Specially dedicated to:
iv
ACKNOWLEDGEMENT
Praise to the Almighty...
First of all, thanks to our Creator for the continuous blessing and for giving me
the strength and chances in completing this thesis.
Special thanks to my project supervisor, Ir. Dr. Ahmad ‘Athif bin Mohd Faudzi
and co-supervisor, Prof. Dr. Mohd Fua’ad bin Rahmat, for their guidance, support and
helpful comments in doing this research.
My family deserves special mention for their constant support and for their role
of being the driving force towards the success of my project. My sincere appreciation
also goes to everyone whom I may not have mentioned above who have helped directly
or indirectly in the completion of my PhD thesis.
I would also like to thank Universiti Teknologi Malaysia (UTM), Ministry of
Education (MOE) Malaysia under Skim Latihan Akademik IPTA (SLAI), Universiti
Teknikal Malaysia Melaka (UTeM) and Okayama University for their support. Thanks
ABSTRACT
This thesis presents System Identification (SI) model development and controller
design using Predictive Functional Control with Observer (PFC-O) algorithm for
real-time control of Intelligent Pneumatic Actuator (IPA). An application of Ankle-Foot
Rehabilitation Exerciser (AFRE) device uses the IPA system. The plant mathematical
model in discrete transfer functions was approximated using the MATLAB system
identification toolbox for open-loop input-output experimental data. The SI process was
conducted through a series of activities including observation and data gathering, Auto
Regressive with Exogenous Input (ARX) model structure selection, model estimation,
model validation and the implementation of PFC-O algorithm designed to prove the
operation of IPA is acceptable. PFC-O algorithm was selected as a new control strategy
for IPA to overcome the real-time nonlinearities and uncertain characteristics. PFC-O
algorithm was used for position control, force control and realized compliance control
for stiffness characteristic through MODBUS communication protocol. Performance
assessment of the controller was programmed into MATLAB and validated through two
real-time experiments: Personal Computer (PC) based (using National Instrument (NI)
devices) and embedded based (using Programmable System on Chip (PSoC)
microcontroller). The results between simulation, theoretical calculation and both
real-time experiment matched closely and achieved the control objectives. Towards the
AFRE application, the IPA can be configured through MATLAB Graphical User
Interface (GUI) via personal computer where user can adjust the required Range of
Motion (ROM) and resistance in real-time. The AFRE system testing was conducted
successfully on selected subjects for various ROM and resistance using the proposed
algorithm. The significant finding demonstrates that the new PFC-O control algorithm
reduces the control effort and gives better performance in terms of tracking accuracy as
vi
ABSTRAK
Tesis ini membentangkan pembangunan model Pengenalpastian Sistem (SI) dan
reka bentuk Kawalan Fungsian Ramalan dengan Pemerhati (PFC-O) algoritma untuk
kawalan masa nyata Penggerak Pneumatik Pintar (IPA). Aplikasi peranti Senaman
Pemulihan Buku Lali-Kaki (AFRE) menggunakan sistem IPA itu. Model matematika
dalam rangkap pindah diskret telah dianggarkan menggunakan kotak alat
pengenalpastian sistem MATLAB untuk gelung-buka masukan-keluaran data
eksperimen. Proses SI telah dijalankan melalui satu siri aktiviti termasuk pemerhatian
dan perhimpunan data, pemilihan struktur model Auto Regresif bersama Input Luaran
(ARX), penganggaran model, pengesahan model dan implimentasi PFC-O algoritma
direka untuk membuktikan operasi IPA boleh diterima. PFC-O algoritma dipilih sebagai
strategi kawalan baru untuk IPA bagi mengatasi parameter tak lelurus masa sebenar dan
ciri-ciri yang tidak menentu. PFC-O algoritma digunakan untuk kawalan kedudukan,
kawalan kuasa dan sifat kawalan kelembutan direalisasikan melalui protokol komunikasi
MODBUS. Penilaian prestasi pengawal diprogramkan ke MATLAB dan disahkan
melalui dua ujikaji masa nyata: berdasarkan Komputer Peribadi (PC) (dengan
menggunakan peranti Instrumen Nasional (NI)) dan berdasarkan terbenam
(menggunakan mikropengawal Sistem Atur Cara pada Cip (PSoC)). Keputusan antara
simulasi, pengiraan teori dan ujikaji kedua-dua masa nyata dipadankan dan mencapai
objektif kawalan. Bagi mencapai aplikasi AFRE, IPA boleh dikonfigurasikan menerusi
grafik Antara Muka Pengguna (GUI) MATLAB melalui komputer peribadi di mana
pengguna boleh menyesuaikan Julat Pergerakan (ROM) yang diperlukan dan rintangan
dalam masa nyata. Ujian sistem AFRE yang telah dijalankan ke atas subjek yang dipilih
untuk pelbagai ROM dan rintangan berjaya menggunakan algoritma yang dicadangkan.
