MODELING, POSITION AND VISCOSITY CONTROL OF INTELLIGENT PNEUMATIC ACTUATOR
TEH CHUAN ENN
A project report submitted in partial fulfilment
of the requirements for the award of degree of
Master of Engineering (Electrical - Mechatronics and Automatic Control)
Faculty of Electrical Engineering
Universiti Teknologi Malaysia
22 JANUARY 2013
iii
Specially dedicated to
my beloved parent, siblings, and friends who always give spiritual support to me
iv
ACKNOWLEGDEMENT
First of all, I would like to express my highy appreciation to my supervisor,
Dr. Ahmad ‘Athif Mohd Faudzi for his advices and guidance throughout the whole
project. He is a great, friendly and excellent mentor, and always shares his valuable
experiences to inspire me for completing this project. I also would like to thanks to
his willingness to spend time for conducting several favorable meetings, so that I can
grab opportunities to obtain knowledge from these meetings. Secondly, I would like
to address my gratitude to my fellow postgraduate students who are also researching
their projects under similar field. Specially thank to Khairuddin bin Osman, who are
currently still going on in his PhD research. I would like to express my sincere
appreciation to his technically support and help towards achieving this success.
Besides, I would like to thanks to the authority of Faculty of Electrical (FKE) for
providing such a good environment and facilities to complete this project. Last but
not least, I would like to convey my deepest thanks to my family members,
especially my parent who always encourage and give concern to me in completing
my master degree in University Technology Malaysia (UTM).
v
ABSTRACT
Intelligent pneumatic actuator (IPA) is a new developed actuator which
integrates actuator, and others new features such as microcontroller and various
micro sensors. This type of actuator has the capability to communicate with
computer to give better control, higher position and force accuracy. In prior to that,
several experimental setup for the stiffness and viscosity control had been done using
conventional PI controller. The previous experimental results showed that these
control algorithms were feasible for the real IPA system. In this project, the work
focuses more on the reverse engineering method, which is from existing real IPA
system which had been developed by Dr. Ahmad ‘Athif Mohd Faudzi, to simulation
analysis for the validation of other controllers. The objectives of this project are to
develop a simulation model to represent the real IPA system, and design other
controllers to be applied in this developed simulation model. For the simulation
model, nonlinear mathematical modeling based on fundamental physical derivation
is presented. Open-loop and closed-loop simulation works are done to confirm this
model based on this derivation. Closed-loop IPA system is divided into two main
control algorithms, which are position control for position tracking control and
viscosity control for force tracking control. Several controllers which are related to
the fuzzy logic are designed and applied to these control algorithms. The simulation
results from these controllers are then validated and compared with result of using
conventional PI controller. The comparison is made by analyzing their performances
based on control theory. Lastly, due to the nonlinearities problem exist in nonlinear
mathematical model, linearization method is proposed to obtain a new linear model
to ease the controller design and analysis. For the future research, it is recommended
to implement all the proposed controllers to the real-time IPA system.
vi
ABSTRAK
Aktuator pneumatik pintar (IPA) adalah aktuator baru yang menggabungkan
aktuator, dan ciri-ciri baru lain seperti pengendali mikro dan pelbagai sensor mikro.
Aktuator jenis ini mempunyai keupayaan untuk berkomunikasi dengan komputer
untuk memberi kawalan yang lebih baik, serta kedudukan dan daya yang tepat.
Sebelum itu, beberapa setup eksperimen berdasarkan kekukuhan dan kelikatan
dengan menggunakan pengawal PI konvensional, telah dijalankan. Keputusan
daripada setup eksperimen itu menunjukan bahawa algoritma kawalan itu boleh
dilaksanakan untuk sistem IPA yand sebenar. Dalam projek ini, kerja-kerja adalah
ditumpu kepada kaedah kejuruteraan undur, bermaksud daripada sistem IPA yang
sebenar kepada simulasi analisis untuk pegesahan pengawal lain. Tujuan-tujuan bagi
projek ini adalah untuk membuat satu model simulasi bagi mewakili sistem IPA
sebenar, serta mereka pengawal lain untuk diaplikasikan dalam model simulasi ini.
