Reprinted by permission from Chapter 1: Introduction to Modern Control Theory,
in: F.L. Lewis, Applied Optimal Control and Estimation, PrenticeHall, 1992.
A BRIEF HISTORY OF FEEDBACK CONTROL
Contents
 Outline
 A Brief History of Automatic Control
 Water Clocks of the Greeks and Arabs
 The Industrial Revolution
 The Millwrights
 Temperature Regulators
 Float Regulators
 Pressure Regulators
 Centrifugal Governors
 The Pendule Sympathique
 The Birth of Mathematical Control Theory
 Differential Equations
 Stability Theory
 System Theory
 Mass Communication and The Bell Telephone System
 FrequencyDomain Analysis
 The World Wars and Classical Control
 Ship Control
 Weapons Development and Gun Pointing
 M.I.T. Radiation Laboratory
 Stochastic Analysis
 The Classical Period of Control Theory
 The Space/Computer Age and Modern Control
 TimeDomain Design For Nonlinear Systems
 Sputnik  1957
 Navigation
 Optimality In Natural Systems
 Optimal Control and Estimation Theory
 Nonlinear Control Theory
 Computers in Controls Design and Implementation
 The Development of Digital Computers
 Digital Control and Filtering Theory
 The Personal Computer
 The Union of Modern and Classical Control
 The Philosophy of Classical Control
 The Philosophy of Modern Control
 References
Outline
In this chapter we introduce modern control theory by two approaches. First, a short history
of automatic control theory is provided. Then, we describe the philosophies of classical and
modern control theory.
Feedback control is the basic mechanism by which systems, whether mechanical, electrical, or
biological, maintain their equilibrium or homeostasis. In the higher life forms, the conditions
under which life can continue are quite narrow. A change in body temperature of half a degree
is generally a sign of illness. The homeostasis of the body is maintained through the use of
feedback control [Wiener 1948]. A primary contribution of C.R. Darwin during the last century
was the theory that feedback over long time periods is responsible for the evolution of species.
In 1931 V. Volterra explained the balance between two populations of fish in a closed pond using
the theory of feedback.
Feedback control may be defined as the use of difference signals, determined by comparing the
actual values of system variables to their desired values, as a means of controlling a system.
An everyday example of a feedback control system is an automobile speed control, which uses the
difference between the actual and the desired speed to vary the fuel flow rate. Since the system
output is used to regulate its input, such a device is said to be a closedloop control system.
In this book we shall show how to use modern control theory to design feedback control systems.
Thus, we are concerned not with natural control systems, such as those that occur in living organisms
or in society, but with manmade control systems such as those used to control aircraft, automobilies,
satellites, robots, and industrial processes.
Realizing that the best way to understand an area is to examine its evolution and the reasons for its
existence, we shall first provide a short history of automatic control theory. Then, we give a brief
discussion of the philosophies of classical and modern control theory.
The references for Chapter 1 are at the end of this chapter. The references for the remainder of the
book appear at the end of the book.
1.1 A BRIEF HISTORY OF AUTOMATIC CONTROL
There have been many developments in automatic control theory during recent years. It is difficult
to provide an impartial analysis of an area while it is still developing; however, looking back on the
progress of feedback control theory it is by now possible to distinguish some main trends and point out
some key advances.
Feedback control is an engineering discipline. As such, its progress is closely tied to the practical
problems that needed to be solved during any phase of human history. The key developments in the history
of mankind that affected the progress of feedback control were:
1. The preoccupation of the Greeks and Arabs with keeping accurate track of time. This represents a
period from about 300 BC to about 1200 AD.
2. The Industrial Revolution in Europe. The Industrial Revolution is generally agreed to have started
in the third quarter of the eighteenth century; however, its roots can be traced back into the 1600's.
3. The beginning of mass communication and the First and Second World Wars. This represents a period
from about 1910 to 1945.
4. The beginning of the space/computer age in 1957.
One may consider these as phases in the development of man, where he first became concerned with
understanding his place in space and time, then with taming his environment and making his existence
more comfortable, then with establishing his place in a global community, and finally with his place
in the cosmos.
At a point between the Industrial Revolution and the World Wars, there was an extremely important
development. Namely, control theory began to acquire its written language the language of mathematics.
J.C. Maxwell provided the first rigorous mathematical analysis of a feedback control system in 1868.
Thus, relative to this written language, we could call the period before about 1868 the prehistory
of automatic control.
Following Friedland [1986], we may call the period from 1868 to the early 1900's the primitive period
of automatic control. It is standard to call the period from then until 1960 the classical period,
and the period from 1960 through present times the modern period.
Let us now progress quickly through the history of automatic controls. A reference for the period 300 through
the Industrial Revolution is provided by [Mayr 1970], which we shall draw on and at times quote. See also
[Fuller 1976]. Other important references used in preparing this section included [M. Bokharaie 1973] and
personal discussions with J.D. Aplevich of the University of Waterloo, K.M. Przyluski of the Polish Academy
of Sciences, and W. Askew, a former Fellow at LTV Missiles and Space Corporation and vicepresident of ESystems.
Water Clocks of the Greeks and Arabs
The primary motivation for feedback control in times of antiquity was the need for the accurate
determination of time. Thus, in about 270 the Greek Ktesibios invented a float regulator
for a water clock. The function of this regulator was to keep the water level in a tank at a constant depth.
This constant depth yielded a constant flow of water through a tube at the bottom of the tank which filled
a second tank at a constant rate. The level of water in the second tank thus depended on time elapsed.
