Fuzzy
Logic
On/off,
PID, microprocessor based, Fuzzy Logic; the evolutionary changes
of controls. This brief paper outlines some background of standard
PID control and how the implementation of Fuzzy Logic can improve
your single feedback control systems. Fuzzy Logic is a particular
area of concentration in the study of Artificial Intelligence
and is based on the value of that information which is neither
definitely true nor false. The information which humans use in
their everyday lives to base intuitive decisions and apply general
rules of thumb can and should be applied to those control situations
which demand them. Acquired knowledge can be a powerful weapon
to combat the undesired effects of the system response.
Control
software utilizing fuzzy programs use a very flexible set of ifthen
rules. The solution is then applied to appropriate membership
functions. Referring to figure 1, values which lie within the
shaded area are called true beyond a shadow of a doubt. Those
values which lie within the cross hatched area are called false
beyond a shadow of a doubt. If all data falls to one side or the
other of the overlap area, then Fuzzy Logic probably would be
of little benefit. In most applications there are some points
which lie in the common area. Information which lies within the
common area has to be studied, stored, and used to quantify and
to classify the data. This allows for smart manipulation of the
data structure in order to make inference to a solution. Information
which falls in that common area can be ranked, aged, and "best
guess" made after evaluation of this "gray" information.
Another
benefit of Fuzzy Logic in a control system is to quantify the
input signal in a sometimes "noisy" environment. This noise, which
tends to corrupt the integrity of the actual signal, is dealt
with through the common sense of the competent operator. Mathematically,
the information must be judged and prepared for use in decision
making. If an operator took the time to plot the process information
on an XY coordinate system, the operator could visually apply
a curve fit to the data and come up with a fairly accurate generic
representation. Mathematically, fitting a curve of lower order
would produce a fairly inaccurate representation. Therefore, a
higher order curve fit would be appropriate to accommodate the
noisy signal. Fuzzy Logic attempts to emulate what the human response
would be and apply the most intelligent fit to the data.
Currently
there are many applications of Fuzzy Logic utilized by common
household devices, products which most people are familiar with.
The benefit of Fuzzy Logic becomes transparent to the user of
consumer devices since the Fuzzy Module or function is embedded
within the product. The advantage of this approach takes the need
for the operator to understand the theory of fuzzy operation away.
Operation only requires the application of common knowledge to
the standard parameters. A few products which have benefited from
the implementation of Fuzzy Logic are: camcorders with automatic
compensation for operator injected noise such as shaking and moving;
elevators with decreased wait time, making intelligent floor decisions
and minimizing travel and power consumption; anti lock braking
systems with quick reacting independent wheel decisions based
on current and acquired knowledge; television with automatic color,
brightness, and acoustic control based on signal and environmental
conditions; and finally, most importantly to this article, single
loop temperature and process controls.
ZieglerNichols
control theory provides for PID (proportional, integral, and derivative)
numbers which aid in the operation of controls. The development
of the inexpensive microprocessor based PID control has replaced
a majority of the thermostat (on/off) type devices. Most controllers
that are microprocessor based have an autotune function which
operates a system experiment as shown in figure 2. This experiment
helps to determine the thermal characteristics of a particular
system. In most cases, the method of autotune is to make a step
input into the final control element and monitor the output. This
produces a gain term directly related to proportional band. A
delay time between the application of the step input and an observed
response influences the derivative number. The rise time of the
response to the step input produces a value to be used in integration.In
some systems, the delay time to produce response is much different
than the time to give up heat as shown in figure 3. This is common
with many extruder applications making a Fuzzy Logic approach
quite beneficial. If the response of the final control element
as shown in figure 4 is nonlinear, for whatever reason, a linear
response from proportioning action only would result in less than
acceptable control. In addition, if the system tends to have changing
thermal properties or some thermal irregularities, Fuzzy Logic
control should offer a better alternative to the constant adjustment
of PID parameters. Most Fuzzy Logic software begins building its
information base during the autotune function. In fact, the majority
of the information used in the early stages of system startup
come from the autotune solutions.
Until
the 1990's, using computationally intensive Fuzzy Logic methods
of control was not worth the cost to incorporate. As microprocessors
become faster and memory becomes cheaper, the benefit to cost
ratio has climbed significantly. Looking at the typical response
of a standard PID control strategy, shown in figure 5, the response
curve demonstrates a quarter wave decay phenomenon. This method
works adequately in the steady state. However, in real applications
the overshoot and undershoot may sometimes be unacceptable. Methods
derived prior to Fuzzy Logic involve setpoint adjustment to control
this oscillation below a critical level. Ramp to setpoint was
introduced into single loop controls to reduce the rate of ascent
of the process variable in order to knock down the initial overshoot.
Finally, slight detuning of the steady state PID parameters is
commonly done to minimize the destructive oscillatory response.
Fuzzy Logic can incorporate an intelligent response to deal with
these situations.
With
a single feedback control architecture, some of the obvious information
which is readily available to the algorithms is; the error signal,
the difference between Fuzzy Logic, the process variable and the
setpoint variable; change in error from previous cycles to the
current cycle; changes to the setpoint variable; change of the
manipulated variable from cycle to cycle; as well as the change
in the process from past to present. Also available, in addition
to the above, are the multiple combinations of the system response
data. As long as the irregularity lies in that dimension which
fuzzy decisions are being based or associated, the result should
be enhanced performance. This enhanced performance should be demonstrated
in both the transient and steady state response.
The
benefit of accumulating system information is the capability to
predict a problem or upset developing rather than waiting until
the unwanted situation is fully underway as seen in figure 6.
A simple analogy to this type of action is to consult your medical
expert immediately if you experience any symptoms of heart trouble
such as shortness of breath, dizziness, or chest pain rather than
waiting until you are experiencing myocardial infarction. Early
preventative care just makes good sense. The same holds true for
control theory. Fuzzy Logic should offer more intelligent control
now, when it is needed, rather than waiting until the problem
has become catastrophic.
There
are many areas of Artificial Intelligence and much research is
underway throughout the AI spectrum. The portion of AI which is
termed "Fuzzy Logic" invoked in this article, is truly in its
infancy. Research and development into "Fuzzy Controls" continues.
There are many new and revamped products being introduced every
day that incorporate this new technology, Fuzzy Logic. This Engineer
is excited with the solutions offered by many control manufacturers
to address your particular needs. Those applications which were
once thought of as impossible to control with a standard PID controller
now have a new cost effective alternative. Controls incorporating
Fuzzy Logic appear to be the most efficient solution to your difficult
single loop control applications. The frugal must wait no longer.
Truly, our machines are evolving into emulators of mankind.
A
note about the author
Douglas S. Dewey was born in Burlington, Vermont and received
his BSEE from the University of Vermont in 1984. His list of collegiate
honors include Student Chairman of IEEE, Dean's list, and membership
in the Engineering Honor society, Tau Beta Pi. He worked for the
Naval Underwater Systems Center where he was engaged in the development
of advanced acoustic ranging systems for submarines. Currently
Mr. Dewey is the Chief Engineer for Total Temperature Instrumentation,
Incorporated. His current job description includes the development
of specifications for control products using PID loops and advanced
control technologies as manufactured by the Fuji Electric Company
of Tokyo, Japan.
Reproduced
with permission of Total Temperature Instrumentation, Inc.
