Improved Tracking Performances Of A Hot Air Blower System Using Generalized Minimum Variance (GMV) Controller With Particle Swarm Optimization (PSO) And Harmony Search Algorithm (HSA) Tuning Method.
1
IMPROVED TRACKING PERFORMANCES OF A HOT AIR BLOWER
SYSTEM USING GENERALIZED MINIMUM VARIANCE (GMV)
CONTROLLER WITH PARTICLE SWARM OPTIMIZATION (PSO) AND
HARMONY SEARCH ALGORITHM (HSA) TUNING METHOD
LIM HOOI CHEN
This Report Is Submitted In Partial Fulfillment Of Requirements For The
Bachelor Degree of Electronic Engineering (Industrial Electronic)
Faculty of Electronic and Computer Engineering
Universiti Teknikal Malaysia Melaka
JUNE 2015
2
3
4
5
To my beloved father and mother
6
ACKNOWLEDGEMENT
In performing this project, I had to take the help and guideline of some
respected persons, who deserve my greatest gratitude. First of all, I would like to show
my gratitude to Pn. Sharatul Izah Binti Samsudin and Engr. Siti Fatimah Bte Sulaiman
for giving me a good advice throughout numerous consultations. I would also like to
expand my deepest gratitude to all those who have directly and indirectly guided me
in writing this project.
7
ABSTRACT
Hot air blower system is the process of heating of the air flowing in the tube
up to the desired temperature level. The crucial part that can be seen from this system
is to control the temperature of a flowing air. In this project, a PT326 process trainer,
which is a hot air blower system is used. This project is conducted due to this problem.
The scope of work for this research include modelling and controller design of a PT326
process trainer. Generalized minimum variance (GMV) controller is designed with
MATLAB software to control the purpose of maintaining the process temperature at a
desired value. The simulation result aim to make a comparison of the performances of
the process temperature when using particle swarm optimization (PSO) and Harmony
Search Algorithm (HSA). Through simulation, the performances of the hot air blower
system with the use of GMV controller with PSO tuning method is better than HSA.
8
ABSTRAK
Sistem penghembus udara panas adalah proses pemanasan udara yang
mengalir dalam tiub ke tahap suhu yang dikehendaki. Bahagian yang penting dari
sistem ini adalah untuk mengawal suhu udara yang mengalir. Dalam projek ini, sistem
penghembus udara panas PT326 akan digunakan. Projek ini dikendalikan kerana
masalah ini. Skop kerja bagi kajian ini termasuk pemodelan dan kawalan reka bentuk
PT326. Pengawal varians minimum umum (GMV) direka dengan perisian MATLAB
untuk mengawal dan mengekalkan suhu proses pada nilai yang dikehendaki. Hasil
simulasi bertujuan untuk membuat perbandingan prestasi suhu proses apabila
menggunakan pengoptimum kumpulan zarah (PSO) dan Algoritma Carian Harmonik
(HSA) Melalui simulasi, prestasi penghembus udara panas dengan penggunaan GMV
pengawal dengan kaedah PSO adalah lebih baik daripada HSA.
