DEVELOPMENT OF PC BASED FUZZY LOGIC CONTROLLER FOR DC

MOTOR

WAN HASSAN BIN WAN HAMAT

A thesis submitted in

fulfillment of the requirement for the award of the

Master of Electrical Engineering

Faculty of Electrical and Electronic Engineering

Universiti Tun Hussein Onn Malaysia

JANUARY 2014

v

ABSTRACT

This Master project report presents a development of PC Based Fuzzy Logic

Controller for DC motor. The project consist of hardware and software parts. For

hardware, Data Acquisition (DAQ) USB Card is used as interface hardware between

PC and DC motor. Personel Computer (PC) with Labview is used as main software

in this system. Fuzzy Logic Controller (FLC) is design as a system controller to

control DC motor movement. Flexible Robot Manipulator (FRM) is use as hardware

to show the performance of DC motor. This project also presents the modelling of

FRM by using System Identification to get the transfer function of a model. By using

Labview, Graphical User Interface (GUI) and FLC are integrated to produce

function of FRM. In this project, FRM is used to control the vibration link to point

the accurate position. FLC has been used to get the better performance of vibration

control through feedback signal upon FRM. Meanwhile, the strain gauge and encoder are

devices act to produce the feedback signal for FRM. Result of this project is a graph

output of strain gauge and encoder can be compared between using FLC and without

FLC experiment of vibration control.

vi

ABSTRAK

Laporan projek sarjana ini membentangkan pembangunan PC berdasarkan Pengawal

Logic Kabur (FLC) untuk DC motor. Projek ini terdiri daripada bahagian perkakasan

dan perisian. Bagi perkakasan, Perolehan Data (DAQ) USB Kad digunakan sebagai

perkakasan antara muka antara PC dan DC motor. Personel Komputer ( PC) dengan

LabVIEW digunakan sebagai perisian utama sistem ini. FLC digunakan sebagai

pengawal sistem untuk mengawal pergerakan DC motor. Fleksibel Robot

Manipulator (FRM) digunakan sebagai perkakasan untuk menunjukkan prestasi DC

motor. Projek ini memperkenalkan pemodelan FRM dengan Sistem Pengenalan

untuk mendapatkan rangkap pindah model. Menggunakan Antara Muka Pengguna

grafik (GUI) dan FLC diintegrasi untuk mengawal fungsi FRM. Dalam projek ini,

FRM digunakan untuk mengawal getaran pautan untuk mencapai kedudukan yang

tepat. FLC telah digunakan untuk mendapatkan prestasi yang lebih baik dengan

mengawal getaran pada FRM. Isyarat suapbalik telah dicadangkan untuk kawalan

getaran FRM. Tolok terikan dan pengekod telah digunakan untuk menghasilkan

isyarat suapbalik FRM. Keputusan daripada projek ini adalah graf output tolok

terikan dan pengekod yang boleh dibandingkan menggunakan FLC dan tanpa FLC

dalam eksperimen kawalan getaran.

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CONTENTS

TITLE i

DECLARATION ii

DEDICATION iii

ACKNOWLEDGEMENT iv

ABSTRACT v

CONTENTS vii

LIST OF TABLES ix

LIST OF FIGURES x

LIST OF SYMBOLS AND ABBREVIATIONS xii

CHAPTER 1 INTRODUCTION 1

1.1 Project background 1

1.2 Problem Statements 2

1.3 Project Objectives 2

1.4 Project Scopes 3

1.5 Thesis Overview 3

CHAPTER 2 LITERATURE REVIEW 3

2.1 Introduction 4

2.2 Fuzzy Logic Controller (FLC) 7

2.3 Feedback signal 8

2.4 System Identification 8

CHAPTER 3 METHODOLOGY 9

3.1 Introduction 10

3.2 Project Methodology 10

3.3 Project Arrangement Setup 11

3.4 Equipment 12

3.4.1 Personal Computer 13

3.4.2 LabVIEW Software 13

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3.4.3 Flexible Robot Manipulator (FRM) 15