The regulator of Ktesibios used a float to control the inflow of water through a valve; as the level of
water fell the valve opened and replenished the reservoir. This float regulator performed the same function
as the ball and cock in a modern flush toilet.
A float regulator was used by Philon of Byzantium in 250 to keep a constant level of oil in a lamp.
During the first century AD Heron of Alexandria developed float regulators for water clocks. The Greeks
used the float regulator and similar devices for purposes such as the automatic dispensing of wine, the
design of syphons for maintaining constant water level differences between two tanks, the opening of temple
doors, and so on. These devices could be called "gadgets" since they were among the earliest examples of
an idea looking for an application.
In 800 through 1200 various Arab engineers such as the three brothers Musa, AlJazari, and Ibn alSa'ati
used float regulators for water clocks and other applications. During this period the important feedback
principle of "on/off" control was used, which comes up again in connection with minimumtime problems in the 1950's.
When Baghdad fell to the Mongols in 1258 all creative thought along these lines came to an end. Moreover,
the invention of the mechanical clock in the 14th century made the water clock and its feedback control
system obsolete. (The mechanical clock is not a feedback control system.) The float regulator does not
appear again until its use in the Industrial Revolution.
Along with a concern for his place in time, early man had a concern for his place in space. It is worth
mentioning that a pseudofeedback control system was developed in China in the 12th century for navigational
purposes. The southpointing chariot had a statue which was turned by a gearing mechanism attached
to the wheels of the chariot so that it continuously pointed south. Using the directional information
provided by the statue, the charioteer could steer a straight course. We call this a "pseudofeedback"
control system since it does not technically involve feedback unless the actions of the charioteer are
considered as part of the system. Thus, it is not an automatic control system.
The Industrial Revolution
The Industrial Revolution in Europe followed the introduction of prime movers, or selfdriven
machines. It was marked by the invention of advanced grain mills, furnaces, boilers, and the steam engine.
These devices could not be adequately regulated by hand, and so arose a new requirement for automatic control
systems. A variety of control devices was invented, including float regulators, temperature regulators,
pressure regulators, and speed control devices.
J. Watt invented his steam engine in 1769, and this date marks the accepted beginning of the Industrial
Revolution. However, the roots of the Industrial Revolution can be traced back to the 1600's or earlier with
the development of grain mills and the furnace.
One should be aware that others, primarily T. Newcomen in 1712, built the first steam engines. However,
the early steam engines were inefficient and regulated by hand, making them less suited to industrial use.
It is extremely important to realize that the Industrial Revolution did not start until the invention of
improved engines and automatic control systems to regulate them.
The Millwrights
The millwrights of Britain developed a variety of feedback control devices. The fantail, invented in
1745 by British blacksmith E. Lee, consisted of a small fan mounted at right angles to the main wheel of a
windmill. Its function was to point the windmill continuously into the wind.
The millhopper was a device which regulated the flow of grain in a mill depending on the speed of rotation
of the millstone. It was in use in a fairly refined form by about 1588.
To build a feedback controller, it is important to have adequate measuring devices. The millwrights
developed several devices for sensing speed of rotation. Using these sensors, several speed regulation
devices were invented, including selfregulating windmill sails. Much of the technology of the millwrights
was later developed for use in the regulation of the steam engine.
Temperature Regulators
Cornelis Drebbel of Holland spent some time in England and a brief period with the Holy Roman Emperor
Rudolf II in Prague, together with his contemporary J. Kepler. Around 1624 he developed an automatic
temperature control system for a furnace, motivated by his belief that base metals could be turned to gold
by holding them at a precise constant temperature for long periods of time. He also used this temperature
regulator in an incubator for hatching chickens.
Temperature regulators were studied by J.J. Becher in 1680, and used again in an incubator by the Prince de
Conti and R.A.F. de Réaumur in 1754. The "sentinel register" was developed in America by W. Henry
around 1771, who suggested its use in chemical furnaces, in the manufacture of steel and porcelain, and in
the temperature control of a hospital. It was not until 1777, however, that a temperature regulator suitable
for industrial use was developed by Bonnemain, who used it for an incubator. His device was later installed
on the furnace of a hotwater heating plant.
Float Regulators
Regulation of the level of a liquid was needed in two main areas in the late 1700's: in the boiler of a steam
engine and in domestic water distribution systems. Therefore, the float regulator received new interest,
especially in Britain.
In his book of 1746, W. Salmon quoted prices for ballandcock float regulators used for maintaining the level
of house water reservoirs. This regulator was used in the first patents for the flush toilet around 1775.
The flush toilet was further refined by Thomas Crapper, a London plumber, who was knighted by Queen Victoria
for his inventions.
The earliest known use of a float valve regulator in a steam boiler is described in a patent issued to J. Brindley
in 1758. He used the regulator in a steam engine for pumping water. S.T. Wood used a float regulator for a
steam engine in his brewery in 1784. In Russian Siberia, the coal miner I.I. Polzunov developed in 1765 a float
regulator for a steam engine that drove fans for blast furnaces.
By 1791, when it was adopted by the firm of Boulton and Watt, the float regulator was in common use in steam engines.
Pressure Regulators
Another problem associated with the steam engine is that of steampressure regulation in the boiler, for the
steam that drives the engine should be at a constant pressure. In 1681 D. Papin invented a safety valve for
a pressure cooker, and in 1707 he used it as a regulating device on his steam engine. Thereafter, it was a
standard feature on steam engines.
The pressure regulator was further refined in 1799 by R. Delap and also by M. Murray. In 1803 a pressure
regulator was combined with a float regulator by Boulton and Watt for use in their steam engines.