Model matematik tidak linear berdasarkan terbitan fizikal asas dibentangkan bagi
model simulasi ini. Kerja-kerja untuk simulasi gelung terbuka dan gelung tertutup
dilakukan untuk mengesahkan model ini adalah betul. Kemudian sistem IPA gelung
tertutup boleh dibahagikan kepada dua algoritma kawalan utama, iaitu kawalan
kedudukan untuk mengawal kedudukan dan kawalan kelikatan untuk mengawal
daya. Beberapa pengawal yang berdasarkan fuzzy logik direka and digunakan untuk
kedua-dua algoritma kawalan itu. Selepas itu, semua keputusan daripada simulasi
dibandingkan dengan hasil daripada menggunakan pengawal PI konventional.
Perbandingan itu dibuat dengan menganalisis prestasi mereka berdasarkan teori
kawalan. Akhir sekali, disebabkan masalah tidak lelurus wujud dalam model
matematik tidak linear, maka kaedah ‘pelinearan’ dicadangkan untuk mendapatkan
model linear yang baru. Bagi penyelidikan masa depan, pengawal-pengawal baru
yang direka boleh diimplikasikan kepada sistem IPA yang sebenar.
vii
TABLE OF CONTENTS
CHAPTER TITLE PAGE
DECLARATION OF THESIS ii
DEDICATION iii
ACKNOWLEDGEMENT iv
ABSTRACT v
ABSTRAK vi
TABLE OF CONTENTS vii
LIST OF TABLES xi
LIST OF FIGURES xii
LIST OF SYMBOLS AND ABBREVIATIONS xvi
LIST OF AXPENDICES xvii
1 INTRODUCTION
1.1 Overview of the project 1
1.2 Problem Statements 4
1.3 Objectives 4
1.4 Scope of Works 5
2 LITERATURE REVIEW
2.1 Introduction 6
2.2 Terminology: Viscosity 6
2.3 Earlier Model (Prototype) of Intelligent 8
Pneumatic Actuator
viii
2.4 Control Algorithm based on the Previous 9
Experimental Setup
2.5 Fuzzy Logic Speed Control of an Inductor 11
Motor
2.6 Fuzzy Control of the Compressor Speed in a 12
Refrigeration Plant
2.7 Fuzzy and Neural Controllers for a Pneumatic 13
Actuator
2.8 Speed Control of Separately Excited DC Motor 14
using Self Tuned Fuzzy PID Controller
2.9 A Comparative Analysis of PI, Fuzzy Logic 15
and ANFIS Speed Control of Permanent
Magnet Synchronous Motor
2.10 Non-linear Modeling and Cascade Control of 16
an Industrial Pneumatic Actuator System
2.11 Summary 17
3 METHODOLOGY
3.1 Overview 18
3.2 Flow Chart of Methodology 19
3.3 Modeling and Control Approaches 20
3.4 Structure of Intelligent Pneumatic Actuator 21
3.5 Operation of the IPA System 22
3.6 Nonlinear Mathematical Modeling 24
3.6.1 Piston-Load Dynamic Model 25
3.6.2 Modeling of the Cylinder Chambers 27
3.6.3 Valve Model 28
3.7 Development of the IPA Simulation Model 30
3.7.1 Position Control (Closed-loop) 31
3.7.2 Viscosity Control (Closed-loop) 31
3.8 Controller Design 32
3.8.1 Fuzzy Logic Controller 33
3.8.1.1 Fuzzy Logic Controller Design 37
ix
3.8.2 Adaptive Neuro-Fuzzy Inference System 40
(ANFIS) Controller
3.8.2.1 ANFIS Controller Design 44
3.8.3 ANFIS Cascade Controller 47
3.8.4 Self-tuning Fuzzy PI Controller 48
3.8.4.1 Self-tuning Fuzzy PI Controller Design 49
3.9 Linearization 51
3.10 Summary 54
4 RESULTS AND DISCUSSIONS
4.1 Introduction 55
4.2 Open-loop Response of Nonlinear 56
Mathematical Model
4.3 Closed-loop Response of Nonlinear 58
Mathematical Model
4.3.1 Position Control (Nonlinear Mathematical 58
Model)
4.3.1.1 Conventional PI Controller (Position 59
Control)
4.3.1.2 Fuzzy Logic Controller (Position 62
Control)
4.3.1.3 ANFIS Controller (Position Control) 64
4.3.1.4 ANFIS Cascade Controller (Position 65
Control)
4.3.1.5 Self-tuning Fuzzy PI Controller 68
(Position Control)
4.3.1.6 Comparison of Controllers applied for 70
Position Control
4.3.2 Viscosity Control (Nonlinear Mathematical 71
Model)
4.4 Linearization 76
4.4.1 Linear Differential Equation (Taylor Series 76
Expansion)
x
4.4.2 Linear Transfer Function (Control and 81
Estimation Tools Manager)
4.5 3D Animation for IPA System 85
4.6 Summary 86
5 CONCLUSION AND FUTURE WORK
5.1 Conclusion 87
5.2 Future Work 89
REFERENCES 90
Appendices A - B 94 - 101
xi
LIST OF TABLES
TABLE NO. TITLE PAGE
2.1 Specifications of the prototype of intelligent cylinder 9
2.2 Specification of parameter settings for each experiment 10
