F. Greg Shinskey. \"PID Control.\" Copyright 2000 CRC Press LLC ...
F. Greg Shinskey. "PID Control."
Copyright 2000 CRC Press LLC. .
97.2 Open and Closed Loops
Open-Loop Responses • Closed-Loop Responses
97.3 Mode Selection
Proportional Control • Integral Control • PI Control • PD
Control • PID Controllers
97.4 Controller Hardware
Pneumatic Controllers • Electronic Controllers • Digital
F. Greg Shinskey 97.5 Tuning Controllers
Process Control Consultant Manual Tuning • Autotuning • Self-Turning
Process control plays an essential role in the safe manufacture of quality products at market demand,
while protecting the environment. Flow rates, pressures and temperatures within pipes and vessels,
inventories of liquids and solids, and product quality are all examples of measured variables that must
be controlled to meet the above objectives. While there are several means available for controlling these
variables, the PID family of controllers has historically carried the major share of this responsibility and,
because of their simplicity and reliability, will continue to do so in the future.
The acronym PID stands for the three principal modes of the controller, each of which bears a
mathematical relationship between the controlled variable c and the manipulated variable m driven by
the controller output. The Proportional mode relates changes in m to changes in c through a proportional
gain. The Integral mode moves the output at a rate related to the deviation of c from its desired value,
known as the set point or reference r. Finally, the Derivative mode moves the output in response to the
time derivative or rate of change of c. Interestingly, the mathematical relationships were actually deter-
mined after controllers had already been created to solve process-control problems. The integral mode
was initially called automatic reset, and the derivative mode hyper-reset or pre-act .
97.2 Open and Closed Loops
Figure 97.1 describes the process and controller in functional blocks connected in a loop. Inputs to the
process are manipulated m and load q variables, usually rates of ﬂow of material or energy into or out
of the process. The load may be a single variable or an aggregate, either independent or manipulated or
controlled by another controller. If independent, it is often unmeasured, with its value inferred by the
level of controller output required to maintain the controlled variable at set point. Noise u is shown
affecting the controlled variable directly, typically caused by local turbulence in ﬂow, pressure, and liquid-
level measurements, or by nonuniformity of streams whose composition is measured.
© 1999 by CRC Press LLC
FIGURE 97.1 Process and controller connected in a loop.
In the absence of automatic control, the controlled variable is subject to variations in the load, and to
manual adjustments to m intermittently introduced by operators. These cause variations in c following
both the steady-state and dynamic functions appearing in Fig. 97.1:
c = K p mgm - qgq + u ) (97.1)
where Kp is the steady-state gain, gm and gq are the dynamic-gain vectors in the manipulated and load
paths, respectively, and u is the noise level. The vectors have both magnitude and phase angle which are
functions of the frequency or period of the signal passing through. In the open loop, variations in m are
likely to be steps introduced by operators, but variations in q could take any form — steps, ramps, random
variations, or cycles — depending on the source of the disturbance. Steps are easily introduced manually,
and contain all the frequencies from zero to inﬁnity; consequently, they are useful for evaluating loop
response and testing the effectiveness of controllers. Noise is typiﬁed by random variations in a frequency
range above the bandwidth of the control loop.
The dynamic elements typically consist of deadtime and lags. However, liquid level is the integral of
the difference between inﬂow and outﬂow , in which case Kp in Fig. 97.1 is replaced by an integrator.
Any difference between inﬂow and outﬂow will then be integrated, causing liquid level to continue rising
or falling until the vessel limit is reached. This process has no self-regulation, and therefore cannot be
left indeﬁnitely in an open loop.
In the closed-loop, c responds to load and set point as follows:
c= - +u (97.2)
1 + 1 K p g m K c gc g m K c gc + 1 K p
where Kc is the proportional gain of the controller, gc is the dynamic gain of its integral and derivative
modes, and gr that of its set-point ﬁlter. For the typical self-regulating process in the open loop, c will
take an exponential path following a step change in load as shown by the dashed curve in Fig. 97.2. If
the loop is closed, the controller can move m to return c to r along the solid curve in Fig. 97.2, an
optimum trajectory having a minimum integrated absolute error (IAE) between c and r. The leading
© 1999 by CRC Press LLC
FIGURE 97.2 Closed-loop control minimizes error.
FIGURE 97.3 A lead–lag ﬁlter reduces overshoot.
edge of the curve depends on gq and the trailing edge depends on gm and the PID settings. If the PID
settings are optimized for load response, they will usually cause c to overshoot a change in set point r,
as shown by the dashed curve in Fig. 97.3. If the PID settings are then readjusted to reduce set-point
overshoot, load response is extended. A preferred solution is to insert a lead-lag ﬁlter in the set-point
path, which produced the solid curve in Fig. 97.3.
