217 lines
7.7 KiB
Plaintext
217 lines
7.7 KiB
Plaintext
INTRODUCTION
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Recently fuzzy logic has found increasing applicability in the
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field of vehicle control. Applications include automatic
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transmission, engine control, cruise control, antiskid braking,
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and air conditioning, among others. This application note focuses
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on automatic transmission control.
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AUTOMATIC TRANSMISSION : BASIC MODEL
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A basic automatic transmission system is shown in Figure 1. Fuzzy
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logic is employed to infer the best gear selection. The four
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fuzzy inference unit inputs are sensor based signals from the car
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itself. Using throttle, vehicle speed, engine speed, engine load,
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the fuzzy inference unit determines a shift, i.e., gear number,
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for the car.
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Figure 1 Automatic Transmission System
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Definitions of Input/Output Variables
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To create a fuzzy inference unit, we first need to define labels
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(membership functions) for input and output variables. Examples
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of such labels are shown in Figures 2, 3, 4, 5, and 6. The output
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variable Shift uses singleton membership functions because the
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TVFI (Truth Value Flow Inference) method is the preferred method
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of defuzzification.
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Figure 2 Labels and Membership Functions of Throttle
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Figure 3 Labels and Membership Functions of Vehicle_Speed
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Figure 4 Labels and Membership Functions of Engine_Speed
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Figure 5 Labels and Membership Functions of Engine_Load
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Figure 6 Labels and Membership Functions of Shift
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Rules
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Using labels as defined above, we can write rules for the fuzzy
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inference unit shown in Figure 1. Rules embody the knowledge base
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required for decision making. They are represented as English
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like if-then statements.
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For example, the following is a rule:
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IF Throttle is Low and
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Vehicle_Speed is Low and
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Engine_Speed is Low and
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Engine_Load is High
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THEN Shift is No_1
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We can write many such rules to cover the different situations
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encountered in transmission of power to wheel. The totality of
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such rules constitutes a fuzzy inference unit for gear selection
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in an automobile.
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AUTOMATIC TRANSMISSION : MODIFIED MODEL
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The performance of the above automatic transmission model is not
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very good. The gear shifting procedure is implemented without
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taking into account the driving environment. We, as humans, drive
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in different "modes" depending on road conditions. For example,
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we sometimes drive at a constant low gear when negotiating a
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windy mountainous road. This avoids unnecessary gear shifting,
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which can add to engine wear and make for a less than smooth ride
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for passengers.
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With this in mind, a modified transmission system is shown in
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Figure 7. We have added an extra input, mode, to the fuzzy
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inference unit to influence gear shift behavior. This new driving
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mode can be inferred by fuzzy logic(FIU B) as well.
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Figure 7 Modified Automatic Transmission System
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Figure 8 Fuzzy Inference Unit for Driving Mode
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Figure 8 shows a fuzzy inference unit for inferring driving mode.
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To create an FIU, we develop rules such as the following:
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If Vehicle_Speed is Low and
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Variation_of_Vehicle_Speed is Small and
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Slope_Resistance is Positive_Large and
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Accelerator is Medium then
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Mode is Steep_Uphill_Mode
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If Vehicle_Speed is Medium and
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Variation_of_Vehicle_Speed is Small and
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Slope_Resistance is Negative_Large and
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Accelerator is Small then
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Mode is Gentle_Downhill_Mode
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The driving mode output of FIU B can then be further used to
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affect the gear shifting procedure. For example, if mode is
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Steep_Uphill_Mode, a downshift is necessary in order to obtain
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greater engine power. If mode is Gentle_Downhill_Mode, we also
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need a lower gear than would be the case for a flat smooth road.
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The lower gear provides engine braking power. Typical gear
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selection rules could look as follows:
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If Mode is Steep_Uphill_Mode then
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Shift is No_2
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If Mode is Gentle_Downhill_Mode then
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Shift is No_3
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COMMENTS
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In actuality, the inputs to fuzzy inference unit B in Figure 8
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could include other factors, such as steering angle, to determine
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a more accurate driving mode. With steering angle data, we can
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determine whether or not the vehicle is on a winding road. Gear
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shifting practices can be quite different on a winding road than
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on a straight road.
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Again, fuzzy logic provides us with a powerful tool to deal with
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complex situations that are intractable using conventional
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approaches. We simply include additional variables and rules to
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take into account factors that could improve the behavior of our
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control system.
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(Weijing Zhang, Applications Engineer, Aptronix Inc.)
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For Further Information Please Contact:
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Aptronix Incorporated
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2150 North First Street #300
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San Jose, CA 95131
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Tel (408) 428-1888
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Fax (408) 428-1884
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FuzzyNet (408) 428-1883 data 8/N/1
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Aptronix Company Overview
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Headquartered in San Jose, California, Aptronix develops and
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markets fuzzy logic-based software, systems and development tools
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for a complete range of commercial applications. The company was
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founded in 1989 and has been responsible for a number of
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important innovations in fuzzy technology.
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Aptronix's product Fide (Fuzzy Inference Development Environment)
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-- is a complete environment for the development of fuzzy
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logic-based systems. Fide provides system engineers with the
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most effective fuzzy tools in the industry and runs in
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MS-WindowsTM on 386/486 hardware. The price for Fide is $1495
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and can be ordered from any authorized Motorola distributor. For
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a list of authorized distributors or more information, please
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call Aptronix. The software package comes with complete
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documentation on how
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FIDE Application Notes Available:
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#001
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Washing Machine
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Decision Making, Determining Wash Time
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#002
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Automatic Focusing System
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Decision Making, Determining Focus
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#003
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Servo Motor Force Control
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Servo Control, Grasping Object
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#004
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Temperature Control(1)
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Process Control, Glass Melting Furnace
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#005
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Temperature Control(2)
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Process Control, Air Conditioner
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#006
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Temperature Control(3)
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Process Control, Reactor
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#007
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Automatic Transmission
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Decision Making, Determining Gear Shift
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FIDE Application Note 007-920929 Aptronix Inc., 1992
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Automatic Transmission
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