Project Proposal 01

SIMULATION AND EXPERIMENTAL ANALYSIS OF AN ACTIVE

VEHICLE SUSPENSION SYSTEM

SIMULATION AND EXPERIMENTAL ANALYSIS OF AN ACTIVE

VEHICLE SUSPENSION SYSTEM

A project report submitted in partial fulfilment of the

requirements for the award of the degree of

Master of Engineering (Mechanical)

v

ABSTRACT

This project was carried out to study the performance of a two degree-of-

freedom (DOF) active vehicle suspension system with active force control (AFC) as

the main proposed control technique. The overall control system essentially

comprises two feedback control loops. First is intermediate AFC control loop for the

compensation of the disturbances and second is the outermost Proportional-Integral-

Derivative (PID) control loop for the computation of the optimum commanded

force. Iterative learning method (ILM) and crude approximation (CA) were used as

methods to approximate the estimated mass in the AFC loop. Both simulation and

experimental studies were applied in this project. A quarter car model consists of

sprung and unsprung masses is considered in developing of the computer simulation

model in Simulink and also in the experimental set-up. Both simulation and

experimental work were carried out and the results between the two of them are

compared. The results of the simulation study show that active suspension system

using AFC with CA and ILM gives better performance compared to PID controller

and passive suspension system. Experimental results obtained in the study further

verified the potential and superiority of the performance of the active suspension

system with AFC strategy compared to the PID control.