97.3 Mode Selection
The family of PID controllers includes P, I, PI, PD, and two PID controllers. Each has its own advantages
and limitations, and therefore its preferred range of applications. Each is outlined along with an appli-
In a proportional controller, the deviation between c and r must change for the output to change:
m = b ± K c (r - c) (97.3)
© 1999 by CRC Press LLC
where b is an adjustable bias, also known as “manual reset,” and the sign of the deviation is selected to
provide negative feedback. Some controllers have proportional gain adjustable as percent proportional
band P, where Kc = 100/P. If m must move to a different steady-state value because of a change in load
or set point, the deviation will also change. Therefore the proportional controller allows a steady-state
offset between c and r whenever m does not equal b which can only be eliminated by manually resetting
b. Proportional control is recommended for liquid level, where Kc can be set quite high without loss of
stability and there is no economic penalty for offset.
Proportional control of liquid level is actually preferred when manipulating the ﬂow leaving a tank as
the feed to a critical downstream process. Setting Kc slightly above 1.0 will keep the level in the tank for
all rates of inﬂow, while minimizing the rate of change of outﬂow. This application is called averaging
level control .
The integral mode eliminates offset by driving the output at a rate proportional to the deviation:
where t is time, d is the differential operator, and I is the integral time setting; C is the constant of
integration, i.e., the initial value of the controller output when placed on automatic. Some controllers
have integral time adjusted as integral gain or “reset rate” 1/I in inverse time as “repeats per minute.”
Integration produces a phase lag of 90° between input and output, which increases the response time
and period of oscillation of the loop. The integral controller cannot be used for liquid level, because two
integrators in series form an unstable closed loop . Its use is limited to optimizing the set points of
other closed loops which are already stable .
The PI controller combines the proportional and integral modes:
ò (r - c)dt úû
m = C ± K c êr - c + (97.5)
The deviation and its integral are added vectorially, producing a phase lag falling between 0 and 90°.
This combination provides stability with elimination of offset, making it the most common controller
used in the ﬂuid-processing industries. It is used almost universally, even in those applications where
other controllers are better suited.
The addition of derivative action to a proportional controller adds response for the rate of change of
the controlled variable:
m = b ± Kc æ r - c - D ö
è dt ø
where D is the derivative time setting. Derivative action is preferably applied only to c as indicated and
not to the set point, which would only increase set-point overshoot. A pure derivative function has a
dynamic gain increasing indeﬁnitely with frequency — to avoid instability within the controller, it is
usually limited to about 10 by ﬁltering. This provides an optimum combination of responsiveness and
© 1999 by CRC Press LLC
FIGURE 97.4 Commercial interacting PID controller.
noise rejection. Still, the high-frequency gain ampliﬁes noise in ﬂow and liquid-level measurements
enough to prevent the use of the derivative mode on those loops; temperature measurements are usually
noise-free, allowing it to be used to advantage there. The phase lead provided by the derivative mode
reduces the period of oscillation and settling time of a loop, and improves stability.
PD controllers are recommended for batch processes, where operation begins with c away from r, and
ends ideally with c = r and ﬂows at zero. The PD controller must be biased for this ﬁnal output state,
with D adjusted to eliminate overshoot, which for a zero-load process is permanent .
The phase lead of derivative action more than offsets the phase lag of integral in a properly adjusted PID
controller, resulting in a net phase lead and typically half the IAE of a PI controller applied to the same
process. There are two types of PID controllers in common use, which differ in the way the modes are
combined . Early controllers combined PD and PI action in series, multiplying those functions rather
than adding them. These interacting PID controllers remain in common use, even implemented digitally.
Noninteracting controllers combine the integral and derivative modes in parallel, producing a more
mathematically pure PID expression:
é dc ù
ò (r - c)dt - D dt úû
m = C ± K c êr - c - (97.7)
The interacting controller can be described in the same form, but the coefﬁcients of the individual terms
are different . The effective integral time of the interacting controller is I + D, its effective derivative
time is 1/(1/I + 1/D), and its proportional gain is augmented by 1 + D/I. Thus, the principal result of
mode interaction is to require different PID settings for the two controllers applied to the same process.
A block diagram of a commercial interacting PID controller appears in Fig. 97.4, with transfer functions
described in Laplace transforms whose operator s is equivalent to the differential operator d/dt. Integra-
tion is achieved by positive feedback of the output through a ﬁrst-order lag set at the integral time. The
feedback signal f is taken downstream of the high and low limits, and may be replaced with a constant
or other variable to stop integration, in which case, the controller behaves as PD with remote bias. This
feature is extremely valuable in preventing reset windup, which occurs whenever a controller with integral
action remains in automatic while the loop is open, and results in a large overshoot when the loop is
then closed . Most noninteracting controllers lack this feature.
