POSITION CONTROL USING FUZZY-BASED CONTROLLER FOR

PNEUMATIC-SERVO CYLINDER IN BALL AND BEAM APPLICATION

MUHAMMAD ASYRAF BIN AZMAN

A thesis submitted in fulfilment of the

requirements for the award of the degree of

Master of Engineering (Electrical)

Faculty of Electrical Engineering

Universiti Teknologi Malaysia

AUGUST 2014

iii

Specially dedicated to

my family for their encouragement and blessings,

my wife CK. Farhana for her support and care.

Thanks for always being there for me.

iv

ACKNOWLEDGEMENT

All praise to Allah the Almighty, the Most Gracious, Most Merciful, and

Most Benevolent for His blessings and for giving me this opportunity to complete

this research successfully. My sincere appreciation goes to my supervisor, Ir. Dr.

Ahmad ‘Athif Mohd Faudzi for his guidance, patience and support throughout the

whole project until completion.

My appreciation also goes to my beloved family for their endless support and

unconditional love. From the day I was born until now they are the ones that taught

me everything. Without them I am nothing. I am also very thankful to my research

group members especially Engr. Khairuddin bin Osman, Engr. Nu’man Din bin

Mustafa, Engr. Omer Faris, Engr, Amar Faiz and Engr. Chai Chang Kai.

Last but not least, to all my friends that helped and shared ideas and material

for this research. Thank you very much for the support given either directly or

indirectly during the completion of this thesis. Once again thank you very much to

all.

v

ABSTRACT

Pneumatic actuator is widely used in the automation industry and in the field

of automatic control due to its advantages such as high power to weight ratio, cost-

effective and uses air as a clean medium to drive it. However, pneumatic actuator

also has some drawback in control due to the nonlinear factors such as air

compressibility and friction. Therefore, the purpose of this research is to design a

controller that will control the position of the cylinder pneumatic stroke. Fuzzy Logic

Control is proposed because of its simplicity in terms of less mathematical equation

and also its performance in controlling the nonlinearities. Three different types of

Fuzzy Logic controller were designed and compared to observe the performance of

the controller in controlling the pneumatic actuator. An optimization method of the

Particle Swarm Optimization (PSO) algorithm is used in tuning the fuzzy control

parameter. PSO is used to find the best value of the parameter involved in the

controller design. Both controllers and optimization method are designed using

MATLAB/Simulink platform from position transfer function obtained by System

Identification (SI) technique. Then, the simulation results are analyzed and validated

with real-time experiment using the Data Acquisition (DAQ) card. The experiment

has been done to the pneumatic actuator with different loads and positions target. A

Pneumatic Ball and Beam System (PABBS) is proposed as the application of the

position controller. The mathematical model of the system is developed and tested

with simulation and experiments for its fast response and stability in controlling the

ball movement. Results show that Proportional-Derivative Fuzzy Logic Controller

(PD-Fuzzy) offers better control compared to other controllers in terms of stability

and robustness for the pneumatic actuator and cascaded PD-Fuzzy controller gives

better control compared with position and rate feedback controller for the PABBS

application.

vi

ABSTRAK

Sistem pneumatik banyak digunakan di dalam sektor industri automasi dan juga

di dalam bidang kawalan automatik kerana kelebihan yang dimiliki seperti kuasa yang

tinggi dinisbahkan kepada berat, kos yang efektif dan menggunakan udara yang bersih

sebagai medium penggerak. Walau bagaimanapun, penggerak pneumatik juga terdapat

beberapa kelemahan untuk mengawal disebabkan oleh tak lelurus yang terlibat seperti

kemamapatan udara and geseran. Oleh itu, tujuan kajian ini adalah untuk mereka bentuk

satu pengawal yang akan mengawal kedudukan lejang pneumatik silinder. Kawalan

Logik Kabur adalah dicadangkan kerana kemudahan dari segi persamaan matematik

yang kurang dan juga prestasi dalam mengawal tak lelurus. Tiga Kawalan Logik Kabur

yang berbeza telah direka dan dibandingkan untuk melihat prestasi kawalan dalam

mengawal penggerak pneumatik. Satu kaedah pengoptimuman algoritma

Pengoptimuman Kerumunan Zarah (PSO) digunakan dalam mencari parameter yang

sesuai. PSO digunakan untuk mencari nilai terbaik parameter yang terlibat dalam reka

bentuk pengawal. Kedua-dua pengawal dan kaedah pengoptimuman direka

menggunakan MATLAB/Simulink platform dari fungsi pemindahan kedudukan yang

diperolehi dari teknik Sistem Pengenalan (SI). Kemudian, keputusan simulasi dianalisis

dan disahkan dengan eksperimen masa nyata menggunakan kad Perolehan Data (DAQ)

dengan beban dan kedudukan sasaran yang berbeza. Satu sistem pneumatik bola dan

rasuk (PABBS) dicadangkan sebagai aplikasi pengawal kedudukan. Model matematik

sistem ini dibangunkan dan diuji secara simulasi dan eksperimen untuk tindak balas yang

cepat dan kestabilan dalam mengawal pergerakan bola. Keputusan menunjukkan bahawa

Pengawal Logik Kabur Berkadar-Derivatif (PD-Fuzzy) memberi pengawal kawalan yang

lebih baik berbanding dengan pengawal lain dari segi kestabilan dan kekukuhan untuk

penggerak pneumatik dan pengawal cascaded PD-Fuzzy memberikan kawalan yang

lebih baik berbanding dengan kedudukan dan pengawal maklum balas kadar untuk

aplikasi PABBS.

