controller design for industrial hydraulic...
Post on 18-Mar-2019
214 Views
Preview:
TRANSCRIPT
CONTROLLER DESIGN FOR INDUSTRIAL HYDRAULIC ACTUATOR USING
ARTIFICIAL NEURAL NETWORK
NASRUL BIN SALIM PAKHERI
A project report submitted in partial fulfilment of the
requirements for the award of the degree of
Master of Mechatronic and Automation Control Engineering
Faculty of Electrical Engineering
Universiti Teknologi Malaysia
MAY 2011
iii
To my beloved father, mother, father in law, mother in law, my wife, my daugther
and all of my family member
iv
ACKNOWLEDGEMENTS
Alhamdullillah, a lot of praise to All-Mighty Allah. I wish to express my
sincere gratitude and appreciation to my thesis supervisor Associate Professor Dr. Mohd
Fua’ad bin Hj. Rahmat for encouragement, guidance, critics and friendship. Without his
determination and never ending support, this thesis would not have been the same as
presented here. I am also very thankful UTM’s Phd student, Mr. Zulfatman, for his idea,
assistance and motivation in helping me to handle the Industrial Hydraulic Plant at
UTM. Thank you so much!
To Universiti Malaysia Pahang (UMP), thanks a lot for allowing me to further
my study in part time mode. I also wish to my friend at Faculty Electrical Engineering
& Electronics, Mr. Syakirin Ramli and Associates Professor Ahmed N. Abd Alla for
brilliant idea and contribution in helping me completing this project.
Very special thanks to my beloved parents, Hj. Salim Pakheri and Hajjah
Azimah Abdullah, for their enduring patience, understanding and pray. My sincere
appreciation extends to my fellow colleagues and others who have provided assistance at
various occasions. Finally, I would like to express my appreciation to my beloved wife,
Nurulasma, my daughter Awatif and Alisya for the sacrifice and constant moral support.
I love u so much!
Thank you to all of you.
v
ABSTRACT
Electro-hydraulic actuators are widely used in motion control application. Its
valve needs to be controlled to determine direction of the actuator. Mathematical
modeling is a description of a system in terms of equations. It can be divided into two
parts, which is physical modeling and system identification. The objective of this study
was to determine the mathematical modeling of Industrial Hydraulic Actuator by using
System Identification technique by estimating model using System Identification
Toolbox in MATLAB. Then, an ANN controller is designed in order to control the
displacement of the hydraulic actuator. Finally the controller is validated by
implementing in the real time experiments. Experimental works were done to collect
input and output data for model estimation and ARX model was chosen as model
structure of the system. The best model was accepted based on the best fit criterion and
residuals analysis of autocorrelation and cross correlation of the system input and output.
Then, PIDNN controller was designed for the model through simulation in SIMULINK.
The neural network weights and controller’s parameters is tuning by The Particles
Swarm Optimization (PSO) method. The simulation work was verified by applying
the controller to the real system to achieve the best performance of the system.
The result showed that the output of the system with PIDNN controller in simulation
mode and experimental works was improved and almost similar. The designed PIDNN
with PSO tuning method controller can be applied to the electro-hydraulic system either
in simulation or real-time mode. The others automatic tuning method controller could be
developed in future work to increase the reliability of the PIDNN controller. Besides, the
hydraulic actuator system with non linear model could be modeled.
vi
ABSTRAK
Penggerak elektro hidraulik digunakan dengan meluas dalam applikasi kawalan
pergerakan. Injapnya perlu dikawal bagi menentukan haluan penggerak. Permodelan
matematik adalah perihal suatu sistem dalam terma persamaan. Ia boleh dibahagikan
kepada dua bahagian, iaitu permodelan fizikal dan sistem pengecaman. Objektif kajian
adalah untuk mengenalpasti pemodelan matematik bagi penggerak hidraulik industri
dengan cara menggunakan teknik sistem pengecaman, iaitu dengan mengangar model
dengan menggunakan kotak alatan Sistem Pengecaman di dalam Matlab. Selepas itu,
Pengawalan ANN direka bentuk bagi tujuan mengawal pergerakkan penggerak
hidraulik. Akhir sekali, pengawal yang direka bentuk disahkan dengan melaksanakan
pada ekperimen masa sebenar. Eksperimen dilakukan untuk mengumpul data masukan
dan keluaran bagi anggaran model dan model ARX dipilih sebagai struktur model bagi
sistem. Model terbaik adalah diterima bepandukan kreteria padanan terbaik dan analisis
baki pada hubungkait automatik dan hubungkait silang untuk sistem masukan dan
keluaran. Pengawal PIDNN direkabentuk untuk model melaui simulasi di dalam
SIMULINK. Pemberat rangkaian saraf dan parameter penggawal adalah ditala dengan
kaedah Pengoptimuman Kerumunan Zarah (PSO) dengan mengaplikasikan pengawal
pada sistem sebenar bagi mencapai prestasi terbaik sistem. Keputusan menunjukkan
keluaran daripada sistem bersama pengawal PIDNN dalam mod simulasi dan
eksperimen adalah bertambah baik berserta hampir sama. Pengawal PIDNN yang
direkabentuk bersama kaedah larasan pengawal PSO boleh diaplikasikan pada sistem
penggerak elektro hidraulik, sama ada dalam mod simulasi ataupun pada masa sebenar.
