25-08-2017, 09:32 PM

AUTOMATED CAR BRAKING SYSTEM USING FUZZY LOGIC CONTROLLER

ABSTRACT

This paper deals with a Fuzzy Logic Controller (FLC) for an automated car

braking system. The response of the system will be simulated by using Fuzzy Logic

Toolbox in MATLAB and PID controller. The purpose of this controller is to brake a

car when the car approaches for an obstacle at a specific range. For this, the Fuzzy

Logic Controller is design using the Fuzzy Logic Toolbox in MATLAB. The system

uses four rules and three membership function. The two parameters such as distance

and speed will be observed for both controllers and the ability to attenuate

disturbance will be simulated. Output of the controller will determine the force of the

car brake. Base on the simulation, it can be concluded that the response of Fuzzy

Logic Controller is better than PID. However, PID controller can be used to

constitute a reference for the performance of the fuzzy logic controller.

INTRODUCTION

1.1 Fuzzy Logic Controller (FLC)

Fuzzy logic was formulated by Lotfi Zadeh of the University of California at

Berkeley in the mid-1960s, based on earlier work in the area of fuzzy set theory.

Zadeh also formulated the notion of fuzzy control that allows a small set of 'intuitive

rules' to be used in order to control the operation of electronic devices. In the 1980s

fuzzy control became a huge industry in Japan and other countries where it was

integrated into home appliances such as vacuum cleaners, microwave ovens and

video cameras. Such appliances could adapt automatically to different conditions; for

instance, a vacuum cleaner would apply more suction to an especially dirty area. One

of the benefits of fuzzy control is that it can be easily implemented on a standard

computer.

Fuzzy controllers appear in consumer products such as washing machines,

video cameras, cars. As for in industry, for controlling cement kilns, underground

trains, and robots. A fuzzy controller is an automatic controller, a self-acting or selfregulating

mechanism that controls an object in accordance with a desired behavior.

The object can be, for instance, a robot set to follow a certain path. A fuzzy

controller acts or regulates by means of rules in a more or less natural language,

based on the distinguishing feature: fuzzy logic. The rules are invented by plant

operators or design engineers, and fuzzy control is thus a branch of intelligent

control.

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1.2 Proportional Integral Derivative (PID) Controller

A proportional-integral-derivative controller (PID controller) is a generic

control loop feedback mechanism widely used in industrial control systems. A PID

controller attempts to correct the error between a measured process variable and a

desired set point by calculating and then outputting a corrective action that can adjust

the process accordingly.

The PID controller calculation (algorithm) involves three separate

parameters; the Proportional, the Integral and Derivative values. The Proportional

value determines the reaction to the current error, the Integral determines the reaction

based on the sum of recent errors and the Derivative determines the reaction to the

rate at which the error has been changing. The weighted sum of these three actions is

used to adjust the process via a control element such as the position of a control

valve or the power supply of a heating element.

1.3 Car Braking System

Braking system is the most important system in a car. If the brakes fail, the

result can be disastrous. The brakes are in essence energy conversion devices, which

convert the kinetic energy of the vehicle into thermal energy.

In this project, a car brake system will be controlled by the Fuzzy Logic

Controller (FLC) and the Proportional Integral Derivative (PID) controller. The

purpose of the automated car braking system is to develop an automated control

system that would maintain a safe driving distance from obstacles while in traffic.

The system will successfully detect an obstacle ahead at a specific range and create a

way for the system to avoid collision by braking the car. By that, it will results in a

more enjoyable and less stressful drive. The system will be developed in fuzzy logic

toolbox available in MATLAB and will be simulated to see the performance of the

car braking system.

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1.4 Problem Statement

The increasing rate of road accident had been increasing nowadays. At

present, there are four deaths per 10,000 vehicles. In many such cases, the cause of

the accident is driver distraction and failure to react in time. Generally, a car brake

system operated manually as the driver push the brake pedal. Therefore, to overcome

this problem, an automated car braking system will be implemented to avoid such

accident.

1.5 Objectives

The objectives of this project are:

I. To develop a Fuzzy Logic Controller and Proportional Integral

Derivative Controller using MATLAB for an automated vehicle due to

an obstacle.

II. To evaluate and analyze the performance of the systems.

1.6 Scope of Project

This project is to design a Fuzzy Logic Controller and Proportional Integral

Derivative Controller that can be use to control a car brake automatically. Thus, the

scopes that need to be considered in this project are:

I. Car brake

The car brake will be controlled by the fuzzy logic controller from the

MATLAB toolbox and PID controller designed according to the range

detected to the obstacle ahead by reducing the speed from the

specified speed desired.