vii

TABLE OF CONTENTS

CHAPTER TITLE PAGE

DECLARATION ii

DEDICATION iii

ACKNOWLEDGEMENT iv

ABSTRACT v

ABSTRAK vi

TABLE OF CONTENTS vii

LIST OF TABLES x

LIST OF FIGURES xii

LIST OF ABBREVIATIONS xvi

1 INTRODUCTION 1

1.1 Overview 1

1.2 Motivation 3

1.3 Problem Statement 3

1.4 Objective 4

1.5 Scope 5

1.6 Thesis Outline 5

2 LITERATURE REVIEW 7

2.1 Introduction 7

2.2 Pneumatic Cylinder Actuator (Intelligent Actuator) 7

2.3 Position Control Algorithm 11

2.4 Optimization Technique 15

2.5 Ball and Beam System 16

2.6 Summary 18

viii

3 METHODOLOGY 19

3.1 Introduction 19

3.2 Methodologies Approaches 20

3.3 System Modelling and Designs 21

3.3.1 The Pneumatic-servo Cylinder Plant 21

3.3.2 Pneumatic Actuated Ball and Beam System

(PABBS) 25

3.4 Controllers Design 32

3.4.1 Pneumatic-servo Cylinder Controller

Design – Inner Loop 33

3.4.2 PABBS Controller Design – Outer Loop 44

3.5 Swarm Intelligent – Particle Swarm Optimization

(PSO) 50

3.6 Design of Pneumatic Actuated Ball

and Beam System Structure 54

3.6.1 PABBS CAD Design 55

3.6.2 PABBS Dimension 57

3.6.3 PABBS Parts 58

4 RESULTS AND DISCUSSIONS 60

4.1 Introduction 60

4.2 Experimental Setup 61

4.2.1 Real-time Experimental Setup for

Position Control 61

4.2.2 Real-time Experimental Setup with

variation of loads 62

4.2.3 Real-time Experimental Setup for PABBS 64

4.3 Implementation, Results and Discussion for

Pneumatic-servo Cylinder 65

4.3.1 Simulation-based for Pneumatic-servo Cylinder 65

4.3.2 Real-time Experimental-based for

Pneumatic-servo Cylinder 75

4.3.3 Comparison Between Simulation and

Experiment for Pneumatic-servo Cylinder 85

ix

4.4 Implementation, Results and Discussion for

PABBS Application 86

4.4.1 Simulation-based for PABBS 88

4.4.2 Real-time Experimental-based for

PABBS Application 94

4.4.3 Comparison Between Simulation and

Experiment for PABBS 100

5 CONCLUSION AND FUTURE WORK 101

5.1 Conclusion 101

5.2 Future Work 102

REFERENCES 103

Appendices A - B 111-114

x

LIST OF TABLES

TABLE NO. TITLE PAGE

3.1 Valve Configuration 23

3.2 Specification of the pneumatic actuator system. 24

3.3 System Parameter for PABBS 27

3.4 MATLAB m-file 31

3.5 Defuzzification Technique 36

3.6 Criteria for designing fuzzy logic controller 39

3.7 Linguistic rules of the fuzzy controller design - Version 2 41

3.8 Criteria for designing fuzzy logic controller 48

3.9 Linguistic rules of the fuzzy controller design - PABBS 50

3.10 PABBS dimension 55

4.1 PSO parameters and the value of the IAE obtained 69

4.2 Optimized parameters 69

4.3 PSO Parameters and the value of the IAE obtained 71

4.4 Optimized parameters 72

4.5 Comparisons for step response position tracking analysis

results - Simulation 73

4.6 Comparisons of the performance controlled by overall

fuzzy-based controller - Simulation 75

4.7 Comparisons for step response position tracking analysis

results - Simulation 78

4.8 Comparisons of the performance controlled by overall

fuzzy-based controller - Experimental 80

4.9 Performance for pneumatic-servo cylinder controlled by

PD-Fuzzy-based on load variation (horizontal) 83

xi

4.10 Performance for pneumatic-servo cylinder controlled by

PD-Fuzzy-based on load variation (vertical) 84

4.11 PD-Fuzzy position experimental results compared with

other controllers 85

4.12 Comparison performance between simulation and

experimental result 86

4.13 PSO Parameters 91

4.14 Optimized parameters 92

4.15 Step response analysis results both controllers - simulation 93

4.16 Step response ball tracking for PABBS analysis results 98

4.17 Performance analysis of real-time experiment for PABBS 99

4.18 Comparison performance between simulation and

experimental result 100

xii

LIST OF FIGURES

FIGURE NO. TITLE PAGE

2.1 The PRSD and the intelligent linear actuator [24] 8

2.2 The intelligent pneumatic cylinder and its application to

active link mechanism [25] 9

2.3 Intelligent pneumatic design (a) side view (b) top view 10

2.4 Developed intelligent cylinder 10

2.5 Test platform with nozzle and three pneumatic muscles

[34] 13

2.6 Ball and Beam system [19] 17

2.7 Magnetic suspension actuator ball and beam system

[56] 17

3.1 Research methodology 20

3.2 Structure of Pneumatic-servo cylinder [38] 21

3.3 Schematic diagram and valve connection of the

pneumatic-servo cylinder 23

3.4 (a) Poles and zeroes (b) Frequency response 25

3.5 (a) PABBS (b) Free body digram 26

3.6 Beam angle vs Pneumatic stroke displacement graph 30

3.7 Pneuamtic-servo cylinder controller designed 32

3.8 Pneumatic Actuated Ball & Beam System (PABBS)

controller designed 32

3.9 Components of the fuzzy logic controller 35

3.10 Fuzzy Inference System (FIS) editor for designing

fuzzy logic controller 38

3.11 MATLAB graphical tool for designing fuzzy logic

controller - Version 2 39

xiii

3.12 Membership function of input (a,b) – Version 2 40

3.13 Control surface of the fuzzy controller (a) 3D view (b)

2D view – Version 2 41

3.14 PD-Fuzzy logic controller architecture 43

3.15 Fuzzy-PID controller architecture 44