3.4.4 Data Acquisition Card (DAQ Card) 15

3.5 Block Diagram 16

3.6 Feedback Signal 16

3.6.1 Strain Gauge 17

3.6.2 Encoder 18

3.7 System Identification 18

3.7.1 System Identification using Matlab 19

3.7.2 Step of Identification Using Matlab Toolbox 19

3.8 FLC Implementation 24

3.8.1 Basic operation of FLC 24

3.8.2 Process of FLC 25

3.8.3 FLC in LabVIEW 27

CHAPTER 4 RESULT AND ANALYSIS 30

4.1 Introduction 31

4.2 Modeling using System Identification 31

4.3 Simulation without FLC and with FLC 32

4.3.1 Without FLC (Uncontrolled) 32

4.3.2 With FLC (Controlled) 33

4.4 Result between Uncontrolled and Controlled 38

4.5 Rise Time (Tr), Settling Time (Ts) and Steady State Error

Analysis from Simulation using FLC 39

4.6 Experiment of FRM Control using FLC in LabVIEW. 40

4.6.1 FL Fuzzy Controller (Fuzzysetb.fs) 41

4.6.2 FL Fuzzy Controller (Fuzzyset.fs) 44

4.6.3 Experimental result Uncontrolled and Controlled 46

4.7 Discussion. 50

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CHAPTER 5 CONCLUSIONS AND FUTURE WORKS 51

5.1 Conclusion 52

5.2 Future works 53

REFERENCE 54

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LIST OF TABLE

Table 4.1 : Specification Range 34

Table 4.2 : Range of Membership Function for Input Variable 35

Table 4.3 : Range of Membership Function for Output Variable 36

Table 4.4 : Range of Membership Function for Input Variable Fuzzysetb 42

Table 4.5 : Range of Membership Function for Output Variable Fuzzysetb 42

Table 4.6 : Range of Membership Function for Input Variable fuzzyset 44

Table 4.7 : Range of Membership Function for Output Variable fuzzyset 45

Table 4.8 : Value of Tr, Ts And ess 50

Table 4.9 : Uncontrolled and Controlled CW 51

Table 4.10 : Uncontrolled and Controlled CCW 51

xi

LIST OF FIGURE

Figure 2.1 : DC motor modeling armature voltage control method 4

Figure 2.2 : DC motor function block diagram 6

Figure 2.3 : Block Diagram of DC Motor 7

Figure 3.1 : Project Methodology 11

Figure 3.2 : Connection between FRM, DAQ Interface Card and Laptop 12

Figure 3.3 : Equipment of Project Setup 12

Figure 3.4 : Labview Software Interface 14

Figure 3.5 : Flexible Robot Manipulator 15

Figure 3.6 : DAQ Card 15

Figure 3.7 : Block Diagram of System 16

Figure 3.8 : Strain Gauge 17

Figure 3.9 : Encoder 18

Figure 3.10 : System Identification Application Flow Chart 18

Figure 3.11 : Waveform of Encoder 19

Figure 3.12 : Waveform of Strain Gauge 20

Figure 3.13 : Matlab Command Window 20

Figure 3.14 : Main Identification Information Window 21

Figure 3.15 : Selecting Time Domain Data In Import Data Window 21

Figure 3.16 : Import Data Dialog Box 22

Figure 3.17 : Transfer Function for Estimate 22

Figure 3.18 : Model Output 23

Figure 3.19 : Transfer Function 23

Figure 3.20 : Basic Operation of FLC 24

Figure 3.21 : FLC Operations 25

Figure 3.22 : Process of FLC 25

Figure 3.23 : Fuzzy Logic Controller 27

Figure 3.24 : Select The Fuzzy System Designer 28

Figure 3.25 : Editor of Fuzzy System Designer 28

Figure 3.26 : Edit Variable Dialog Box 29

Figure 3.27 : Edit Variable for Setup The Range 29

Figure 3.28 : Rule of Fuzzy 30

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Figure 3.29 : Test System or Fuzzy Controller 30

