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MODELING AND CONTROLLER DESIGN OF A HOT AIR BLOWER SYSTEM

KHAIRUL NIZAR SYAZWAN BIN WAN SALIHIN WONG

A project report submitted in partial fulfilment of the

requirements for the award of the degree of

Master of Engineering (Electrical - Mechatronics & Automatic Control)

Faculty of Electrical Engineering

Universiti Teknologi Malaysia

JUNE 2014

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Dedicated to my beloved father, mother and family for their never ending support

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ACKNOWLEDGMENTS

First First and foremost, I would like to express my sincere gratitude and

appreciation to my supervisor, Professor Dr Mohd Fua'ad Rahmat for his invaluable

and critical comment throughout this project. His guidance and assistance have given

me a strong basis in handling this project.

I would like to take this opportunity to thank my beloved father, Wan Salihin

Wong Abdullah, my beloved mother, Zuliani Ghazali and my beloved family for

their never ending support. Their supports at times of need are crucial to me.

Finally, i would like to extend my appreciation to my course mates for

discussing and supporting each other throughout the whole time.

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ABSTRACT

This research describes the modeling and controller design of a hot air blower

system (HABS). The purpose of this research is to obtain model estimation best fit

over 90%. Furthermore, the purpose of this research is to design controllers which

the overshoot percentage close to zero percent and obtain a shorter rise time compare

to current hot air blower system. The research divided into 3 area of focus, first focus

on the development of Pseudo-Random Binary Sequences (PRBS) controller

prototype to use as a System Identification Tool. The development of the controller

was designed using PIC controllers. Testing was being conducted to ensure the

controller is fully operational. Next, the output data from the hot air blower system

(HABS) was captured for analysis. Second, the research focused on the modeling of

a hot air blower system using System Identification and Estimation approach. By

obtaining the mathematical model of a hot air blower system, tuning can be made

possible. The output data was being run on MATLAB to compare between several of

model structures. Model structures selected for this research are Auto-Regressive

with eXogeneous inputs (ARX), Auto-Regressive Moving-Average with

eXogeneous inputs (ARMAX), Output Error (OE) and Box–Jenkins (BJ). Result

shows the eXogeneous inputs (ARX) obtained the highest best fit which best

resemble the dynamic system of a hot air blower system (HABS) and was selected to

be implemented in the controllers. Third area of focus is the simulation design of

controllers; controllers which were selected for this research were PID controllers,

Self-tuning controllers and Fuzzy controllers. The result obtained are shown in the

Result and Analysis chapter, all controllers obtained zero overshoot percentage and

have a shorter rise time then the current hot air blower system. Results showed the

different in rise time, peak time, delay time and percentage overshoot varies

depending on the controller. So, depending on the purpose in the industrial

application, engineers can pick any controller to meet their desire task.

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ABSTRAK

Kajian ini menerangkan model dan reka bentuk pengawal sistem penghembus

udara panas. Tujuan kajian ini adalah untuk mendapatkan anggaran model terbaik

lebih daripada 90%. Tambahan pula, tujuan kajian ini adalah untuk mereka bentuk

pengawal yang peratusan terlajak hampir dengan sifar peratus dan untuk

memendekan masa kenaikan berbanding dengan sistem penghembus udara panas.

Kajian ini dibahagikan kepada 3 bahagian, fokus pertama kepada pembangunan

Pseudo-Random Binary Sequences untuk digunakan sebagai system alat Pengenalan.

Pembangunan pengawalan telah direka menggunakan pengawalan jenis PIC. Ujian

telah dijalankan untuk memastikan pengawal itu beroperasi sepenuhnya. Seterusnya,

data output dari sistem penghembus udara panas telah disimpan untuk analisis.

Kedua, kajian ini memberi tumpuan kepada sistem penghembus udara panas

menggunakan Sistem Pengenalan dan pendekatan Anggaran. Dengan mendapatkan

model matematik sistem blower udara panas, pembaikan boleh dijalankan. Data

akhir dijalankan dalam MATLAB untuk membandingkan antara beberapa struktur

model. Struktur model dipilih untuk kajian ini adalah Auto-Regressive with

eXogeneous inputs (ARX), Auto-Regressive Moving-Average with eXogeneous

inputs (ARMAX), Output Error (OE) dan Box–Jenkins (BJ). Keputusan

menunjukkan Auto-Regressive with eXogeneous inputs (ARX) yang memerolehi

model tertinggi yang terbaik menyerupai sistem dinamik sistem penghembus udara

panas dan telah dipilih untuk dilaksanakan dalam sistem pengawalan. Tumpuan

ketiga tumpuan adalah reka bentuk simulasi system pengawalan; pengawal yang

telah dipilih untuk kajian ini adalah pengawalan PID, pengawalan Self-Tuning dan

pengawalan Fuzzy. Keputusan menunjukkan yang berbeza dalam masa kenaikan,

masa puncak, masa tunda dan peratusan terlajak bergantung bergantung pada

pengawalan. Jadi, bergantung kepada tujuan dalam perindustrian, jurutera boleh

memilih mana-mana pengawalan untuk memenuhi tugas dan keinginan mereka.