3.16 Feedback controller architecture 45

3.17 Percentage overshoot versus damping ratio [72] 46

3.18 Cascade PD-Fuzzy controller architecture 47

3.19 MATLAB graphical tool for designing fuzzy logic

controller – PABBS 48

3.20 Membership function of (a) input 1 (b) input 2 –

PABBS 49

3.21 Control surface of the fuzzy controller (a) 3D view (b)

2D view – PABBS 50

3.22 Flow chart of the PSO algorithm 53

3.23 Example block diagram for PSO PD-Fuzzy control

system 54

3.24 Ball and Beam structure development 54

3.25 Acceleration of the ball vs beam length graph 56

3.26 Final design of PABBS from (a) front, (b) left angle

and (c) the beam 56

3.27 Dimension of PABBS (a) front view (b) side view 57

3.28 Beam angle change depends on h 58

3.29 The Pneumatic Actuated Ball & Beam System

(PABBS) 58

3.30 Softpot Membrane Potentiometer – 500mm 59

3.31 Sensor attached (left) and ball (right) on the beam 59

4.1 Flow of the procedures 60

4.2 Experimental setup for position control 62

4.3 Block diagram for real-time experiment 62

4.4 Experimantal setup for variation of loads (a) horizontal

(b) vertical 63

4.5 Experimental setup for position control 64

4.6 Block diagram for PABBS 64

xiv

4.7 Simulink diagram for FLC position controller 66

4.8 Response for Fuzzy simulation (a) Multistep response

(b) Step response 67

4.9 Simulink diagram for PD-Fuzzy position controller 68

4.10 The value of IAE in successive generations of the PSO 68

4.11 Response for PD-Fuzzy simulation (a) Multistep

response (b) Step response 70

4.12 Simulink diagram for Fuzzy-PID position controller 70

4.13 The value of IAE in successive generations of the PSO 71

4.14 Response for Fuzzy-PID simulation (a) Multistep

response (b) Step response 72

4.15 Simulated step response for overall controller design 73

4.16 Simulated multistep response for overall controllers

design 74

4.17 DAQ card configuration in MATLAB/Simulink 76

4.18 Simulink diagram for real-time experiment 76

4.19 Experimental step response for (a) Horizontal position

(b) Vertical position 77

4.20 Experimental multistep response for (a) Horizontal

position (b) Vertical position 79

4.21 Experimental multistep response for Horizontal

position with load (3kg) 81

4.22 Experimental step response based on load variation (a)

Horizontal position (b) Vertical position 82

4.23 Experimental multistep response based on load

variation (a) Horizontal position (b) Vertical position

using PD-Fuzzy 83

4.24 The variation of performance index, IAE with respect

to the load changes 84

4.25 Comparison between simulation and experiment result

for PD-Fuzzy controller – Pneumatic-servo Cylinder 86

4.26 Simulink diagram for PABBS system - simulation 87

4.27 Step response using both PABBS models 88

xv

4.28 Simulink diagram using position and rate feedback

controller 89

4.29 Response for simulated PABBS using feedback

controller (a) step response (b) multistep response 89

4.30 Simulink diagram for PD-Fuzzy position controller 90

4.31 Response for simulated PABBS using cascade

controller (a) step response (b) multistep response 92

4.32 Simulated step response for both controllers design 93

4.33 Simulated multistep response for both controllers

design 94

4.34 DAQ card configuration in MATLAB/Simulink 95

4.35 Simulink diagram for real-time experiment of PABBS 96

4.36 Real-time experiment results for step response (a)

Feedback controller (b) cascade PD-Fuzzy controller 97

4.37 Comparisons of dynamic response of both controllers –

real-time experiment 99

xvi

LIST OF ABBREVIATIONS

ARMAX - Auto-Regressive Moving Average with Exogenous

CAD - Computer –aided Design

DAQ - Data Acquisition

FIS - Fuzzy Inference System

FLC - Fuzzy Logic Control

GA - Genetic Algorithm

GPC - Generalized Predictive Control

IAE - Integral Absolute Error

ICT - Intelligent Chair Tool

IPA - Intelligent Pneumatic Actuator

ISE - Integral Squared Error

ITAE - Integral of Time Absolute Error

I/O - Input/ Output

LED - Light-emitting Diode

MF - Membership Function

MISO - Multiple Input Single Output

NN - Neural Network

PABBS - Pneumatic Actuated Ball and Beam System

PD - Proportional-Derivative

PI - Proportional-Integral

PID - Proportional-Integral-Derivative

PWM - Pulse Width Modulation

PSO - Particle Swarm Optimization

SI - System Identification

SISO - Single Input Single Output

TF - Transfer Function

CHAPTER 1

INTRODUCTION

1.1 Overview

The number of fields in which actuators are applied has increased over the

past few years. Actuators can be categorized as piezoelectric, electromagnetic,

pneumatic, and hydraulic actuators, which range in size and force from nano to mega

range [1]. Actuators are used for automation applications in many industries. In the

process control plants, actuators regulate the respective material, mass or energy

flows by adjusting valves, flaps or slide valves [2]. In home application, actuators are

applied in human friendly robot, and other robot's application [3, 4]. They are also

used in micro machines, medical application and in special environment [4].