3.1 Important parameters of the IPA system 24
3.2 Upstream and downstream pressures according to the process 29
of cylinder chamber
3.3 Criteria for designing fuzzy logic controller for IPA system 39
3.4 Fuzzy rules matrix for fuzzy controller design 40
3.5 ANFIS editor training criteria 46
3.6 Fuzzy rule matrix of the fuzzy PI controller 51
4.1 Performance of step response using conventional PI controller 60
4.2 Performance of step response using fuzzy logic controller 63
4.3 Performance of step response using ANFIS controller 64
4.4 Performance of step response using ANFIS cascade controller 66
4.5 Performance of step response using self-tuning fuzzy PI 69
controller
4.6 Performances of step response using different kinds of 71
controllers
4.7 Comparison of RMSE (m) values for sine wave tracking 71
4.8 Output forces (N) for different value of velocity and viscosity 75
parameter
xii
LIST OF FIGURES
FIGURE NO. TITLE PAGE
1.1 Basic structure of a pneumatic actuator 2
1.2 New LA36 intelligent pneumatic actuator from LINAK 3
1.3 Intelligent pneumatic actuator 3
2.1 A mass-spring-damper system 7
2.2 Damping effect when external force is being applied to an 8
object
2.3 Comparison between Intelligent cylinder and commercial 8
cylinder
2.4 Structure of previous experiment setup 9
2.5 A unified control block diagram 10
2.6 Control scheme for an induction motor voltage-source inverter 11
drive
2.7 Fuzzy controller block diagram 12
2.8 Vapor compression experimental plant 12
2.9 Comparison of energy consumption using both the fuzzy 13
control and thermostatic control
2.10 Fuzzy force - position feedback system 14
2.11 Separately excited DC motor model 14
2.12 Speed vs time response of fuzzy tuned PID controlled DC 15
motor
2.13 d-axis equivalent circuits of the sinusoidal PMSM brushless 15
machine
2.14 Step response for fuzzy logic and ANFIS controller 16
xiii
2.15 Closed loop system with cascade controller 17
3.1 Flow chart for the modeling and control approaches 19
3.2 Organization K-chart for whole project 20
3.3 Structure of new intelligent pneumatic actuator 21
3.4 Simplify diagram of the IPA structure 22
3.5 Schematic diagram and valve connection of the intelligent 23
pneumatic actuator
3.6 Translational mechanical systems and their relationships 25
3.7 Model of IPA plant 30
3.8 Position control simulation model 31
3.9 Viscosity control simulation model 32
3.10 Components of the fuzzy logic controller 34
3.11 Process of fuzzification 35
3.12 Computation of the centroid defuzzification method 36
3.13 Operations of fuzzy controller 37
3.14 Flow chart of the design procedure of a fuzzy logic control 37
system
3.15 MATLAB graphical tool for designing fuzzy logic controller 38
3.16 Membership function of error (e) 39
3.17 Membership function of control signal (u) 39
3.18 A typical architecture of an ANFIS model 41
3.19 Flow chart of training procedure of ANFIS 43
3.20 Graphical user interface of ANFIS editor 44
3.21 Collection of input output training data 45
3.22 Input and output training data for ANFIS system 45
3.23 Result from training data 46
3.24 Generated membership functions for input variable through 46
ANFIS editor
3.25 Simulation model using ANFIS cascade controller 48
3.26 Structure of self-tuning fuzzy PI controller 49
3.27 Structure of fuzzy logic for fuzzy PI controller 50
3.28 Membership function of input푒 50
3.29 Membership function of input ∆푒 50
xiv
3.30 Membership function of output퐾 50
3.31 Membership function of input퐾 50
3.32 Some examples of graph showing nonlinearities problem 52
3.33 Graphs showing comparison of (a) linear equation and (b) 53
nonlinear equation
3.34 GUI of Control and Estimation Tools Manager 54
4.1 Simulink block diagram for nonlinear mathematical IPA 56
model
4.2 Open-loop step response 57
4.3 Open-loop sine wave response 57
4.4 Simulink block diagram for position control 58
4.5 Step response for position tracking using conventional PI 59