9
CONTENTS
CHAPTER CONTENTS
I
II
PAGE
PROJECT’S TITLE
i
DECLARATION
iii
DEDICATION
v
ACKNOWLEDGEMENT
vi
ABSTRACT
vii
ABSTRAK
vii
CONTENTS
ix
LIST OF TABLES
xii
LIST OF FIGURES
xiii
LIST OF ABBREVIATION
xv
LIST OF APPENDICES
xvi
INTRODUCTION
1
1.1 OVERVIEW
1
1.2 PROBLEM STATEMENT
2
1.3 OBJECTIVES
2
1.4 SCOPE
2
1.5 METHODOLOGY’S SUMMARY
3
1.6 REPORT STRUCTURE
3
LITERATURE REVIEW
5
10
2.1 OVERVIEW OF PT326
5
2.2 RESEARCH ANALYSIS
9
2.3 AUTO REGRESSIVE WITH EXOGENOUS
11
INPUT (ARX) MODEL
III
2.4 AKAIKE’S FINAL PREDICTION ERROR(FPE)
11
2.5 GMV CONTROLLER
12
2.5.1 Self-Tuning GMVC Algorithm
13
2.5.2 Recursive Least Squares (RLS) Estimation
13
RESEARCH METHODOLOGY
15
3.1 RESEARCH METHODOLOGY
15
3.1.1 Project Planning
IV
16
3.2 PARTICLE SWARM OPTIMIZATION (PSO)
19
3.3 HARMONY SEARCH ALGORITHM (HSA)
21
RESULT AND DISCUSSION
23
4.1 INTRODUCTION
23
4.2 MATHEMATICAL MODEL OF THE PT326
24
PROCESS TRAINER
4.2.1 Preparing Data for System Identification
24
4.2.2 Import Data Arrays into System Identification
24
4.2.3 Remove Mean
26
4.2.4 Estimation Data and Validation Data
27
4.2.5 Appropriate Order of ARX Model
29
4.2.6 Zeros and Poles
31
4.2.7 ARX 333
32
4.3 GMV CONTROLLER DESIGN
34
4.4 THE EFFECT OF CHANGING VARIABLES
38
11
4.4.1 Number of Iteration (nt) Change In PSO Tuning
38
Method
4.4.2 Number of Particle (NOP) Change In PSO
39
Tuning Method
4.4.3 Number of Iteration (nt) Change In HSA Tuning
41
Method
4.4.4 Number of Particle (Nhm) Change In HSA
42
Tuning Method
4.5 SIMULATION RESULT
V
43
4.5.1 GMV
44
4.5.2 GMC with PSO
44
4.5.3 GMV with HSA
45
4.5.4 GMV with HSA (different stopping criteria)
46
4.6 COMPARISON AND JUSTIFICATION
46
4.7 DISCUSSIONS
49
CONCLUSION AND RECOMMENDATION
50
5.1 CONCLUSION
50
5.2 RECOMMENDATION
51
REFERENCES
53
APPENDIX A
55
12
LIST OF TABLES
NO TITLE
PAGE
2.1
Performances of the controllers designed by other researcher
8
2.2
The different technique of modelling and controller design for hot
9
air blower system
4.1
Comparison of different ARX model
30
4.2
The performances of the GMV controller with PSO when the
38
number of iteration is different
4.3
Performances of the controller designed
47
4.4
Value of GMV parameters used in different algorithm
47
13
LIST OF FIGURES
NO
TITLE
PAGE
2.1
PT326 hot air blower trainer kit
5
2.2
Basic elements of a closed loop process control system
6
3.1
Flow chart of project methodology
17
3.2
Gantt chart
18
3.3
Flow chart of basic PSO
20
3.4
Flow chart of basic HSA
22
4.1
Import data dialog box
25
4.2
The system identification tool window displays a ‘dry’ icon to
25
represent the data
4.3
The data is plot in time plot window
26
4.4
Time plot window to display the original and the detrended data
26
4.5
‘dryd’ icons added in system identification tool GUI
27
4.6
Selected ranges for model estimation
28
4.7
Selected ranges for model validation
28
4.8
‘dryde’ and ‘drydv’ icons added in system identification tool GUI
28
4.9
Selection of linear parametric models
29
4.10 ARX model structure
29
4.11 The ARX orders of 441, 331, 332, 333, 223 and 233
29
4.12 Best fits
30
4.13 Zero and poles plot of ARXs models with different parameters
32
4.14 Zero and poles plot for ARX333
32
4.15 Data/model info: ARX 333
33
14
4.16 Simulation to track the performances of a hot air blower system
(without controller)
4.17 Coding in parameters estimation
33
4.18 Block diagram of the design GMV controller subsystem
37
4.19 Output response of the GMV controller with PSO when the
39
37
number of iteration is different
4.20 Output response of the GMV controller with PSO when the
40
number of particle (NOP) is different
4.21 Line graph of the system response characteristics when the
40
number of particle is different
4.22 Bar chart of the system steady state error (%) when the number of
41
iteration is different
4.23 Output response of the GMV controller with HSA when the
42
number of iteration (nt) is different
4.24 Output response of the GMV controller with HSA when the
42
number of particle (Nhm) is different
4.25 Bar chart of the system steady state error (%) when the number of
43
particle (Nhm) is different
4.26 Output response of the GMV controller
44
4.27 Output response of the GMV controller with PSO
45
4.28 Output response of the GMV controller with HSA
45
4.29 Output response of the GMV controller with HSA (different
46
stopping criteria)
4.30 Output responses of the GMV controller (with PSO and HSA)
48
15
LIST OF ABBREVIATION
ARX
-
Auto Regressive with Exogenous
AWC
-
Anti-Windup Compensator
FPE
-
Final Prediction Error
GMV
-
Generalized Minimum Variance
GUI
-
Graphical User Interface
HMCR
-
Harmony Memory Considering Rate
HM
-
Harmony Memory
HSA
-
Harmony Search Algorithm
NOP
-
No of particles
PAR
-
Pitching Adjust Rate
PBRS
-
Pseudorandom Binary Sequence signal
PI
-
Proportional Integral
PID
-
Proportional-Integral-Derivative
PSO
-
Particle Swarm Optimization
RLS
-
Recursive Least Squares
SISO
-
Single-Input/Single-Output
W&P
-
Weston and Postlethwaite
16
LIST OF APPENDICES
NO TITLE
A
GMV controller (without algorithm) and GMV controller (with
algorithm)
PAGE
55
17
CHAPTER 1
INTRODUCTION
This chapter gives a general overview of the project, problem statement,
project objectives and limitation of the project. Besides that, a brief methodology and
report structure are also included in this chapter.