Lain-lain kaedah pelaras automatik bagi pengawal boleh dibangunkan pada masa akan
datang untuk meningkatkan kebolehpercayaan bagi pengawal PIDNN. Disamping itu,
sistem penggerak hidraulik juga boleh dimodelkan dengan model tidak linear.
TABLE OF CONTENTS
CHAPTER TITLE PAGE
DECLARATION ii
DEDICATION iii
ACKNOWLEDGEMENTS iv
ABSTRACT v
ABSTRAK vi
TABLE OF CONTENTS vii
LIST OF TABLES x
LIST OF FIGURES xi
LIST OF ABBREVIATIONS xiii
LIST OF SYMBOLS xiv
LIST OF APPENDICES xv
1 PROJECT INTRODUCTION
1.1 Project Background 1
1.2 Objectives 3
1.3 Project Scopes 4
1.4 Project Report Overview 5
2 LITERATURE REVIEWS
2.1 Electro-Hydraulic Actuator System 6
2.2 Overview of System Identification 9
2.2.1 Model Structure Selection 10
2.2.1.1 AR Model 11
2.2.1.2 ARX Model 12
2.2.1.3 ARMAX Model 13
viii
2.2.1.4 Box-Jenkins Model 13
2.2.1.5 Output-Error Model 14
2.2.2 Parameter Estimation 15
2.2.3 Model Validation 15
2.2.3.1 Model Validity Criterion 15
2.2.3.2 Pole-Zero plots 16
2.2.3.3 Bode Diagram 16
2.2.3.4 Residual Analysis 16
2.3 Artificial Neural Network Controller 17
2.3.1 PID Neural 18
2.3.2 PID Neural Network Structure 19
2.4 Background to Particle Swarm Optimization (PSO) 21
2.4.1 Basic PSO Algorithm 21
2.4.2 Fitness Function 24
2.4.3 Variations to the PSO Algorithm 25
2.4.4 Steps in Implementing the PSO Method 26
2.4.5 Factors Affecting PSO Performance 27
3 METHODOLOGY
3.1 Design an Experiment and Experimental Setup 30
3.2 Model Identification Using Matlab 31
3.3 Control System Design 41
3.3.1 PID Neural Network Controller 41
3.3.2 Particles Swarm Optimization Tuning Method 43
3.3.3 Implementation of PSO algorithm in Matlab 44
3.3.4 PID Controller 45
4 ANALYSIS AND RESULT
4.1 System Modeling 47
4.2 Controller design via simulation 50
4.2.1 Comparison Performance between PSO
Tuning PIDNN with Conventional PID (Z-N) 54
4.2.2 Training Neural Network Weight with
different Number of iterations 55
ix
4.2.3 Validation of PIDNN (PSO) with Real Time
Industrial Hydraulic Actuator 56
4.3 Conclusion 58
5 CONCLUSIONS AND RECOMMENDATIONS
5.1 Conclusion 59
5.2 Future Works 60
REFERENCES 61
APPENDICES 65
x
LIST OF TABLES
TABLE NO. TITLE PAGE
2.1 Activation Function for each type of neuron 19
3.1 Zeigler-Nichols tuning method 46
4.1 Model Order Analysis 48
4.2 Parameter of industrial hydraulic actuator 51
4.3 Initialization value of PSO 51
4.4 Initialization values of position, i of agent 52
4.5 Initiliazation values of velocity, i of agent 52
4.6 Performance comparison of PIDNN Controller with Conventional
PID controller 55
xi
LIST OF FIGURES
FIGURE NO. TITLE PAGE
1.0 General idea for this project 3
2.0 Valve and piston schematic 7
2.1 System identification approach 10
2.2 General Linear Model Structure 11
2.3 AR Model Structure 12
2.4 ARX Model Structure 12
2.5 ARMAX Model Structure 13
2.6 BJ Model Structure 14
2.7 Output-Error Model Structure 14
2.8 Biological Neuron 17
2.9 Neuron Form 18
2.10 Sub-PID neural network 20
2.11 PID Neural Multivariable Control System 20
2.12 Concept of modification of a searching point by PSO 23
2.13 Flow chart for general PSO algorithm 27
3.1 Flow Chart of System Identification 29
3.2 Experimental workbenches for Industrial Hydraulic Actuator 30
3.3 Actual displacement range of hydraulic actuator with applied
input voltage 31
3.4 Open loop Hydraulic Data Collection Block 31
3.5 Main ident identification windows 33
3.6 Import data dialog box 33
3.7 Additional windows in Import Data dialog box 34
3.8 Time plot of input-output signal of the measured data 34
3.9 Selecting range in Preprocess window 35
xii
3.10 Select range dialog box 35
3.11 Model validation data, datav and model estimation data,