vii

TABLE OF CONTENTS

CHAPTER TITLE PAGE

TITLE i

DECLARATION ii

DEDICATION iii

ACKNOWLEDGEMENT iv

ABSTRACT v

ABSTRAK vi

TABLE OF CONTENTS vii

LIST OF TABLES x

LIST OF FIGURES xi

LIST OF SYMBOLS xiv

LIST OF ABBREVIATIONS xvi

LIST OF APPENDICES xvii

1 INTRODUCTION 1

1.1 General Introduction 1

1.2 Objective 2

1.3 Scope of Work 2

1.4 Project Implementation 3

1.5 Organisation of Thesis 7

viii

2 THEORETICAL BACKGROUND AND

LITERATURE REVIEW

8

2.1 Introduction 8

2.2 Definition of Suspension System 8

2.3 Functions of a Vehicle Suspension 10

2.4 Types of Suspension System 11

2.4.1 Passive Suspension 12

2.4.2 Semi-active Suspension 13

2.4.3 Active Suspension 13

2.5 PID Controller 15

2.6 Active Force Control (AFC) 17

2.7 Iterative Learning Method 19

2.8 Review on Previous Research 20

2.9 Conclusion 21

3 MATHEMATICAL MODELLING AND

SIMULATION

23

3.1 Introduction 23

3.2 Quarter Car Model 23

3.3 Disturbance Models 26

3.4 Passive Suspension System Model 28

3.5 Active Suspension System Model 29

3.5.1 Active Suspension System with AFC-CA

Strategy

30

3.5.2 Active Suspension System Model with

AFC-ILM

31

3.6 Modelling and Simulation Parameters 33

3.7 Conclusion 37

4 SIMULATION RESULTS 35

4.1 Introduction 35

ix

4.2 Passive Suspension 35

4.3 Active Suspension 37

4.4 Active Suspension with AFC-CA 38

4.5 Active Suspension with AFC-ILM 40

4.6 Conclusion 41

5 EXPERIMENTAL SET-UP 42

5.1 Introduction 42

5.2 Simulink Model in Real-Time Workshop (RTW) 42

5.3 Experimental Set-up 47

5.3.1 Mechanical System 49

5.3.2 Electrical/Electronic Device 49

5.3.3 Computer Control 52

5.4 Parameters for Experiments 53

5.5 Conclusion 54

6 EXPERIMENTAL RESULTS AND DISCUSSION 55

6.1 Introduction 55

6.2 System Response Without Disturbance 56

6.3 System Response with the Sinusoidal Disturbance 59

6.4 System Response with the Step Disturbance 62

6.5 Conclusion 65

7 CONCLUSION AND RECOMMENDATION 66

7.1 Conclusion 66

7.2 Recommendation for Future Works 67

REFERENCES 68

APPENDICES 71

x

LIST OF TABLES

TABLE NO. TITLE PAGE

3.1 Parameters for suspension model 33

3.2 Simulation parameters 33

5.1 Suspension and pneumatic actuator parameters 53

xi

LIST OF FIGURES

FIGURE NO. TITLE PAGE

1.1 Flow chart of the project implementation 5

1.2 Gantt Chart of the project schedule 6

2.1 A suspension system 10

2.2 Passive suspension system 12

2.3 Semi-active suspension 13

2.4 Active suspension system 14

2.5 A block diagram of suspension system using PID controller 16

2.6 The schematic diagram of AFC strategy 18

2.7 A model of iterative learning method 19

3.1 Quarter car vehicle passive suspension 24

3.2 Quarter car vehicle active suspension 25

3.3 (a) Step input 27

3.3 (b) Bump and hole 27

3.3 (c) Sinusoidal 28

3.4 Simulink model of passive suspension system 29

3.5 Simulink model of active suspension system 30

3.6 Simulink model of active suspension system with AFC-CA 31

3.7 Simulink model of active suspension system with AFC-ILM 32

3.8 Subsystem of iterative learning method in AFC 32

4.1 (a) Passive suspension response to step input disturbance 36

4.1 (b) Passive suspension response to sinusoidal disturbance 36

4.2 (a) Active suspension response to step input disturbance. 37

4.2 (b) Active suspension response to sinusoidal disturbance 38

xii

4.3 (a) AFC-CA suspension response to step input disturbance 39

4.3 (b) AFC-CA suspension response to sinusoidal disturbance 39

4.4 (a) AFC-ILM suspension response to step input disturbance 40

4.4 (b) AFC-ILM suspension response to sinusoidal disturbance 41

5.1 Simulink model with RTW related to PID and AFC-ILM

control

43

5.2 Active suspension Simulink model in RTW 44

5.3 Pneumatic actuator subsystem 44

5.4 Body acceleration subsystem 45

5.5 Tyre acceleration subsystem 45

5.6 Disturbance subsystem 45

5.7 Suspension deflection system 46

5.8 Force tracking subsystem 46

5.9 AFC with ILM subsystem 47

5.10 Fotograph of the suspension system 48

5.11 The schematic of the experimental set-up 48

5.12 Accelerometer to measure body acceleration 50

5.13 Laser sensor to measure suspension deflection 50

5.14 LVDT to measure disturbance 51

5.15 Pressure sensor to measure actuator force 51

5.16 A computer set as the main controller 52

5.17 DAS 1602 interface card slotted in the CPU 53

6.1 Graph for body displacement response without disturbance 56

6.2 The close-up of body displacement response 57

6.3 Body displacement response without disturbance for B vary 58

6.4 Close-up body displacement response without disturbance

for B vary

58

6.5 Disturbance model type sinusoidal 59

6.6 Body displacement response with the sinusoidal disturbance 60

6.7 Body acceleration response with the sinusoidal disturbance 60

6.8 Suspension deflection response with the sinusoidal

disturbance

61

6.9 Tyre deflection response with the sinusoidal disturbance 61

6.10 Disturbance model type step. 62

xiii

6.11 Body displacement response with the step disturbance 63

6.12 Body acceleration response with the step disturbance 63

6.13 Suspension deflection response with the step disturbance 64

6.14 Tyre deflection response with the step disturbance 64

xiv

LIST OF SYMBOLS

a - Acceleration of the body

A - Proportional learning parameter

B - Derivative learning parameter

sb - Damping coefficient

D - Derivative

( )e t - Error (output – input)

( )e t� - Derivative error

af - Actuator force

aF - Actuated force

*F - Estimated force

I - Integral

dK - Derivative controller gain

iK - Integral controller gain

pK - Proportional controller gain

sk - Spring stiffness

tk - Tyre stiffness

( )m t - Control signal

sm - Sprung mass

um - Unsprung mass

*M - Estimated mass of the body

P - Proportional

kTE - Error value/current root of sum squared position track error

xv

ku - Current estimate value

1ku + - Next estimated value

rz - Displacement of road

sz - Displacement of sprung mass

uz - Displacement of unsprung mass

sz� - Velocity of sprung mass

uz� - Velocity of unsprung mass

sz�� - Acceleration of sprung mass

uz�� - Acceleration of unsprung mass

s uz z− - Deflection of suspension

u rz z− - Deflection of tyre

xvi

LIST OF ABBREVIATIONS

ADC - Analoque-to-digital converter

AF-AFC - Adaptive fuzzy active force control

AFC - Active force control

AFC-CA - Active force control with crude approximation

AFC-ILM - Active force control with iterative learning method

CA - Crude approximation

DAS - Data acquisition system

DCA - Digital-to-analoque converter

DOF - Degree of freedom

FLC - Fuzzy logic control

I/O - Input/output

IAFCRG - Intelligent Active Force Control Research Group

ILM - Iterative learning method

LVDT - Linear variable differential transformer

PC - Personal computer

PD - Proportional-Derivative

PID - Proportional-Integral-Derivative

PLC - programmable logic control

RTW - Real-Time Workshop

SANAFC - Skyhook and adaptive neuro active force control

xvii

LIST OF APPENDICES

APPENDIX TITLE PAGE

A Simulation Result for Various Conditions 71

B Experimental Results for Various Learning Parameter 72

C Experimental Results for Different Conditions 80

D The Sketch of the Experimental Rig 83

E The LVDT 84

F The Pressure Sensor 86

G The Data Acquisition System Card DAS 1602 88

H The Accelerometer 90

CHAPTER 1

INTRODUCTION

1.1 General Introduction

Traditionally, automotive suspension designs have been a compromise

between three conflicting criteria of road holding, load carrying and passenger

comfort. The suspension system must support the vehicle, provide directional

control during handling manouevres and provide effective isolation of passenger

payload from road disturbances [1]. Good ride comfort requires a soft suspension

wheras insentivity to applied load requires stiff suspension. Good handling requires

a suspension setting somewhere between the two.

Due to these conflicting demands, suspension design has had to be

something of a compromise, largely determined by the type of use for which the

vehicle was designed. Active suspensions are considered to be a way of increasing

the freedom one has to specify independently the characteristics of load carrying,

handling and ride quality.

2

A passive suspension system has the ability to storage energy via a spring

and to dissipate it via a damper. Its parameters are generally fixed, being chosen to

achive a certain level of compromise between road holding, load carrying and

comfort.

An active suspension system has the ability to store, dissipate and to

introduce energy to the system. It may vary its parameters depending upon operating

conditions and can have knowledge other than the strut deflection the passive system

is limited to.

1.2 Objective

The main objective of this project is to study the performance of an active

suspension system using active force control (AFC) through simulation and

experimental works.

1.3 Scope of work

The scope of this study consists of two major parts. The first is simulation

works and the second is experimental works. For the simulation works, the scope

involve is as follows:

i) To use an existing mathematical model of an active suspension.

ii) Apply active force control (AFC) with crude approximation (CA) and

3

iterative learning method (ILM) to active suspension system.

iii) Simulate active suspension system with active force control with

crude approximation (AFC-CA) and active force control with

iterative learning method (AFC-ILM) strategy incorporated with

different road profile.

iv) Study the performance of active suspension using AFC-CA and AFC-

ILM strategy compare to PID control.

The scopes involved in an experimental works is as follows:

i) Prepare experimental set-up.

ii) Develop Simulink model in Real-Time Workshop (RTW).

iii) Run experiment.

iv) Study the performance of active suspension system using AFC-ILM

strategy compare to PID control with the different disturbance.

v) Compare simulation results with the experimental results.

In experimental works, AFC strategy is used with iterative learning method

(ILM) is applied to approximate the estimated mass. The study in experimental

work will compare the result between PID and AFC-ILM only with different type

of disturbance. A quarter car model is considered in both simulation and

experimental study.

1.4 Project Implementation

The research is started with deriving the mathematical model of the main

dynamic system for the vehicle suspension system using Newton’s Second Law.

First, dynamic equation for passive suspension system is derived followed by active

4

suspension system. The model used is a two degree of freedom (DOF) system

representing a class of passenger car. Disturbances also were modeled

mathematically. Then, control scheme was developed and modelled. The schemes

include PID and AFC strategy employing both crude estimation and iterative

learning method.

Based on the derived models, a simulation study using MATLAB and

Simulink was carried out. Started with the passive suspension system with open loop

system followed by closed loop system of active suspension system. The results of

the simulations were then compared for both passive and active suspension. The

simulation results of active suspension system using AFC strategy and PID

controller also were compared.

Experimental set-up for the proposed system then was prepared. The work

involve during the set-up preparation is to develop experimental modules in the

MATLAB which known as Simulink model with Real-Time Workshop (RTW).

Then the experiment was carry out and the results obtained are analysed. Then

experimental results was compared to simulation results in order to validate the

results obtained for both method. This project implementation can be illustrated in a

form of a flow chart as shown in Figure 1.1. Gantt Chart of the project schedule is

shown in Figure 1.2.