1.1
Overview
In this project, a PT326 process trainer of a hot air blower system is used. The
crucial part that can be seen from this system is to control the temperature of a flowing
air. This project is conducted due to this problem. The scope of work for this research
include modelling and controller design of a PT326 process trainer. GMV controller
is designed with MATLAB software to control the purpose of maintaining the process
temperature at a desired value. The simulation result aim to make a comparison of the
performances of the process temperature when using Particle Swarm Optimization
(PSO) and Harmony Search Algorithm (HSA).
18
1.2
Problem Statement
A GMV controller is a controller to be designed in this project. The crucial part
is to tune the GMV parameter in order to control the temperature of a flowing air. In
order to solve this problem, two tuning methods (PSO and HSA) will be used to autotune the GMV parameters.
1.3
Objectives
The objectives of this project are:
1) To determine the mathematical model of the PT326 process trainer using
System Identification approach based on Real Laboratory Process Data.
2) To design a GMV controller for the purpose of controlling the temperature of
air flowing.
3) To implement PSO and HSA in GMV controller for the purpose of tuning the
GMV parameters.
4) To make a comparison and justification based on the controller performances
obtained from the simulation.
1.4
Scope
The scope of work for this project consists of modeling and controller design
of a PT326 process trainer using MATLAB software. The controller design for this
project is a GMV controller. Two algorithms will be included in GMV controller for
tuning the GMV parameters, which are PSO and HSA. Real Laboratory Process Data
come from the data store in MATLAB.
19
1.5
Methodology’s Summary
The software used in this project is MATLAB. First of all, the mathematical
model of the PT326 process trainer using System Identification approach based on
Real Laboratory Process Data is determined. Then a GMV controller for the purpose
of controlling the temperature of air flowing is designed. After that, PSO Algorithm
will be included in GMV controller for tuning the GMV parameters.
If the output response of the system is correspond to the input signal apply,
then the simulated result of GMV controller and GMV with PSO algorithm are
compared and evaluated. Next, the HSA will be included in GMV controller. If the
performance of the controller is not desire, troubleshooting is make before moving to
the next step. Finally, a comparison and justification is make based on the controller
performances obtained.
1.6
Report Structure
This report is organized in five chapters accordingly. The first chapter gives a
general overview of the project, problem statement, project objectives and limitation
of the project. Besides that, a brief methodology and report structure are also included
in this chapter.
Chapter two is a literature review that highlight past studies related to the hot
air blower system, PT326. Other than that, the background theory also be included in
this chapter.
Chapter three contains the required steps and procedures to achieve the main
objectives of this research. Flow chart is used to give a clear explanation to present the
project methodology, PSO and HSA.
20
Chapter four presents the findings of the project. The comparison and
justification based on the results obtained are discussed in this chapter.
Chapter five contains the summary of this project, some recommendations for
future work and the contributions of this project.
21
CHAPTER 2
LITERATURE REVIEW
Chapter two is a literature review that highlight past studies related to the hot
air blower system, PT326. Other than that, the background theory also be included in
this chapter.