controller
4.6 Multistep response for position tracking using conventional PI 59
controller
4.7 Sine wave response for position tracking using conventional 60
PI controller
4.8 Input and piston speed response 61
4.9 Change of pressure of chamber 1 versus time 61
4.10 Mass flow rate versus time 62
4.11 The structure of the fuzzy logic controller 62
4.12 Step response for position tracking using fuzzy logic 63
controller
4.13 Step response for position tracking using ANFIS controller 64
4.14 Simulink block diagram of closed-loop system using ANFIS 65
cascade controller
4.15 Step response for position tracking using ANFIS cascade 65
controller
4.16 Multistep response for position tracking using ANFIS cascade 66
controller
4.17 Square wave response for position tracking using ANFIS 67
cascade controller
xv
4.18 Sine wave response for position tracking using ANFIS 67
cascade controller
4.19 Simulink block diagram of fuzzy PI controller 68
4.20 Tuning process of PI controller’s parameters 68
4.21 Step response for position tracking using self-tuning fuzzy PI 69
controller
4.22 Step responses for position tracking using different types of 70
controllers
4.23 Simulink block diagram for the viscosity control 72
4.24 Sinusoidal wave responses for force tracking 73
4.25 Force reference with output force versus time, based on four 74
different value of viscosity coefficient
4.26 Force reference with output force versus time, based on four 74
different value of velocity
4.27 Viscosity plot for intelligent pneumatic actuator 75
4.28 Simulation result of the position control using conventional PI 77
controller
4.29 Simulation graph for spool displacement form time 0 s to 78
0.377 s
4.30 Position tracking of 0.1 m step responses for the linear and 80
nonlinear system
4.31 Steps in linearizing the nonlinear system 81
4.32 Pole-zero map of the linear transfer function 82
4.33 Bode diagram of the linear transfer function 83
4.34 Simulink block diagram for position control using linear 83
transfer function
4.35 Position tracking of 0.1m step responses for the linear systems 84
(mathematical model and transfer function)
4.36 Complete 3D animation for the IPA system 85
xvi
LIST OF SYMBOLS AND ABBREVIATIONS
3D - Three dimension
ANFIS - Adaptive neuro-fuzzy inference system
CAD - Computer-aided design
CAM - Computer-aided manufacturing
DC - Direct current FIS - Fuzzy inference system
FLC - Fuzzy logic controller
GUI - Graphical user interface
IC - Integrated circuit
IPA - Intelligent pneumatic actuator
LED - Light emitting diode
MIMO - Multi input multi output
MISO - Multi input single output
PASS - Pneumatic Actuator Seating System
PI - Proportional-integral
PSoC - Programmable system on chip PSPM - Permanent magnet synchronous motor
PWM - Pulse-width modulation
RMSE - Root mean square error
SI - System identification
VRML - Virtual reality modeling language
xvii
LIST OF APPENDICES
APPENDIX TITLE PAGE
A Gantt chart for MEM 1813 – Research Project 94
Proposal
Gantt chart for MEM 1825 – Master Project 94
B1 IPA complete parameter 95
B2 Complete Simulink block diagram for position 96
control (conventional PI)
B3 Complete Simulink block diagram for viscosity 97
control (conventional PI)
B4 Complete Simulink block diagram + Simulink 3D 98
Animation™
B5 Design of IPA 3D Model using V-realm Builder 2.0 99
B6 MATLAB coding for Linearization using Taylor series 100
expansion
CHAPTER 1
INTRODUCTION
1.1 Overview of the project
In the new era of technology world, actuators were invented and applied in
various fields. There are many types of actuators: electromagnetic, electrostatic,
piezoelectric, pneumatic and hydraulic actuators. These actuators not only used for
automation applications in many industries, they are also being used in others
applications such as home applications, micro machines, medical applications, in
special environments and in physical human-machine applications.