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Figure 1.1: Flow chart of the project implementation

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Figure 1.2: Gantt chart of the project schedule

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1.5 Organisation of Thesis

This thesis is organised into seven chapters. General introduction to the

suspension system, the objective and the scope of the project and how the project is

implemented is presented in Chapter 1. Chapter 2 discussed about theoretical information

and literature review related to the project backgraound. This includes the definition of

the suspension systems and its function. Explanation of types of suspension system and

the concept of proportional-integral-derivative (PID) controller, active force control

(AFC) and iterative learning method (ILM) also done in this chapter. A number of related

research is reviewed adequately in this chapter.

Mathematical modelling based on quarter car model is presented in Chapter 3.

Disturbances model and proposed simulation models also discussed in detail. Parameters

used for simulation is highlighted in this chapter. Chapter 4 presents the simulation

results for the different types of suspension system with various control strategies.

In chapter 5, experimental set-up for this project is explained in detail. This

includes the development of the Simulink model with Real-Time Workshop (RTW)

complete with all related subsystems in the model. Hardware components that used in the

set-up also described. Then, parameters for experiment is presented.

Chapter 6 presents experimental results. System response with various conditions

are presented and disscussed adequately. Chapter 7 gives the overall conclusion on the

study that has been done and recommend of future works could be considered as

extension to this study.

CHAPTER 2

THEORETICAL BACKGROUND AND LITERATURE REVIEW

2.1 Introduction

This chapter includes the study of the suspension definition, function of the

vehicle suspension and the vehicle dynamics. Three competing types of suspension

systems are also described in this chapter which are passive, semi active and active

suspension system. PID control, active force control (AFC) strategies and iterative

learning method (ILM) are also discussed. Then, the review of the previous

research related to the active suspension system is given.

2.2 Definition of Suspension System

Suspension system is a system that supports a load from above and isolates

the occupants of a vehicle from the road disturbances. Springs in the suspension

system are flexible elements. They able to store the energy applied to them in the

form of loads and deflections. They have the ability to absorb energy and bend when

9

they are compressed to shorter lengths. When a tyre meets an obstruction, it is forced

upward and the spring absorbs energy of this upward motion.

However, the spring absorbs this energy for a short time only and it will

release the energy by extending back to its original condition. When a spring

releases its stored energy, it does so with such quickness and momentum that the end

of the spring usually extends too far. The spring will go through a series of

oscillations, contractions and extension until all of the energy in the spring is

released. The natural frequency of the spring and suspension will determine the

speed of the oscillations.

The energy that released by spring is converted to heat and dissipated partly by

friction in the system by damper. Dampers usually in the form of piston working in

cylinders filled with hydraulic fluid. They exert a force which is proportional to the

square of the piston velocity. The function of damper is to restrain undesirable bounce

characteristic of the sprung mass. It also used to ensure the wheel assembly always

contact with the road by being excited at its natural vibration frequency.

Other mechanical elements in a suspension system are the wheel assemblies

and control geometry of their movement. Some of these elements are simple links

and multi-role members such as transverse torsion bars used to stabilize the vehicle

in corners by restricting roll. A suspension system comprises many elements that

include spring, damper, tyres, bushes, locating links and anti-roll bars are shown in

Figure 2.1.

10

Figure 2.1: A suspension system [2]

2.3 Functions of a Vehicle Suspension

A vehicle suspension system is a complicated system as it has to fulfill a large

number of partly contradictory requirements. Ride comfort, safety, handling, body

leveling and noise comfort are among the most important requirements that has to

fulfill.

Ride comfort can be determined by the acceleration of the vehicle body.

Acceleration forces are experienced by the passengers as a disturbance and set

demands on the load and the vehicle. The suspension system has the task to isolate

these disturbances from the vehicle body which caused by the uneven road profile.

The lower the acceleration, the better the rides comfort.

11

The safety of the vehicle during traveling is determined by the wheels ability

to transfer the longitudinal and lateral forces onto the road. The vehicle suspension

system is required to keep the wheels as close the road surface as possible. Wheel

vibration must be dampened and the dangerous lifting the wheels must be avoided. If

the dynamic forces occurring between the wheels and the road surface are small, the

braking, driving and lateral forces can be transferred to the road in an optimal

manner. The necessity of dampening the tyre system is the reason for the known

conflict of aims between comfortable and safety tuning.

Another function of the suspension system is the isolation of the vehicle body

from high frequency road disturbances. The passengers in the car note these

disturbances acoustically and thus the noise comfort is reduced. When there is

changes in loading, the suspension system has to keep the vehicle level as constant

as possible, so that the complete suspension travel is available for the wheel

movements. A lower suspension travel means that lower suspension working space

and this is a good suspension design. In order to fulfill all these contradict

requirements certain marginal conditions have to be considered.

2.4 Types of Suspension System

Generally there are three types of the suspension system. They are:

i) passive suspension

ii) semi-active suspension

iii) active suspension

12

2.4.1 Passive Suspension

Passive suspension system is the conventional suspension system. However

it is still to be found on majority of production car. It consists two elements namely

dampers and springs. The function of the dampers in this passive suspension is to

dissipate the energy and the springs is to store the energy. If a load exerted to the

spring, it will compress until the force produced by the compression is equal to the

load force. When the load is disturbed by an external force, it will oscillate around

its original position for a period of time. Dampers will absorb this oscillation so that

it would only bounce for a short period of time. Damping coefficient and spring

stiffness for this type of suspension system are fixed so that this is the major

weakness as parameters for ride comfort and good handling vary with different road

surfaces, vehicle speed and disturbances.

Figure 2.2: Passive suspension system

13

2.4.2 Semi-active Suspension

The element in the semi-active suspension system is same with passive

suspension system and it uses the same application of the active suspension system

where external energy is needed in the system. The difference is the damping

coefficient can be controlled. The fully active suspension is modified so that the

actuator is only capable of dissipating power rather than supplying it as well. The

actuator then becomes a continuously variable damper which is theoretically capable

of tracking force demand signal independently of instantaneous velocity across it

[3]. This suspension system exhibits high performance while having low system

cost, light system weight and low energy consumption.

Figure 2.3: Semi-active suspension

2.4.3 Active Suspension

The concept of active suspension system was introduced as early as 1958.

