INTRODUCTION
Temperature control is widely used in various processes. These
processes, no matter if it is in a large industrial plant, or in
a home appliance, share several unfavorable features. These
include non-linearity, interference, dead time, and external
disturbances, among others. Conventional approaches usually do
not result in satisfactory temperature control.
In this Application Note we provide examples of fuzzy logic used
to control temperature in several different situations. These
examples are developed using FIDE, an integrated fuzzy inference
development environment.
FUZZY CONTROLLER FOR AIR CONDITIONING SYSTEM
In the following discussion, we give examples of air conditioning
systems, ranging from a basic model to an advanced model. We do
not provide FIU(Fuzzy Inference Unit) source code as we have in
previous application notes. Instead, this time we concentrate on
the input/output variables of the fuzzy controller for an air
conditioning system.
A Basic Model
Let us start with the simplest air conditioning system, which is
shown in Figure 1. The only control target in this system is
temperature. There are two adjustment valves to change
temperature. An example provided in directory /fide/examples/fans
in the FIDE software package is similar to this basic model.
There is a sensor in the room to monitor temperature for feedback
control, and there are two control elements, cooling valve and
heating valve, to adjust the air supply temperature to the room.
Figure 2 diagrams a fuzzy controller for an air conditioning
system basic model. Rules for this controller may be formulated
using statements similar to:
If temperature is low then open heating valve greatly
Values such as low are defined by fuzzy sets (membership
functions). We can use the MF-edit function in FIDE to define the
fuzzy sets. Generally, membership functions of fuzzy sets take on
a triangular shape because they are effective and easy to
manipulate.
A Modified Model
In the real world, however, it is usually not enough to manage an
air conditioning system with temperature control only. We need to
control humidity as well. A modified air conditioning system is
shown in Figure 3. There are two sensors in this system: one to
monitor temperature and one to monitor humidity. There are three
control elements: cooling valve, heating valve, and humidifying
valve, to adjust temperature and humidity of the air supply.
A fuzzy controller for this modified air conditioning system is
shown in Figure 4. The two inputs to the controller are measured
temperature and humidity. The three outputs are control signals
to the three valves.
Rules for this controller can be formulated by adding rules for
humidity control to those already formulated for temperature
control in the basic model. Additional rules must take the
interference between temperature and humidity into account. For
example, in the winter, when we use heat to raise temperature,
humidity is usually reduced. The air thus becomes too dry. To
address this condition, a rule statement similar to the following
is appropriate:
If temperature is low then open humidifying valve slightly
This rule acts as a predictor of humidity (it leads the humidity
value) and is also designed to prevent overshoot in the output
humidity curve. We could have used the following rule:
If humidity is low then open humidifying valve slightly
But it's action, if acting as the only rule for low humidity,
will be late when low humidity is already the case.
An Advanced Model for Automobile Passenger Environment
Temperature control in an automobile passenger environment is
more complex than that of a static room in a building. To address
driver and passenger comfort and safety, many factors must be
taken into account. Temperature and humidity should be controlled
to provide an enjoyable ride. However, it is also critical to
keep windows from being fogged, which is caused by a temperature
differential between inside and outside air in combination with
the interior humidity. To obtain satisfactory control results,
the strength of sunshine radiation and the automobile speed must
also be factored in.
Figure 5 shows a fuzzy controller which employs five sensors to
obtain data for temperature control and humidity control in an
automobile. A recent industry report on the application of such a
controller on a new model automobile indicates this controller
outperforms conventional control systems substantially. It
prevents rapid change of temperature in the car when doors or
windows are opened and then closed. It even reacts to weather
changes because interior humidity changes caused by the weather
can be detected by sensors.
COMMENTS
Air conditioning systems are essential in most of our daily
lives. Our expectations of such systems have been raised to
demand more than just temperature control, and it is increasingly
desirable to apply these systems in varying situations and
environments. A comfortable and safe environment is often
difficult to define and affected by sometimes contradictory
factors. Fuzzy logic control provides an effective and economic
approach to this problem. Fuzzy controllers incorporated in the
latest model automobiles designed by Japanese auto makers
provide proof that temperature control in diverse environments
can be solved. The key to a good solution lies in thorough
analysis of factors affecting the control target and the kinds
of sensors and sensing techniques used to detect these factors.
We did not provide FIU source code in this note. However we give
examples of the types of rules required. For further
investigation, FIU source code for a temperature control system
can be found in the directory /fide/examples/fans in the FIDE
system provided with the FIDE or FIDE DEMO package.
For an engineer, an ideal machine would be one in which human
requests are automatically interpreted and responded to by
adjusting itself appropriately to variations in the environment.
Fuzzy logic can help make this ideal a reality. At the least, it
makes the effort easier.
(Weijing Zhang, Applications Engineer, Aptronix Inc.)
For Further Information Please Contact:
Aptronix Incorporated
2150 North First Street #300
San Jose, CA 95131
Tel (408) 428-1888
Fax (408) 428-1884
FuzzyNet (408) 428-1883 data 8/N/1
Aptronix Company Overview
Headquartered in San Jose, California, Aptronix develops and
markets fuzzy logic-based software, systems and development tools
for a complete range of commercial applications. The company was
founded in 1989 and has been responsible for a number of
important innovations in fuzzy technology.
Aptronix's product Fide (Fuzzy Inference Development Environment)
-- is a complete environment for the development of fuzzy
logic-based systems. Fide provides system engineers with the
most effective fuzzy tools in the industry and runs in
MS-WindowsTM on 386/486 hardware. The price for Fide is $1495
and can be ordered from any authorized Motorola distributor. For
a list of authorized distributors or more information, please
call Aptronix. The software package comes with complete
documentation on how to develop fuzzy logic based applications,
free telephone support for 90 days and access to the Aptronix
FuzzyNet information exchange.
Temperature Control (2)
FIDE Application Note 005-920903
Aptronix Inc., 1992