                          INTRODUCTION
     
     Recently fuzzy logic has found increasing applicability in the
     field of vehicle control. Applications include automatic
     transmission, engine control, cruise control, antiskid braking,
     and air conditioning, among others. This application note focuses
     on automatic transmission control.
     
     AUTOMATIC TRANSMISSION : BASIC MODEL
     
     A basic automatic transmission system is shown in Figure 1. Fuzzy
     logic is employed to infer the best gear selection. The four
     fuzzy inference unit inputs are sensor based signals from the car
     itself. Using throttle, vehicle speed, engine speed, engine load,
     the fuzzy inference unit determines a shift, i.e., gear number,
     for the car. 
     
      
     Figure 1   Automatic Transmission System 
     
     Definitions of Input/Output Variables
     
     To create a fuzzy inference unit, we first need to define labels
     (membership functions) for input and output variables. Examples
     of such labels are shown in Figures 2, 3, 4, 5, and 6. The output
     variable Shift uses singleton membership functions because the
     TVFI (Truth Value Flow Inference) method is the preferred method
     of defuzzification. 
     
     Figure  2   Labels and Membership Functions of  Throttle
     
     
     Figure  3   Labels and Membership Functions of  Vehicle_Speed
     
     
     Figure  4   Labels and Membership Functions of  Engine_Speed
     
     
     Figure  5   Labels and Membership Functions of  Engine_Load
     
     
     Figure  6   Labels and Membership Functions of  Shift
     
     
     Rules
     
     Using labels as defined above, we can write rules for the fuzzy
     inference unit shown in Figure 1. Rules embody the knowledge base
     required for decision making. They are represented as English
     like if-then statements. 
     
     For example, the following is a rule: 
     
     	IF        Throttle            is Low and 
     	          Vehicle_Speed       is Low and 
     	          Engine_Speed        is Low and 
     	          Engine_Load         is High 
     	THEN      Shift               is No_1
     
     We can write many such rules to cover the different situations
     encountered in transmission of power to wheel. The totality of
     such rules constitutes a fuzzy inference unit for gear selection
     in an automobile. 
     
     
     AUTOMATIC TRANSMISSION : MODIFIED MODEL
     
     The performance of the above automatic transmission model is not
     very good. The gear shifting procedure is implemented without
     taking into account the driving environment. We, as humans, drive
     in different "modes" depending on road conditions. For example,
     we sometimes drive at a constant low gear when negotiating a
     windy mountainous road. This avoids unnecessary gear shifting,
     which can add to engine wear and make for a less than smooth ride
     for passengers. 
     
     With this in mind, a modified transmission system is shown in
     Figure 7. We have added an extra input, mode, to the fuzzy
     inference unit to influence gear shift behavior. This new driving
     mode can be inferred by fuzzy logic(FIU B) as well. 
     
     
     Figure 7   Modified Automatic Transmission System 
     
     
     Figure 8   Fuzzy Inference Unit for Driving Mode
     
     Figure 8 shows a fuzzy inference unit for inferring driving mode.
     To create an FIU, we develop rules such as the following:
     
     If        Vehicle_Speed                 is Low and 
               Variation_of_Vehicle_Speed    is Small and 
               Slope_Resistance              is Positive_Large and 
               Accelerator                   is Medium then 
               Mode                          is Steep_Uphill_Mode
     
     
     If        Vehicle_Speed                 is Medium and 
               Variation_of_Vehicle_Speed    is Small and 
               Slope_Resistance              is Negative_Large and 
               Accelerator                   is Small then
               Mode                          is Gentle_Downhill_Mode
     
     The driving mode output of FIU B can then be further used to
     affect the gear shifting procedure. For example, if mode is
     Steep_Uphill_Mode, a downshift is necessary in order to obtain
     greater engine power. If mode is Gentle_Downhill_Mode, we also
     need a lower gear than would be the case for a flat smooth road.
     The lower gear provides engine braking power. Typical gear
     selection rules could look as follows: 
     
     If   Mode      is Steep_Uphill_Mode then     
          Shift     is No_2 
     
     If   Mode      is Gentle_Downhill_Mode then 
          Shift     is No_3
     
     COMMENTS
     
     In actuality, the inputs to fuzzy inference unit B in Figure 8
     could include other factors, such as steering angle, to determine
     a more accurate driving mode. With steering angle data, we can
     determine whether or not the vehicle is on a winding road. Gear
     shifting practices can be quite different on a winding road than
     on a straight road. 
     
     Again, fuzzy logic provides us with a powerful tool to deal with
     complex situations that are intractable using conventional
     approaches. We simply include additional variables and rules to
     take into account factors that could improve the behavior of our
     control system. 
     
     (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 
     
     
     
     
     FIDE Application Notes Available:
     
     
     #001
     Washing Machine
     Decision Making, Determining Wash Time
     
     #002
     Automatic Focusing System
     Decision Making, Determining Focus
     
     #003
     Servo Motor Force Control
     Servo Control, Grasping Object
     
     #004
     Temperature Control(1)
     Process Control, Glass Melting Furnace
     
     #005
     Temperature Control(2)
     Process Control, Air Conditioner
     
     #006
     Temperature Control(3)
     Process Control, Reactor
     
     #007
     Automatic Transmission
     Decision Making, Determining Gear Shift
     
     
     
     FIDE Application Note 007-920929	Aptronix Inc., 1992 	
     
                     Automatic Transmission
     
     
     
     
     
     
     
     
     
     
     
     
     
     