The difference compare to conventional suspension is active suspension system able

14

to inject energy into vehicle dynamic system via actuators rather than dissipate

energy. Active suspension can make use of more degrees of freedom in assigning

transfer functions and thus improve performance. The active suspension system

consists an extra element in the conventional suspension which is basically an

actuator that is controlled by a high frequency response servo valve and which

involves a force feedback loop. The demand foce signal, typically generated in a

microprocessor, is governed by a control law which is normally obtained by

application of various forms of optimal control theory [3]. Theoretically, this

suspension provides optimum ride and handling characteristics. It is done by

maintaining an approximately constant tire contact force, maintaining a level vehicle

geometry and by minimizing vertical accelerations to the vehicle. How ever due to

its complexity, cost and power requirements, it has not yet put into mass production.

Figure 2.4 shows an active suspension system.

An important issue in active suspension is energy consumption. It is

recognized that full active suspension, which must carry the full weight of vehicle,

would consume a considerable amount of energy and need high bandwidth actuators

(30 Hz) and control valves (100 Hz) [4,5]. Consequently, it was only installed in

some expensive and exclusive car or Formula One cars, and has not been mass

produced.

Figure 2.4: Active suspension system

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2.5 PID Controller

PID control is a particular control structure that has become almost

universally used in industrial control. The letters ‘PID’ stand for Proportional,

Integral and Derivative. They have proven to be quite robust in the control of many

important applications for specific operating conditions. It structure is simple but

very effective feedback control method applied to dynamical systems. PID also most

conveniently integrated with other more advanced control techniques which more

often than not results in better overall performance. Pure PID control is excellent for

slow speed operation and with very small or no disturbances, the performance

severely degrades in the adverse conditions.

However, the simplicity of these controllers is also their weakness where it

limits the range of plants that they can control satisfactorily. Indeed, there exists a set

of unstable plants that they cannot be stabilized with any member of the PID family.

Nevertheless, the versatility of PID control ensures continued relevance and

popularity for this controller.

The PID method is error driven and largely relies on the proper tuning of the

controller gains and accurate information from the feedback element (sensor). The

basic algorithm of the PID is expressed as follows:

( ) ( ) ( ) ( )p i dm t K e t K e t dt K e t= + +∫ � (2.1)

where,

( )m t = control signal

pK = proportional controller gain

iK = integral controller gain

dK = derivative controller gain

16

( )e t = error (output – input)

( )e t� = derivative error

A simple PID controller applied to a vehicle suspension system can be

illustrated as shown in Figure 2.5.

Figure 2.5: A block diagram of suspension system using PID controller.

The effects of the P, I and D parameters to the system are as follows:

a) Proportional (P) action

This parameter provides a contribution which depends on the instantaneous

value of the control error. A proportional controller can control unstable plant but it

provides limited performance and non zero steady-state errors. This later limitation is

due to the fact that its frequency response is bounded for all frequencies.

b) Integral (I) action

Integral parameter gives a controller output that is proportional to the

accumulated error, which implies that it is a slow reaction mode. This

characteristic is also evident in its low-pass frequency response. The integral mode

plays a fundamental role in achieving perfect plant inversion at zero frequency.

17

This forces the steady-state error to zero in the presence of a step reference and

disturbance.

c) Derivative (D) action

Derivative action acts on the rate of change of the control error.

Consequently, it is a fast mode which ultimately disappears in the presence of

constant errors. It sometimes referred to as a predictive mode because of its

dependence on the error trend. The main limitation of the derivative mode is its

tendency to yield large control signals in response to high-frequency control errors,

such as errors induced by set-point changes or measurement noise.

2.6 Active Force Control (AFC)

Active force control strategy applied to dynamic system was proposed in the

early 80s by Hewit [6]. High robustness system can be achieved such that the system

remains stable and effective even in the presence of known or unknown

disturbances, uncertainties and varied operating conditions [7]. One of the succesful

AFC strategy applications is controlling robot arm, done by Mailah [8]. The study

has been demonstrated that AFC is superior compared to the conventional method in

controlling the robot arm.

The essence of the AFC is to determine the estimated force *F by measuring

two importants parameters. These parameters are the actuated force, aF (measured

by force sensor) and acceleration of the body, a (measured by accelerometer). An

appropriate estimation of the estimated mass of the body, *M was then multiplied

with the acceleration of the body, a yielding the estimated force. The mathematical

model for AFC can be written as follows:

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* *aF F M a= − ⋅ (2.2)

If equation ( 2.2 ) can be fulfilled, it is expected that very robust system can

be achieved. Thus, it is the main aim of the study to apply the AFC method to

control a suspension effectively.

Figure 2.6 shows a schematic of the AFC strategy applied to a dynamic

system. Note that the estimated mass, *M in Figure 2.5 can be determined by a

number of methods such as crude approximation method, neural network, fuzzy

logic, iterative learning and genetic algorithms. However in this project, crude

approximation (CA) and iterative learning method (ILM) were used.

Figure 2.6: The schematic diagram of AFC strategy

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2.7 Iterative Learning Method (ILM)

Iterative learning method (ILM) is one of the popular method in estimate the

next value. It has been applied to control a number of dynamic system [9]. As the

number of iteration increases, the track error converges to near zero datum and the

dynamic system is then said to operate effectively. In this project, the proposed

iterative learning algorithm takes the following form;

1 ( ) ( )k k k k

du u A TE B TE

dt+ = + + (2.3)

where

1ku + = next estimated value

ku = current estimate value

kTE = error value/current root of sum squared position track error

,A B = learning parameter

Figure 2.7 shows a graphical representation of the ILM algorithm.

A

1

Me(u k+1)

IM

IM(uk)

kdu/dt

Derivative

k

B

Add

1

TEk

Figure 2.7: A model of iterative learning method

20

2.8 Review on Previous Research

Active suspensions have been extensively studied nowadays compare to

passive suspension. Many researchers have studied and proposed a number of

control methods for vehicle suspensions. The first preview in the control of an active

system for a 1-DOF model was to introduced by Bender in 1967. Bender assumed

an integrated white noise terrain profile. He developed an optimal pair of damping

coefficient and spring stiffness by using Wiener filter theory to provide a wide range

of vibration isolation [10].

Tomizuka applied a discrete time, state space approach to Bender's problem

[11]. The optimal control scheme of that study involved both feedforward and

feedback elements. Tomizuka suggested his control logic could be realized in

practice by moving previewed samples through shift registers. The potential of the

preview control was demonstrated by subsequent studies for 2-DOF models by

Thomson.

In their paper, D’Amato and Viassolo described that the goal of this paper is

to minimize vertical car body acceleration, and to avoid hitting suspension limits

using fuzzy logic control (FLC) [12]. A controller consisting of two control loops is

proposed to attain this goal. The inner loop controls a nonlinear hydraulic actuator to

achieve tracking of a desired actuation force. The outer loop implements a FLC to

provide the desired actuation force. Controller parameters are computed by genetic

algorithm based optimization. The methodology proved effective when applied to a

quarter car model of suspension system.