One of the popular types of actuators is pneumatic actuators. Terminology of
pneumatics came from a Greek word pnein, meaning to blow or breath. Pneumatics
is a kind of technology involves the study and application of pressurized gas to
generate mechanical motion. Recently, pneumatic system has been widely
implemented in various kinds of applications such as industrial manipulators and
robotics fields. Pneumatic cylinder or actuator plays vital roles in the pneumatic
system. Since many years ago, pneumatic system was extensively used in lots of
machines in factories to help or assist operations. A pneumatic actuator mainly
consists of a piston, a cylinder, and valves as shown in Figure 1.1.
2
Figure 1.1: Basic structure of a pneumatic actuator. [31]
Intelligent actuator was initially developed by P. Strickland et al. [1] where
this research focused on the development of the actuators and highlighted new
approach of designing servo systems. The development consists of control and fault
tolerant software, communication links, microcontroller, sensor system and actuator.
By having the assistant from a CAD/CAM facility, ‘SERVOCAM’, they managed to
construct intelligent servo systems for robotic systems and the results shows that the
approach is feasible. Development of an intelligent pneumatic cylinder and its
application to pneumatic servo mechanism was then presented by K. Suzumori et al.
[2] where this research contributed to develop intelligent cylinder that can control
position and speed. Micro functional elements such as optical encoder, micro
pneumatic control valves and a micro-processor were started to be utilized to achieve
high control performance.
Intelligent Pneumatic Actuator (IPA) is a new type of developed actuator
which represents the upgraded version of earlier actuator model. Many researchers
and industrial technicians are interesting in applying these new pneumatic actuators
due to their advantages over others actuator model. The positive advantages of
pneumatic actuators are low in cost, high-power-to-weight ratio, light, easier to
maintenance, and have a simpler structure design if compared to other types of
actuators. Moreover, pneumatics actuators are less sensitive to temperature changes
and substance contamination. Figure 1.2 shows one example of intelligent pneumatic
actuator.
Piston
Cylinder
3
Figure 1.2: New LA36 intelligent pneumatic actuator from LINAK. [32]
Intelligent pneumatic actuator have been developed by A.A.Mohd Faudzi et
al. [3] where in this research, IPA was developed and applied to Pneumatic Actuator
Seating System (PASS) as an application. The appearance of the IPA is shown in
Figure 1.3. Intelligent pneumatic actuator integrates microprocessors and some micro
sensors to give better control and thus can achieve higher position and force accuracy.
The movement of the stroke inside the chamber of actuator is controlled by on/off
valve. The benefit of intelligent pneumatic actuator is it can provide accurate position
tracking, deal with different value of stiffness and damping function, and movement
of piston can be controlled by regulating the air pressure of only one chamber inside
the cylinder.
Figure 1.3: Intelligent pneumatic actuator. [3]
4
1.2 Problem Statements
The new intelligent pneumatic actuator (IPA) which had been developed by
A.A.Mohd Faudzi [3], was used on chair type human-machine interaction tool,
Pneumatic Actuated Seating System (PASS). Sufficient information was required on
this human-machine interaction chair in order to obtain how position, stiffness and
viscous coefficient can affect the contour shape, spring and damping characteristics.
Thus, several previous experimental setups have been conducted for these control
algorithms. Based on his research, real IPA closed loop system was controlled by
using conventional PI controller for position and viscosity control. However, this
IPA system is not yet controlled with other controllers to achieve higher control
performances. Therefore, better controllers need to be designed, for the purpose of
giving high control performances and minimizing the errors. In this study, to ease the
controllers design, the works are done in MATLAB simulation. On the other hand,
previous mathematical modeling of pneumatic actuator which had been derived from
other researchers was a nonlinear system; therefore a new linear model is needed for
controller design.
