Post on 27-Aug-2019
INTELLIGENT ROBUST CONTROLLER FOR SEMI ACTIVE SUSPENSION
SYSTEM
MOHAMMADJAVAD ZEINALI
UNIVERSITI TEKNOLOGI MALAYSIA
INTELLIGENT ROBUST CONTROLLER FOR SEMI ACTIVE SUSPENSION
SYSTEM
MOHAMMADJAVAD ZEINALI
A project report submitted in fulfillment of the
requirements for the award of the degree of
Master of Engineering (Mechanical – Pure)
Faculty of Mechanical Engineering
Universiti Teknologi Malaysia
JULY 2012
iii
I dedicate this thesis to my lovely family, who offered me unconditional love and
support throughout the course of this thesis
iv
ACKNOWLEDGMENT
In the name of God
I would like to express my gratitude to Dr. Intan Zaurah Mat Darus for being
an outstanding advisor and excellent professor. Her constant encouragement,
support, and invaluable suggestions made this work successful. This research project
would not have been possible without the support of many people.
Special thanks to my family for the support they provided me through my
entire life.
v
ABSTRACT
A variety of semi-active suspension studies have been researched on
decreasing the energy consumption and improving the system performance as fully
active suspension systems because of the expense of fully active suspension systems
for its actuators and execution equipments. However, the fully active suspension
systems are much more powerful than semi active suspension. This project has
proposed an intelligent robust controller for semi active suspension due to its
applications on automotive industry using Magnetorheological (MR) damper. The
entire model has been simulated in Matlab/SIMULINK environment and the results
are gathered to compare the influences of the different controllers on ride comfort
and handling performance. Four different controllers; PID, Fuzzy, Fuzzy PD+I and
Fuzzy PID controller with parallel structure were implemented into the system also
two different models of MR damper, polynomial and Adaptive-Network-based
Inference System (ANFIS) model, were studied to simulate the shock absorber. The
results illustrate that the Fuzzy PD+I controller and Fuzzy PID controller with
parallel structure have successfully reduced the effects of road profile as a
disturbance. The best results are owned by Fuzzy PID controller with parallel
structure because of better ride comfort and handling performance.
.
vi
ABSTRAK
Pelbagai kajian penggantungan separa-aktif telah dikaji untuk mengurangkan
penggunaan tenaga dan memperbaiki prestasi sistem sebagai sistem penggantungan
sepenuhnya aktif kerana perbelanjaan sepenuhnya sistem gantungan aktif untuk
penggerak dan peralatan pelaksanaan. Walau bagaimanapun, sistem penggantungan
sepenuhnya aktif adalah lebih berkuasa daripada penggantungan aktif separuh.
Projek ini telah dicadangkan pengawal pintar yang teguh bagi penggantungan separa
aktif kerana penggunaannya ke atas industri automotif menggunakan
Magnetorheological (MR) peredam. Seluruh model telah simulasi dalam persekitaran
Matlab / SIMULINK dan keputusan berkumpul untuk membandingkan pengaruh
pengawal berbeza pada keselesaan prestasi pemanduan dan pengendalian. Empat
pengawal yang berbeza; PID, Kabur, Kabur PD + I dan pengawal PID Kabur dengan
struktur selari telah dilaksanakan ke dalam sistem juga dua model yang berbeza MR
peredam, polinomial dan Adaptif-Rangkaian-berasaskan Sistem Pentaabiran
(ANFIS) model, telah dikaji untuk mensimulasikan penyerap hentakan. Keputusan
ini menunjukkan bahawa PD Kabur + I pengawal dan pengawal PID Kabur dengan
struktur selari telah berjaya mengurangkan kesan profil jalan sebagai gangguan.
Hasil yang terbaik yang dimiliki oleh pengawal PID Kabur dengan struktur selari
kerana selesa pemanduan yang lebih baik dan prestasi pengendalian.
vii
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
1 INTRODUCTION 1
1.1 Background 1
1.2 Objectives 2
1.3 Scopes 2
1.4 Thesis outline 2
1.5 Research Methodology 5
2 LITERATURE REVIEW 6
2.1 Introduction 6
2.2 Passive suspension system 9
2.2.1 Introduction 9
2.2.2 System modeling 9
2.2.3 Controlling strategies 12
2.3 Semi-Active suspension system 12
viii
2.3.1 Introduction 12
2.3.2 Magnetorheological damper (MR damper) 14
2.3.3 System modeling 15
2.3.4 Controlling strategies 18
2.4 Active suspension system 20
2.4.1 Introduction 20
2.4.2 System modeling 21
2.4.3 Controlling strategies 23
2.5 Methodology 24
2.6 Conclusion 27
3 DESIGN AND TUNE THE CONTROLLERS 28
3.1 Introduction 28
3.2 System modeling 28
3.3 Magnetorheological damper 34
3.3.1 Polynomial model 34
3.3.2 Adaptive-Network-based Fuzzy Inference System (ANFIS) 37
3.4 Inverse model of the MR damper 40
3.5 PID controller 42
3.6 Fuzzy controller 44
3.7 Fuzzy-PID controller 47
3.7.1 Fuzzy PD+I controller (FPD+I) 48
3.7.2 Second model of Fuzzy-PID controller 52
4 RESULTS AND DISCUSSION 56
4.1 Introduction 56
4.2 Road Profiles 57
4.2.1 Band-limited White noise 57
4.2.2 Step Bump profile 58
4.2.3 Ramp Bump profile 59
4.3 Simulation Test Results 60
4.3.1 PID Controller 61
ix
4.3.2 Fuzzy Controller 62
4.3.3 FPD+I Controller 65
4.3.4 Fuzzy PID Controller with parallel structure 67
5 CONCLUSION 69
5.1 Conclusion 69
References 71
Appendices
x
LIST OF TABLES
Table
No Title Page
2.1 Classification of suspension system 7
3.1 Transfer functions of the passive suspension system for different
characters of the suspension (S.M. Savaresi, et al., 2010) 29
3.2 The value of suspension system parameters 31
3.3 The transfer functions of passive suspension system for this
project 31
3.4 Invariant points of designed passive suspension system 32
3.5 Values of bi and ci in the polynomial model (Du, et al., 2005) 35