Omar introduced a novel approach to control vehicle suspension system

using AFC strategy [13]. A proportional-derivative (PD) controller was incorporated

into the AFC control scheme. Crude approximation and iterative learning method

21

were used to estimated the initial mass in the AFC to effect the control action. The

simulation results have shown that the AFC is able to compensate the presence of

known or unknown disturbances to ensure that the system achieve the desired input.

Mailah and Priyandoko proposed an adaptive fuzzy active force control (AF-

AFC) to control vehicle active suspension system [14]. The technique proposed are

mainly for simplicity of the control low and to reduce the computational burden.

Non linear hydraulic actuator are used in the study. The simulation result shows the

performance of the proposed control method is found to be significantly superior

compared to the other systems considered in the study.

Priyandoko et al. introduced the practical design of a control technique apply

to a vehicle active suspension system [15]. Skyhook and adaptive neuro active force

control (SANAFC) are used as a control scheme. From the experimental result it

shows that SANAFC controller is very effective in isolating the vibration effects on

the sprung mass which in turn considerably improve the overall system

performance.

2.9 Conclusion

The theoretical backgrounds and previous research related to this study have

been outlined in this chapter. The information of the suspension in term of

definition, functions and type of suspension were adequately discussed. The

conventional PID controller and the fundamental concept of active force control

(AFC) applied to the dynamic system were also explained. Iterative learning method

(ILM) that will apply to estimate the estimated mass in AFC also discussed. It is

found that, many research papers discussed on optimization of various types of

22

suspension system to improve ride quality and road handling by using various types

of control schemes.

CHAPTER 3

MATHEMATICAL MODELLING AND SIMULATION

3.1 Introduction

In this chapter, a full modeling of the system dynamics related to the vehicle

suspension system, proposed control strategies and road disturbances are described.

This shall provide the basis for the rigorous computer simulation study to be carried

out using MATLAB and Simulink software package. The mathematical modelling

of the dynamic system is performed using the Newtonian mechanics. The suspension

system is modelled based on a quarter car configuration. The active suspension

system is specifically designed and modelled with the feedback control element

embedded into the system. A number of assumption that are made throughout the

modeling and simulation study is also described.

3.2 Quarter Car Model

Quarter car model are used to derive the mathematical model of the active

suspension system. The quarter car model is popularly used in suspension analysis

24

and design because it is simple to analyze but yet able to capture many important

characteristics of the full model. It is also realistic enough to validate the suspension

simulations.

Figure 3.1 shows a quarter car vehicle passive suspension system. Single

wheel and axle are connected to the quarter portion of the car body (sprung mass)

through a passive spring and damper. The tyre (unsprung mass) is assumed to have

only the spring feature and is in contact with the road terrain at the other end. The

road terrain serves as an external disturbance input to the system.

Figure 3.1: Quarter car vehicle passive suspension

The equations of motion for the the passive system are based on Newtonian

mechanics and given as: [14]

( ) ( )( ) ( ) ( )

s s s s u s s u

u u s s u s s u t u r

m z k z z b z z

m z k z z b z z k z z

=− − − −

= − + − − −

�� � �

�� � �

(3.1)

where

sm and um : sprung mass and unsprung mass respectively

sb : damping coefficient

25

sk and tk : stiffness of spring and tyre respectively

sz and uz : displacement of sprung mass and unsprung mass respectively

rz : displacement of road

s uz z− : deflection of suspension

u rz z− : deflection of tyre

sz� and uz� : velocity of sprung mass and unsprung mass respectively

sz�� and uz�� : acceleration of sprung mass and unsprung mass respectively

Active suspension system for a quarter car model can be constructed by

adding an actuator parallel to spring and dampe. Figure 3.2 shows a schematic of a

quarter car vehicle active suspension system.

Sprung MassMs

Ks

Unsprung MassMu

Kt

Bs

road profile

Zs

Zu

Zr

fa

Figure 3.2: Quarter car vehicle active suspension

The equations of motion for an active system are as follows:

( ) ( )( ) ( ) ( )

s s s s u s s u a

u u s s u s s u t u r a

m z k z z b z z f

m z k z z b z z k z z f

=− − − − +

= − + − − − −

�� � �

�� � �

Eq.(3.2)

where

af : actuator force

26

Some assumptions are made in the process of modeling the active suspension

system. The assumptions are:

i. the behaviour of the vehicle can be represented accurately by a quarter

car model.

ii. the suspension spring stiffness and tyre stiffness are linear in their

operation ranges and tyre does not leave the ground. The displacements

of both the body and tyre can be measured from the static equilibrium

point.

iii. The actuator is assumed to be linear with a constant gain.

3.3 Disturbance Models

There are three types of disturbances introduced to the vehicle suspension

system in this study. They are the step input, bump and hole, and sinusoidal

disturbance. Both bump and hole and sinusoidal are called the road disturbances

which represent the irregular road profile.

Figures 3.3 (a), (b) and (c) show the disturbances, step input, bump and hole

and sinusoidal respectively. The bump followed by a hole disturbance model is

adapted from the study by Roh and Park in [16] and the sinusoidal road input is

adapted from the work by Roukieh and Titli [17].

27

Figure 3.3 (a): Step input

Figure 3.3 (b): Bump and hole

28

Figure 3.3 (c): Sinusoidal

3.4 Passive Suspension System Model

The suspension system Simulink model is started basically with developing

the passive suspension system of a quarter car model. The dynamical system is

separated into two systems as the suspension system involves two degrees of

freedoms. This passive suspension model was modeled in Simulink form as shown

in Figure 3.4. This model was built based on the equation (3.1). There is an open

loop system with no feedback element for appropriate adjustment of parameters.

29

zrzu zszudotzuddot

zsdotzsddot

1/m2

mu

1/m1

ms

k2

ktk1

ks

b1

bs

XY GraphRoad profil e

1s

1s

1s

1s

du/dt

Derivative

Clock

Figure 3.4: Simulink model of passive suspension system

3.5 Active Suspension System Model

Active suspension system requires an actuator force to provide a better ride

and handling than the passive suspension system. The actuator force, Fa is an

additional input to the suspension system model. The model in Simulink was built

based on the equation (3.2) and shown in Figure 3.5. The actuator force is controlled

by the PID controller which involves a feedback loop.