Figure 4.1 : Transfer Function From System Identification Toolbox 31

Figure 4.2 : Simulation Using Uncontrolled 32

Figure 4.3 : Angular Position Vs Time Using Uncontrolled 32

Figure 4.4 : Simulation Using FLC 33

Figure 4.5 : Angular Position Vs Time Using FLC 33

Figure 4.6 : FIS Editor 34

Figure 4.7 : Input Variable 35

Figure 4.8 : Output Variable 36

Figure 4.9 : Rule Base 37

Figure 4.10 : Rule Viewer 37

Figure 4.11 : Surface Viewer 38

Figure 4.12 : Angular Position Vs Time Using Uncontrolled And Flc 39

Figure 4.13 : Value of Tr, Ts And ss 40

Figure 4.14 : GUI in LabVIEW 40

Figure 4.15 : Block diagram of FLC in LabVIEW 41

Figure 4.16 : Input Variable of Encoder Fuzzysetb 41

Figure 4.17 : Output Variable of Strain Gauge Fuzzysetb 42

Figure 4.18 : Rule Base Fuzzysetb 43

Figure 4.18 : Test System Fuzzysetb 43

Figure 4.19 : Input Variable of Encoder fuzzyset 43

Figure 4.20 : Output Variable of Strain Gauge fuzzyset 44

Figure 4.21 : Rule Base fuzzyset 45

Figure 4.22 : Test System fuzzyset 46

Figure 4.23 : Waveform of Strain Gauge Uncontrolled CW 46

Figure 4.24 : Waveform of Encoder Uncontrolled CW 47

Figure 4.25 : Waveform of Strain Gauge Controlled CW 47

Figure 4.26 : Waveform of Encoder Controlled CW 47

Figure 4.27 : Waveform of Strain Gauge Uncontrolled CCW 48

Figure 4.28 : Waveform of Encoder Uncontrolled CCW 49

Figure 4.29 : Waveform of Strain Gauge Controlled CCW 49

Figure 4.30 : Waveform of Encoder Controlled CCW 49

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LIST OF SYMBOLS AND ABBREVIATIONS

Symbol

change in resistance caused by strain

resistance of the unformed gauge

strain

armature resistance

armature inductance

armature current

field current

input voltage

back electromotive force (EMF)

motor torque

angular velocity of rotor

rotating inertial measurement of motor bearing

damping coefficient

Tr Rise time

Ts Settling time

ss Steady state error

FLC Fuzzy Logic Controller

GUI Graphical User Interface

DAQ Data Acquisition

FRM Flexible Robot Manipulator

PC Personel Computer

CW Clockwise

CCW Counter-Clockwise

GF Gauge Factor

DCM Discontinous Conduction Mode

COA Centre Of Area

COG Centre Of Gravity

xiv

LIST OF APPENDICES

APPENDIX TITLE PAGE

A Data Sheet for Strain Gauge 57

B Data Sheet for Encoder 58

C Data Sheet for DAQ USB Card 59

D Data Sheet for DC Motor 62

CHAPTER 1

INTRODUCTION

1.1 Project background

The DC motors have been popular in the industry control for a long time, because

it’s have many good characteristics. For example, high start torque characteristic,

high response performance and easier to be linear control. The different control

approach depends on the different performance of motors.

Because the peripheral control and devices are enough, there is the more

extensive application in the industry control system. Therefore, the DC motor control

is larger than other kinds of motors then are no matter in the theoretic study in the

research and development application. The measurement and control can be achieved

by PC-based. However, the technique of Instrument design also moves forward the

times of “virtual instrument”, not only the designing time is shorten, but also the

designing space is more elastic extension. This project presents to guide the motor

speed control field with the various advanced computer technology and the

development platform of software/hardware. Let the dynamic state response of motor

have a better efficiency.