Centrifugal Governors
The first steam engines provided a reciprocating output motion that was regulated using a device known as a
cataract, similar to a float valve. The cataract originated in the pumping engines of the Cornwall coal mines.
J. Watt's steam engine with a rotary output motion had reached maturity by 1783, when the first one was sold.
The main incentive for its development was evidently the hope of introducing a prime mover into milling.
Using the rotary output engine, the Albion steam mill began operation early in 1786.
A problem associated with the rotary steam engine is that of regulating its speed of revolution. Some of the
speed regulation technology of the millwrights was developed and extended for this purpose.
In 1788 Watt completed the design of the centrifugal flyball governor for regulating the speed of the rotary
steam engine. This device employed two pivoted rotating flyballs which were flung outward by centrifugal force.
As the speed of rotation increased, the flyweights swung further out and up, operating a steam flow throttling
valve which slowed the engine down. Thus, a constant speed was achieved automatically.
The feedback devices mentioned previously either remained obscure or played an inconspicuous role as a part
of the machinery they controlled. On the other hand, the operation of the flyball governor was clearly visible
even to the untrained eye, and its principle had an exotic flavor which seemed to many to embody the nature
of the new industrial age. Therefore, the governor reached the consciousness of the engineering world and
became a sensation throughout Europe. This was the first use of feedback control of which there was popular
awareness.
It is worth noting that the Greek word for governor is
kußernan.
In 1947, Norbert Wiener at MIT was searching for a name for his new discipline of automata theory control
and communication in man and machine. In investigating the flyball governor of Watt, he investigated also
the etymology of the word kußernan
and came across the Greek word for steersman,
kußerntV.
Thus, he selected the name cybernetics for his fledgling field.
Around 1790 in France, the brothers Périer developed a float regulator to control the speed of a steam
engine, but their technique was no match for the centrifugal governor, and was soon supplanted.
The Pendule Sympathique
Having begun our history of automatic control with the water clocks of ancient Greece, we round out this
portion of the story with a return to mankind's preoccupation with time.
The mechanical clock invented in the 14th century is not a closedloop feedback control system, but a
precision openloop oscillatory device whose accuracy is ensured by protection against external disturbances.
In 1793 the FrenchSwiss A.L. Breguet, the foremost watchmaker of his day, invented a closedloop feedback
system to synchronize pocket watches.
The pendule sympathique of Breguet used a special case of speed regulation. It consisted of a large,
accurate precision chronometer with a mount for a pocket watch. The pocket watch to be synchronized is
placed into the mount slightly before 12 o'clock, at which time a pin emerges from the chronometer, inserts
into the watch, and begins a process of automatically adjusting the regulating arm of the watch's balance spring.
After a few placements of the watch in the pendule sympathique, the regulating arm is adjusted automatically.
In a sense, this device was used to transmit the accuracy of the large chronometer to the small portable pocket watch.
The Birth of Mathematical Control Theory
The design of feedback control systems up through the Industrial Revolution was by trialanderror together
with a great deal of engineering intuition. Thus, it was more of an art than a science. In the mid 1800's
mathematics was first used to analyze the stability of feedback control systems. Since mathematics is the
formal language of automatic control theory, we could call the period before this time the prehistory
of control theory.
Differential Equations
In 1840, the British Astronomer Royal at Greenwich, G.B. Airy, developed a feedback device for pointing a
telescope. His device was a speed control system which turned the telescope automatically to compensate for
the earth's rotation, affording the ability to study a given star for an extended time.
Unfortunately, Airy discovered that by improper design of the feedback control loop, wild oscillations were
introduced into the system. He was the first to discuss the instability of closedloop systems, and
the first to use differential equations in their analysis [Airy 1840]. The theory of differential
equations was by then well developed, due to the discovery of the infinitesimal calculus by I. Newton (16421727)
and G.W. Leibniz (16461716), and the work of the brothers Bernoulli (late 1600's and early 1700's), J.F. Riccati
(16761754), and others. The use of differential equations in analyzing the motion of dynamical systems was
established by J.L. Lagrange (17361813) and W.R. Hamilton (18051865).
Stability Theory
The early work in the mathematical analysis of control systems was in terms of differential equations.
J.C. Maxwell analyzed the stability of Watt's flyball governor [Maxwell 1868]. His technique was to linearize
the differential equations of motion to find the characteristic equation of the system. He studied
the effect of the system parameters on stability and showed that the system is stable if the roots of the
characteristic equation have negative real parts. With the work of Maxwell we can say that the theory
of control systems was firmly established.
E.J. Routh provided a numerical technique for determining when a characteristic equation has stable
roots [Routh 1877].
The Russian I.I. Vishnegradsky [1877] analyzed the stability of regulators using differential equations
independently of Maxwell. In 1893, A.B. Stodola studied the regulation of a water turbine using the techniques
of Vishnegradsky. He modeled the actuator dynamics and included the delay of the actuating mechanism in his analysis.
He was the first to mention the notion of the system time constant. Unaware of the work of Maxwell and
Routh, he posed the problem of determing the stability of the characteristic equation to A. Hurwitz [1895], who
solved it independently.
The work of A.M. Lyapunov was seminal in control theory. He studied the stability of nonlinear differential
equations using a generalized notion of energy in 1892 [Lyapunov 1893]. Unfortunately, though his work was
applied and continued in Russia, the time was not ripe in the West for his elegant theory, and it remained unknown
there until approximately 1960, when its importance was finally realized.