datae added to Data View Board 36
3.12 Inserting ‘datae’ into Working Data and ‘datav’ into Validation
Data window 36
3.13 Select Linear parametric models 37
3.14 Parametric Model dialog box 37
3.15 The icon arx331 is added to Model Views board 38
3.16 Model Output window Views 39
3.17 Residual Analysis window 39
3.18 Pole and zeros plot window 40
3.19 Model info window 40
3.20 Basic PID-NN 41
3.21 SISO process with PID-NN control system 42
3.22 Positioning the PSO optimization algorithm within SISO System 43
3.23 Schematic diagram of the industrial hydraulic actuator system
with PID controllers in Simulink 46
4.1 Input and output signal for system identification 47
4.2 Model validation 49
4.3 Residual analysis using correlation function 49
4.4 Pole zero plot for model ARX331 50
4.5 The subsystem block of PIDNN 52
4.6 The Simulink block for the overall System of PIDNN
Controller with Industrial Hydraulic Actuator System 53
4.7 Output signal with step input 53
4.8 Output signal with square input 54
4.9 The step response signal with different type of controller used 55
4.10 Output signal of step input with various number iteration
of PSO tuning Method 56
4.11 Matlab Simulink real time PID-NN controller 56
4.12 Real time industrial hydraulic actuator system 57
4.13 Output Response of Industrial Hydraulic Actuator in real time
experimental with step input 57
xiii
LIST OF ABBREVIATIONS
ANN - Artificial Neural Network
PSO - Particle Swarm Optimization
PID - Proportional Derivative Integral Controller
N-Z - Zeigler-Nichols tuning method
SI - System Identification
PRBS - Pseudorandom Binary Sequences
ARX - Auto-regressive with Exogenous Input
ARMAX - Auto-regressive Moving Average with Exogenous Input
AR - Auto-regressive
OE - Output Error
BJ - Box-Jenkins
DAQ - Data Acquisition Systems
LS - Least Square Method
IV - Instrumental Variables Method
FPE - Final Prediction Error
MSE - Mean Square Error
ISE - Integral Square Error
RMS - Root Mean Square
GA - Genetic Algorithm
PIDNN - Proportional-Integral-Derivative-Neural-Network
NN - Neural Network
FPE - Final Prediction Error
NI - National Instrument
PCI - Peripheral Component Interconnect
IV - Instrumental Variable method
LC - Least Squares method
xiv
LIST OF SYMBOLS
- Piston velocity
- Hydraulic pressure
- External load disturbance
- Spool value diplacement
- Piston surface area
- Mass of the load
- Effective bulk mod ulus
- Viscous damping coefficient
- Total volume of hydraulic oil in the piston chamber & connection line
- Sampling Time
- Number of Sample
- Proportional Gain
- Integral Gain
- Derivative Gain
- Perturbation in controller output signal from the bias or base value
corresponding to the normal operating condition
- Error between the reference input and the process output
- Current velocity of agent at iteration
- New velocity of agent at iteration
- Adjustable cognitive acceleration constants (self confidence)
- Adjustable social acceleration constant (swarm confidence)
- Random number between 0 and 1
- Current position of agent at iteration
- Personal best of agent
- Global best of the population
xv
LIST OF APPENDICES
APPENDIX TITLE PAGE
A Soft code for computation flow of PSO technique 65
B Soft code for computation of fitness function 70
CHAPTER 1
PROJECT INTRODUCTION
1.1 Project background
Hydraulic Actuators are crucial in engineering field as used in industrial
process control. With capability to provide very high forces, high control accuracies,
high power to weight ratio, good positioning capability and also have a compact
structure to employ hydraulic pressure to drive an output member (Ljung, L., 1987).