2.1
Overview of PT326
The PT326 hot air blower trainer kit is a self-contained process and control
equipment. Figure 2.1 shows the PT326 apparatus.
Figure 2.1: PT326 hot air blower trainer kit
22
In this equipment, a centrifugal blower draws air from atmosphere and drove
it past a heater grid and through a length of tubing to atmosphere again. The process
involved in this equipment is the heating of the air flowing in the tube up to the desired
temperature level. Whereas, a control equipment will measure the air temperature and
compare it with a value set by the operator. It then generate a control signal which
determines how much electrical power to be supplied to a correcting element. The
basic elements that involve in this closed loop process control system is shown in
Figure 2.2.
Correcting
element
Process
Detecting
element
Motor
element
Measuring
element
Controlling
element
Comparing
element
Set value
Figure 2.2: Basic elements of a closed loop process control system
In general, the input signal voltage range between 0 to -10V and the output
signal voltage range 0 to +10V. The measured and set value meter scales from 0 to
80°C only. The minimum resistive load on any output is 5k�. For controller, the
continuous control proportional band is range from 200% to 5%. The temperature of
the set value and measured value range from 30°C to 60°C. While the set value
adjustment scale can up to 10 [12].
23
In 2005, Rahmat, Hoe, Usman and Abdul Wahab [1] use pseudorandom binary
sequence (PRBS) signal of five different maximum lengths as an input signal to
determine the open-loop and closed-loop model of PT326 process trainer. After that,
the transfer function is obtain by using Cross-Correlation Technique.
The impulse response of the system can decays effectively to zero when the
sequence of PRBS is increase and the bit interval is chosen wisely. Thus, it will
increase the accuracy of the result.
Hot air blower system have output delays, noise and under actuator saturation
[2]. Thus, Rehan, Ahmed, Iqbal and Hong have design a proportional-integral (PI)
controller with Anti-Windup Compensator (AWC) to ensuring global stability and
performance of the industrial application. The simulation and the experiment result
show that the response without AWC and with actuator saturation has a lag. This is
due to the windup caused by integral action of PI controller. Windup is prevented with
the proposed AWC. It can be seen that the closed-loop response has no delay due to
saturation. This paper suggested that Pade approximation can be used to reduce the
memory consumption that is caused by the output delay term.
To control the hot air blower system precisely, Siti Fatimah [7] did a research
about the system identification, estimation and controller design of a PT326 process
trainer. Three types of controllers, which are self- tuning pole assignment servoregulator
controller,
Proportional-Integral-Derivative
(PID)
Controller
and
Generalized Minimum Variance (GMV) Controller have been designed with two
different tuning methods. The simulation results is shown in Table 2.1.
From Table 2.1, the performance is improve significantly when using SelfTuning Pole Assignment Servo-Regulator controller to control and maintain the
temperature of the system. The research found out that the zero percent overshoot is
because its capability to reject noise and tracking the set point of the system. While
the controller with the lower settling time and rise time is GMV controller with PSO
24
tuning method. The performance of PID controller is not very well as it need a longer
settling time as compared to the other type of controller.
In industrial applications, automatic temperature control of furnaces is
essential for melting, studying the physical and chemical properties of elements and
decomposing [7]. For industry, temperature control is very important when concerns
with the safety of the equipment. Thus, Ijaz, Riaz, Rehan and Ahmed have develop
three PI (proportional-integral) controller with slightly different parameters. This is
used to control the temperature of a nonlinear hot air blower system. Three different
regions of input signals will be consider when conduct system identification. The
writer observed close loop response have small overshoot because of nonlinear
dynamics of the system and actuator saturation. This paper discover that the amplitude
of the actuator signal had risen for high frequency variations when temperature is
increased.