30

zs

zu

zr

ref1/m2

mu

1/m1

ms

k2kt

k1

ks

b1

bs

-K-

actuator XY Graph

Road disturbance

PID

PID Controller

1s

1s

1s

1s

du/dt

Derivative

Clock

Figure 3.5: Simulink model of active suspension system

3.5.1 Active Suspension System Model with AFC-CA Strategy

Instead of using only PID controller, active suspension system in Simulink

model was further develop by introduced active force control with crude

approximation (AFC-CA) in the system. This model is shown in Figure 3.6. The

AFC-CA control Simulink blocks include the estimated mass gain, parameter 1/Ka

gain and the percentage of AFC application gain. The input to the AFC control is the

sprung mass acceleration and the output is summed with the PID controller output

before multiply with the actuator gain which finally results the generated actuator

force. Crude approximation method is used to estimated the estimated mass in the

AFC.

31

zs

zu

zr

ref1/m2

mu

1/m1

ms

k2kt

k1

ks

b1

bs

1

-K-

-K-

actuator XY Graph

Road disturbance

PID

PID Controller

-K-

Me

1s

1s

1s

1s

du/dt

Derivative

Clock

Figure 3.6: Simulink model of active suspension system with AFC-CA

3.5.2 Active Suspension System Model with AFC-ILM

To estimate the estimated mass for AFC, systematic method such as

intelligent method is appropriate to use rather than try and error. One of the

intelligent method is iterative learning method (ILM). This type of method applied

with AFC can be modelled as shown in Figure 3.7 .

32

zs

zu

zr

ref1/m2

mu

1/m1

ms

k2kt

k1

ks

b1

bs

1

-K-

-K-

actuator XY Graph

In1 Out1

Subsystem

Road disturbance

Product

PID

PID Control ler

1s

1s

1s

1s

y(n)=Cx(n)+Du(n)x(n+1)=Ax(n)+Bu(n)

Discrete State-Space1

du/dt

Derivative

Clock

Figure 3.7: Simulink model of active suspension system with AFC-ILM

Subsytem for iterative learning method is shown in Figure 3.8.

A

1

Me(u k+1)

IM

IM(uk)

kdu/dt

Derivative

k

B

Add

1

TEk

Figure 3.8: Subsystem of iterative learning method in AFC

33

3.6 Modelling and Simulation Parameters

The suspension parameters used in this study are adopted from the previous

study [15]. The detail of suspension model parameters are shown in Table 3.1 and

the simulation parameters are shown in Table 3.2.

Table 3.1: Parameters for suspension model.

Parameters Value

Sprung mass ( sm ) 170 kg

Unsprung mass ( um ) 25 kg

Spring stiffness ( sk ) 10,520 N/m

Damping coefficient ( sb ) 1,130 Ns/m

Tyre stiffness ( tk ) 86,240 N/m

Table 3.2: Simulation parameters

Parameters Value

Solver Ode45 (Dormand Prince)

Type Variable-step

Simulation time 10 s

Minimum step size Auto

Maximum step size Auto

Initial step size Auto

Relative tolerance 1e3

Absolute tolerance Auto

Zero crossing control use local setting

34

3.7 Conclusion

The mathematical equations of vehicle suspension system are derived using

quarter car model based on Newtonian mechanics. Then, the Simulink models for

passive and active suspension system were constructed. The disturbances also

modelled in the Simulink. The active suspension control systems in particular were

fully modelled complete with the control scheme with intelligent element to be

simulated to observe their responses. The simulation results for all models are

presented in the next chapter.

CHAPTER 4

SIMULATION RESULTS

4.1 Introduction

This chapter presents the simulated suspension responses results for all

suspension systems that are described in the previous chapter. The main concern of

the simulated suspension system responses results is the sprung mass displacement.

Comparisons of the results between types of the suspension system, different type of

disturbances and different type of control system are also discussed.

4.2 Passive Suspension

Figure 4.1 (a) and (b) show the response of passive suspension system to the

step and sinusoidal inputs respectively. Response shown for the step input is not

stable and need some time to settle down while under sinusoidal disturbance the

passive suspension could not adapt to the force given. This causes the sprung mass

displacement to occur for a long period of time.

36

0 1 2 3 4 5 6 7 8 9 100

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8Body Displacement

Time(s)

Dis

plac

emen

t (c

m)

Figure 4.1 (a): Passive suspension response to step input disturbance

0 1 2 3 4 5 6 7 8 9 10-1.5

-1

-0.5

0

0.5

1

1.5Body Displacement

Time(s)

Dis

plac

emen

t (c

m)

Figure 4.1 (b): Passive suspension response to sinusoidal disturbance

37

4.3 Active Suspension

Figure 4.2 (a) and (b) show the response given by active suspension to the

step input and sinusoidal respectively. PID controller is used in this suspension and

it is a close loop system. PID is tuned optimisely so that the response for the step

input disturbance is good. However, for sinusoidal disturbance the response is not

good and not much different with the passive suspension. PID gain used are as

follows; Kp = 12, Ki = 5 and Kd = 4. These values are remain for the following sub

chapter 4.4 and 4.5 to observe their response.

0 1 2 3 4 5 6 7 8 9 100

0.2

0.4

0.6

0.8

1

1.2

1.4Body Displacement

Time(s)

Dis

plac

emen

t (c

m)

Figure 4.2 (a): Active suspension response to step input disturbance

38

0 1 2 3 4 5 6 7 8 9 10-1.5

-1

-0.5

0

0.5

1

1.5Body Displacement

Time(s)

Dis

plac

emen

t (c

m)

Figure 4.2 (b): Active suspension response to sinusoidal disturbance

4.4 Active Suspension with AFC-CA

Figure 4.3 (a) and (b) show the response given by active suspension with

AFC strategy and crude approximation method to the step input and sinusoidal

respectively. Both response, under step input and sinusoidal disturbance is much

better compare to PID controller only. AFC gives a good result although disturbance

is change. Estimated mass used in this simulation is 300 kg.

39

0 1 2 3 4 5 6 7 8 9 100

0.2

0.4

0.6

0.8

1

1.2

1.4Body Displacement

Time(s)

Dis

plac

emen

t (c

m)

Figure 4.3 (a): AFC-CA suspension response to step input disturbance

0 1 2 3 4 5 6 7 8 9 100

0.2

0.4

0.6

0.8

1

1.2

1.4Body Displacement

Time(s)

Dis

plac

emen

t (c

m)

Figure 4.3 (b): AFC-CA suspension response to sinusoidal disturbance

40

4.5 Active Suspension with AFC-ILM

Figure 4.4 (a) and (b) show the response of active suspension with AFC

strategy and iterative learning method to the step input and sinusoidal disturbance

respectively. The response given for both disturbance also as good as AFC-CA

suspension. This condition shows that the active suspension system with AFC

strategy still gives a good response although the disturbance is change. In other

words the active suspension with AFC is not affected by the changing of the

disturbance. Value of learning parameter A is set to 4 and B = 5. Initial condition

used is 200.