This project proposal is to design FLC controller to supervise and control the

speed response of the DC motor with the virtual instrument graphic monitor software

using Labview.

2

1.2 Problem Statements

Basically in industrial applications is used DC motors, because the speed and torque

relationship can be varied and regeneration applications in either direction of

rotation. DC motor is use in Robotic Machine, Lift Motor, Crane Motor and etc. So

the motor must be operates in perfect condition to avoid the machine cannot run

smoothly. DC motor also needs additional controller to control the performance

rotation the motor on starts conditions. Mostly in industries used DC Motor without

controller in order to save the cost. The effect of this case, DC motor is possibly

damage because at certain times run with on and off condition. The DC motor also

cannot run smooth and precisely without controller.

In this project is propose to design FLC to control the speed of DC motor

using the Labview software program and display the speed of the motor in real-time

in order to obtain the system response of FLC. The real-time monitor of application

on the motor not only can substitute the traditional instrument but also can be as the

monitor basis of the machine operating normally or not. This programming system is

based on a structure of the PC, and combines the DC motor supervision needed

instrument which then replaces other hardware equipment with the cheaper and more

efficient method to facilitate operators with the graphical interface of an easy and

kind operation.

1.3 Project Objectives

The objectives of this project are;

i) To design the modeling system using FLC to control the speed of DC

motor using Matlab.

ii) To develop the FLC in Labview to control DC motor using FRM

hardware by PC Based method.

iii) To analyze the response of system performance for FRM hardware.

3

1.4 Project Scopes

The scopes of this project is to simulate the FLC response of DC motor with

MATLAB Simulink software. For the Hardware, FRM hardware is used to show the

analysis of DC motor response.

1.5 Thesis Overview

This project report is organized as follows;

i) Chapter 1 briefs the overall background of the study. A quick glimpse of

study touched in first sub-topic. The heart of study such as problem

statement, project objective, and project scope and project report layout is

present well through this chapter.

ii) Chapter 2 covers the literature review of previous case study based on FLC

controller background and development. Besides, general information about

hardware interface and DC motor modeling and theoretical revision on FLC

also described in this chapter.

iii) Chapter 3 presents the methodology used to design Fuzzy Logic controller

and PC based hardware model system. All the components that have been

used in designing FLC are described well in this chapter.

iv) Chapter 4 reports and discuss on the results obtained based on the problem

statements as mentioned in the first chapter. The simulation results from

output controller will be analyzed with helps from set of figures and tables.

v) Chapter 5 will go through about the conclusion and recommendation for

future study. References cited and supporting appendices are given at the end

of this project report.

CHAPTER 2

LITERATURE REVIEW

2.1 Introduction

This DC motor system is a separately excited DC motor, which is often used to the

velocity tuning and the position adjustment. This project focuses on the study of DC

motor linear speed control, therefore, the separately excited DC motor is adopted.

Make use of the armature voltage control method to control the DC motor velocity,

the armature voltage is controls distinguishing feature of method as the flux fixed,

and current fixed as well. The control equivalent circuit of the DC motor by the

armature voltage control method is shown in Figure 1.

Fig. 2.1: DC motor modeling armature voltage control method

where;

: the armature resistance

5

: the armature inductance

: the armature current

the field current

: the input voltage

the back electromotive force (EMF)

the motor torque

an angular velocity of rotor

rotating inertial measurement of motor bearing

a damping coefficient

Because the back EMF is proportional to speed ω directly, then

Making use of the KCL voltage law can get

From Newton law, the motor torque can obtain

Take 2.1 – 2.3 into Laplace transform, respectively, the equations can be formulated

as follows:

Fig. 2.2 describes the DC motor armature control system function block diagram

from equations (1) to (6).

