intelligent robust controller for semi active...

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


2.3.2 Magnetorheological damper (MR damper) 14



2.4 Active suspension system 20




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


controller 61


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:

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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