methods

and tools for modeling and control of high level, networked, distributed sys-

tems, and rigorous techniques for reliable, embedded, real-time software.

2. Substantially increase research in control at higher levels of decision making,

moving toward enterprise level systems. This includes work in dynamic re-

source allocation in the presence of uncertainty, learning and adaptation, and

artificial intelligence for dynamic systems.

3. Explore high-risk, long-range applications of control to new domains such

as nanotechnology, quantum mechanics, electromagnetics, biology, and envi-

ronmental science. Dual investigator, interdisciplinary funding might be a

particularly useful mechanism in this context.

4. Maintain support for theory and interaction with mathematics, broadly in-

terpreted. The strength of the field relies on its close contact with rigorous

mathematics, and this will be increasingly important in the future.

5. Invest in new approaches to education and outreach for the dissemination of

control concepts and tools to non-traditional audiences. The community must

do a better job of educating a broader range of scientists and engineers on the

principles of feedback and the use of control to alter the dynamics of systems

and manage uncertainty.

The impact of control is one which will come through many applications, in

aerospace and transportation, information and networking, robotics and intelligent

machines, materials and processing, and biology and medicine. It will enable us to

build more complex systems and to ensure that the systems we build are reliable,

efficient, and robust. The Panel’s recommendations are founded on the diverse

heritage of rigorous work in control and are key actions to realize the opportunities

of control in an information rich world.

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,

Chapter 2. Overview of the Field

Actuators

System

Sensors

D/A

Computer

A/D

operator input

noise

external disturbances

noise

Output

Controller

Plant

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?

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

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?

Actuators

Sensors

Functions

Projecte

(a) Engine Control Electronics

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

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

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

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

(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

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

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.

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

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

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

(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.

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

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.

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

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.

Chapter 2. Overview of the Field

Chapter 3

Applications,

Opportunities, and

Challenges

In this chapter, we consider some of the opportunities and challenges for control

in different application areas. The Panel decided to organize the treatment of

applications around five main areas to identify the overarching themes that would

guide its recommendations. These are:

•

Aerospace and transportation

•

Information and networks

•

Robotics and intelligent machines

•

Biology and medicine

•

Materials and processing

In addition, several additional areas arose over the course of the Panel’s delibera-

tions, including environmental science and engineering, economics and finance, and

molecular and quantum systems. Taken together, these represent an enormous col-

lection of applications and demonstrate the breadth of applicability of ideas from

control.

The opportunities and challenges in each of these application areas form the

basis for the major recommendations in this report. In each area, we have sought

the advice and insights not only of control researchers, but also experts in the

application domains who might not consider themselves to be control researchers.

In this way, we hoped to identify the true challenges in each area, rather than

simply identifying interesting control problems that may not have a substantial

opportunity for impact. We hope that the findings will be of interest not only to

control researchers, but also to scientists and engineers seeking to understand how

control tools might be applied to their discipline.

There were several overarching themes that arose across all of the areas con-

sidered by the Panel. The use of systematic and rigorous tools is considered critical

to future success and is an important trademark of the field. At the same time, the

Chapter 3. Applications, Opportunities, and Challenges

next generation of problems will require a paradigm shift in control research and

education. The increased information available across all application areas requires

more integration with ideas from computer science and communications, as well as

improved tools for modeling, analysis, and synthesis for complex decision systems

that contain a mixture of symbolic and continuous dynamics. The need to continue

research in the theoretical foundations that will underly future advances was also

common across all of the applications.

In each section that follows we give a brief description of the background and

history of control in that domain, followed by a selected set of topics which are used

to explore the future potential for control and the technical challenges that must be

addressed. As in the rest of the report, we do not attempt to be comprehensive in

our choice of topics, but rather highlight some of the areas where we see the greatest

potential for impact. Throughout these sections, we have limited the references to

those that provide historical context, future directions, or broad overviews in the

topic area, rather than specific technical contributions (which are too numerous to

properly document).

3.1. Aerospace and Transportation

3.1 Aerospace and Transportation

Men already know how to construct wings or airplanes, which when driven through

the air at sufficient speed, will not only sustain the weight of the wings themselves,

but also that of the engine, and of the engineer as well. Men also know how to

build engines and screws of sufficient lightness and power to drive these planes at

sustaining speed ... Inability to balance and steer still confronts students of the flying

problem. ... When this one feature has been worked out, the age of flying will have

arrived, for all other difficulties are of minor importance.

Wilbur Wright, lecturing to the Western Society of Engineers in 1901 [30].

Aerospace and transportation encompasses a collection of critically important

application areas where control is a key enabling technology. These application areas

represent a very large part of the modern world’s overall technological capability.

They are also a major part of its economic strength, and they contribute greatly to

the well being of its people. The historical role of control in these application areas,

the current challenges in these areas, and the projected future needs all strongly

support the recommendations of this report.

The Historical Role

In aerospace, specifically, control has been a key technological capability tracing

back to the very beginning of the 20th Century. Indeed, the Wright brothers are cor-

rectly famous not simply for demonstrating powered flight—they actually demon-

strated

controlled

powered flight. Their early Wright Flyer incorporated moving

control surfaces (vertical fins and canards) and warpable wings that allowed the

pilot to regulate the aircraft’s flight. In fact, the aircraft itself was not stable, so

continuous pilot corrections were mandatory. This early example of controlled flight

is followed by a fascinating success story of continuous improvements in flight con-

trol technology, culminating in the very high performance, highly reliable automatic

flight control systems we see on modern commercial and military aircraft today (see

Fighter Aircraft and Missiles Vignette, page 17).

Similar success stories for control technology occurred in many other aerospace

application areas. Early World War II bombsights and fire control servo systems

have evolved into today’s highly accurate radar guided guns and precision guided

weapons. Early failure-prone space missions have evolved into routine launch oper-

ations, manned landings on the moon, permanently manned space stations, robotic

vehicles roving Mars, orbiting vehicles at the outer planets, and a host of commer-

cial and military satellites serving various surveillance, communication, navigation

and earth observation needs.

Similarly, control technology has played a key role in the continuing improve-

ment and evolution of transportation—in our cars, highways, trains, ships and air

transportation systems. Control’s contribution to the dramatic increases of safety,

reliability and fuel economy of the automobile is particularly noteworthy. Cars

have advanced from manually tuned mechanical/pneumatic technology to computer

controlled operation of all major functions including fuel injection, emission con-

trol, cruise control, braking, cabin comfort, etc. Indeed, modern automobiles carry