𝑒𝑏 𝑡 = K𝑏

𝑑𝜃 𝑡

𝑑𝑡= K𝑏𝜔 𝑡 (2.1)

𝑇𝑚 𝑡 = J 𝑑2 𝜃 𝑡

𝑑𝑡 2 + 𝐵

𝑑𝜃 𝑡

𝑑𝑡 = 𝐾𝑡 i𝑎 (2.3)

𝐸𝑎 𝑆 = R𝑎 + L𝑎 𝑆 i𝑎 𝑆 + 𝐸𝑎 𝑆 (2.4)

𝐸𝑏 𝑆 = 𝐾𝑏 Ω 𝑆 (2.5)

𝑇𝑚 𝑆 = 𝐵Ω 𝑆 + 𝐽𝑆Ω 𝑆 = 𝐾𝑡 i𝑎 (2.6)

6

Fig. 2.2: DC motor armature voltage control system function block diagram.

The transfer function of DC motor speed with respect to the input voltage can be

written as follows;

From equation (2.7) the armature inductance is very small in practices, hence, the

transfer function of DC motor speed to the input voltage can be simplified as

follows;

Where,

is a motor gain,

is a motor time constant.

𝐺 𝑆 = Ω 𝑆

𝐸𝑎 𝑆 = J

𝐾𝑇 L𝑎𝑆 + 𝑅𝑎 𝐽𝑆 + 𝐵 + 𝐾𝑏 𝐾𝑇

(2.7)

Ω 𝑆

𝐸𝑎 𝑆 =

𝐾𝑚𝜏𝑆 + 1

(2.8)

𝐾𝑚 =𝐾𝑇

𝑅𝑎𝐵 + 𝐾𝑏 𝐾𝑇

(2.9)

𝜏 = 𝑅𝑎 𝐽

𝑅𝑎𝐵 + 𝐾𝑏 𝐾𝑇

(2.10)

+

-

1

L +

i 1

+

K

K

Ω

7

From equation (2.8), the transfer function can be drawn the DC motor system block

diagram which is shown in Figure 2.3.

Fig. 2.3: Block diagram of DC motor

2.2 Fuzzy Logic Controller (FLC)

The previous researcher develops the FLC for trajectory tracking of tip angular

position and vibration control of flexible joint manipulator. Initially a PD-type FLC

is developed for trajectory tracking of tip angular position. This is extended to

incorporate non-collected FLC for vibration control of the manipulator. The

performances are examined in term of input tracking capability, level of reduction

and time response specification [1].

FLC is the better control for control the vibration of flexible robot. Based on

research paper in [5] mention for improvement process, and since then many papers

have addressed robot control in combination with FLC. Furthermore, by using FLC

application it can help to control active vibration of a very flexible link manipulator

[6]. Then, other researchers mention using FLC for active vibrations control [7].

The fuzzy control method is quite useful in terms of reliability and

robustness. The FLC has been increased interest in applying the concepts of fuzzy set

theory to flexible structural control. Fuzzy controllers afford a simple and robust

framework to specify nonlinear control laws that accommodate uncertainly and

imprecision [8].

FLC have some advantage compared to other classical controller such as

simplicity of control, low cost and the possibility to design without knowing the

exact mathematical model of the process. Fuzzy logic incorporates an alternative

way of thinking which allows modeling complex systems using higher level of

abstraction originating from the knowledge and experience. Fuzzy logic can be

describes simply as “computing words rather than numbers” or “control with

sentence rather than equations” [9].

+ 1

8

FLC have been successfully employed to universal approximate

mathematical model of dynamic system in the recent years. The FLC offer an

efficient alternative to classical methods of modelling and control of nonlinear

system. Although a number of fuzzy position control system for robot manipulator

are available, only a few have consideration of torque or current limits [10].