The British engineer O. Heaviside invented operational calculus in 18921898. He studied the transient behavior
of systems, introducing a notion equivalent to that of the transfer function.
System Theory
It is within the study of systems that feedback control theory has its place in the organization of
human knowledge. Thus, the concept of a system as a dynamical entity with definite "inputs" and "outputs"
joining it to other systems and to the environment was a key prerequisite for the further development of
automatic control theory. The history of system theory requires an entire study on its own, but a brief
sketch follows.
During the eighteenth and nineteenth centuries, the work of A. Smith in economics [The Wealth of Nations,
1776], the discoveries of C.R. Darwin [On the Origin of Species By Means of Natural Selection 1859],
and other developments in politics, sociology, and elswehere were having a great impact on the human consciousness.
The study of Natural Philosophy was an outgrowth of the work of the Greek and Arab philosophers, and contributions
were made by Nicholas of Cusa (1463), Leibniz, and others. The developments of the nineteenth century, flavored
by the Industrial Revolution and an expanding sense of awareness in global geopolitics and in astronomy had a
profound influence on this Natural Philosophy, causing it to change its personality.
By the early 1900's A.N. Whitehead [1925], with his philosophy of "organic mechanism", L. von Bertalanffy [1938],
with his hierarchical principles of organization, and others had begun to speak of a "general system theory".
In this context, the evolution of control theory could proceed.
Mass Communication and The Bell Telephone System
At the beginning of the 20th century there were two important occurences from the point of view of control theory:
the development of the telephone and mass communications, and the World Wars.
FrequencyDomain Analysis
The mathematical analysis of control systems had heretofore been carried out using differential equations in the
time domain. At Bell Telephone Laboratories during the 1920's and 1930's, the frequency domain
approaches developed by P.S. de Laplace (17491827), J. Fourier (17681830), A.L. Cauchy (17891857), and others
were explored and used in communication systems.
A major problem with the development of a mass communication system extending over long distances is the need to
periodically amplify the voice signal in long telephone lines. Unfortunately, unless care is exercised, not only
the information but also the noise is amplified. Thus, the design of suitable repeater amplifiers is of prime
importance.
To reduce distortion in repeater amplifiers, H.S. Black demonstrated the usefulness of negative feedback
in 1927 [Black 1934]. The design problem was to introduce a phase shift at the correct frequencies in the system.
Regeneration Theory for the design of stable amplifiers was developed by H. Nyquist [1932]. He derived his
Nyquist stability criterion based on the polar plot of a complex function. H.W. Bode in 1938 used the
magnitude and phase frequency response plots of a complex function [Bode 1940]. He investigated
closedloop stability using the notions of gain and phase margin.
The World Wars and Classical Control
As mass communications and faster modes of travel made the world smaller, there was much tension as men tested
their place in a global society. The result was the World Wars, during which the development of feedback control
systems became a matter of survival.
Ship Control
An important military problem during this period was the control and navigation of ships, which were becoming
more advanced in their design. Among the first developments was the design of sensors for the purpose of
closedloop control. In 1910, E.A. Sperry invented the gyroscope, which he used in the stabilization
and steering of ships, and later in aircraft control.
N. Minorsky [1922] introduced his threeterm controller for the steering of ships, thereby becoming the first
to use the proportionalintegralderivative (PID) controller. He considered nonlinear effects in the
closedloop system.
Weapons Development and Gun Pointing
A main problem during the period of the World Wars was that of the accurate pointing of guns aboard moving
ship and aircraft. With the publication of "Theory of Servomechanisms" by H.L. Házen [1934], the use
of mathematical control theory in such problems was initiated. In his paper, Házen coined the word
servomechanisms, which implies a master/slave relationship in systems.
The Norden bombsight, developed during World War II, used synchro repeaters to relay information on aircraft
altitude and velocity and wind disturbances to the bombsight, ensuring accurate weapons delivery.
M.I.T. Radiation Laboratory
To study the control and information processing problems associated with the newly invented radar, the Radiation
Laboratory was established at the Massachusetts Institute of Technology in 1940. Much of the work in control
theory during the 1940's came out of this lab.
While working on an M.I.T./Sperry Corporation joint project in 1941, A.C. Hall recognized the deleterious
effects of ignoring noise in control system design. He realized that the frequencydomain technology developed
at Bell Labs could be employed to confront noise effects, and used this approach to design a control system for
an airborne radar. This success demonstrated conclusively the importance of frequencydomain techniques in
control system design [Hall 1946].
Using design approaches based on the transfer function, the block diagram, and frequencydomain methods, there
was great success in controls design at the Radiation Lab. In 1947, N.B. Nichols developed his Nichols
Chart for the design of feedback systems. With the M.I.T. work, the theory of linear servomechanisms was
firmly established. A summary of the M.I.T. Radiation Lab work is provided in Theory of Servomechanisms
[James, Nichols, and Phillips, 1947].
Working at North American Aviation, W.R. Evans [1948] presented his root locus technique, which provided
a direct way to determine the closedloop pole locations in the splane. Subsequently, during the 1950's, much
controls work was focused on the splane, and on obtaining desirable closedloop stepresponse characterictics in
terms of rise time, percent overshoot, and so on.
Stochastic Analysis
During this period also, stochastic techniques were introduced into control and communication theory.
At M.I.T in 1942, N. Wiener [1949] analyzed information processing systems using models of stochastic processes.
Working in the frequency domain, he developed a statistically optimal filter for stationary
continuoustime signals that improved the signaltonoise ratio in a communication system. The Russian A.N.
Kolmogorov [1941] provided a theory for discretetime stationary stochastic processes.