0 1 2 3 4 5 6 7 8 9 10-3

-2

-1

0

1

2

3

4

5

Time(s)

Dis

plac

emen

t (c

m)

Body Displacemnt

Figure 4.4 (a): AFC-ILM suspension response to step input disturbance

41

0 1 2 3 4 5 6 7 8 9 10-3

-2

-1

0

1

2

3

4

5

Time(s)

Dis

plac

emen

t (c

m)

Body Displacement

Figure 4.4 (b): AFC-ILM suspension response to sinusoidal disturbance

4.6 Conclusion

From the results it is proven that by using AFC, active suspension will

response much much better than without AFC. Passive suspension is the weakest

suspension to absorb any disturbance exerted to the system. Active suspension with

PID controller can give good performance if we can tune the PID controller gain

optimally. But when there is changes in the disturbances, PID controller is not

capable to compensate for that disturbance.

Active suspension with AFC strategy is proven not affected by the changing

of the disturbances. This means with AFC the high robust of suspension system can

be achieved. The system will remain stable and effective even in the presence of

known or unknown disturbance.

CHAPTER 5

EXPERIMENTAL SET-UP

5.1 Introduction

This chapter presents about the experimental work that was done in this

project. Experimental rig was developed using MATLAB, Simulink and Real-Time

Workshop after which a number of experiments were carried out.

This project used existing rig that was developed by the Intelligent Active

Force Control Research Group (IAFCRG). The quarter car rig was developed based

on the modified Perodua Kelisa suspension system. The details of the experimental

set-up will describe in this chapter.

5.2 Simulink Model in Real-Time Workshop (RTW)

Simulink model with Real-Time Workshop (RTW) developed in MATLAB

Software. This is the important element in the experiment because the RTW has an

43

ability to communicate with the outside world (suspension rig in this case) via an

interface card such as data acquisition system (DAS). DAS 1602 card is used in this

experiment as an interface card. With an aid of Simulink model in RTW and DAS

1602 card, the control of software and hardware of the rig is made possible through

the ‘hardware-in-the-loop’ concept.

Figure 5.1 shows the Simulink model with RTW that used in this

experiment. This model can be used for PID only control and also PID and AFC

with iterative learning method (AFC-ILM). A switch that is inserted into the middle

of the model will switch the system from the pure PID controller to the AFC-ILM

control scheme. Figure 5.2 shows the Simulink model with RTW for the physical

active suspension system.

tyre deflection

tyre acc

susp deflection

force

disturbance

body pos

body acc

time Active Suspension

PID

u-200

6.5

Iterative Learning-AFC

Figure 5.1: Simulink model with RTW related to PID and AFC-ILM control

44

7

body pos

6

tyre acc

5

force

4

susp deflc

3

disturbance

2

tyre deflection

1

body acceleration

ty re def lection

ty re acc

tyre accelerometer

Out1

susp def lc

susp deflection laser lvdt

to actuator desired press

pneumatic actuator

f orce

pressure1

force / pressure sensor

disturbance

Out2

disturbance lvdt

body position

body acceleration

body accelerometer

bddispl

bddispl.mat

uU( : )

|u|

Abs

-K-

AREA

1

to actuator

Figure 5.2: Active suspension Simulink model in RTW

Figures 5.3 to 5.8 show the subsystem models of the pneumatic actuator,

body acceleration, tyre accleration, disturbance model, suspension deflection and

force tracking model respectively.

1

desired press

desired pressure1

actuator

-K-

f(u)

f(u)

uf(u)

U( : )

Bad Link

butter

|u|1

to actuator

Figure 5.3: Pneumatic actuator subsystem

45

2

body acceleration

1

body position

bposbacc

bpos.matbacc.mat

1s

1s

uf(u) U( : )Bad Linkbutter

butterbutter

@1

Figure 5.4: Body acceleration subsystem

2

tyre acc

1

tyre deflection

acc

tdef

tacc

tdef.mat

tacc.mat

1s

1s

uf(u) U( : )Bad Linkbutter butterbutter

@3

Figure 5.5: Tyre acceleration subsystem

2

Out2

1

disturbance

distubance

distr

distr.mat

-1 f(u)f(u) U( : )Bad Linkbutter

@4

Figure 5.6: Disturbance subsystem

46

2

susp deflc

1

Out1

sus deflc

susdef

susdef.mat

-1

u

f(u)

U( : )

Bad Linkbutter

@7

Figure 5.7: Suspension deflection subsystem

2

pressure1

1

force

pressure

force1

press

force

press.mat

force.mat

1 f(u)

u

u

U( : )

U( : )

Bad Linkbutter

-K-

AREA

@8

Figure 5.8: Force tracking subsystem.

Figure 5.9 shows the most important element in the control system in the

experiment. That is active force control with iterative learning method (AFC-ILM)

simulink model.

47

2

pressure1

1

force

pressure

force1

press

force

press.mat

force.mat

1 f(u)

u

u

U( : )

U( : )

Bad Linkbutter

-K-

AREA

@8

Figure 5.9: AFC with ILM subsystem

5.3 Experimental Set-up

Figure 5.10 shows a photograph of an actual rig of active suspension system.

The schematic of the experimental set-up is shown in Figure 5.11.

Physical sensors required for input/output (I/O) signal were connected to a

PC-based data acquisition and control system using Matlab, Simulink and Real-

Time Workshop (RTW) that essentially constitute a hardware in the loop

configuration, implying that the simulation can be effectively converted to the

equivalent practical scheme without much fuss. A 100 Hz sampling frequency was

used in conjunction with a data acquisition card (DAS 1602) that is fitted into one of

the expansion slots of the personal computer (PC). Appropriate signals are processed

using the analoque-to-digital converter (ADC) and digital-to-analoque converter

(DCA) channels which are already embedded in the DAS card.

48

Figure 5.10: Photograph of the suspension system.

Figure 5.11: The schematic of the experimental set-up.

D/A

A/D

Suspension Test Rig PC-based control

MATLAB/CST/ Simulink/RTW

DAS1602 I/O card

to pneumatic actuator

from sensors (LVDTs, pressure sensor

& accelerometers)

PID, AFC and ILM,

Programmable Logic

Controller (PLC)

Disturbances

49

Accelerometers were installed at the sprung and unsprung mass of the

vehicle suspension system to measure body acceleration and tyre deflection. A laser

sensor was placed in the between of the sprung and unsprung mass to measure

suspension deflection. A linear variable differential transformer (LVDT) was used to

measure the vertical displacement of the road profile or disturbance. The

disturbances were used in this experiment was generated by a specially design

pneumatic system control by a programmable logic control (PLC).