Penemuan penting menunjukkan kawalan algoritma PFC-O baru dapat mengurangkan
usaha kawalan dan memberikan prestasi yang lebih baik dari segi ketepatan pengesanan
TABLE OF CONTENTS
CHAPTER TITLE PAGE
DECLARATION ii
ACKNOWLEDGEMENT iv
ABSTRACT v
ABSTRAK vi
TABLE OF CONTENTS vii
LIST OF TABLE xi
LIST OF FIGURES xii
LIST OF ABBREVIATIONS xv
LIST OF SYMBOLS xvii
LIST OF APPENDICES xix
1 INTRODUCTION 1
1.1 Research Background 1
1.2 Problem Statement 4
1.3 Research Objectives 5
1.4 Scope of Work 5
1.5 Contribution of the Work 6
1.6 Organization of the Thesis 6
2 LITERATURE REVIEW 8
2.1 Introduction 8
viii
2.3 System Identification and Controller
Selection 15
2.4 Ankle Rehabilitation and its Associated
Challenges 21
4 MODEL AND CONTROL ALGORITHM
STRATEGIES 31
4.1 Introduction 31
4.2 Intelligent Pneumatic Actuator (IPA) System
Operations 31
4.3 System Identification Technique 34
4.3.1 Position Model 37
4.3.2 Force Model 39
4.4 Predictive Functional Control with Observer
(PFC-O) Design 42
4.4.1 Predictive Functional Control (PFC) 42
4.4.2 Observer 46
4.5 Stiffness Characteristic 48
4.6 Embedded Controller Algorithm with Stiffness
Characteristics 50
4.6.1 Simplification of Algorithm 50
4.6.2 Control Stability Test 54
4.6.3 Profiling Algorithm Execution
Performance 55
5.2 Experimental Setup 59
5.3 IPA System Experiments 64
5.3.1 Models Tracking Analysis 64
5.3.2 Stiffness Characteristics with Deflection
Analysis 65
5.3.3 Position Control with Loading Effects
Analysis 66
5.4 Model Validations 67
5.4.1 Position Control 68
5.4.2 Force Control 71
5.5 Embedded System Validations 75
5.5.1 Stiffness Characteristics with Deflection
Analysis 75
5.5.2 Position Control with Loading Effect
Analysis 78
5.6 Summary 87
6 ANKLE-FOOT REHABILITATION EXERCISER
SYSTEM 88
6.1 Introduction 88
6.2 Design and Development of AFRE 89
6.2.1 Characteristics 89
6.2.2 Prototype 90
6.2.3 MODBUS Communication Protocol 93
6.2.4 Graphical User Interface (GUI) 98
6.3 AFRE System Experiments 99
6.3.1 Testing with Measurement Tool 99
x
6.3.2.1 Fixed Movement 101
6.3.2.1 Two-way movement 102
6.4 Data Collection for AFRE System 103
6.4.1 Testing with Measurement Tool 103
6.4.2 Testing with Selected Subject 104
6.4.2.1 Fixed movement 104
6.4.2.2Two-way movement 105
6.5 Summary 109
7 CONCLUSIONS AND FUTURE WORKS 110
7.1 Conclusions 110
7.2 Suggestions for Future Works 111
REFERENCES 113
LIST OF TABLE
TABLE NO. TITLE PAGE
4.1 Valve Configuration 32
5.1 Comparison of the simulated performances for position
control 69
5.2 Comparison of simulated and experimental
performance for position control using PFC-O 71
5.3 Comparison of the simulated performances for force
control 73
5.4 Comparison of simulated and experimental performance
for force control using PFC-O 74
5.5 Comparison of deflection results 77
5.6 Comparison of horizontal payloads for
experimental PFC-O performances 81
5.7 Comparison of vertical payloads for experimental PFC-O
performances 84
xii
LIST OF FIGURES
FIGURE NO. TITLE PAGE
2.1 IPA system and its parts 14
2.2 Ankle rehabilitation trends 26
3.1 Model development flow chart 28
4.1 IPA schematic operations 33
4.2 Process model for SI and its implementation 35
4.3 Response of system to step input signal 38
4.4 Position Model views 39
4.5 Response of system to PRBS signal 40
4.6 Force Model views 41
4.7 Block diagram of PFC-O for plant model 48
4.8 Coil Spring illustration 49
4.9 Block diagram for control system with stiffness
characteristic 50
4.10 PFC controller stage 51
4.11 Observer stage 53
4.12 Eigenvalues of the closed-loop control system 55
4.13 PSoC CY8C27243 56
4.14 Measurement of effective algorithm execution time 57
4.15 Measurement of effective algorithm sampling time 58
5.1 National Instrument (NI) devices connection 60
5.5 Real experiment setup using NI devices 65
5.6 Pneumatic actuator with mass 66
5.7 Loading effect experimental setup 67
5.8 Simulation step responses for position 68
5.9 Simulation multi-step responses for position 69
5.10 Position step responses 70
5.11 Position multi-step responses 70
5.12 Simulation step responses for force 72
5.13 Simulation multi-step responses for force 72
5.14 Force step responses 73