Table 2.1: Performances of the controllers designed by other researcher
Response
Controller
Characteristic Self- Tuning Pole
Proportional-
Generalized
Assignment
Integral-Derivative
Minimum Variance
Servo- Regulator
(PID) Controller
(GMV) Controller
Controller
Pole at
Pole at
0.2
0.8
0%
0%
Peak Time
0s
Settling Time
Rise Time
Percent
ZN-PID
PSO-
ST-
PSO-
PID
GMVC
GMVC
2.5%
0%
180%
2.6%
0s
23s
0s
6.6s
6.7s
4.8s
17s
19.2s
12.5s
4.8s
3.8s
2s
9.5s
8s
8s
0.4s
0.6s
Overshoot
(%OS)
IMPROVED TRACKING PERFORMANCES OF A HOT AIR BLOWER
SYSTEM USING GENERALIZED MINIMUM VARIANCE (GMV)
CONTROLLER WITH PARTICLE SWARM OPTIMIZATION (PSO) AND
HARMONY SEARCH ALGORITHM (HSA) TUNING METHOD
LIM HOOI CHEN
This Report Is Submitted In Partial Fulfillment Of Requirements For The
Bachelor Degree of Electronic Engineering (Industrial Electronic)
Faculty of Electronic and Computer Engineering
Universiti Teknikal Malaysia Melaka
JUNE 2015
2
3
4
5
To my beloved father and mother
6
ACKNOWLEDGEMENT
In performing this project, I had to take the help and guideline of some
respected persons, who deserve my greatest gratitude. First of all, I would like to show
my gratitude to Pn. Sharatul Izah Binti Samsudin and Engr. Siti Fatimah Bte Sulaiman
for giving me a good advice throughout numerous consultations. I would also like to
expand my deepest gratitude to all those who have directly and indirectly guided me
in writing this project.
7
ABSTRACT
Hot air blower system is the process of heating of the air flowing in the tube
up to the desired temperature level. The crucial part that can be seen from this system
is to control the temperature of a flowing air. In this project, a PT326 process trainer,
which is a hot air blower system is used. This project is conducted due to this problem.
The scope of work for this research include modelling and controller design of a PT326
process trainer. Generalized minimum variance (GMV) controller is designed with
MATLAB software to control the purpose of maintaining the process temperature at a
desired value. The simulation result aim to make a comparison of the performances of
the process temperature when using particle swarm optimization (PSO) and Harmony
Search Algorithm (HSA). Through simulation, the performances of the hot air blower
system with the use of GMV controller with PSO tuning method is better than HSA.
8
ABSTRAK
Sistem penghembus udara panas adalah proses pemanasan udara yang
mengalir dalam tiub ke tahap suhu yang dikehendaki. Bahagian yang penting dari
sistem ini adalah untuk mengawal suhu udara yang mengalir. Dalam projek ini, sistem
penghembus udara panas PT326 akan digunakan. Projek ini dikendalikan kerana
masalah ini. Skop kerja bagi kajian ini termasuk pemodelan dan kawalan reka bentuk
PT326. Pengawal varians minimum umum (GMV) direka dengan perisian MATLAB
untuk mengawal dan mengekalkan suhu proses pada nilai yang dikehendaki. Hasil
simulasi bertujuan untuk membuat perbandingan prestasi suhu proses apabila
menggunakan pengoptimum kumpulan zarah (PSO) dan Algoritma Carian Harmonik
(HSA) Melalui simulasi, prestasi penghembus udara panas dengan penggunaan GMV
pengawal dengan kaedah PSO adalah lebih baik daripada HSA.
9
CONTENTS
CHAPTER CONTENTS
I
II
PAGE
PROJECT’S TITLE
i
DECLARATION
iii
DEDICATION
v
ACKNOWLEDGEMENT
vi
ABSTRACT
vii
ABSTRAK
vii
CONTENTS
ix
LIST OF TABLES
xii
LIST OF FIGURES
xiii
LIST OF ABBREVIATION
xv
LIST OF APPENDICES
xvi
INTRODUCTION
1
1.1 OVERVIEW
1
1.2 PROBLEM STATEMENT
2
1.3 OBJECTIVES
2
1.4 SCOPE
2
1.5 METHODOLOGY’S SUMMARY
3
1.6 REPORT STRUCTURE
3
LITERATURE REVIEW
5
10
2.1 OVERVIEW OF PT326
5
2.2 RESEARCH ANALYSIS
9
2.3 AUTO REGRESSIVE WITH EXOGENOUS
11
INPUT (ARX) MODEL
III
2.4 AKAIKE’S FINAL PREDICTION ERROR(FPE)
11
2.5 GMV CONTROLLER
12
2.5.1 Self-Tuning GMVC Algorithm
13
2.5.2 Recursive Least Squares (RLS) Estimation
13
RESEARCH METHODOLOGY
15
3.1 RESEARCH METHODOLOGY
15
3.1.1 Project Planning
IV
16
3.2 PARTICLE SWARM OPTIMIZATION (PSO)
19
3.3 HARMONY SEARCH ALGORITHM (HSA)
21
RESULT AND DISCUSSION
23
4.1 INTRODUCTION
23
4.2 MATHEMATICAL MODEL OF THE PT326
24
PROCESS TRAINER
4.2.1 Preparing Data for System Identification
24
4.2.2 Import Data Arrays into System Identification
24
4.2.3 Remove Mean
26
4.2.4 Estimation Data and Validation Data
27