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TABLE OF CONTENTS

CHAPTER TITLE PAGE

DECLARATION ii

DEDICATION iii

ACKNOWLEDGEMENTS iv

ABSTRACT v

ABSTRAK vi

TABLE OF CONTENTS vii

LIST OF TABLES x

LIST OF FIGURES xi

LIST OF SYMBOLS xv

1 INTRODUCTION 1

1. 1 Introduction 1

1. 2 Problem Statement 3

1. 3 Objective 4

1. 4 Scopes of project 4

1. 5 Thesis Organization 5

2 LITERATURE REVIEW 6

2. 1 Literature Review 6

3 MATHEMATICAL MODEL 8

3. 1 Experimental Setup 8

3. 2 Modeling and Controller Design 10

3. 3 Controller Design 13

3.3.1 PID controller 13

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3.3.2 Self-Tuning controller 16

3.3.3 Fuzzy controller 17

4 OPTIMIZATION PROBLEM 19

4. 1 Introduction 19

4. 2 Prototype setup stage 19

4.2.1 Step 1: MATLAB Simulink diagram 19

4.2.2 Step 2: Construct Hardware using Proteus 22

4.2.3 Step 3: Prototype Pseudo Random Binary

Sequence signals (PRBS) parts

23

4.2.4 Step 4: Programming 26

4.2.5 Step 5: Capture Output using oscilloscope 27

4.2.6 Step 6: Analysis RLC data to validate the

prototype functionality

28

4. 3 Data collecting and analysis of a hot air blower

stage

30

4.3.1 Step 1: Capture data using the PRBS

prototype development

30

4.3.2 Step 2: Model presentation using different

model structure

32

4.3.3 Step 3 & Step 4: Parameter Estimation

using the first 500 data

35

4.3.4 Step 5 & Step 6: Model Validation 37

4. 4 Controller simulation design using MATLAB 37

4.4.1 PID controller 38

4.4.2 Self-Tuning controller 40

4.4.2 Fuzzy controller 42

4.4.2 Comparison between controllers and

reference model

46

5 CONCLUSION 48

REFERENCES 50

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APPENDIX 51

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LIST OF TABLES

TABLE NO. TITLE PAGE

3.1 Effects of increasing a parameter independently 14

3.2 Ziegler-Nichols formula to calculate , and 15

4.1 Menu selection on LCD 24

4.2 Comparison Controller analysis 47

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LIST OF FIGURES

TABLE NO. TITLE PAGE

1.0 Various applications of a hot air blower in industrial 2

3.1 PT 326 Process Trainer system 10

3.2 Methods analysis of a hot air blower system 11

4.1 MATLAB simulation for feedback 1 ⊕ 7 20

4.2 MATLAB simulation for feedback 4 ⊕ 7 20

4.3 Output MATLAB simulation for feedback 1 ⊕ 7 20

4.4 Output MATLAB simulation for feedback 1 ⊕ 7 21

4.5 Hardware simulation using Proteus 22

4.6 Microcontroller chip PIC16F877a 22

4.7 Voltage regulator 23

4.8 Hardware construction 23

4.9 RLC circuits 25

4.10 Algorithm of the Pseudo Random Binary Sequence signals 26

4.11 Output oscilloscope n is 7 and feedback 1 ⊕ 7 27

4.12 Output oscilloscope n is 7 and feedback 1 ⊕ 7 27

4.13 Data and model info 28

4.14 Poles and Zeros 29

4.15 Model Selection Criterion 29

4.16 PRBS overall block diagram 31

4.17 Pseudo Random Binary Sequence signals (PRBS) signal

input

31

4.18 Output signal captured by using oscilloscope 32

4.19 System Identification Tool 33

4.20 Recursive method for system identification 34

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4.21 Model output 37

4.22 PID controller Simulink design 38

4.23 Graph comparison between PID controller and reference

model

39

4.24 Self-Tuning controller Simulink design 40

4.25 Graph comparison between Self-Tuning controller and

reference model

41

4.26 Fuzzy Editor 42

4.27 Membership Function Editor: Input1 43

4.28 Membership Function Editor: Input2 43

4.29 Membership Function Editor: Output 44

4.30 Fuzzy controller Simulink design 45

4.31 Graph comparison between Fuzzy controller and reference

model

35

4.32 Graph comparison between various controllers and

reference model

46

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LIST OF SYMBOLS

D - diameter

h - heat transfer coefficient

k - thermal conductivity

l - length

R - resistance

T - temperature

U - overall heat transfer coefficient

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CHAPTER 1

INTRODUCTION

1.1 Introduction

Hot-air blowers are used in all areas in which hot air is conceivable in the

manufacturing process: In the automobile industry, the food industry, the packaging

industry, in mechanical engineering and etc. Figure 1.0 is examples of hot air blower

application which are used in various industrial. Hot air blower applications include

wrapping packaging, drying paint, sealing, heating ball bearing and a finishing touch

on the items. Many people still underestimate the importance of having an adequate

temperature control in place, but failing to have one could mean higher chances of

damaging the goods.