The latest technology has now reached actuators as they become part of

intelligent field devices [1]. While the conventional (normal/basic) actuator requires

fast rotation, strong, efficient, and precise, the requirements for new actuators also

have become numerous and in many varieties depending on their application. For

example, micro robots and active catheters require powerful micro actuators,

whereas nursing robots and pet robots require actuators with intelligence, force

control, and safety. On the other hand, the motions of actuators have been changing

where not only rotation and linear motion are applied; but motions with multiple

degrees of freedom and bending motions are also needed. These motions correspond

to the contacting objects, for example, actuators that have several applications.

Currently, there are various applications that require new actuators as part of the

2

system such as methods of communication between human and machines which are

urgently desired by engineers; therefore, a new intelligent actuator for such

applications is expected.

In this research, a pneumatic intelligent actuator (is then called as the

pneumatic-servo cylinder) is selected because of its advantages such as

communication ability and local control functions, while reducing the number of

cables connected, as well as having more delicate and high-performance actuator

motions. This actuator has a different characteristic because most of the pneumatic

actuator has full extend and contract while this actuator, the pneumatic stroke can be

control accordingly. A particularly well suited application for pneumatic actuators is

the position control of robotic manipulators, end effectors, pick and place system,

and grippers, where stiff and lightweight structures are critical. Unfortunately,

pneumatic actuators are subjected to high friction forces, dead time (due to the

compressibility of air), valve dead zone problems, mass flow rate parameters, the

compliance variation and the generating force [5, 6]. These nonlinearities make

accurate position control of a pneumatic actuator is difficult to achieve [7].

As a result, a considerable amount of research work has been devoted to the

development of various position-control systems for pneumatic actuators [8-11]. The

controller will be focused on the position control and will be tested in simulation and

experimental (real-time experiment) to see the performance of the controller. In order

to test the controller robustness and performance, an external load will be attached at

the end effector of the pneumatic. To this end, a pneumatic actuated ball and beam

system will be proposed as a linear stabilizing system. The system will use the

pneumatic-servo cylinder as the actuator to move the ball along the beam. The ball

and beam system is viewed as a benchmark in control engineering setup where the

underlying concept can be applied in the stabilization problem for diverse system

such as the balance problem dealing with goods to be carried by a moving robot, the

spaceship position control systems in aerospace engineering [12, 13] and to test

pneumatic actuator to its limit.

3

The proposed controller method is Artificial Intelligent, which will be using

Fuzzy Logic Control (Fuzzy-based controller) and will be validated with different

controllers by other researchers [14, 15]. An optimization method of the Particle

Swarm Optimization (PSO) algorithm is used in tuning the parameter. PSO is used to

find the best value of the parameter (scaling factors) to get an optimum response.

Previous researcher has tried to implement this intelligent actuator in physical

human-machine interaction [4, 16, 17]. By using position control as the main

objective, this research will implement it on Pneumatic Actuated Ball and Beam

System (PABBS).

1.2 Motivation

Pneumatic systems are widely known in the field of engineering and

industries. Both sectors are fast emerging and the same also goes to the pneumatic

system. Pneumatic system evolves to suit the demand of the industries and different

types of pneumatic system have different characteristics. Different pneumatic

systems need different types of controller to give a better response. Therefore

controller design is important as different pneumatic been developed.

1.3 Problem Statement

Pneumatic cylinders are used to provide linear motion in machines and tools

in industrial operations. Some key advantages of pneumatic cylinders are regarding

their speed, high power to weight ratio, versatility, cost effectiveness, and the use of

a clean medium to drive them. In spite of these advantages, it is difficult to get

accurate position control for pneumatic actuators due to compressibility of air, high

friction forces and valve dead zone problems [18]. Position control is important in

determining the accuracy and response of the pneumatic cylinder which is very

critical in the application of stabilizing system (i.e. ball beam system). Therefore, this

4

research is focused on the position controller for pneumatic-servo cylinder using

Fuzzy-based controller.

The ball and beam system is chosen because the system is one of the most

popular and important benchmark systems for studying control systems [19]. The

common type of ball and beam system usually comprises a mechanical combination

of motor, gear and pull belt. It also uses the servo motor as an actuator to control the

angle of the beam. With this proposed system, pneumatic-servo cylinder is used as an

actuator to control the angle of the beam and it will give more challenging in control

system. Therefore, a good control algorithm is important in determined the

performance of the proposed system.

1.4 Objective

The main objective of this research is to develop a position controller for

pneumatic-servo cylinder to give better position of the pneumatic stroke. In order to

achieve the objective, several sub-objectives are outlined below

The sub-objectives of the research are:

I. To design a position controller using fuzzy-based controller for the

pneumatic-servo cylinder.

