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