1.3 Objectives
The objectives for this project are:
1. To develop the nonlinear modeling of the Intelligent Pneumatic Actuator.
2. To design controllers (using fuzzy logic) for the position and viscosity control
of IPA.
3. To do the simulation analysis through MATLAB Simulink and compare
results with other controllers.
4. Obtain the linear model of IPA by using linearization technique.
5
1.4 Scope of Works
Basically, the scope of works for this project is shown as following:
1. Study and understand the nonlinear mathematical modeling of the IPA.
2. Do the modeling from the nonlinear mathematical equations to represent IPA
plant by using MATLAB Simulink.
2. Design various kinds of controllers for position and viscosity control for the
nonlinear model.
3. Obtain result using Simulink MATLAB, and compare result from
conventional PI controller.
4. Linearize the current nonlinear model into linear system using theory of
Taylor Series Expansion.
90
REFERENCES
[1] P. Strickland, J. E. L. Hollis, J. Billingsley and B. Haynes, "Intelligent
Actuators", Intelligent Control and Instrumentation, 1992. SICICI '92.
Proceedings., Singapore International Conference on, 1992, pp. 630-635.
[2] Koichi Suzumori, Junichi Tanaka and Takefumi Kanda, “Development of an
Intelligent Pneumatic Cylinder and Its Application to Pneumatic Servo
Mechanism”, International Conference on Advanced Intelligent Mechatronics,
2005.
[3] Ahmad 'Athif Mohd Faudzi, "Development of Intelligent Pneumatic Actuators
and Their Applications to Physical Human-Mechine Interaction System", Ph.D.
thesis, The Graduate School of Natural Science and Technology, Okayama
University, Japan, vol. September, 2010.
[4] Jaime Fonseca, João L. Afonso and Julio S. Martins, “Fuzzy Logic Speed
Control of an Inductor Motor”, Minho University, 1998.
[5] C. Aprea, R. Mastrullo and C. Renno, “Fuzzy Control of the Compressor Speed
in a Refrigeration Plant”, International Journal of Refrigeration 27 (2004), pp.
639-648.
[6] Tiberiu Vesselenyi, Simona Dziţac, Ioan Dziţac, Mişu-Jan Manolescu, “Fuzzy
and Neural Controllers for a Pneumatic Actuator”, International Journal on,
2009, Vol. II (2007), No. 4, pp. 375-387.
91
[7] Prahlad Kumar Sahoo and Nirmal Kumar Barik, “Speed Control of Separately
Excited DC Motor using Self Tuned Fuzzy PID Controller”, Bachelor thesis,
National Institute of Technology Rourkela, 2010-2011.
[8] K.H. Chua, W.P. Hew and C.P. Ooi, “A Comparative Analysis of PI, Fuzzy
Logic And ANFIS Speed Control of Permanent Magnet Synchronous Motor”,
ELEKTROPIKA, Vol. 1, 10-22, 2011.
[9] M.F. Rahmat and Sy Najib Sy Salim, “Non-linear Modeling and Cascade
Control of an Industrial Pneumatic Actuator System”, Australian Journal of
Basic and Applied Sciences, 5(8): 465-477, 2011.
[10] Edmond Richer and Yildirim Hurmuzlu, “A High Performance Pneumatic
Force Actuator System Part 1 - Nonlinear Mathematical Model”, Mechanical
Engineering Department Dallas, 2001.
[11] John F. Blackbum, Gerald Reethof, and J. Lowen Shearer, “Fluid Power
Control. New York and London: Technology Press of M.I.T. and Wiley”, 1960.
[12] D. Ben-Dov and S. E. Salcudean, “A Force-Controlled Pneumatic Actuator”,
IEEE Transactions on Robotics and Automation, vol. 11, no. 6, pp. 906-911,
1995.
[13] Xue-kui Wang and Xu-hong Yang, “Adaptive Neuro-fuzzy Inference System
PID Controller For Sg Water Level Of Nuclear Power Plant”, 8th International
Conference on Machine Learning and Cybernetics, Baoding, July 2009.