2.3 Feedback signal

The feedback sensors in this project are encoder and strain gauge to control the

vibration on system. The researcher on [11] mention previous researcher (Luo) used

a strain gauge sensor to measure the flexible robot. According [12] mention

experiment on flexible robot manipulator purpose controlling the end tip position of

the flexible link, the passive velocity feedback and strain feedback approaches to

control the vibration of flexible link robot. The purpose of using strain feedback is

trying to damp out the flexible link vibration. The researcher mention base on

nonlinear dynamical model, the nonlinear control schemes such as, those using

computed torque method, inverse dynamic, feedback linearization and sliding mode

control have been proposed for control of flexible robot arm. Based on the

development models by researcher, an output feedback nonlinear control strategy is

proposed with motion duration of 0.5 second. It means that the tracking capability of

the flexible robot arm follow fast motion duration improvement.

The vibration control strategy separated into feed forward and feedback part.

The idea of feed forward approaches is to shape the original reference signal in a way

that minimizes the excitation of Eigen frequencies in the flexible structure. Feedback

strategies in contrast include direct measurements or estimate of vibration states and

outputs to control the vibration motion. In this way robustness against system

parameter uncertainties, modeling errors and disturbance is achieved [13].

2.4 System Identification

The most important in the System Control Design is a modelling for the plant that

wants to control. There are many techniques in identifying the method to build the

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modeling of system using system identification. The researchers in [14] have built

the modeling by using system identification base on measured data. The parameters

need to adjust within a given model until the output accordance with the measure

output.

In [15], they introduce the MATLAB as a software package included of high

performance and interactive numerical computation, data analysis, and graphics. The

MATLAB toolboxes make this software most powerful in the specific field include

one of them is System Identification Toolbox. The command of the toolbox is

divided into five layers include of basics tools, model structure selection, more

methods to examine models and multi-input system, recursive identification, and

state-space modelling.

The researcher in [16] provides a graphical user interface (GUI) for System

Identification Toolbox. The GUI has been successfully in modeling and developing

the mathematical model of the system. The researcher was introduced the system

identification problem, the graphical user interface of the system identification,

common terms used in system identification, basic information about the dynamic

system, the basic steps of system identification and a start-up identification

procedure. The close look from the model’s output is used to get a good test compare

to the measured one of a data set that was not used for the fit of validation data.

CHAPTER 3

METHODOLOGY

3.1 Introduction

In this section, all the procedures, steps and flow chart that have been used in the

simulation and experiment to archive the project objective is presented. The method of

DC Motor control by using FLC has been discussed in this chapter. For the DC Motor

model, FRM is used to show the application of DC Motor movement.

3.2 Project Methodology

Firstly, literature review have been done by referring to previous chapter decide the

method that can be used for project include the knowledge about modelling of DC Motor

and FLC. Next, the mechanical structure of FRM has been studied to know the function

for each component in FRM. After that, the modeling by using System Identification

needs to build. From the System Identification by using either LabVIEW or MATLAB

need to construct the transfer function for the FRM model. Then, the encoder and strain

gauge are used in this system for the purpose of feedback signal. The FLC have been

study to use as a controller on FRM.

The software that has been used for this project is LabVIEW to run the

simulation and experiment on FRM. After running the project in LabVIEW, the

comparison between simulation and experiment need to analysis. From the comparison,

the controller is suitable for the performance of vibration control on FRM. Lastly, all the

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data need to compile into the documentation of the project. Project methodology for this

project was shown in Figure 3.1.

Figure 3.1: Project methodology

3.3 Project Arrangement Setup

As shown in Figure 3.2 is arrangement setup to operate correctly which involve FRM,

interface card and personal computer. Connection from personal computer is used to

transmit and receive data from FRM through DAQ interface card. DAQ interface card

used as analog to digital converter also as digital to analog converter. Connection from

interface card to personal computer is using universal serial bus cable (USB) while

connection to FRM is using two cable, serial cable and parallel cable for different

purposes. DAQ interface card doesn’t need any power supply as it is provided 5V from

USB cable.