4.2.5 Appropriate Order of ARX Model
29
4.2.6 Zeros and Poles
31
4.2.7 ARX 333
32
4.3 GMV CONTROLLER DESIGN
34
4.4 THE EFFECT OF CHANGING VARIABLES
38
11
4.4.1 Number of Iteration (nt) Change In PSO Tuning
38
Method
4.4.2 Number of Particle (NOP) Change In PSO
39
Tuning Method
4.4.3 Number of Iteration (nt) Change In HSA Tuning
41
Method
4.4.4 Number of Particle (Nhm) Change In HSA
42
Tuning Method
4.5 SIMULATION RESULT
V
43
4.5.1 GMV
44
4.5.2 GMC with PSO
44
4.5.3 GMV with HSA
45
4.5.4 GMV with HSA (different stopping criteria)
46
4.6 COMPARISON AND JUSTIFICATION
46
4.7 DISCUSSIONS
49
CONCLUSION AND RECOMMENDATION
50
5.1 CONCLUSION
50
5.2 RECOMMENDATION
51
REFERENCES
53
APPENDIX A
55
12
LIST OF TABLES
NO TITLE
PAGE
2.1
Performances of the controllers designed by other researcher
8
2.2
The different technique of modelling and controller design for hot
9
air blower system
4.1
Comparison of different ARX model
30
4.2
The performances of the GMV controller with PSO when the
38
number of iteration is different
4.3
Performances of the controller designed
47
4.4
Value of GMV parameters used in different algorithm
47
13
LIST OF FIGURES
NO
TITLE
PAGE
2.1
PT326 hot air blower trainer kit
5
2.2
Basic elements of a closed loop process control system
6
3.1
Flow chart of project methodology
17
3.2
Gantt chart
18
3.3
Flow chart of basic PSO
20
3.4
Flow chart of basic HSA
22
4.1
Import data dialog box
25
4.2
The system identification tool window displays a ‘dry’ icon to
25
represent the data
4.3
The data is plot in time plot window
26
4.4
Time plot window to display the original and the detrended data
26
4.5
‘dryd’ icons added in system identification tool GUI
27
4.6
Selected ranges for model estimation
28
4.7
Selected ranges for model validation
28
4.8
‘dryde’ and ‘drydv’ icons added in system identification tool GUI
28
4.9
Selection of linear parametric models
29
4.10 ARX model structure
29
4.11 The ARX orders of 441, 331, 332, 333, 223 and 233
29
4.12 Best fits
30
4.13 Zero and poles plot of ARXs models with different parameters
32
4.14 Zero and poles plot for ARX333
32
4.15 Data/model info: ARX 333
33
14
4.16 Simulation to track the performances of a hot air blower system
(without controller)
4.17 Coding in parameters estimation
33
4.18 Block diagram of the design GMV controller subsystem
37
4.19 Output response of the GMV controller with PSO when the
39
37
number of iteration is different
4.20 Output response of the GMV controller with PSO when the
40
number of particle (NOP) is different
4.21 Line graph of the system response characteristics when the
40
number of particle is different
4.22 Bar chart of the system steady state error (%) when the number of
41
iteration is different
4.23 Output response of the GMV controller with HSA when the
42
number of iteration (nt) is different
4.24 Output response of the GMV controller with HSA when the
42
number of particle (Nhm) is different
4.25 Bar chart of the system steady state error (%) when the number of
43
particle (Nhm) is different
4.26 Output response of the GMV controller
44
4.27 Output response of the GMV controller with PSO
45
4.28 Output response of the GMV controller with HSA
45
4.29 Output response of the GMV controller with HSA (different
46
stopping criteria)
4.30 Output responses of the GMV controller (with PSO and HSA)
48
15
LIST OF ABBREVIATION
ARX
-
Auto Regressive with Exogenous
AWC
-
Anti-Windup Compensator
FPE
-
Final Prediction Error
GMV
-
Generalized Minimum Variance
GUI
-
Graphical User Interface
HMCR
-
Harmony Memory Considering Rate
HM
-
Harmony Memory
HSA
-
Harmony Search Algorithm
NOP
-
No of particles
PAR
-
Pitching Adjust Rate
PBRS
-
Pseudorandom Binary Sequence signal
PI
-
Proportional Integral
PID
-
Proportional-Integral-Derivative
PSO
-
Particle Swarm Optimization
RLS
-
Recursive Least Squares
SISO
-
Single-Input/Single-Output
W&P
-
Weston and Postlethwaite
16
LIST OF APPENDICES
NO TITLE
A
GMV controller (without algorithm) and GMV controller (with
algorithm)
PAGE
55
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CHAPTER 1
INTRODUCTION
This chapter gives a general overview of the project, problem statement,
project objectives and limitation of the project. Besides that, a brief methodology and
report structure are also included in this chapter.