Derivative action combines a differentiator Ds with a lag (ﬁlter) of time constant aD, where a represents
the inverse of the high-frequency gain limit of typically 10. Figure 97.4 also shows a lead-lag set-point
ﬁlter having a lag time constant of integral time I and lead time of GrI, which produces a gain of Gr to
a step in set point. This gain is adjustable from 0 to 1, with 1 eliminating the ﬁlter and zero imposing a
ﬁrst-order lag. The effect of this lag is equivalent to eliminating proportional action from r in Eqs. 97.5
and 97.7, an optional feature in some PID controllers. The gain adjustment offers more ﬂexibility in
© 1999 by CRC Press LLC
optimizing set-point response as in Fig. 97.3; Gr is set around 0.3 for lag-dominant processes such as
that described in Figs. 97.2 and 97.3, and closer to 1 for deadtime-dominant processes .
97.4 Controller Hardware
Pneumatic proportional controllers were ﬁrst used early in this century, with integral and then derivative
functions added around 1930. Pneumatic controllers are still in use in hazardous areas, in remote gas
ﬁelds where they are operated by gas, and for simple tasks such as regulating temperatures in buildings.
Electronic analog controllers began to replace pneumatic controllers in the late 1950s, but their functions
were implemented using digital microprocessors beginning around 1980. Digital control began in main-
frames in the 1960s, but now is available is programmable logic controllers (PLC), personal computers
(PC), and in distributed control systems (DCS).
The simplest pneumatic controllers are proportional units used to control heating, ventilating, and air-
conditioning (HVAC) in buildings. However, complete PID controllers with functionality similar to that
shown in Fig. 97.4 are also available, used for such demanding tasks as temperature control of batch
exothermic reactors and regulation of ﬂows and pressures on offshore oil platforms. There are panel-
mounted units available as well as weatherproof models used for ﬁeld installation. Most have auto–manual
transfer stations with bumpless transfer between the two modes of operation. Their principal limitations
are a speed of response limited by transmission lines (lengths beyond 30 m are unsuitable for ﬂow and
pressure control) and lack of computing capability (required for nonlinear characterization and automatic
The ﬁrst electronic controllers mimicked their pneumatic counterparts while eliminating transmission
lags. Eventually remote auto–manual transfer and remote tuning were added, and microprocessors
brought these controllers calculation and logic functions, signal conditioning and characterization, and
auto- and self-tuning features as well. Multiple controllers are even available in a single station for
implementing cascade and feedforward systems. The bandwidth of electronic analog controllers extends
to 10 Hz, and in some units even further. Digital controllers execute their calculations intermittently
rather than continuously; most are limited to 10 Hz operation, which with digital ﬁltering results in an
effective deadtime of 0.1 s, reducing bandwidth to about 1.3 Hz.
Electronic controllers are used extensively in ﬁeld locations and dedicated to control of individual
units such as pumps, dryers, wastewater-treatment facilities — wherever only a few loops are required.
Peer-to-peer communication is available in some controllers for incorporation into networks, and most
can communicate with personal computers where data can be displayed on a graphic interface. Conﬁg-
uration (selection of scales, control modes, alarms, and other functions) can be done either via keys on
the controller faceplate or through a PC.
While the stand-alone digital controller evolved from electronic analog models, centralized digital con-
trollers originated with digital computers. Mainframe computers were ﬁrst used for direct digital control
(DDC) where valves were manipulated by the computer, and for supervisory control, where the computer
positioned set points of analog controllers. Gradually minicomputers and microprocessors replaced the
mainframe, with functions becoming distributed among ﬁeld input–output modules, workstations, con-
trol modules, etc., in clusters and other conﬁgurations as DCS. PCs are used for some workstations, and
even for direct control in some plants.
© 1999 by CRC Press LLC
Where many loops are controlled by a single processor, the interval between samples is likely to be
0.5 s or even longer. Users tend to keep expanding the functions demanded of the processor, thereby
extending the interval between samples. This reduces the bandwidth of DCS controllers to values too
low for combustion and compressor controls. Some digital PID algorithms produce an incremental output
Dm, requiring an integrator downstream to produce m. These are not available in proportional or PD
action because they have no ﬁxed bias b, only a constant of integration C which is subject to change
whenever the controller is operated manually.
The PLC was originally a replacement for relay logic. Eventually PID loops were added, which generally
operate much faster (e.g., 100 Hz) than other digital controllers. However, many have nonstandard
algorithms and lack the functionality of other PID controllers, such as proportional and PD control,
windup protection, derivative ﬁltering, set-point ﬁltering, nonlinear characterization, and autotuning.