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Figure 3.2: Connection between FRM, DAQ Interface Card and Laptop.

In order to configure the personal computer to work with specific interface card a

driver in LabView software needed to be set compatible driver with the same interface

card model number. Input and output of interface card are fixed by manufacturer and can

be referred to data sheet provided for troubleshooting purposes. In this arrangement,

FRM used as experimental equipment where educational boards, sensors and motors are

located inside.

3.4 Equipment

Figure 3.3 Equipment of Project setup

13

Figure 3.3 shows the equipment that used for project arrangement include of

Personal Computer, LabVIEW Genuine Licence Software, FRM and DAQ Interface

Card (NI USB 6211).

3.4.1 Personal Computer

The Personal Computer is used to control the whole system that using in this project.

It also needs to process the data between hardware and software. From Personal

Computer, all the data and result were shown to observe after running the simulation

and experiment.

3.4.2 LabVIEW Software

The LabVIEW (Laboratory Virtual Instrumentation Engineering Workbench)

is a system design platform and highly productive development environment for a

visual programming language from National Instruments. Furthermore, LabVIEW is

a graphical programming language and unprecedented hardware integration to

rapidly design and deploy measurement and control systems. It has been widely

adopted throughout industry, academia and research labs as the standard for data

acquisition and instrument control software. LabVIEW is a powerful and flexible

instrumentation and analysis software system that is multiplatform.

Its graphical programming is:

i. Easy to use

ii. Faster Development Time

iii. Graphical User Interface

iv. Graphical Source Code

v. Easily Modularized

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vi. Application Builder to create stand-alone executable

The most advantage when using LabVIEW over other development an

environment is the extensive support for accessing instrumentation hardware.

Figure 3.4 shows the LabVIEW Software Interface that used to develop the Fuzzy Logic

Controller for controlling the vibration of FRM.

Figure 3.4: LabVIEW Software Interface

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3.4.3 Flexible Robot Manipulator (FRM)

Figure 3.5: Flexible Robot Manipulator

Figure 3.5 shows the several items on FRM include of Encoder, Motor Strain Gauge

as a sensor and the important part of robot to use for experiment is the link of FRM.

The FRM system consists of:

i. DC motor

ii. Strain Gauge

iii. Motor position sensor (encoder)

iv. DC Motor

v. Power supply 24V

vi. Power Supply 9V

vii. Stopper Switch (safety)

3.4.4 Data Acquisition Card (DAQ Card)

Figure 3.6: DAQ Card

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Data acquisition is the process from the sampling of the real world to generate and

converting the data into digital numeric that can manipulate by computer. Data

acquisition typically involves acquisitions of signals and waveform and processing

the signals to obtain desired information.

Figure 3.6 shows the DAQ Card type of NI USB-6211 that use for this

project to process the data acquisition include as interfaces between FRM and laptop.

3.5 Block Diagram

Figure 3.7: Block Diagram of system

Figure 3.7 shows block diagram of implement the FLC as a controller also encoder

and strain gauge as a feedback signal in FRM system. The input for the system which

is called desired angle while output for the system is called actual output after goes

through certain process. The main component in this project refers to Fuzzy Logic

Controller (FLC) block where it is used to control all operation in this system.

Basically the FLC will consider feedback input signal such as encoder and strain

gauge to the controller. All block diagrams shown involved all equipment mention

earlier.

3.6 Feedback Signal

Feedback signal is a process return signal that results from a measurement of the

directly controlled variable. It is a part of a closed-loop to control the system. The

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actual value detected by a sensor which is strain gauge and encoder as a process is

taking place.

3.6.1 Strain Gauge

A strain gauge is a device that used change in electrical resistance to measure the

strain of an object. The Strain Gauge has a structure such that a grip-shaped sensing

element of thin metallic resistive foil that put on a base of thin plastic film and is

laminated with a thin film. In Figure 3.8 shows the structure of strain gauge.