1.1
Overview
In this project, a PT326 process trainer of a hot air blower system is used. The
crucial part that can be seen from this system is to control the temperature of a flowing
air. This project is conducted due to this problem. The scope of work for this research
include modelling and controller design of a PT326 process trainer. GMV controller
is designed with MATLAB software to control the purpose of maintaining the process
temperature at a desired value. The simulation result aim to make a comparison of the
performances of the process temperature when using Particle Swarm Optimization
(PSO) and Harmony Search Algorithm (HSA).
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1.2
Problem Statement
A GMV controller is a controller to be designed in this project. The crucial part
is to tune the GMV parameter in order to control the temperature of a flowing air. In
order to solve this problem, two tuning methods (PSO and HSA) will be used to autotune the GMV parameters.
1.3
Objectives
The objectives of this project are:
1) To determine the mathematical model of the PT326 process trainer using
System Identification approach based on Real Laboratory Process Data.
2) To design a GMV controller for the purpose of controlling the temperature of
air flowing.
3) To implement PSO and HSA in GMV controller for the purpose of tuning the
GMV parameters.
4) To make a comparison and justification based on the controller performances
obtained from the simulation.
1.4
Scope
The scope of work for this project consists of modeling and controller design
of a PT326 process trainer using MATLAB software. The controller design for this
project is a GMV controller. Two algorithms will be included in GMV controller for
tuning the GMV parameters, which are PSO and HSA. Real Laboratory Process Data
come from the data store in MATLAB.
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1.5
Methodology’s Summary
The software used in this project is MATLAB. First of all, the mathematical
model of the PT326 process trainer using System Identification approach based on
Real Laboratory Process Data is determined. Then a GMV controller for the purpose
of controlling the temperature of air flowing is designed. After that, PSO Algorithm
will be included in GMV controller for tuning the GMV parameters.
If the output response of the system is correspond to the input signal apply,
then the simulated result of GMV controller and GMV with PSO algorithm are
compared and evaluated. Next, the HSA will be included in GMV controller. If the
performance of the controller is not desire, troubleshooting is make before moving to
the next step. Finally, a comparison and justification is make based on the controller
performances obtained.
1.6
Report Structure
This report is organized in five chapters accordingly. The first chapter gives a
general overview of the project, problem statement, project objectives and limitation
of the project. Besides that, a brief methodology and report structure are also included
in this chapter.
Chapter two is a literature review that highlight past studies related to the hot
air blower system, PT326. Other than that, the background theory also be included in
this chapter.
Chapter three contains the required steps and procedures to achieve the main
objectives of this research. Flow chart is used to give a clear explanation to present the
project methodology, PSO and HSA.
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Chapter four presents the findings of the project. The comparison and
justification based on the results obtained are discussed in this chapter.
Chapter five contains the summary of this project, some recommendations for
future work and the contributions of this project.
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CHAPTER 2
LITERATURE REVIEW
Chapter two is a literature review that highlight past studies related to the hot
air blower system, PT326. Other than that, the background theory also be included in
this chapter.
2.1
Overview of PT326
The PT326 hot air blower trainer kit is a self-contained process and control
equipment. Figure 2.1 shows the PT326 apparatus.