The fluid used in hydraulic actuator is highly incompressible and so that pressure
applied can be transmitted instantaneously to the member attached to it. Due to that
reason, research for the control of force and position of electro-hydraulic system
attract a great interest to both the researchers and engineers.
An important step in designing a control strategy is to having a proper model
for the plant system. An exact system model should produce output responses similar
to the actual system. The complexity of most physical systems, however, meets any
difficulties in developing the exact models. In case the model and parameters are
unknown, appropriate techniques that called System Identification can be applied to
overcome all those limitations.
Currently, a number of techniques of system identification have been applied
to estimate the hydraulic actuator model in form linear models, non-linear models
and intelligent models. Linear model such as Auto-regressive Exogenous (ARX)
model with PRBS signal as input signal (Huang, S.H. and Chen, Y.H.C., 2006) and
ARX model with multi sine signal with three different frequencies is chosen as the
2
input to the system (Rahmat, M.F. et al, 2010). Nonlinear model has proposed in
observer canonical form using a modified Recursive Instrument Variable (Jelali, M.
and Schwarz, H., 2005), and Hammerstein model which makes the assumption that
the nonlinearities of the systems can be separated from the system dynamics (Kwak,
B.-J. et al., 1998). Since neural networks have been successfully used in various
fields, back-propagation neural networks were applied in identification of electro-
hydraulic actuator model (Anyi, H. et al., 1997). In the last few years, neural
networks have been developed in form online identification using Recurrent High
Order Neural Networks (RHONN) method (Lizarde, C. et al., 2005). Another online
identification of the systems parameters is based on recursive least square algorithm,
with constant trace (Kaddissi, C. et al., 2007). In this Project, the linear model such
as Auto-regressive Exogenous (ARX) model with multi sine with different input
frequencies is used.
Regarding on the controller propose for the industrial hydraulic actuator,
there are several type of controller has been designed before starting with linear
control, which applied a simple poles placement to a linearized model of an electro-
hydraulic system (Lim,T.J.,1997) and following by classic cascaded loops and
proportional-integral-derivative (PID) controllers were employed respectively for the
position control of a hydraulic actuator (Plahuta, M.J. et al., 1997), (Zeng, W. and
Hu, J.,1999). The next control design is an indirect adaptive controller, based on pole
placement for the speed and position feedback of electro-hydraulic systems (Yu,
W.S. and Kuo, T.S., 1996). However, the controllers which are based on a linear
model of the plant, imposes certain limitations on the efficiency and robustness of
the controller. With dynamic characteristics of fluid power leads to uncertain
dynamic modeling and thus robust control strategies can increase safety, reliability
and availability of hydraulic actuators. As a controller designing stage for the
industrial hydraulic actuator, the Proportional Integral Derivative Neural Network
(PIDNN) controller is implemented. PID neural network which is proposed by
Huaillin et al. (2000) is a new kind of networks and its hidden layer neurons simply
work as PID controller terms through their activation functions thus it simultaneously
utilizes advantages of both PID controller and neural structure.
3
To obtain the optimal parameter tuning for the controller, it is highly
desirable to increase the capabilities of PIDNN controllers by adding new features.
Most in common, artificial intelligence (AI) techniques have been employed to
improve the controller performances for a wide range of plants while retaining the
basic characteristics. Artificial Intelligent techniques such as artificial neural
network, fuzzy system and neural-fuzzy logic have been widely applied in order to
get proper tuning of PID controller parameters. In this project is focuses on utilizing
a soft computing strategy, namely the particle swarm optimization (PSO)
technique that was first proposed by Kennedy, J. and Eberhart, R. (1995), as an
optimization strategy to tuning of PID neural network weights adjustment and fine
tuning the controller’s parameters. Below Figure 1.1 is shown the general idea on
this project.
Figure 1.1: General idea for this project
1.2 Objectives
In carrying out of this project, the objectives are as follows in order to fulfill
the requirement of system identification and controller design for industrial hydarulic
actuator:
i. First objective of this project is determining the mathematical modeling of
Industrial Hydraulic Actuator by using System identification technique
utilizing real experiment data.