Figure 3.8: Strain Gauge

A fundamental parameter for the strain gauge is its sensitivity to strain that expressed

quantitatively as the gauge factor (GF). The GF defined as the ratio of fractional

change in electrical change in electrical resistance to the fractional change in length

strain:

Where

= the change in resistance caused by strain

= the resistance of the unformed gauge

= the strain

𝐺𝐹 =∆𝑅/𝑅𝐺

(3.1)

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3.6.2 Encoder

Encoder is sensors that generate the digital signals in response to movement the link

of FRM. Both shaft encoders, which is response to rotation, and linear encoder which

is response to motions in a line are variable. Encoder act as feedback transducers for

motor- speed control and as sensor for measuring the position between -90˚ to 90˚ of

FRM. Figure 3.9 shows the encoder that using for feedback signal on FRM.

Figure 3.9: Encoder

3.7 System Identification

Figure 3.10: System Identification application flow chart

Figure 3.10 shows the System Identification application flow chart to build the

modelling of system. System Identification is a technique to build mathematical

models of a dynamic system. The dynamic system based on a set of measured

19

stimulus and response data samples. System Identification is fundamental for

communication and control engineering, and plays as roles in many other areas.

3.7.1 System Identification using Matlab

The MATLAB provide System Identification Toolkit an interactive tool for

identifying the Transfer Function of the system. This toolkit encompasses the entire

identification process from raw data analysis to validation of identified models. To

investigate the System Identification of the model in the MATLAB software, the

input and output data was from the FRM model.

3.7.2 Step of System Identification Using Matlab Toolbox

This part describes the procedures to obtain the Transfer Function model from the

input and output data using MATLAB System Identification Toolbox. The steps

taken are as follows;

i. The data of encoder and strain gauge is taken from the movement of link on

FRM. Figure 3.11 shows the value of encoder as an input of variable in

LabVIEW.

Figure 3.11: Waveform of Encoder

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Figure 3.12: Waveform of Strain Gauge

ii. The command ‘ident’ was type on MATLAB command windows as shown in

Figure 3.13. The main identification windows are opened as shown in Figure

3.14.

Figure 3.13: MATLAB command window

21

Figure 3.14: Main identification information window

iii. At the import data window, the Time domain data was selected. This is

shown in Figure 3.15. The Import data dialog box is open as shown in Figure

3.16.

Figure 3.15: Selecting Time domain data in Import data window

22

Figure 3.16: Import data dialog box

iv. At the Workspace Variable window as shown in Figure 3.16, the Input was

fill-in with ‘Encoder’ and the Output with ‘Strain Gauge’. Next, Import

button was clicked to import the information data into a main indent

information window.

v. Next, in Figure 3.17 shows the Transfer Function was clicked for estimate the

input and output. Then in Figure 3.18 and Figure 3.19 shows the estimation

result includes value of encoder and strain gauge.

Figure 3.17: Transfer Function for Estimate

23

Figure 3.18: Model output

Figure 3.19: Transfer Function

24

3.8 FLC Implementation

3.8.1 Basic operation of FLC

Figure 3.20: Basic Operation of FLC

Figure 3.20 shows the basic operation of FLC to control the vibration of FRM. There

are many types of controller that can be implemented to control the vibration of

FRM. FLC effective to control a discrete system that needs high level of sensitivity

in order to control feedback signal. FLC refer to a system that reads signals from

feedback system (encoder and strain gauge) and control the output pattern (motor

movement) by considering the feedback signal as error.

FLC mainly used to reduce the vibration rate produce when the motor move

which cause vibration to links. In this project FLC represented in LabVIEW

software. FLC provides a nonanalytic alternative to the classical analytic control

theory. “But what is striking is that its most important and visible application today is

in a realm not anticipated when fuzzy logic was conceived, namely, the realm of

fuzzy-logic-based process control” [4].

54

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