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 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

I 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

II LITERATURE REVIEW 5

10

2.1 OVERVIEW OF PT326 5

2.2 RESEARCH ANALYSIS 9

2.3 AUTO REGRESSIVE WITH EXOGENOUS

INPUT (ARX) MODEL

11

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

III RESEARCH METHODOLOGY 15

3.1 RESEARCH METHODOLOGY 15

3.1.1 Project Planning 16

3.2 PARTICLE SWARM OPTIMIZATION (PSO) 19

3.3 HARMONY SEARCH ALGORITHM (HSA) 21

IV RESULT AND DISCUSSION 23

4.1 INTRODUCTION 23

4.2 MATHEMATICAL MODEL OF THE PT326

PROCESS TRAINER

24

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

Method

38

4.4.2 Number of Particle (NOP) Change In PSO

Tuning Method

39

4.4.3 Number of Iteration (nt) Change In HSA Tuning

Method

41

4.4.4 Number of Particle (Nhm) Change In HSA

Tuning Method

42

4.5 SIMULATION RESULT 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

V 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

air blower system

9

4.1 Comparison of different ARX model 30

4.2 The performances of the GMV controller with PSO when the

number of iteration is different

38

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

represent the data

25

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)

33

4.17 Coding in parameters estimation 37

4.18 Block diagram of the design GMV controller subsystem 37

4.19 Output response of the GMV controller with PSO when the

number of iteration is different

39

4.20 Output response of the GMV controller with PSO when the

number of particle (NOP) is different

40

4.21 Line graph of the system response characteristics when the

number of particle is different

40

4.22 Bar chart of the system steady state error (%) when the number of

iteration is different

41

4.23 Output response of the GMV controller with HSA when the

number of iteration (nt) is different

42

4.24 Output response of the GMV controller with HSA when the

number of particle (Nhm) is different

42

4.25 Bar chart of the system steady state error (%) when the number of

particle (Nhm) is different

43

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

stopping criteria)

46

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 PAGE

A GMV controller (without algorithm) and GMV controller (with

algorithm)

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 auto-

tune 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.

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 5k𝛺. 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].

Correcting element

Process

Detecting element

Measuring element

Comparing element

Controlling element

Motor element

Set value

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 servo-

regulator 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 Self-

Tuning 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

Characteristic

Controller

Self- Tuning Pole

Assignment

Servo- Regulator

Controller

Proportional-

Integral-Derivative

(PID) Controller

Generalized

Minimum Variance

(GMV) Controller

Pole at

0.2

Pole at

0.8

ZN-PID PSO-

PID

ST-

GMVC

PSO-

GMVC

Percent

Overshoot

(%OS)

0% 0% 2.5% 0% 180% 2.6%

Peak Time 0s 0s 23s 0s 6.6s 6.7s

Settling Time 4.8s 17s 19.2s 12.5s 4.8s 3.8s

Rise Time 2s 9.5s 8s 8s 0.4s 0.6s