This document discusses applying fuzzy logic to anti-lock braking systems (ABS). It begins with an introduction to ABS and fuzzy logic. It then reviews literature on ABS control methods. The components of ABS are described, including wheel speed sensors, brake calipers, and a computer. Fuzzy logic is proposed to model the nonlinear relationships in ABS. Reasons for using fuzzy logic include its conceptual ease of understanding and flexibility. The document provides an example of a fuzzy rule and membership functions. Finally, it discusses building an ABS fuzzy logic system in MATLAB and some companies involved in ABS technologies.

1. Application of Fuzzy Logic for Effective
Anti-lock Braking System
Presented By : Under Guidance of :
- Amrita B. Chavan (04) - Dr. K. Rajeswari
ME 1st year (CE) HOD of (CE)
PCCOE, Nigdi, Pune. PCCOE, Nigdi, Pune.
Pimpri Chinchwad College of Engineering, Pune

2. Introduction
1. Anti-lock Braking System (ABS)
a. The Anti-lock Braking System (ABS) was invented by Gabriel Voisin in 1929
b. It is automobile safety system
c. Monitor the operating condition of the tire and controls the applied braking torque
d. Prevents the wheels of vehicle from locking up
e. Steering stays under control and stopping distances are generally reduced.
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3. Introduction contd...
2. Fuzzy Logic (FL)
a. Fuzzy Logic was initiated in 1965 by Lotfi A. Zadeh, Professor for Computer Science at the University
of California in Berkeley
b. Fuzzy logic is a form of multi-valued logic as well derived from fuzzy set theory to deal with reasoning
that is approximate
c. Fuzzy Logic variables may have truth values that ranges between 0 and 1
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4. Literature Survey
Sr.No. Paper Name Author Name Strength Weakness
1. Anti-lock Braking
Systems
Data-driven Control
using Q-Learning
Mircea-Bogdan Radac
Radu-Emil Precup
Raul-Cristian Roman
It doesn’t require an
initial stabilizing
controller
More complex with more
parameter to be tuned
4ME 1st year (Computer Engineering)05.15.18

5. Literature Survey contd...
Sr.No. Paper Name Author Name Strength Weakness
2. Adaptive Control of
Anti-lock Braking System
Using Grey Multilayer
Feedforward Multilayer
Neural Network
Erdal Kayacan
Yesim Oniz
Okyay Kaynak
Andon V. Topalov
2008
Ability to control
nonlinear system
accurately, Robustness
of system can be
guaranteed
System is getting unstable
at the end of simulation
3. Anti-lock-Braking System
Using Fuzzy Logic
K. Subbulakshmi
2014
Ability to modify and
tune certain parts of
nonlinear characteristic
surface easily
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6. ABS in cars
● You have more control on your car during situations such as
sudden braking
● It is designed to help the driver maintain some steering ability
and avoid skidding while braking.
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7. Need of ABS
● It may help the driver stop quicker under wet or slippery conditions
● You’ll stop faster, and you’ll be able to steer while you stop.
● It prevent you from running into the immovable object that you might otherwise have tried to move
● With ABS, you have more control on your car
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8. Components of ABS
An ABS system consists :
● Some wheel speed sensors (is a type of tachometer)
a. It is a sender device used for reading the speed of a vehicle's
wheel rotation
● Brake calipers
a. Brake calipers squeeze the brake pads against the surface of the
brake rotor to slow or stop the vehicle.
b. The job of the caliper is to slow the car's wheels by creating
friction with the rotors
8
Fig 1: Brake caliper (1)
wheel speed sensor (2)
(Source :http://www.drivingfast.net/abs/ )
ME 1st year (Computer Engineering)05.15.18

9. Components of ABS
● A hydraulic motor
a. is a mechanical actuator that converts hydraulic pressure and flow into torque and angular displacement
● Some pressure release valves
● A quick thinking computer which coordinates the whole process
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10. Fuzzy Logic (FL)
● Fuzzy logic is a convenient way to map an input space to an output space.
● With information about how good your service was at a restaurant, a fuzzy logic system can tell you what the
tip should be.
● With information about how fast the car is going and how hard the motor is working, a fuzzy logic system can
shift gears for you.
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11. Fuzzy Logic (FL) contd...
A graphical example of an input-output map is shown in the following figure.
(Image source : mathworks.com)
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12. An Intoductory Example: Tipping Problem The Non-Fuzzy Approach
The Extended Tipping Problem: Given two sets of numbers between 0 and 10 (where 10 is excellent) that
respectively represent the quality of the service and the quality of the food at a restaurant,
what should the tip be?
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13. An Intoductory Example: Tipping Problem The Non-Fuzzy Approach
The Extended Tipping Problem: Given two sets of numbers between 0 and 10 (where 10 is excellent) that respectively represent the
quality of the service and the quality of the food at a restaurant,
Suppose you want the service to be a more important factor than the food quality. Specify that service accounts for
80% of the overall tipping grade and the food makes up the other 20%.
what should the tip be?
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14. An Intoductory Example: Tipping Problem The Fuzzy Approach
The Extended Tipping Problem: Given two sets of numbers between 0 and 10 (where 10 is excellent) that
respectively represent the quality of the service and the quality of the food at a restaurant,
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15. An Intoductory Example: Tipping Problem The Fuzzy Approach
15
The Extended Tipping Problem: Given two sets of numbers between 0
and 10 (where 10 is excellent) that respectively represent the quality of
the service and the quality of the food at a restaurant,
Tipping Problem Rules — Service Factor
If service is poor, then tip is cheap
If service is good, then tip is average
If service is excellent, then tip is generous
Tipping Problem Rules — Food Factor
If food is rancid, then tip is cheap
If food is delicious, then tip is generous
05.15.18 ME 1st year (Computer Engineering)