In the industrial application, temperature control is a process in which change

of temperature of a space is measured or otherwise detected, and the passage of heat

energy into or out of the space is adjusted to achieve a desired average temperature.

Further enhancements using the accumulated error signal and the rate at which the

error is changing are used to form more complex Controllers which is the form

usually seen in industry. Increasing the number of hot air blower in the industrial

application, lead researcher to boost productivity and efficiency without neglecting

the aspect of safety and power consumption.

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To accurately control process temperature without extensive operator

involvement, a temperature control system relies upon a controller, which accepts a

temperature sensor such as a thermocouple or RTD as input. It compares the actual

temperature to the desired control temperature, or set point, and provides an output to

a control element. The controller is one part of the entire control system, and the

whole system should be analyzed in selecting the proper controller.

Figure 1.0 Various applications of a hot air blower in industrial

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1.2 Problem Statement

Hot air blower plays in an important role in the industry. Hot air blower use

highly amount of electricity to run during the process. In order to increase safety,

quality and reduce cost, developing controllers for the current system will benefit the

industry and also the environment. So the focus in design area is to avoid overshoot

in temperature and shorter the rise time. This approach will benefit in a long run

where cost electricity can be reduce significantly and maximize the quality of goods

in the same time. The main component of the hot air blower is the temperature

control; temperature control will be a challenge since it requires time to respond to

temperature. Furthermore, to optimize the current system, faster response time need

to be purposed. Hence, in order to optimize, researcher need to achieve the model of

the system which best represent the system dynamic. With the aid of software, trial

and error way can be avoided to obtain results and it helps to save cost. In order to

enhance the current system, a Pseudo-Random Binary Sequences (PRBS) controller

prototype needs to be designed in order to obtain mathematical model. The PRBS

controller needs to be careful design to fulfill the requirement 0.08 second time

sampling for this research. Besides, modeling of hot air blower using System

Identification and Estimation approach using the normal GUI approach is time

consuming. Finding a way to speed up the estimation and trying all possible

combination parameter will be vital to the research. Besides, temperature control in

the real world requires time to transfer energy into other particles. The respond time

taken for heat to transfer takes longer than other controls. For sensitive goods, the

temperature control shall minimize the percentage overshoot compare to other heavy

industries which allow some overshoot to happen.

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1.3 Project Objective

Objectives of this project are

1) Develop a prototype Pseudo-Random Binary Sequences (PRBS) to

use as a system identification tool.

2) Modeling of hot air blower using System Identification and

Estimation approach.

3) Design and testing several controllers for a Hot Air Blower

System.

1.4 Project Scope and Limitation

This paper consists of three areas of studies. First, the hardware development

of the PRBS and injecting PRBS input signal to a Hot Air Blower system. Second,

Various model structures is being tested and analyzed to best represent the dynamic

model of the system. Both mathematical models are used to estimate the temperature

of the pipeline. This is to obtain the Best Fit Curve experimental model which is

above 90%. Third, from the model obtained several controllers are simulated by

using MATLAB software to best describe a Hot Air Blower system. The result is

then shown and discussed. During the duration of the research author encountered

some limitation, one of it was the limited size memory of the microcontroller used.

The memory size used is 256 kb which only fit 5 series of PRBS. By using a bigger

memory size, the controller can fit a lot more series and enhance the studies of the

project. Second limitation was the study on nonlinear System Identification of a hot

air blower system, the nonlinear model structure used was Nonlinear Auto-

Regressive with eXogeneous (NARX) model structure. The research went to a stop

after author cannot find suitable software to run the nonlinear model structure.

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1.5 Thesis Organization

The preceding sections briefly summarized the introduction and objectives of

the research work. This section presents the outline of the thesis. The remainder of

the theses is organized into five main chapters. Chapter 2 discusses literature review

on modeling and controller design of a hot air blower. It also gives a summary on the

approach and the direction of the research. The chapter will tell the overview on the

development of the Pseudo Random Binary Sequence (PRBS) prototype, the use of

this controller to model a mathematical and the design of controllers to improve the

current hot air blower system. The Pseudo Random Binary Sequence (PRBS)

prototype was then discussed in detail in Chapter 3. In this chapter the development

of the prototype was discussed from start to the end in a deliberate way. For the

estimation model by using system identification tool from MATLAB, the approach

on how the estimation model was being performed on the software. Then this chapter

describes the design and implementation of a linear controller for a hot air blower

system. The implementation focused on to eliminates overshoot with the best settling

time. Chapter 4 showed the result and analysis of the research by using the method

discussed previously. Figures of the controllers, graph outputs and screenshots were

displayed in the chapter for easy visual. While Chapter 5 is the conclusion and

summarization on the finding discovery during the research.

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