II. To tune the parameter (scaling factors) available in the controller

design using Particle Swarm Optimization (PSO) technique.

III. To do simulation analysis and then to validate it with real-time

experiment through MATLAB/Simulink using Data Acquisition

(DAQ) card.

IV. To propose and develop a Pneumatic Actuated Ball and Beam System

(applying the position controller).

5

1.5 Scope

This research intends to concentrate on several scopes such as listed below:

I. Designing a fuzzy-based controller based on previous transfer

function model of the pneumatic-servo cylinder using

MATLAB/Simulink for simulation evaluation.

II. The parameters that have to be optimized are the scaling factors

available in the controller design.

III. Validating the designed controller with real-time experiment for

position control and tested with external load for robustness and

performance checking using NI PCI/PXI 6221 DAQ card as a

communication tool.

IV. Developing Pneumatic Actuated Ball and Beam System by using two

servo-pneumatic actuators (one fixed and the other one moving).

1.6 Thesis Outline

Chapter 1 provides a brief introduction of pneumatic actuator and its

background. The objectives and the problem statement of the research were also

stated. The areas that the project will be focused on were explained in the scope of

research.

Chapter 2 provides a literature review in the area of the pneumatic actuator.

Types of the pneumatic actuator, the position controls algorithm, optimization

technique and the application of the pneumatic actuator are discussed in detail. The

summary of all the literature reviews are also included in this chapter.

Chapter 3 describes the methodology used throughout the research. This

covers the system designs and models, the controller designs, optimization technique

6

that have been used and the experiment setups done. The controller simulation and

experimental analysis were performed using MATLAB/Simulink.

Chapter 4 explains the details of the design of the Pneumatic Actuated Ball

and Beam System (PABBS) structure. The CAD design of the system was designed

using SolidWorks software. The overall dimension of the structure is provided in this

chapter.

Chapter 5 discusses the implementation of the controller designs and the

proposed application. The simulated results are obtained and compared with real-

time experiments. An analysis of the results is made and compared with other types

of controller.

Chapter 6 discusses the conclusion of the research and future work that can

be done to improve the research project.

REFERENCE

[1] J. Ochi, Hashimoto, T., Suzumori, K. and Kanda, T. (2003). Active link

mechanism for physical man-machine interaction. International Symposium on

Micromechatronics and Human Science, MHS 2003. . 19-22 Oct. 2003. 141-

146

[2] Hazem I. Ali, Samsul Bahari B Mohd Noor, S. M. Bashi and M. H. Marhaban

(2009). A Review of Pneumatic Actuators (Modeling and Control). Australian

Journal of Basic and Applied Science. 3 (2), 440-454

[3] S. N. S. S. M.F. Rahmat, Ahmad ‘Athif Mohd Faudzi, Zool Hilmi Ismail, S.I.

Samsudin, N.H.Sunar and K.Jusoff (2011). Non-linear Modeling and Cascade

Control of an Industrial Pneumatic Actuator System. Australian Journal of

Basic and Applied Science. 5 (8), 465-477

[4] A. A. M. Faudzi. Development of Intelligent Pneumatic Actuators and Their

Applications to Physical Human-Mechine Interaction System. PhD. The

Graduate School of Natural Science and Technology, Okayama University;

2010

[5] G. Priyandoko, M. Mailah and H. Jamaluddin (2009). Vehicle active

suspension system using skyhook adaptive neuro active force control.

Mechanical Systems and Signal Processing. 23 (3), 855-868

[6] H. M. Chen, Y. P. Shyu and C. H. Chen (2010). Design and realization of a

sliding mode control scheme for a pneumatic cylinder X-Y axles position servo

system. Frontier Computing. Theory, Technologies and Applications, 2010 IET

International Conference on. 4-6 Aug. 2010. 416-421

[7] R. B. van Varseveld and G. M. Bone (1997). Accurate position control of a

pneumatic actuator using on/off solenoid valves. Mechatronics, IEEE/ASME

Transactions on. 2 (3), 195-204

[8] S. Kaitwanidvilai and M. Parnichkun (2004). Position control of a pneumatic

servo system by genetic algorithm based fixed-structure robust H∞ loop

104

shaping control. Industrial Electronics Society, 2004. IECON 2004. 30th

Annual Conference of IEEE. 2-6 Nov. 2004. 2246-2251 Vol. 3

[9] B. W. Surgenor and N. D. Vaughan (1997). Continuous Sliding Mode Control

of a Pneumatic Actuator. Journal of Dynamic Systems, Measurement, and

Control. 119 (3), 578-581

[10] S. Qiang and L. Fang (2006). Improved Control of a Pneumatic Actuator

Pulsed with PWM. Mechatronic and Embedded Systems and Applications,

Proceedings of the 2nd IEEE/ASME International Conference on. 13-16 Aug.

2006. 1-4

[11] J.-Y. Lai, C. H. Menq and R. Singh (1989). Accurate Position Control of a

Pneumatic Actuator. American Control Conference, 1989. 21-23 June 1989.