97.5 Tuning Controllers
A controller is only as effective as its tuning: the adjustment of the PID settings relative to the process
parameters to optimize load and set-point response as described in Figs. 97.2 and 97.3. Tuning is required
when the controller is ﬁrst commissioned on a loop, and may have to be repeated if process parameters
change appreciably with time, load, set point, etc. Tuning requires the introduction of a disturbance in
either the open or closed loop, and interpretation of the resulting response. Autotuning essentially
automates manual procedures, whereas self-tuning can recognize and correct an unsatisfactory response
Ziegler and Nichols  developed the ﬁrst effective tuning methods, and these are still used today. Their
open-loop method steps the controller output to produce a response like the broken curve in Fig. 97.2.
The apparent deadtime (delay) in the response and the steepest slope of the curve are then converted
into appropriate settings for PI and PID controllers using simple formulas. The open-loop method is
most accurate for lag-dominant processes, but the closed-loop method is more broadly applicable. It is
based on inducing a uniform cycle by adjusting the gain of a controller with only its proportional mode
effective (D is set to 0 and I at maximum). The period of the cycle and the controller gain are then used
to calculate appropriate PID settings.
The open-loop method has been extended to other processes , but remains limited to the accuracy
of step-test results. Fine tuning must be done with the loop closed: with the deviation zero, place the
controller in manual, step the output, and immediately transfer to automatic — this simulates a closed-
loop load change. The resulting response should appear like the solid curve in Fig. 97.2, having a ﬁrst
peak which is symmetrical, followed by a slight overshoot and well-damped cycle. If the overshoot is
excessive, I and/or D time should be increased; in the case of undershoot, they should be decreased. (To
simplify tuning of PID controllers, I and D can be moved together, keeping the I/D ratio at about 4 for
noninteracting controllers and 3 for interacting controllers.) If damping is light, lower the proportional
gain; if recovery is slow (producing an unsymmetrical ﬁrst peak), raise the proportional gain. The set
point should not be stepped for test purposes unless set-point response is important, as for ﬂow con-
Some autotuning controllers use a step test in the open loop, implementing Ziegler–Nichols rules. A
single pulse produces more accurate identiﬁcation, and a doublet pulse is better still . A closed-loop
method replaces proportional cycling with relay cycling, where the output switches between high and
low limits as c crosses r . The period of the resulting cycle is used to set I and D, and its amplitude
relative to the distance between output limits to set Kc. The principal limitation of the step and relay
© 1999 by CRC Press LLC
autotuning methods is that only two response features are used to identify a complex process. The
estimated PID settings may not ﬁt the speciﬁc process particularly well, resulting in instability in some
loops and sluggishness in others. The autotuning function cannot monitor the effectiveness of its work,
as self-tuning does.
A self-tuning controller need not test the process, but simply observe its closed-loop response to set-
point and load changes with its current PID settings. It then performs ﬁne-tuning as described above to
bring the overshoot or damping or symmetry of the response curve closer to optimum. This may require
several iterations if the PID settings are far from optimum, but can eventually converge on optimum
response, and readjust as necessary whenever process parameters change. Without a test disturbance,
mischaracterization is possible, especially for a sinusoidal disturbance, where detuning may result when
tightening would give better control.
Averaging level control: Allowing the liquid level in a tank to vary in an effort to minimize changes
in its outﬂow.
Closed-loop response: The response of a controlled variable to changes in set point or load with the
controller in the automatic mode.
Derivative action: The change in controller output responding to a rate of change in the controlled
Dynamic gain: The ratio of the change in output from a function to a change in its input which varies
with time or with the frequency of the input.
Integral action: The rate of change of controller output responding to a deviation between the
controlled variable and set point.
Load: A variable or aggregate of variables which affects the controlled variable.
Manipulated variable: A variable changed by the controller to move the controlled variable.
Noise: A disturbance having a frequency range too high for the controller to affect.
Offset: Steady-state deviation between the controlled variable and set point.
Open-loop response: The response of a controlled variable to process inputs in the absence of control
Proportional action: The change in controller output responding directly to a change in the controlled
Proportional band: The percentage change in the controlled variable required to drive the controller
output full scale.
Self-regulation: The property of a process through which a change in the controlled variable affects
either the ﬂow into or out of a process in such a way as to reduce further changes in the controlled
Tuning: Adjusting the settings of a controller to affect its performance.
Windup: Saturation of the integral model of a controller which results in the controlled variable
subsequently overshooting the set point.
1. J. G. Ziegler and N. B. Nichols, Optimum settings for automatic controllers, Trans. ASME, Novem-
ber 1942, 759–768.
2. F. G. Shinskey, Process Control Systems, 4th ed., New York: McGraw-Hill, 1996, 22–25, 25–28,
3. F. G. Shinskey, Feedback Controllers for the Process Industries, New York: McGraw-Hill, 1994, 68–70,
176–178, 157–164, 148–151, 155–156.
© 1999 by CRC Press LLC
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