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.

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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.

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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

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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

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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

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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

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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

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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

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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

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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

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