The Classical Period of Control Theory
By now, automatic control theory using frequencydomain techniques had come of age, establishing itself as a
paradigm (in the sense of Kuhn [1962]). On the one hand, a firm mathematical theory for servomechanisms had
been established, and on the other, engineering design techniques were provided. The period after the Second
World War can be called the classical period of control theory. It was characterized by the appearance
of the first textbooks [MacColl 1945; Lauer, Lesnick, and Matdon 1947; Brown and Campbell 1948; Chestnut and Mayer
1951; Truxall 1955], and by straightforward design tools that provided great intuition and guaranteed solutions
to design problems. These tools were applied using hand calculations, or at most slide rules, together with
graphical techniques.
The Space/Computer Age and Modern Control
With the advent of the space age, controls design in the United States turned away from the frequencydomain
techniques of classical control theory and back to the differential equation techniques of the late 1800's,
which were couched in the time domain. The reasons for this development are as follows.
TimeDomain Design For Nonlinear Systems
The paradigm of classical control theory was very suitable for controls design problems during and immediately
after the World Wars. The frequencydomain approach was appropriate for linear timeinvariant systems.
It is at its best when dealing with singleinput/singleoutput systems, for the graphical techniques were
inconvenient to apply with multiple inputs and outputs.
Classical controls design had some successes with nonlinear systems. Using the noiserejection properties of
frequencydomain techniques, a control system can be designed that is robust to variations in the system
parameters, and to measurement errors and external disturbances. Thus, classical techniques can be used on a
linearized version of a nonlinear system, giving good results at an equilibrium point about which the system
behavior is approximately linear.
Frequencydomain techniques can also be applied to systems with simple types of nonlinearities using the
describing function approach, which relies on the Nyquist criterion. This technique was first used
by the Pole J. Groszkowski in radio transmitter design before the Second World War and formalized in 1964 by
J. Kudrewicz.
Unfortunately, it is not possible to design control systems for advanced nonlinear multivariable systems, such
as those arising in aerospace applications, using the assumption of linearity and treating the
singleinput/singleoutput transmission pairs one at a time.
In the Soviet Union, there was a great deal of activity in nonlinear controls design. Following the lead of
Lyapunov, attention was focused on timedomain techniques. In 1948, Ivachenko had investigated the principle
of relay control, where the control signal is switched discontinuously between discrete values.
Tsypkin used the phase plane for nonlinear controls design in 1955. V.M. Popov [1961] provided his
circle criterion for nonlinear stability analysis.
Sputnik  1957
Given the history of control theory in the Soviet Union, it is only natural that the first satellite, Sputnik,
was launched there in 1957. The first conference of the newly formed International Federation of Automatic
Control (IFAC) was fittingly held in Moscow in 1960.
The launch of Sputnik engendered tremendous activity in the United States in automatic controls design.
On the failure of any paradigm, a return to historical and natural first principles is required. Thus, it
was clear that a return was needed to the timedomain techniques of the "primitive" period of control theory,
which were based on differential equations. It should be realized that the work of Lagrange and Hamilton makes
it straightforward to write nonlinear equations of motion for many dynamical systems. Thus, a control theory
was needed that could deal with such nonlinear differential equations.
It is quite remarkable that in almost exactly 1960, major developments occurred independently on several fronts
in the theory of communication and control.
Navigation
In 1960, C.S. Draper invented his inertial navigation system, which used gyroscopes to provided accurate
information on the position of a body moving in space, such as a ship, aircraft, or spacecraft. Thus, the
sensors appropriate for navigation and controls design were developed.
Optimality In Natural Systems
Johann Bernoulli first mentioned the Principle of Optimality in connection with the Brachistochrone
Problem in 1696. This problem was solved by the brothers Bernoulli and by I. Newton, and it became clear that
the quest for optimality is a fundamental property of motion in natural systems. Various optimality principles
were investigated, including the minimumtime principle in optics of P. de Fermat (1600's), the work of L. Euler
in 1744, and Hamilton's result that a system moves in such a way as to minimize the time integral of the
difference between the kinetic and potential energies.
These optimality principles are all minimum principles. Interestingly enough, in the early 1900's,
A. Einstein showed that, relative to the 4D spacetime coordinate system, the motion of systems occurs in
such as way as to maximize the time.
Optimal Control and Estimation Theory
Since naturallyoccurring systems exhibit optimality in their motion, it makes sense to design manmade control
systems in an optimal fashion. A major advantage is that this design may be accomplished in the time domain.
In the context of modern controls design, it is usual to minimize the time of transit, or a quadratic generalized
energy functional or performance index, possibly with some constraints on the allowed controls.
R. Bellman [1957] applied dynamic programming to the optimal control of discretetime systems, demonstrating
that the natural direction for solving optimal control problems is backwards in time. His procedure
resulted in closedloop, generally nonlinear, feedback schemes.
By 1958, L.S. Pontryagin had developed his maximum principle, which solved optimal control problems
relying on the calculus of variations developed by L. Euler (17071783). He solved the minimumtime
problem, deriving an on/off relay control law as the optimal control [Pontryagin, Boltyansky, Gamkrelidze, and
Mishchenko 1962]. In the U.S. during the 1950's, the calculus of variations was applied to general optimal
control problems at the University of Chicago and elsewhere.
In 1960 three major papers were published by R. Kalman and coworkers, working in the U.S. One of these [Kalman
and Bertram 1960], publicized the vital work of Lyapunov in the timedomain control of nonlinear systems.