1497-1502

[12] J. Hauser, S. Sastry and P. Kokotovic (1992). Nonlinear control via

approximate input-output linearization: the ball and beam example. IEEE

Transactions on Automatic Control. 37 (3), 392-398

[13] B. C. Chang, H. Kwatny and H. Shr-Shiung (1998). An application of robust

feedback linearization to a ball and beam control problem. Proceedings of the

1998 IEEE International Conference on Control Applications. 1-4 Sep 1998.

694-698 vol.1

[14] M. O. E. Abdelrahman. System Identification and PI Neuro-Fuzzy Control of A

Pneumatic Actuator. Master Universiti Teknologi Malaysia; 2012

[15] O. F. Hikmat. Observer-Based Receding Horizon Controller for Position and

Force Control of a Pneumatic Actuator. Master. Universiti Teknologi

Malaysia; 2013

[16] A. A. M. Faudzi, K. Suzumori and S. Wakimoto (2009). Development of an

Intelligent Pneumatic Cylinder for Distributed Physical Human–Machine

Interaction. Advanced Robotics. 23 (1-2), 203-225

[17] A. A. M. Faudzi, K. Suzumori and S. Wakimoto (2010). Development of an

Intelligent Chair Tool System Applying New Intelligent Pneumatic Actuators.

Advanced Robotics. 24 (10), 1503-1528

[18] K. b. O. Ahmad ‘Athif Mohd Faudzi, Rahmat, M. F., Nu’man Din Mustafa, M.

Asyraf Azman and Koichi Suzumori (2012). Controller Design for Simulation

Control of Intelligent Pneumatic Actuators (IPA) System. Procedia

Engineering. 41 (0), 593-599

105

[19] Mohammad Keshmiri, Ali Fellah Jahromi, Abolfazl Mohebbi, M. H. A. and

and W.-F. Xie (2012). Modeling and Control of Ball and Beam System using

Model Based and Non-Model Based Control Approaches. International

Journal on Smart Sensing and Intelligent Systems. 5 (1),

[20] K. Ahn and S. Yokota (2005). Intelligent switching control of pneumatic

actuator using on/off solenoid valves. Mechatronics. 15 (6), 683-702

[21] X. G. Wang and K. Chris Kyung-Hyun (2001). Improved control of pneumatic

lumber-handling systems. Control Systems Technology, IEEE Transactions on.

9 (3), 458-472

[22] J. Wang, J. Pu and P. Moore (1999). Accurate position control of servo

pneumatic actuator systems: an application to food packaging. Control

Engineering Practice. 7 (6), 699-706

[23] J. E. L. H. Paul Strickland, John Billingsley and Barry Haynes (1992).

Intelligent Actuator. Singapore International Conference on Intelligent Control

and Instrumentation, 1992. SICICI '92. Proceedings., . 17-21 Feb 1992. 630-

635

[24] Y. Boulenger, Kramer, E., Hong, Liu, Seitz, N. and Hirzinger, G. (2001). An

intelligent linear actuator and its control system. Advanced Intelligent

Mechatronics, 2001. Proceedings. 2001 IEEE/ASME International Conference

on. 2001. 521-526 vol.1

[25] K. Suzumori, J. Tanaka and T. Kanda (2005). Development of an intelligent

pneumatic cylinder and its application to pneumatic servo mechanism.

Advanced Intelligent Mechatronics. Proceedings, 2005 IEEE/ASME

International Conference on. 24-28 July 2005. 479-484

[26] A. A. M. Faudzi, Suzumori, K. and Wakimoto, S. (2008). Distributed Physical

Human Machine Interaction Using Intelligent Pneumatic Cylinders. Micro-

NanoMechatronics and Human Science, 2008. MHS 2008. International

Symposium on. 6-9 Nov. 2008. 249-254

[27] T. Kubo, Kobayashi, Y., Faudzi, A. M. and Suzumori, K. (2009). Development

of Active 80-faced Polyhedron for haptic physical human-machine interface.

Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ International

Conference on. 10-15 Oct. 2009. 1259-1264

106

[28] I. G. French and C. S. Cox (1988). The robust control of a modern

electropneumatic actuator. Control, 1988. CONTROL 88., International

Conference on. 13-15 Apr 1988. 47-52

[29] C. Gi Sang, L. Han Koo and C. Gi Heung (1998). A study on tracking position

control of pneumatic actuators using neural network. Industrial Electronics

Society, 1998. IECON '98. Proceedings of the 24th Annual Conference of the

IEEE. 31 Aug-4 Sep 1998. 1749-1753 vol.3

[30] E. Gómez-Ramírez. Simple Tuning of Fuzzy Controllers. In: O. Castillo, P.

Melin, J. Kacprzyk and W. Pedrycz. Hybrid Intelligent Systems. Springer

Berlin Heidelberg. 115-133; 2007

[31] S. D. Tiberiu Vesselenyi, Ioan Dzitac and Manolescu Misu-Jan. (2007). Fuzzy

and Neural Controllers for a Pneumatic Actuator. International Journal of

Computers Communications & Control. ISSN 1841-9836 2(4): 375-387

[32] M. F. A. a. D. F. H. Ali (2008). Position Control using Fuzzy Logic. Al-

Rafidain Engineering. 16 (No. 1),

[33] J. C. P.-O. Juan Manuel Ramos-Arreguin, Efren Gorrostieta-Hurtado, Rene de

Jesus Romero-Troncoso and Jose Emilio Pneumatic Fuzzy Controller

Simulation vs Practical Results for Flexible Manipulator. In: Automation and

Robotics. 191-200; 2008

[34] V. Bugarski, P. Nikolic and F. Kulic (2008). Realization of control of

pneumatic system for positioning of nozzle based on fuzzy logic. Neural

Network Applications in Electrical Engineering, 2008. NEUREL 2008. 9th

Symposium on. 25-27 Sept. 2008. 211-214

[35] T. Leephakpreeda (2011). Fuzzy logic based PWM control and neural

controlled-variable estimation of pneumatic artificial muscle actuators. Expert

Systems with Applications. 38 (6), 7837-7850

[36] S. Cetin and O. Demir (2008). Fuzzy PID Controller with Coupled Rules for a

Nonlinear Quarter Car Model. World Academy of Science, Engineering and

Technology 41.