SP:Position of hydraulic (cm)
CV:Position of hydraulic (cm)
MV:Voltage input (Hydraulic Valve )(volt )
Out1
1
SENSOR INPUT
Convert
Output Signal
Input Signal
HYDRAULIC SYSTEM MODELLING
Convert
ANN CONTROLLER
Convert
ARX MODEL
PIDNN
DRAW WIRE
SENSOR
4
ii. Second objective is design a controller for an industrial hydraulic actuator’s
position control or displacement control.
iii. Third objective is to validate the controller design obtained via simulation
through experimental procedure.
1.3 Project Scopes
This project is t intends to concentrate on the scopes as follows:
i. Familiarize with the Hydraulic Industrial Actuator system. In order to ensure
the data are taken in the right manner, the study of the system is must be
made. The integration connection between the hydraulic systems with the
MATLAB is performing with the DAQ card.
ii. Experimental data collection with Real Time Workshop. The input-Output for
Hydraulic System Actuator are being collected trough the real time
Workshop. The multi Sine input are used as a input to the system. Then, the
movement of the cylinder that varies with the input is recorded in term of
voltage.
iii. The model is for the industrial hydraulic actuator system performed in linear
discrete model to obtain a discrete transfer function for the system. Model
estimation and validation procedures are done by using System Identification
Toolbox in MATLAB. Data for model estimation is taken from an
experimental works.
iv. Design the artificial neural network (ANN) controller and implement at
modeling of industrial hydraulic actuator.
5
1.4 Project report overview
This document is arranged as follows:
i- Chapter one gives an introduction and general overview of the study. It
focuses on the research problem and motivation for the study.
ii- Chapter two provides a brief outline on Electro hydraulic actuator system,
system identification, artificial neural network controller and some
background for Particles Swarm Optimization (PSO).
iii- Chapter three highlights the methodology of the project. Which is includes
the experimental setup, modeling by using Matlab, PID neural network
controller design and discusses on the PSO tuning approach.
iv- Chapter four discusses about the result of simulation and real time
experimental study that compares the control performance of PIDNN tuning
by PSO with the conventional PID controller.
v- Chapter five concludes the findings of the study and provides direction for
further research that could be pursued in the field.
61
REFERENCES
[1] Aggarwal, V.Meng Mao O'Reilly, U.-M.A. (2006). Self-Tuning Analog
Proportional-Integral-Derivative (PID) Controller in: Adaptive Hardware and
Systems, 2006. pp. 12-19.
[2] Anyi H., Yiming R., Zhongfu Z. and Jianjun Hu (1997). Identification and
Adaptive Control for Electro-hydraulic Servo Systems Using Neural
Networks. In Proceeding of the 1997 IEEE International Conference on
Intelligent Processing Systems. 1997. Beijing, China. 688-692.
[3] Benjamin, C. K. (2005). Automatic Control System. 7th Edition. United
States of America: John Wiley & Sons, Inc.
[4] Clerc, M. (1999). The Swarm and the queen: Towards a deterministic
and adaptive particle swarm optimization. Proceedings of the Conference
on Evolutionary Computation. pp. 1951-1957.
[5] Eberhart R.C. and Shi Y. (1998). Comparison between genetic algorithms and
particle swarm optimization. IEEE International Conferences. Evol. Comput.,
Anchorage. 1998. pp 611-616.
[6] Flynn A.M. and Sanders S.R. (2002). Fundamental limits on energy transfer
and circuit considerations for piezoelectric transformers. IEEE Transactions
on Power Electronics. Jan, 2002. IEEE. Vol.17, (no.1), p.14.
[7] Ghazali R., Sam Y.M., Rahmat M.F. and Zulfatman (2009). Open-loop and
close-loop recursive identification of an electro-hydraulic actuator system.
Paper SCORE International Journal on Smart Sensing and Intelligent
Systems. Vol. 2, No. 2.
62
[8] Huaillin S. and Pi Y. (2000). PID neural networks for time-delay systems.
Computers and Chemical Engineering. September 24, 859-862.
[9] Huang S. H. and Chen Y. H. C. (2006). Adaptive Sliding Control with Self-
Tuning Fuzzy Compensation for Vehicle Suspension Control. Mechatronics.
Vol. 16: 607-622.