The next [Kalman 1960a] discussed the optimal control of systems, providing the design equations for the linear
quadratic regulator (LQR). The third paper [Kalman 1960b] discussed optimal filtering and estimation theory,
providing the design equations for the discrete Kalman filter. The continuous Kalman filter was
developed by Kalman and Bucy [1961].
In the period of a year, the major limitations of classical control theory were overcome, important new
theoretical tools were introduced, and a new era in control theory had begun; we call it the era of modern
control.
The key points of Kalman's work are as follows. It is a timedomain approach, making it more applicable
for timevarying linear systems as well as nonlinear systems. He introduced linear algebra and matrices,
so that systems with multiple inputs and outputs could easily be treated. He employed the concept of the
internal system state; thus, the approach is one that is concerned with the internal dynamics of a system
and not only its input/output behavior.
In control theory, Kalman formalized the notion of optimality in control theory by minimizing a very
general quadratic generalized energy function. In estimation theory, he introduced stochastic notions that applied
to nonstationary timevarying systems, thus providing a recursive solution, the Kalman filter, for the
leastsquares approach first used by C.F. Gauss (17771855) in planetary orbit estimation. The Kalman filter is
the natural extension of the Wiener filter to nonstationary stochastic systems.
Classical frequencydomain techniques provide formal tools for control systems design, yet the design phase
itself remained very much an art and resulted in nonunique feedback systems. By contrast, the theory of Kalman
provided optimal solutions that yielded control systems with guaranteed performance.
These controls were directly found by solving formal matrix design equations which generally had
unique solutions.
It is no accident that from this point the U.S. space program blossomed, with a Kalman filter providing
navigational data for the first lunar landing.
Nonlinear Control Theory
During the 1960's in the U.S., G. Zames [1966], I.W. Sandberg [1964], K.S. Narendra [Narendra and Goldwyn 1964],
C.A. Desoer [1965], and others extended the work of Popov and Lyapunov in nonlinear stability. There was an
extensive application of these results in the study of nonlinear distortion in bandlimited feedback loops,
nonlinear process control, aircraft controls design, and eventually in robotics.
Computers in Controls Design and Implementation
Classical design techniques could be employed by hand using graphical approaches. On the other hand, modern
controls design requires the solution of complicated nonlinear matrix equations. It is fortunate that in 1960
there were major developments in another area digital computer technology. Without computers, modern control
would have had limited applications.
The Development of Digital Computers
In about 1830 C. Babbage introduced modern computer principles, including memory, program control, and branching
capabilities. In 1948, J. von Neumann directed the construction of the IAS storedprogram computer at Princeton.
IBM built its SSEC storedprogram machine. In 1950, Sperry Rand built the first commercial data processing machine,
the UNIVAC I. Soon after, IBM marketed the 701 computer.
In 1960 a major advance occurred, the second generation of computers was introduced which used solidstate
technology. By 1965, Digital Equipment Corporation was building the PDP8, and the minicomputer
industry began. Finally, in 1969 W. Hoff invented the microprocessor.
Digital Control and Filtering Theory
Digital computers are needed for two purposes in modern controls. First, they are required to solve the
matrix design equations that yield the control law. This is accomplished offline during the design process.
Second, since the optimal control laws and filters are generally timevarying, they are needed to implement
modern control and filtering schemes on actual systems.
With the advent of the microprocessor in 1969 a new area developed. Control systems that are implemented on
digital computers must be formulated in discrete time. Therefore, the growth of digital control
theory was natural at this time.
During the 1950's, the theory of sampled data systems was being developed at Columbia by J.R. Ragazzini,
G. Franklin, and L.A. Zadeh [Ragazzini and Zadeh 1952, Ragazzini and Franklin 1958]; as well as by E.I. Jury [1960],
B.C. Kuo [1963], and others. The idea of using digital computers for industrial process control emerged
during this period [Åström and Wittenmark 1984]. Serious work started in 1956 with the collaborative
project between TRW and Texaco, which resulted in a computercontrolled system being installed at the Port Arthur
oil refinery in Texas in 1959.
The development of nuclear reactors during the 1950's was a major motivation for exploring industrial
process control and instrumentation. This work has its roots in the control of chemical plants during the 1940's.
By 1970, with the work of K. Åström [1970] and others, the importance of digital controls in process
applications was firmly established.
The work of C.E. Shannon in the 1950's at Bell Labs had revealed the importance of sampled data techniques in the
processing of signals. The applications of digital filtering theory were investigated at the Analytic
Sciences Corporation [Gelb 1974] and elsewhere.
The Personal Computer
With the introduction of the PC in 1983, the design of modern control systems became possible for the individual
engineer. Thereafter, a plethora of software control systems design packages was developed, including ORACLS,
Program CC, ControlC, PCMatlab, MATRIX_{x}, Easy5, SIMNON, and others.
The Union of Modern and Classical Control
With the publication of the first textbooks in the 1960's, modern control theory established itself as a paradigm
for automatic controls design in the U.S. Intense activity in research and implementation ensued, with the I.R.E.
and the A.I.E.E. merging, largely through the efforts of P. Haggerty at Texas Instruments, to form the Institute
of Electrical and Electronics Engineers (I.E.E.E) in the early 1960's.
With all its power and advantages, modern control was lacking in some aspects. The guaranteed performance obtained
by solving matrix design equations means that it is often possible to design a control system that works in theory
without gaining any engineering intuition about the problem. On the other hand, the frequencydomain
techniques of classical control theory impart a great deal of intuition.