[37] S. Khan, S. F. Abdulazeez, L. W. Adetunji, A. H. M. Z. Alam, M. J. E. Salami,

S. A. Hameed, A. H. Abdalla and M. R. Islam (2008). Design and

implementation of an optimal fuzzy logic controller using genetic algorithm.

Journal of Computer Science. 4 (10), 799-806

107

[38] A. M. Faudzi, K. Suzumori and S. Wakimoto (2009). Design and control of

new intelligent pneumatic cylinder for intelligent chair tool application.

IEEE/ASME International Conference on Advanced Intelligent Mechatronics

(AIM). 14-17 July 2009. 1909-1914

[39] N. m. D. M. Ahmad ‘Athif Mohd Faudzi, Khairuddin bin Osman, M.Asyraf

Azman and Koichi Suzumori (2012). GPC Controller Design for an Intelligent

Pneumatic Actuator. Procedia Engineering. 41 (0), 657-663

[40] A. M. Faudzi, K. Osman, M. F. Rahmat, N. D. Mustafa, M. A. Azman and K.

Suzumori (2012). Nonlinear mathematical model of an Intelligent Pneumatic

Actuator (IPA) systems: Position and force controls. Advanced Intelligent

Mechatronics (AIM), 2012 IEEE/ASME International Conference on. 11-14

July 2012. 1105-1110

[41] A. A. M. F. M. Asyraf Azman, Mohamed Omer Elnimair, Omer F. Hikmat,

Khairuddin Osman and Chai Chang Kai (2013). P-Adaptive Neuro-Fuzzy and

PD-Fuzzy controller design for position control of a modified single acting

pneumatic cylinder. Advanced Intelligent Mechatronics (AIM), 2013

IEEE/ASME International Conference on. 9-12 July 2013. 176-181

[42] M. S. Osman, M. A. Abo-Sinna and A. A. Mousa (2005). A combined genetic

algorithm-fuzzy logic controller (GA–FLC) in nonlinear programming. Applied

Mathematics and Computation. 170 (2), 821-840

[43] F. Gu, K. Ngai Ming and H. Quang (2008). Automatic Fuzzy Membership

Function Tuning Using the Particle Swarm Optimization. Computational

Intelligence and Industrial Application, 2008. PACIIA '08. Pacific-Asia

Workshop on. 19-20 Dec. 2008. 324-328

[44] Z. Yongwei, Q. Fei, L. Jianfeng, W. Lei and W. Qidi (2010). Performance

Criteria Research on PSO-PID Control Systems. Intelligent Computing and

Cognitive Informatics (ICICCI), 2010 International Conference on. 22-23 June

2010. 316-320

[45] S. Yuhui and R. Eberhart (1998). A modified particle swarm optimizer.

Evolutionary Computation Proceedings, 1998. IEEE World Congress on

Computational Intelligence., The 1998 IEEE International Conference on. 4-9

May 1998. 69-73

[46] K. Rajeswari and P. Lakshmi (2010). PSO Optimized Fuzzy Logic Controller

for Active Suspension System. Advances in Recent Technologies in

108

Communication and Computing (ARTCom), 2010 International Conference on.

16-17 Oct. 2010. 278-283

[47] A. Adam, A. F. Z. Abidin, Z. Ibrahim, A. R. Husain, Z. M. Yusof and I.

Ibrahim (2010). A Particle Swarm Optimization Approach to Robotic Drill

Route Optimization. Mathematical/Analytical Modelling and Computer

Simulation (AMS), 2010 Fourth Asia International Conference on. 26-28 May

2010. 60-64

[48] M. N. Ayob, Z. M. Yusof, A. Adam, A. F. Z. Abidin, I. Ibrahim, Z. Ibrahim, S.

Sudin, N. Shaikh-Husin and M. K. Hani (2010). A Particle Swarm

Optimization Approach for Routing in VLSI. Computational Intelligence,

Communication Systems and Networks (CICSyN), 2010 Second International

Conference on. 28-30 July 2010. 49-53

[49] D. Pelusi (2011). Optimization of a fuzzy logic controller using genetic

algorithms. Intelligent Human-Machine Systems and Cybernetics (IHMSC),

2011 International Conference on. 26-27 Aug. 2011. 143-146

[50] M. A. Shamsudin, Z. Ibrahim, S. W. Nawawi, A. R. Husain and J. Abdulla

(2011). An adaptive control of two wheel inverted pendulum robot based on

particle swarm optimization. Automation, Robotics and Applications (ICARA),

2011 5th International Conference on. 6-8 Dec. 2011. 151-156

[51] T.-K. Dao and C.-K. Chen (2012). Tuning Fuzzy-Logic Controllers.

[52] J. Hurel, A. Mandow and A. Garcia-Cerezo (2012). Tuning a fuzzy controller

by particle swarm optimization for an active suspension system. IECON 2012 -

38th Annual Conference on IEEE Industrial Electronics Society. 25-28 Oct.

