New system identification model for predictive functional control with observer for an intelligent pneumatic actuator

KHAIRUDDIN BIN OSMAN

UNIVERSITI TEKNOLOGI MALAYSIA

Author's full name : KHAIRUDDIN BIN OSMAN

Date of birth _: 3_0_J_U_L Y_l 9--'8'-l _ __ _ _ _ _ _ _ _ _ _ _ _ _ _ Title : NEW SYSTEM IDENTIFICATION MODEL FOR PREDICTIVE

FUNCTIONAL CONTROL WITH OBSERVER FOR AN INTELLIGENT PNEUMATIC ACTUATOR

Academic Session : 2014/2015 - 2

- -

- - - -

- - - -

- - -

D

D

Q

(Contains confidential information under the Official Secret Act 1972)*

(Contains restricted information as specified by the organisation where research was done)*

I agree that my thesis to be published as online open access (full text)

I acknowledged that Universiti Teknologi Malaysia reserves the right as follows :

NOTES

1. The thesis is the property of Universiti Teknologi Malaysia.

2. The Library of Universiti Teknologi Malaysia has the right to make copies for the purpose of research only.

3. The Library has the right to make copies of the thesis for academic exchange.

SIGNATURE

810730086243 IR. DR. AHMAD 'ATHIF BIN MOHD FAUDZI

(NEW IC NO./PASSPORT NO.) NAME OF SUPERVISOR

Date : 30 MARCH 2015 Date: 30 MARCH 2015

* If the thesis is CONFIDENTIAL or RESTRICTED, please attach with the letter from theorganisation with period and reasons for confidentiality or restriction.

Signature

Name of Supervisor I : Ir. Dr. Ahmad 'Athifbin Mohd Faudzi

Date 30 March 2015

' ,,

-~

Signature --·---··--·---···-··--·-···---·--··--··--···

Name of Supervisor II: Prof. Dr. Mohd Fua'ad bin Rahmat

Date 3 0 March 2015

Nama Jawatan (Cop rasmi)

*

BAHAGIAN B - Untuk Kegunaan Pejabat Sekolah Pengajian Siswazah Tesis ini telah diperiksa dan diakui oleh:

N ama dan Alamat Pemeriksa Luar

Nama dan Alamat Pemeriksa Dalam

Prof. Dr. Mohd Nasir Taib Faculty Electrical Engineering, Universiti Teknologi Mara (UiTM), 40450 Shah Alam,

Selangor.

Prof. Madya Dr. Yahaya bin Md Sam Fakulti Kejuruteraan Elektrik, UTM Johor Bahru.

Disahkan oleh Timbalan Pendaftar di Sekolah Pengajian Siswazah:

Tandatangan : Tarikh:

Nama ASRAM BIN SULAIMAN @ SAIM

KHAIRUDDIN BIN OSMAN

A thesis submitted in fulfilment of the requirements for the award of the degree of Doctor of Philosophy (Electrical Engineering)

Faculty of Electrical Engineering Universiti Teknologi Malaysia

MARCH2015

I declare that this thesis entitled "New System Identification Model for Predictive Functional Control with Observer for an Intelligent Pneumatic Actuator" is the result of my own research except as cited in the references. The thesis has not been accepted for any degree and is not concurrently submitted in candidature of any other degree.

Signature Name Date

: Khairuddin bin Osman : 30 March 2015

Specially dedicated to:

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 to them.

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 compared to the existing control algorithm.

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 berbanding dengan pengawal algoritma sedia ada.

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

Challenges 21

2.5 Summary 26

3 RESEARCH METHODOLOGY 27

3.1 Introduction 27

3.2 Research Process Flow 27

3.3 Summary 30

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

5.3 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

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

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

5.6 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

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

PSoC - Programmable System on Chip

CPM - Continuous Passive Motion

ROM - - Minimum Range of Motion

GUI - Graphical User Interface

HME - Human Muscle Enhancer

ISAC - Intelligent Soft Arm Control

MP - Myo-Pneumatic

PLC - Programmable Logic Controller

ADC - Analog to Digital Converter

PC - Personal Computer

USB - Universal Serial Bus

CAD - Computer Aided Design

CAM - Computer Aided Manufacturing

PRSD - Planetary Roller Spindle Drive

LED - Light Emitting Diode

PRBS - Pseudo Random Binary Sequence

PAM - Pneumatic Artificial Muscle

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

OE - Output-Error

BJ - Box-Jenkins

FPE - Final Prediction Error

MLS - Maximum Length Sequence

PWM - Pulse-Width Modulation

AIC - Akaike's Information Criteria

PSO - Particle Swarm Optimization

I2_{C - Inter-Integrated Circuit}

UART - Universal Asynchronous Receiver/Transmitter

Sim - Simulation

LIST OF SYMBOLS Fd - force data P1, P2 - pressure data A1, A2 - cross-sectional areas d - time delay na - number of poles nb - number of zeros V - loss function,

na - number of approximated parameter,

N - number of sample

y - true value

yˆ - approximate value

y - mean value

Δt - bit interval or clock pulse time

xk - the state model

uk - the input model

yk - the measured output model

i - value of n

yk - the most recent measured output

Ψ - time constant

F - force reference

ks - coefficient of stiffness

n1 - one coincidence horizon

Kob - gain observer

x', y' - the mobile coordinate system

OS - Overshoot

T_S - Settling Time

T_R - Rise Time

ess - Steady State Error

LIST OF APPENDICES

APPENDIX TITLE PAGE

A List of Publications 124

B List of Awards 130

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 pneumatic plant used in this research was taken from previous researches by