Another problem is that a modern control system with any compensator dynamics can fail to be robust to
disturbances, unmodelled dynamics, and measurement noise. On the other hand, robustness is built in with a
frequencydomain approach using notions like the gain and phase margin.
Therefore, in the 1970's, especially in Great Britain, there was a great deal of activity by H.H. Rosenbrock
[1974], A.G.J. MacFarlane and I. Postlethwaite [1977], and others to extend classical frequencydomain techniques
and the root locus to multivariable systems. Successes were obtained using notions like the characteristic locus,
diagonal dominance, and the inverse Nyquist array.
A major proponent of classical techniques for multivariable systems was I. Horowitz, whose quantitative
feedback theory developed in the early 1970's accomplishes robust design using the Nichols chart.
In 1981 seminal papers appeared by J. Doyle and G. Stein [1981] and M.G. Safonov, A.J. Laub, and G.L. Hartmann
[1981]. Extending the seminal work of MacFarlane and Postlethwaite [1977], they showed the importance of the
singular value plots versus frequency in robust multivariable design. Using these plots, many of the
classical frequencydomain techniques can be incorporated into modern design. This work was pursued in aircraft
and process control by M. Athans [1986] and others. The result is a new control theory that blends the
best features of classical and modern techniques. A survey of this robust modern control theory is
provided by P. Dorato [1987].
1.2 THE PHILOSOPHY OF CLASSICAL CONTROL
Having some understanding of the history of automatic control theory, we may now briefly discuss the philosophies
of classical and modern control theory.
Developing as it did for feedback amplifier design, classical control theory was naturally couched in the
frequency domain and the splane. Relying on transform methods, it is primarily applicable for linear
timeinvariant systems, though some extensions to nonlinear systems were made using, for instance, the
describing function.
The system description needed for controls design using the methods of Nyquist and Bode is the magnitude and phase
of the frequency response. This is advantageous since the frequency response can be experimentally measured.
The transfer function can then be computed. For root locus design, the transfer function is needed. The block
diagram is heavily used to determine transfer functions of composite systems. An exact description of the
internal system dynamics is not needed for classical design; that is, only the input/output behavior of the
system is of importance.
The design may be carried out by hand using graphical techniques. These methods impart a great deal
of intuition and afford the controls designer with a range of design possibilities, so that the resulting
control systems are not unique. The design process is an engineering art.
A real system has disturbances and measurement noise, and may not be described exactly by the mathematical model
the engineer is using for design. Classical theory is natural for designing control systems that are
robust to such disorders, yielding good closedloop performance in spite of them. Robust design is
carried out using notions like the gain and phase margin.
Simple compensators like proportionalintegralderivative (PID), leadlag, or washout circuits are
generally used in the control structure. The effects of such circuits on the Nyquist, Bode, and root locus
plots are easy to understand, so that a suitable compensator structure can be selected. Once designed, the
compensator can be easily tuned on line.
A fundamental concept in classical control is the ability to describe closedloop properties in terms of
openloop properties, which are known or easy to measure. For instance, the Nyquist, Bode, and root locus
plots are in terms of the openloop transfer function. Again, the closedloop disturbance rejection properties
and steadystate error can be described in terms of the return difference and sensitivity.
Classical control theory is difficult to apply in multiinput/multioutput (MIMO), or multiloop systems.
Due to the interaction of the control loops in a multivariable system, each singleinput/singleoutput (SISO)
transfer function can have acceptable properties in terms of step response and robustness, but the coordinated
control motion of the system can fail to be acceptable.
Thus, classical MIMO or multiloop design requires painstaking effort using the approach of closing one loop
at a time by graphical techniques. A root locus, for instance, should be plotted for each gain element,
taking into account the gains previously selected. This is a trialanderror procedure that may require
multiple iterations, and it does not guarantee good results, or even closedloop stability.
The multivariable frequencydomain approaches developed by the British school during the 1970's, as well as
quantitative feedback theory, overcome many of these limitations, providing an effective approach for the design
of many MIMO systems.
1.3 THE PHILOSOPHY OF MODERN CONTROL
Modern controls design is fundamentally a timedomain technique. An exact statespace model of the
system to be controlled, or plant, is required. This is a firstorder vector differential equation
of the form
dx/dt = Ax + Bu
y = Cx
where x(t) is a vector of internal variables or system states, u(t) is a vector of control inputs, and
y(t) is a vector of measured outputs. It is possible to add noise terms to represent process and measurement
noises. Note that the plant is described in the timedomain.
The power of modern control has its roots in the fact that the statespace model can as well represent a MIMO
system as a SISO system. That is, u(t) and y(t) are generally vectors whose entries are the individual scalar
inputs and outputs. Thus, A, B, C are matrices whose elements describe the system dynamical interconnections.
Modern controls techniques were first firmly established for linear systems. Extensions to nonlinear systems can
be made using the Lyapunov approach, which extends easily to MIMO systems, dynamic programming, and other techniques.
Openloop optimal controls designs can be determined for nonlinear systems by solving nonlinear twopoint
boundaryvalue problems.
Exactly as in the classical case, some fundamental questions on the performance of the closedloop system can be
attacked by investigating openloop properties. For instance, the openloop properties of controllability
and observability of (0 (Chapter 2) give insight on what it is possible to achieve using feedback control.
The difference is that, to deal with the statespace model, a good knowledge of matrices and linear algebra is
required.
To achieve suitable closedloop properties, a feedback control of the form
u = Kx
may be used. The feedback gain K is a matrix whose elements are the individual control gains in the system.