3.6 Input space partitioning of the ANFIS model 37
3.7 PID controller parameters (D. Hanafi, 2010) 43
3.8 Characteristics of fuzzy controller 45
3.9 Fuzzy Logic Controller rules for PD and PI parts 53
4.1 Band-limited White noise specifications 57
4.2 Step bump profile specifications 58
4.3 Ramp Bump profile configurations 59
xi
LIST OF FIGURES
Figure
No Title Page
1.1 the process of implementation for this project 4
2.1 Simplified (left) and general form (right) of passive quarter car
suspension model 10
2.2 passive (left) and semi-active suspension model (right) 16
2.3 Active suspension Quarter-car model using EHA 21
2.4 Active suspension Quarter-car model 22
2.5 Process of design and simulate intelligent robust controller for
semi-active suspension system 26
3.1 The model of the semi-active suspension system 29
3.2 Bode diagram of chassis displacement to vertical road
displacement transfer function 32
3.3 Bode diagram of wheel deflection to vertical road displacement
transfer function 33
3.4 Bode diagram of chassis deflection to vertical road displacement
transfer function 33
3.5 Curve fitting of the polynomial model and experimental data 36
3.6 Polynomial model of MR damper in SIMULINK environment 36
3.7 ANFIS architecture for the MR damper model 38
3.8 ANFIS sample rule surface for the MR damper model 40
3.9 Schematic of the inverse polynomial model of MR damper 41
3.10 Schematic of the PID controller applied in suspension system 43
3.11 Schematic of the PID controller applied in suspension system 44
3.12 List of fuzzy rules in the fuzzy controller 45
3.13 Surface rule-based of fuzzy controller 46
xii
3.14 Structure of Fuzzy PD+I controller 49
3.15 Surface rule-based of fuzzy sets for FPD+I controller 51
3.16 Membership functions of input (top) and output (bottom) 51
3.17 Schematic of Fuzzy PID controller with parallel structure 52
3.18 Simulated model of Fuzzy PID controller with parallel structure
in SIMULINK 55
4.1 Band-limited White noise profile 57
4.2 Step Bump profile 58
4.3 Ramp Bump profile 59
4.4 Response of system (Chassis displacement) by applying PID
controller 61
4.5 Response of system (Chassis displacement) by applying PID
controller 61
4.6 Response of system (Chassis displacement) by applying PID
controller 62
4.7 Modeled system and controller in SIMULINK environment 63
4.8 System response of Band-limited and Step bump using Fuzzy
controller 63
4.9 System response of Ramp bump road profile using Fuzzy
controller 63
4.10 System response of Band-limited and Step bump using Fuzzy
controller 64
4.11 System response of Ramp bump road profile using Fuzzy
controller 64
4.12 SIMULINK model of FPD + I controller in semi-active
suspension system 65
4.13 Chassis displacement by applying FPD+I controller 66
4.14 Chassis acceleration by applying FPD+I controller 66
4.15 Chassis displacement by applying Fuzzy PID controller 67
4.16 Chassis acceleration by applying Fuzzy PID controller 68
4.17 Chassis acceleration by applying Fuzzy PID controller 68
CHAPTER 1
INTRODUCTION
1.1 Background
A suspension has the three main elements; elastic element, damping element
and set of mechanical elements. These elements act in different roles. The elastic
element (generally coil spring) conveys the static load, and damping system (shock
absorber) conveys the insignificant force at steady-state, while it has a critical
function in suspension dynamics. The set of mechanical elements have the roles of
joining the sprung mass to the unsprung mass. This project focused on the elastic and
damping system.
Based on usage of energy in the suspension system, electronically controlled
suspensions is categorized into two main parts namely; Active and semi-active
suspension system. In the active suspension system, the energy is added to system,
while in the semi-active suspension the system is modified without energy
supplement. In this project the robust intelligent controllers are designed for semi-
active suspension system.
Moreover, in this project several intelligent robust controllers are designed,
analyzed and their performance for semi-active suspension system are compared.
2
1.2 Objectives
The aim of this project is to design and simulate an intelligent robust
controller for semi-active suspension system using neuro-fuzzy algorithm.
1.3 Scopes
The scopes of the study are concentrated on:
Review of the semi-active suspension, modeling techniques and intelligent
robust controllers.
Modeling of semi-active suspension system within Matlab/SIMULINK
environment.
Development and simulation intelligent robust controller for semi-active
suspension system.
Validation, verification, analysis and comparison of the intelligent
controllers’ performance for semi-active suspension system.
1.4 Thesis outline
The research began with obtaining the mathematical model of the
semi-active suspension system. The process of implementation for this project is
shown in next page:
3
4
Figure 1.1 the process of
implementation for this project
5
1.5 Research Methodology
In this research, a quarter-car model has been developed using simulation
within Matlab/ SIMULINK environment, the dynamic of the suspension system is
used as design and verification platform for the controller. The next step is to design
and simulate the intelligent robust controller for the modeled semi-active suspension
which the parameters of this controller are optimized by neuro-fuzzy algorithm. The
final step is to evaluate the performance of the intelligent robust controller and
validating, verifying and analysis for the modeled semi-active suspension system.
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