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II. Range

The range targeted for the obstacle to be detected is 25m from the car.

Therefore, the car will be brake and stop before it hit the obstacle.

III. Obstacle

Obstacles in this project refer to any objects including cars, human or

animal those were ahead the car. The obstacles will give input to the

controller to brake the car.

1.7 Literature Review

1.7.1 Car Braking Issues

Traffic congestion is a worldwide problem. This problem is mainly due to

human driving which involves reaction times, delays, and judgement errors that may

affect traffic flow and cause accidents. [1] In many such cases, the cause of the

accident is driver distraction and failure to react in time. Advanced system of

auxiliary functions has been develop to help avoid such accident and minimize the

effects of collision should one occur. This is done by reducing the total stopping

distance. [2] By that means, the car brake itself should have a good software system

to assist a driver along the road.

Electronic brake control system has been making the car safer for the past 25

years. In recent years, braking developments have led to significantly greater driving

safety. [3] For the past few years, there are many car brake development that uses

the involvement of the electronic roles such as the Intelligent Cruise Control (ICC),

Antilock Braking Systems (ABS), Traction Control System (TCS) and the

Sensotronic Brake Control (SBC).

Many studies in this field depend upon a precise mathematical model of the

vehicle. In fact, behaviors of the drivers are mostly based on the experience, not the

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exact mathematic computation. The model of vehicle is highly nonlinear function; it

is difficult to find the precise model. Therefore, fuzzy logic systems have been

designed by many researchers for automated driving controller since fuzzy system

emulates the performance of a skilled human operator in the linguistic tulles that do

not need use a mathematic model. [1]

Ordinary cruise control systems for passenger cars are becoming less and

less meaningful because of the increasing traffic density rarely make it possible to

drive at a preselected speed. However, in order to achieve high customer acceptance

an intelligent cruise control system has to perform similarly to an experienced

human driver. Therefore, it is necessary to adjust the following distance and the

control dynamics according to the individual driver’s needs. Applying fuzzy logic to

intelligent cruise control seems to be an appropriate way to achieve this human

behavior, because driver’s experience can be transformed easily into rules. [4]

1.7.2 Fuzzy Logic Toolbox

Fuzzy logic imitates the logic of human thought, which is much less rigid

than the calculations computer generally perform. [5] Intelligent control strategies

mostly involve a large number of inputs. Most of the inputs are relevant for some

specific condition. Using fuzzy logic, this input is only considered in the relevant

rule. This keep the complex system transparent.[6] Whereby using fuzzy logic, the

concept will be much easily to understand as it was based on natural language.

The objective of using fuzzy logic has been to make the computer think like

people. Fuzzy logic can deal with the vagueness intrinsic to human thinking and

natural language and recognize its nature is different from randomness. Using fuzzy

logic algorithm could enable machines to understand and respond to vague human

concept such as hot, cold, large, small, etc. It also could provide a relative simple

approach to reach definite conclusion from imprecise information. [7]

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Fuzzy logic is very adequate to built qualitative models of many kind of

system without an extensive knowledge of their mathematical models. The use of

fuzzy controllers allows achieving a human like vehicle operation. [8]

There are two general types of fuzzy expert system:

I. Fuzzy control

II. Fuzzy reasoning

Although both make use of fuzzy sets, they differ qualitatively in

methodology. [9] Fuzzy control comprises the steps of sense, preprocess, fuzzify,

evaluate, activate, aggregate, defuzzify and act. However, difficulty occurs with the

using of fuzzy logic system. Usually it is difficult to determine the membership

function and fuzzy logic rules. Many cycles of trail-and-error are required to achieve

the desired performance. [1]

The fuzzy logic toolbox is a collection of function built on the MATLAB

numeric computing environment. It provides tools for us to create and edit fuzzy

interference system with the framework of MATLAB or integrate the fuzzy system

into simulation with simulink. The fuzzy logic toolbox for use with MATLAB is a

tool for solving problems with fuzzy logic. It is a fascinating area of research because

it does a good job of trading off between significant and precision. [10]

Although it is possible to use Fuzzy Logic Toolbox by working strictly from

the command line, in general it is much easier to build a system graphically. [10]

There are five primary GUI tools for building, editing, and observing fuzzy inference

systems in Fuzzy Logic Toolbox:

I. Fuzzy Inference System (FIS) Editor

II. Membership Function Editor

III. Rule Editor

IV. Rule Viewer

V. Surface Viewer