Since all the states are used for feedback, this is called statevariable feedback. Note that multiple feedback
gains and large systems are easily handled in this framework. Thus, if there are n state components (where n can be very
large in an aerospace or power distribution system) and m scalar controls, so that u(t) is an mvector, then K is an
mxn matrix with mn entries, corresponding to mn control loops.
In the standard linear quadratic regulator (LQR), the feedback gain K is chosen to minimize a quadratic timedomain
performance index (PI) like
oo
J = / (x^{T}Qx + u^{T}Ru) dt
o
The minimum is sought over all state trajectories. This is an extension to MIMO systems of the sorts of PIs
(ITSE, ITAE, etc.) that were used in classical control. Q and R are weighting matrices that serve as design
parameters. Their elements can be selected to provide suitable performance.
The key to LQR design is the fact that, if the feedback gain matrix K can be successfully chosen to make J finite,
then the integral (0 involving the norms of u(t) and x(t) is bounded. If Q and R are correctly chosen, wellknown
mathematical principles then ensure that x(t) and u(t) go to zero with time. This guarantees closedloop
stability as well as bounded control signals in the closedloop system.
It can be shown (see Chapter 3), that the value of K that minimizes the PI is given by
K = R^{1}B^{T}S
where S is an nxn matrix satisfying the Riccati equation
0 = A^{T}S + SA  SBR^{1}B^{T}S + Q.
Within this LQ framework, several points can be made. First, as long as the system (0 is controllable and Q and
R are suitably chosen, the K given by these equations guarantees the stability of the closedloop system
dx/dt = (ABK)x + Bu.
Second, this technique is easy to apply even for multipleinput plants, since u(t) can be a vector having many
components.
Third, the LQR solution relies on the solution of the matrix design equation (0, and so is unsuited to
hand calculations. Fortunately, many design packages are by now available on digital computers for solving the
Riccati design equation for S, and hence for obtaining K. Thus, computeraided design is an essential
feature of modern controls.
The LQR solution is a formal one that gives a unique answer to the feedback control problem once the
design parameter Q has been selected. In fact, the engineering art in modern design lies in the selection
of the PI weighting matrices Q and R. A body of theory on this selection process has evolved. Once Q is
properly selected, the matrix design equation is formally solved for the unique K that guarantees stability.
Observe that K is computed in terms of the openloop quantities A, B, Q, so that modern and classical
design have this feature of determining closedloop properties in terms of openloop quantities in common.
However, in modern control, all the entries of K are determined at the same time by using the matrix design
equations. This corresponds to closing all the feedback control loops simultaneously, which is in
complete contrast to the oneloopatatime procedure of classical controls design.
Unfortunately, formal LQR design gives very little intuition on the nature or properties of the closedloop
system. In recent years, this deficiency has been addressed from a variety of standpoints.
Although LQR design using state feedback guarantees closedloop stability, all the state components are seldom
available for feedback purposes in a practical design problem. Therefore, output feedback of the form
u = Ky
is more useful. LQR design equations for output feedback are more complicated than (0, but are easily derived
(see Chapter 4).
Modern outputfeedback design allows one to design controllers for complicated systems with multiple inputs
and outputs by formally solving matrix design equations on a digital computer.
Another important factor is the following. While the state feedback (0 involves feedback from all states to all
inputs, offering no structure in the control system, the output feedback control law (0 can be used to design a
compensator with a desired dynamical structure, regaining much of the intuition of classical controls design.
Feedback laws like (0 and (0 are called static, since the control gains are constants, or at most timevarying.
An alternative to static output feedback is to use a dynamic compensator of the form
dz/dt = Fz + Gy + Eu
u = Hz + Dy.
The inputs of this compensator are the system inputs and outputs. This yields
a closedloop and is called dynamic output feedback. The design
problem is to select the matrices F, G, E, H, D for good closedloop
performance. An important result of modern control is that closedloop
stability can be guaranteed by selecting F = ALC for some matrix L which is computed using a Riccati design
equation similar to (0. The other matrices in (0 are then easily determined. This design is based on the vital
separation principle (Chapter 10).
A disadvantage with design using F = ALC is that then the dynamic compensator has the same number of internal
states as the plant. In complicated modern aerospace and power plant applications, this dimension can be very
large. Thus, various techniques for controller reduction and reducedorder design have been
developed.
In standard modern control, the system is assumed to be exactly described by the mathematical model (0.
In actuality, however, this model may be only an approximate description of the real plant. Moreover, in
practice there can be disturbances acting on the plant, as well as measurement noise in determining y(t).
The LQR using full state feedback has some important robustness properties to such disorders, such as an
infinite gain margin, 60°
of phase margin, and robustness to some nonlinearities in the control loops (Chapter 10). On the other hand,
the LQR using static or dynamic output feedback design has no guaranteed robustness properties. With the work
on robust modern control in the early 1980's, there is now a technique (LQG/LTR, Chapter 10) for designing robust
multivariable control systems. LQG/LTR design incorporates rigorous treatments of the effects of modelling
uncertainties on closedloop stability, and of disturbance effects on closedloop performance.
With the work on robust modern design, much of the intuition of classical controls techniques can now be
incorporated in modern multivariable design.
With modern developments in digital control theory and discretetime systems, modern control
is very suitable for the design of control systems that can be implemented on microprocessors (Part III of the
book). This allows the implementation of controller dynamics that are more complicated as well as more effective
than the simple PID and leadlag structures of classical controls.
With recent work in matrixfraction descriptions and polynomial equation design, a MIMO plant
can be described not in statespace form, but in input/output form. This is a direct extension of the classical
transfer function description and, for some applications, is more suitable than the internal description (0.
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