16. An Intoductory Example: Tipping Problem The Fuzzy Approach
Tipping Problem — Both Service and Food Factors
If service is poor OR the food is rancid, then tip is cheap
If service is good, then tip is average
If service is excellent OR food is delicious, then tip is generous
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17. Why Fuzzy Logic
● Fuzzy logic is conceptually easy to understand.
● Fuzzy logic is flexible
● Fuzzy logic is tolerant of imprecise data.
● Fuzzy logic can model nonlinear functions of arbitrary complexity.
● Fuzzy logic can be blended with conventional control techniques.
● Fuzzy logic is based on natural language.
17ME 1st year (Computer Engineering)05.15.18

18. Fuzzy Rule
18
µR(x, y) = f(µA(x), µB(y))
● A Fuzzy rule can be represented by a fuzzy relation on R = A → B
● R can be viewed as a fuzzy set with a two-dimensional membership function
● where f :- fuzzy implication function, performs the task of transforming the membership degrees of x in
A and y in B into those of (x, y) in A × B.
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19. Example
19
T 20 30 40
A
(t) 0.1 0.5 0.9
H 20 50 70 90
B
(h) 0.2 0.6 0.7 1
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20. Example contd...
Calculated R(t,h) through min operator
20
20 50 70 90
20 0.1 0.1 0.1 0.1
30 0.2 0.5 0.5 0.5
40 0.2 0.6 0.7 0.9
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21. Example contd...
● Now suppose, we want to get information about the humidity when there is the following premise about the
temperature:
Temperature is fairly high.
● This fact is rewritten as
R(t): t is A′ where A′ = fairly high.
where the fuzzy term A′ ⊆ T is defined as below
Membership function of A′ in T (temperature)
t 20 30 40 µA′
(t) 0.01 0.25 0.81
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22. Fuzzy Partition Example
● Fuzzy partition of input and output spaces
22
Fig A : Fuzzy Partition in 2-D input
space
Fig B : Fuzzy Partition in having three
rules
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23. Membership Function
● Membership function represents the degree of truth in fuzzy logic.
● Membership functions characterize fuzziness
● Technique to solve practical problems by experience
● Membership functions are represented by graphical forms.
● Rules for defining fuzziness are fuzzy too.
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24. Building System with MATLAB Fuzzy Logic Toolbox
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25. Market Research
25
1. TATA Autocomp Systems LTD (http://www.tacogroup.com)
a. TATA AutoComp Systems Limited - Supply Chain Management (TACO SCM) is a Strategic Business
Unit and leading service provider on
b. Offers its customers full engineering support for the design and installation of braking systems
c. The company is capable of handling the whole life cycle of the product, from design and development
to production, supply and after-sales.
ME 1st year (Computer Engineering)05.15.18

26. Market Research contd...
2. Knorr-Bremse (www.knorr-bremse.com)
a. In the area of bandwidth of innovative solutions ranges from complete braking systems (for example,
ABS and ESP) and transmission control systems
b. These solutions focus on increased efficiency and reduced fuel consumption
c. Specialties :Manufacturer of Braking and Doors
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27. Market Research contd...
3. WABCO (www.wabco-auto.com)
a. WABCO's Anti-lock Braking System (ABS) prevents the wheel from locking during emergency
braking situations and thereby helps commercial vehicle drivers to maintain stability of the vehicle.
b. ABS also helps to bring a vehicle to a complete stop with the shortest possible stopping distance and in
the safest possible way.
c. Specialities : safety technologies, electronic and foundation brakes, air suspension, truck, bus, trailer,
off-highway, car, and aerodynamics
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28. Conclusion
● For Antilock Braking system it is hard to develop the mathematical model
to accurate level as there are highly nonlinear relationship between the
parameters, hence the Fuzzy Logic, linguistic variables come into picture.
● The convincing advantage of fuzzy logic is the ability to modify and tune
certain parts of this characteristic surface easily and carefully
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29. REFERENCES
K. Subbulakshmi “Antilock-Braking System Using Fuzzy Logic” Middle-East J. Sci. Res., 20 (10): 1306-1310, 2014
2. Mircea-Bogdan Radac, Radu-Emil Precup and Raul Cristian Roman “Anti-Lock Braking System Data-Driven Control
Using Q-Learning” 2017 IEEE
3. Erdal Kayacan, Yesim Oniz, Okyay Kaynak and Andon V. Topalov “Adaptive Control of Antilock Braking System Using
Grey Multilayer Feedforward Neural Networks”
4. Radu-Emil Precup, Marius-Csaba Sabau, Emil M. Petriu, “Nature-inspired optimal tuning of input membership
functions of Takagi-Sugeno-Kang fuzzy models for Anti-lock Braking Systems” ELSEVIER, Applied Soft Computing, 27
(2015 ) 575-589
5. Wei-Yen Wang, Ming-Chang Chen, Shun-Feng Su, “Hi erarchical T–S fuzzy-neural control of anti-lock braking system
and active suspension in a vehicle” ELSEVIER, Automatic , 48 (2012) 1698-1706
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