2012. 2524-2529

[53] C. Wenzhuo, S. Xiaomei and X. Yonghui (2012). Modeling and modulation of

nonlinear ball-beam system controller based on matlab. 9th International

Conference on Fuzzy Systems and Knowledge Discovery (FSKD). 29-31 May

2012. 2388-2391

[54] S.-K. Oh, H.-J. Jang and W. Pedrycz (2011). Optimized fuzzy PD cascade

controller: A comparative analysis and design. Simulation Modelling Practice

and Theory. 19 (1), 181-195

[55] M. Amjad, M. I. Kashif, S. S. Abdullah and Z. Shareef (2010). Fuzzy logic

control of ball and beam system. 2nd International Conference on Education

Technology and Computer (ICETC). 22-24 June 2010. V3-489-V3-493

109

[56] C. E. Lin, C. C. Ker, R. T. Wang and C. L. Chen (2005). A new ball and beam

system using magnetic suspension actuator. 31st Annual Conference of IEEE,

Industrial Electronics Society,IECON 2005. . 6-10 Nov. 2005. 6 pp.

[57] A. M. Faudzi, K. Suzumori and S. Wakimoto (2010). Development of

Pneumatic Actuated Seating System to aid chair design. Advanced Intelligent

Mechatronics (AIM), 2010 IEEE/ASME International Conference on. 6-9 July

2010. 1035-1040

[58] G. K. I. Mann, H. Bao-Gang and R. G. Gosine (1999). Analysis of direct action

fuzzy PID controller structures. IEEE Transactions on Systems, Man, and

Cybernetics, Part B: Cybernetics. 29 (3), 371-388

[59] J. Kennedy and R. Eberhart (1995). Particle swarm optimization. Neural

Networks, 1995. Proceedings., IEEE International Conference on. Nov/Dec

1995. 1942-1948 vol.4

[60] M. Clerc and J. Kennedy (2002). The particle swarm - explosion, stability, and

convergence in a multidimensional complex space. Evolutionary Computation,

IEEE Transactions on. 6 (1), 58-73

[61] K. Osman, A. A. M. Faudzi, M. F. Rahmat, N. D. Mustafa, M. A. Azman and

K. Suzumori (2012). System Identification model for an Intelligent Pneumatic

Actuator (IPA) system. Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ

International Conference on. 7-12 Oct. 2012. 628-633

[62] W. Yu (2009). Nonlinear PD regulation for ball and beam system.

International Journal of Electrical Engineering Education. 46 (1), 59-73

[63] F. Nagi and L. Perumal (2009). Optimization of fuzzy controller for minimum

time response. Mechatronics. 19 (3), 325-333

[64] L. A. Zadeh (1973). Outline of a New Approach to the Analysis of Complex

Systems and Decision Processes. IEEE Transactions on Systems, Man and

Cybernetics. SMC-3 (1), 28-44

[65] E. H. Mamdani (1974). Application of fuzzy algorithms for control of simple

dynamic plant. Proceedings of the Institution of Electrical Engineers. 121 (12),

1585-1588

[66] E. H. Mamdani and S. Assilian (1975). An experiment in linguistic synthesis

with a fuzzy logic controller. International Journal of Man-Machine Studies. 7

(1), 1-13

110

[67] T. Takagi and M. Sugeno (1985). Fuzzy identification of systems and its

applications to modeling and control. IEEE Transactions on Systems, Man and

Cybernetics. SMC-15 (1), 116-132

[68] H. Bao-Gang, G. K. I. Mann and R. G. Gosine (2001). A systematic study of

fuzzy PID controllers-function-based evaluation approach. IEEE Transactions

on Fuzzy Systems. 9 (5), 699-712

[69] L. Sang-Hong and J. S. Lim (2007). Forecasting KOSPI by weighted average

defuzzification based on NEWFM. International Conference on Intelligent and

Advanced Systems, ICIAS. 25-28 Nov. 2007. 66-70

[70] T. J. Ross (1995). Fuzzy Logic with Engineering Applications. McGraw-Hill,

Inc

[71] Y. Yu, Y. Huang, M. Wang, B. Zeng and G. Zhong (2004). Fuzzy neural PID

controller and tuning its weight factors using genetic algorithm based on

different location crossover. Systems, Man and Cybernetics, 2004 IEEE

International Conference on. 10-13 Oct. 2004. 3709-3713 vol.4

[72] N. S. Nise (2008). Control Systems Engineering. (5th). John Wiley & Sons,

Inc.

[73] J. Kennedy (1999). Small worlds and mega-minds: effects of neighborhood

topology on particle swarm performance. Evolutionary Computation, 1999.

CEC 99. Proceedings of the 1999 Congress on. 1999. 1938 Vol. 3

[74] J. Kennedy and R. Mendes (2006). Neighborhood topologies in fully informed

and best-of-neighborhood particle swarms. Systems, Man, and Cybernetics,

Part C: Applications and Reviews, IEEE Transactions on. 36 (4), 515-519

[75] H. I. Jaafar, Z. Mohamed, A. F. Z. Abidin and Z. A. Ghani (2012). PSO-tuned

PID controller for a nonlinear gantry crane system. Control System, Computing

and Engineering (ICCSCE), 2012 IEEE International Conference on. 23-25

Nov. 2012. 515-519