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Transcript of POSITION CONTROL USING FUZZY-BASED...
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.
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