5.15 Force multi-step responses 74
5.16 Stiffness characteristic responses 76
5.17 Position step response for different stiffness 76
5.18 Deflection analysis responses 77
5.19 Experimental position step response for PFC-O under
horizontal loads 79
5.20 Experimental position multi-step response for PFC-O
under horizontal loads 80
5.21 Experimental position step response for PFC-O under
vertical loads 82
5.22 Experimental position multi-step response for PFC-O
under vertical loads 83
5.23 PFC-O force outputs during positional multi-step
experiment under horizontal loads 85
5.24 PFC-O force outputs during positional multi-step
experiment under vertical loads 86
xiv
6.2 Position foot and angle movement of dorsiflexion-plantar
flexion (Ortega et al., 2012) 90
6.3 AFRE system design with dimension using SolidWorks 91
6.4 Real AFRE system and its part 91
6.5 Top level function 94
6.6 Turn on IPA control algorithm (servomechanism on) 95
6.7 Write set point to IPA (position, stiffness) 96
6.8 Flow Chart 4 - Read feedback data from IPA
(position, pressure) 97
6.9 AFRE GUI 99
6.10 Validation with measurement tool 100
6.11 Real physical picture for each subject 101
6.12 Comparison of data embedded and measurement
responses 103
6.13 Fixed movement results 105
6.14 Two-way movement result for subject 1 106
6.15 Two-way movement result for subject 2 107
6.16 Two-way movement result for subject 3 108
LIST OF ABBREVIATIONS
IPA - Intelligent Pneumatic Actuator
PASS - Pneumatic Actuator Seating System
PI - Proportional-Integral
SI - System Identification
PFC-O - Predictive Functional Control with observer
NI - National Instrument
xvi
MGA - Modified Genetic Algorithm
MRE - Mixed-Reality Environment
RLS - Recursive Least Squares
ARMA - Auto-Regressive Moving-Average
PID - Proportional-Integral-Derivative
MPC - Model Predictive Control
DMC - Dynamic Matrix Control
MAC - Model Algorithm Control
GPC - Generalized Predictive Control
EPVA - Electro-Pneumatic Valve Actuator
CARMA - Controlled Auto-Regressive Moving Average
DOF - Degrees of Freedoms
ROM - Range of Motion
PPAFO - Powered Portable Devices Ankle-Foot Orthosis
KAFO - Knee Ankle Foot Orthosis
DAQ - Data Acquisition
ARMAX - Auto-Regressive Moving Average with Exogenous
Input
AIC - Akaike's Information Criteria
PSO - Particle Swarm Optimization
I2C - Inter-Integrated Circuit
UART - Universal Asynchronous Receiver/Transmitter
Sim - Simulation
xviii
W - the force exerted on a body by gravity
g - gravitational acceleration
np - PSoC 11-bit delta-sigma
x', y' - the mobile coordinate system
OS - Overshoot
TS - Settling Time
TR - Rise Time
ess - Steady State Error
LIST OF APPENDICES
APPENDIX TITLE PAGE
A List of Publications 124
B List of Awards 130
1
CHAPTER 1
INTRODUCTION
1.1 Research Background
Actuators that can process information from an input given and control the
output independently are highly demanded in applications of mechatronics. Pneumatic
actuating system is normally chosen because of their advantages of high
power-to-weight ratio, lightpower-to-weight, comparative low cost, easy maintenance and having a simple
structure. Moreover, pneumatic actuators are safe and reliable. They are relatively small
in size compared to hydraulic actuators. They have fast response, and at high
temperatures and in nuclear environments, they have the added advantages over
hydraulic actuators. Pneumatic systems have many attributes that make them attractive
for use in difficult environments: gases are not subjected to the temperature limitations
of hydraulic fluids; the actuator exhaust gases need not be collected, so fluid return lines
are unnecessary and long term storage is not a problem because pneumatic systems are
virtually dry and have no organic materials.
Pneumatic systems were first created in the 16th century. Since then, many
developments have been done to the pneumatic actuators to suit the different automation
and industrial purposes according to the desired accuracy and performance and to the
amount of force that is needed for each particular application. In the 20th century,
complex and intelligent pneumatic systems have been developed. The intelligent