[14] Brahim GASBAOUI and Chaker ABDELKADER, “Adaptive Fuzzy PI of
Double Wheeled Electric Vehicle Drive Controlled by Direct Torque Control”,
Leonardo Electronic Journal of Practices and Technologies, p. 27-46, 2010.
[15] Ahmad’ Athif Mohd Faudzi, Koichi Suzumori and Shuici Wakimoto,
“Development of an Intelligent Pneumatic Cylinder for Distributed Physical
Human-Machine Interaction”, Advanced Robotics, 23:1-2, pp.203-225.
92
[16] Ahmad’ Athif Mohd Faudzi, Koichi Suzumori and Shuici Wakimoto, “Design
and Control of New Intelligent Pneumatic Cylinder for Intelligent Chair Tool
Application”, IEEE/ASME International Conference, 2009.
[17] Ahmad’ Athif Mohd Faudzi, Khairuddin Osman and M.F. Rahmat, “Nonlinear
Mathematical Model of an Intelligent Pneumatic Actuator (IPA) Systems:
Position and Force Controls”, 2011.
[18] Kishore Balasubramanian and Kuldip S. Rattan, “Fuzzy Logic Control of a
Pneumatic Muscle System Using a Linearizing Control Scheme”, Wright State
University Dayton, OH- 45435.
[19] Vladislav Blagojević and Miodrag Stojiljković, “Mathematical and Simulink
Model of the Pneumatic System With Bridging of the Dual Action Cylinder
Chambers”, Series: Mechanical Engineering Vol. 5, No 1, 2007, pp. 23-31.
[20] Ahmad ’Athif Mohd Faudzi and Koichi Suzumori, “Stiffness and Viscous
Coefficient Characteristics for Ergonomics Chair Design”, IEEE/ASME
International Conference, 2011.
[21] R. Nejat Tuncay, Zeynep Erenay and Murat Yilmaz, “Rapid Control
Prototyping Approach to Fuzzy Speed Control of Brushless DC Motor”,
Istanbul Technical University, Turkey.
[22] Mohd Shahrieel Mohd Aras and Syed Najib Bin Syed Salim, “Comparison of
Fuzzy Control Rules using MATLAB Toolbox and Simulink for DC Induction
Motor-Speed Control”, International Conference, 2009.
[23] Chen Ying and Zhang Jia-fan, “Design and Hybrid Control of the Pneumatic
Force-feedback Systems for Arm-Exoskeleton by using On/off Valve”,
Mechatronics 17 (2007) 325–335.
[24] Medhat K. Bahr Khalil, “Estimated versus Calculated Viscous Friction
Coefficient in Spool Valve Modeling”, IFPE Technical Conference, 14.2, 2008.
93
[25] J. M. Tressler, T. Clement and H. Kazerooni, “Dynamic Behavior of Pneumatic
Systems for Lower Extremity Extenders”, International Conference on
Robotics and Automation, May 2002.
[26] Xiangrong Shen and Michael Goldfarb, “Simultaneous Force and Stiffness
Control of a Pneumatic Actuator”, Journal of Dynamic Systems, Measurement,
and Control, Vol. 129 / 425-434, 2007.
[27] Hua O. Wang and Kazuo Tanaka, “An Approach to Fuzzy Control of
Nonlinear Systems: Stabilities and Design Issues”, IEEE Transactions on
Fuzzy Systems, vol. 4, no. 1, February 1996.
[28] Mohanadas K P and Shaik Karimulla, “Fuzzy and Neuro-fuzzy Modelling and
Control of Nonlinear Systems”.
[29] Dave Carr. Jeff Shearer, “Nonlinear Control and Decision Making using Fuzzy
Logic in Logix”, Rockwell Automation, Inc., Publication 9324-WP006A-EN-P
– August 2007.
[30] Zhao Dezong, Li Chunwen and Ren Jun, “Fuzzy Speed Control and Stability
Analysis of A Networked Induction Motor System with Time Delays and
Packet Dropouts”, Nonlinear Analysis: Real World Applications, volume 12,
issue 1, pages 273–287, February 2011.
[31] Pneumatic Actuator (http://www.stoneleigh-eng.com/pneumatic_actuators.html)
[32] Actuator to Linear Actuator - LA36 (http://www.projectlink.com.au/actuator-
to-linear-actuator-la36-)