Figure 2.1: PT326 hot air blower trainer kit
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In this equipment, a centrifugal blower draws air from atmosphere and drove
it past a heater grid and through a length of tubing to atmosphere again. The process
involved in this equipment is the heating of the air flowing in the tube up to the desired
temperature level. Whereas, a control equipment will measure the air temperature and
compare it with a value set by the operator. It then generate a control signal which
determines how much electrical power to be supplied to a correcting element. The
basic elements that involve in this closed loop process control system is shown in
Figure 2.2.
Correcting
element
Process
Detecting
element
Motor
element
Measuring
element
Controlling
element
Comparing
element
Set value
Figure 2.2: Basic elements of a closed loop process control system
In general, the input signal voltage range between 0 to -10V and the output
signal voltage range 0 to +10V. The measured and set value meter scales from 0 to
80°C only. The minimum resistive load on any output is 5k�. For controller, the
continuous control proportional band is range from 200% to 5%. The temperature of
the set value and measured value range from 30°C to 60°C. While the set value
adjustment scale can up to 10 [12].
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In 2005, Rahmat, Hoe, Usman and Abdul Wahab [1] use pseudorandom binary
sequence (PRBS) signal of five different maximum lengths as an input signal to
determine the open-loop and closed-loop model of PT326 process trainer. After that,
the transfer function is obtain by using Cross-Correlation Technique.
The impulse response of the system can decays effectively to zero when the
sequence of PRBS is increase and the bit interval is chosen wisely. Thus, it will
increase the accuracy of the result.
Hot air blower system have output delays, noise and under actuator saturation
[2]. Thus, Rehan, Ahmed, Iqbal and Hong have design a proportional-integral (PI)
controller with Anti-Windup Compensator (AWC) to ensuring global stability and
performance of the industrial application. The simulation and the experiment result
show that the response without AWC and with actuator saturation has a lag. This is
due to the windup caused by integral action of PI controller. Windup is prevented with
the proposed AWC. It can be seen that the closed-loop response has no delay due to
saturation. This paper suggested that Pade approximation can be used to reduce the
memory consumption that is caused by the output delay term.
To control the hot air blower system precisely, Siti Fatimah [7] did a research
about the system identification, estimation and controller design of a PT326 process
trainer. Three types of controllers, which are self- tuning pole assignment servoregulator
controller,
Proportional-Integral-Derivative
(PID)
Controller
and
Generalized Minimum Variance (GMV) Controller have been designed with two
different tuning methods. The simulation results is shown in Table 2.1.
From Table 2.1, the performance is improve significantly when using SelfTuning Pole Assignment Servo-Regulator controller to control and maintain the
temperature of the system. The research found out that the zero percent overshoot is
because its capability to reject noise and tracking the set point of the system. While
the controller with the lower settling time and rise time is GMV controller with PSO
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tuning method. The performance of PID controller is not very well as it need a longer
settling time as compared to the other type of controller.
In industrial applications, automatic temperature control of furnaces is
essential for melting, studying the physical and chemical properties of elements and
decomposing [7]. For industry, temperature control is very important when concerns
with the safety of the equipment. Thus, Ijaz, Riaz, Rehan and Ahmed have develop
three PI (proportional-integral) controller with slightly different parameters. This is
used to control the temperature of a nonlinear hot air blower system. Three different
regions of input signals will be consider when conduct system identification. The
writer observed close loop response have small overshoot because of nonlinear
dynamics of the system and actuator saturation. This paper discover that the amplitude
of the actuator signal had risen for high frequency variations when temperature is
increased.
Table 2.1: Performances of the controllers designed by other researcher
Response
Controller
Characteristic Self- Tuning Pole
Proportional-
Generalized
Assignment
Integral-Derivative
Minimum Variance
Servo- Regulator
(PID) Controller
(GMV) Controller
Controller
Pole at
Pole at
0.2
0.8
0%
0%
Peak Time
0s
Settling Time
Rise Time
Percent
ZN-PID
PSO-
ST-
PSO-
PID
GMVC
GMVC
2.5%
0%
180%
2.6%
0s
23s
0s
6.6s
6.7s
4.8s
17s
19.2s
12.5s
4.8s
3.8s
2s
9.5s
8s
8s
0.4s
0.6s
Overshoot
(%OS)