Fuzzy Logic Based Motor Speed Controlling System for Automobile Industry
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Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
Uva Wellassa University of Sri Lanka
Abstract
Road safety is becoming an important case in all over the world today. The number of
deaths is increasing day by day inroads, due to the uncontrollable speed of drivers and
accidental sleepiness while driving. The objective of this project is to develop a system to
protect both vehicles and passengers on the road. The main aim of this project is to
control the speed of a vehicle automatically by controlling the speed of the motor using
fuzzy logic. Which reduces the driver’s task for adjusting the gas pedal and checking the
speedometer frequently. The research introduced the Fuzzy Logic for speed controlling
instead of the commonly used methods such as RF signal controlling method; because
Fuzzy logic controllers (FLC) can be used to utilize the human expertise and experience
for design controllers. The purpose of using a fuzzy logic-based speed controller is to
regulate vehicle speed according to the current speed of the vehicle and the distance
between the vehicle and the object in front of the vehicle. The fuzzy controller has
developed under three main parts as Fuzzification, Rule base, and Defuzzification. Two
inputs were taken to the FLC as ‘Speed error’ and the ‘Distance between vehicle and
object Infront’. 49 Fuzzy rules were designed for the fuzzy logic controller in the rule
base. The ‘Centroid method’ was used as the defuzzification method. The ‘Mamdani’
system which employs fuzzy sets in the consequent part was used in the Fuzzy logic
controller. When the speed and the distance were given, the Fuzzy Inference system gives
the best matching fuzzified values as the output value. Then it sends to the motor
controller. The results show that the fuzzy logic has minimum transient and steady-state
parameters, which show that FLC is more efficient. Finally, the Fuzzy controller
produces the responses with little high rise-time, but it offers a high percentage exceed
and peak amplitude which can result in poor performance of the system.
Keywords: Fuzzy logic, Fuzzy Logic controller (FLC), Rule base, Fuzzification,
Defuzzification
Description
Keywords
Engineering, Automobile, Mechanical Engineering, Robotics