textfiles/programming/FUZZYLOGIC/air_cond.txt

                               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