[10] Jelali M. and Schwarz H. (1995). Nonlinear Identifcation of Hydraulic
ServoDrive Systems. IEEE Control Systems. October 1995, pp. 17-22.
[11] Kaddissi C., Kenne J-P., Saad M. (2007). Identification and Real-time
Control of an Electrohydraulic Servo System Based on Nonlinear
Backstepping. IEEE Transaction on Mechatronics. Vol. 12 No.1:12-21.
[12] Kennedy J., Russell R.C. and Shi Y. (2001). Swarm Intelligence. The
Morgan Kaufmann Series in Evolutionary Computation. 2001.
[13] Kennedy J. and Eberhart R. (1995). Particle swarm optimization. Proc. IEEE
Int. Conferences Neural Networks. Perth, Australia. 1995. Vol. 4, pp 1942-
1948.
[14] Kwak B.-J., Andrew, E. Yagle and Joel A. Levitt. (1998). Nonlinear System
Identification of Hydraulic Actuator Friction Dynamics using a Hammerstein
Model. IEEE: 1998. pp 1933-1936.
[15] Li X., Yu F. and Wang Y. (2007). PSO Algorithm based Online Self-Tuning
of PID Controller. 2007 International Conference on Computational
Intelligence and Security. Sept, 2007. Pg 128-132.
[16] Lim, T. J. (1997). Pole Placement Control of an Electro-hydraulic Servo
Motor. In Proc. of 1997 2nd Int. Conf. Power Electronic Drive System.
Singapore. Part 1, Vol. 1, 350-356.
[17] Lizarde C., Loukianov A. and Sanchez E. (2005). Force Tracking Neural
control for an Electro-hydraulic Actuator via Second Order Sliding Mode. In
Proceeding of the 2005 IEEE International Symposium on Intelligent
Control. Limassol, Cyprus. 2005. pp 292-297.
63
[18] Ljung, L. (1987). System Identification: Theory for the User. 1stedition.
United State of America: Prentice Hall.
[19] Ljung, L. (1999). System Identification: Theory for the User, Upper Saddle
River Prentice-Hall, PTR New Jersey.
[20] Nagaraj B., Subha S. and Rampriya B. (2008). Tuning Algorithms for PID
Controller Using Soft Computing Techniques, IJCSNS International Journal
of Computer Science and Network Security, April 2008 .VOL.8 No.4.
[21] Plahuta M. J., Franchek M. A. and Stern H. (1997). Robust Controller Design
for a Variable Displacement Hydraulic Motor. Proc. of ASME Int. Mech. Eng.
Congr. Expo. 1997. Vol. 4, 169–176.
[22] Pillay, N. (2008). A Particle Swarm Optimization Approach for Tuning of
SISO PID Control Loops. Thesis Masters. Department of Electronics
Engineering, Durban University of Terchnology.
[23] Rahmat M.F., Md Rozali S., Abdul Wahab N., Zulfatman and Kamaruzaman
Jusoff (2010). Modeling and Controller Design of an Electro-Hydraulic
Actuator System. American Journal of Applied Sciences 7 (8): 1100-1108.
[24] Reynolds, C. (1987). Flocks, Herds and Schools: A Distributed Behavioral
Model. Computer Graphics. Vol.21, No. 4, pp. 25-34.
[25] Skarpetis M. G., Koumboulis F. N. and Tzamtzi M. P. (2007). Robust Control
Techniques for Hydraulic Actuator. Proceedings of the 15th
Mediterranean
Conference on Control & Automation.
[26] Soderstrom T. and Stoica P. (1989). System Identification, Prentice Hall
Interational (U) Ltd, Hertfordshire.
[27] Xiaohui Hu. (2006). Particle Swarm Optimization.
http://www.swarmintelligence.org/index.php
64
[28] Yeoh, K. H. (2005). System Identification and Parameter Estimation of a Hot
Air Blower System Using Nonparametric Methods. Universiti Teknologi
Malaysia: Thesis Undergraduate.
[29] Yu W.S., and Kuo T.S. (1996). Robust Indirect Adaptive Control of the
Electro-hydraulic Velocity Control Systems. In Proc. of Inst. Elect. Eng.: J.
Control Theory Appl., 1996. Vol. 143 (5): 448–454.
[30] Zeng W. and Hu J. (1999). Application of Intelligent PDF Control Algorithm
to an Electro-hydraulic Position Servo System. In Proc. of IEEE/ASME Int.
Conf. Adv. Intell. Mechatronics, Atlanta, GA. 1999. 233–238.
top related