The experimental set-up in this project is a mechatronics system. It is

because it involved an integration of the mechanical parts, electric/electronics

devices, and computer control to make the rig function.

5.3.1 Mechanical System

The mechanical system of the experimental set-up consists of the suspension

system itself as shown in Figure 5.10.

5.3.2 Electrical/Electronic Device

The electric/electronics devices were used in the experiment basically consist

of the sensors. Four types of sensors were used in the set-up, namely;

i. accelerometer

ii. laser sensors

iii. linear variable differential transformer (LVDT)

iv. pressure sensor

50

The location or position where all the sensors were placed can be seen in

Figures 5.12 to 5.15.

Figure 5.12: Accelerometer to measure body acceleration.

Figure 5.13: Laser sensor to measure suspension deflection

Accelerometer

Laser sensor

51

Figure 5.14: LVDT to measure disturbance.

Figure 5.15: Pressure sensor to measure actuator force.

LVDT

Accelerometer

Pressure sensor

52

All the signals from the sensors will be sent to the signal conditioners and

driver circuits. The circuits will process the signals to produce suitable signals to the

DAS Card.

5.3.3 Computer Control

The experimental set-up used a Pentium III computer as the main controller

with the software MATLAB/Simulink and RTW facility constituting the PC based

digital control. The DAS 1602 card is interfaced to the computer where the input and

output devices (actuators and sensors) were connected to the controller. Figure 5.16

shows the computer control system while Figure 5.17 shows the DAS 1602

interface card used in this experiment.

Figure 5.16: A Computer set as the main controller

53

Figure 5.17: DAS 1602 interface card slotted in the CPU.

5.4 Parameters for Experiments

Table 5.1 shows the suspension parameters and pneumatic actuator

parameters that have been used in experiment.

Table 5.1: Suspension and pneumatic actuator parameters.

Parameters Value

Sprung mass ( sm ) 170 kg

Unsprung mass (um ) 25 kg

Spring stiffness (sk ) 10520 N/m

Damping coefficient (sb ) 1130 Ns/m

Tyre stiffness ( tk ) 86240 N/m

Stroke length 116 mm

Diameter bore 40 mm

Ram area 0.0076 mm2

54

5.5 Conclusion

The Simulink model with RTW was successfully developed. The details of

the model are explained and all the subsystem models were clearly shown in this

chapter. Then, this Simulink model was integrated to the experimental rig constitutes

a full experimental set-up. A number of experiments were carried out. The results

obtained will be discussed in the next chapter.

CHAPTER 6

EXPERIMENTAL RESULTS AND DISCUSSION

6.1 Introduction

This chapter presents the results of the experiments that was carried out.

Same with the simulation part, in this experiment the main concerned of the

suspension system response result is the sprung mass or body displacement.

Comparisons of the results between different type of control scheme, there are PID

and AFC-ILM will be discussed in this section. The result for different type of

disturbances applied to the system also will presented. The results that are discussed

in this chapter were assume to give the best results obtained in the experiment using

the chosen parameters. Other results for different parameter setting were attached in

the appendix.

The results shown in this chapter are divided into two sections. For the first

200 second, the response belongs to PID controller. Then, for the next 200 seconds,

AFC-ILM control scheme take over. By doing this, we can see directly the different

responses (if any), displayed in single graph.

56

6.2 System Response Without Disturbance

Figure 6.1 shows the body displacement response of an active suspension

system without apply any disturbance into it. PID gain were used in this experiment

are, Kp = 35, Ki = 1.2 and Kd = 350. Learning parameter for the ILM are set as

follows; B=15 and Initial Condition = 25 kg. The value of learning parameter A is

set to vary. Results for the body acceleration, suspension deflection and tyre

deflection for the same conditions are shown in Appendix B.

0 50 100 150 200 250 300 350 400-5

-4

-3

-2

-1

0

1

2

3

4

5Body Displacement

Time(s)

Dis

plac

emen

t (c

m)

A=10A=20A=50

PID

AFC-ILM

Figure 6.1: Graph for body displacement response without disturbance

Figure 6.2 shows the close-up of body displacement response of an active

suspension system without disturbance.

57

50 60 70 80 90 100-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

Body Displacement

Time(s)

Dis

plac

emen

t (c

m)

A=10A=20A=50

Figure 6.2: The close-up of body displacement response without disturbance

For this conditions, the results show that the learning parameter A=50 gives

the best results. However, there is no significant difference in result between pure

PID and AFC-ILM control schemes. The result looks almost similar. This means

that AFC-ILM control scheme was reached at the minimum level. Some tuning still

has to be done to get the better result for the AFC scheme.

Figure 6.3 shows the body displacement response with the fixed value of A

and varied value of B. The rest of the results for the variation of learning parameter

B can be found in Appendix B. Figure 6.4 shows the close-up of of the Figure 6.3.

58

0 50 100 150 200 250 300 350 400-5

-4

-3

-2

-1

0

1

2

3

4

5Body Displacement

Time(s)

Dis

plac

emen

t (c

m)

B=15

B=20B=50

PID

AFC-ILM

Figure 6.3: Body displacement response without disturbance for B vary

40 50 60 70 80 90 100-2

-1.5

-1

-0.5

0

0.5

1

1.5

2Body Displacement

Time(s)

Dis

plac

emen

t (c

m)

B=15

B=20B=50

Figure 6.4: Close-up body displacement response without disturbance for B vary

59

6.3 System Response with the Sinusoidal Disturbance

Disturbances that applied to the active suspension system are step and

sinusoidal. The disturbance is generated by a specially design pneumatic system

controlled by PLC. Figure 6.5 shows the disturbance model and Figure 6.6 shows

the body displacement response to the sinusoidal disturbance. Sinusoidal signal that

gives to the system is high amplitude with high speed ( 2.8 Hz≈ ). Figures 6.7 - 6.9

show the response of the body acceleration, suspension deflection and tyre

deflection respectively to the sinusoidal disturbance.

50 55 60 65 70 75 80 85 90 95 1001.5

1.6

1.7

1.8

1.9

2

2.1

2.2

2.3Sinusoidal Disturbance

Time(s)

Am

plitu

de (

cm)

Figure 6.5: Disturbance model type sinusoidal

60

0 50 100 150 200 250 300 350 400-5

-4

-3

-2

-1

0

1

2

3

4

5Body Displacement

Time(s)

Dis

plac

emen

t (c

m)

PID

AFC-ILM

Figure 6.6: Body displacement response with the sinusoidal disturbance

0 50 100 150 200 250 300 350 400-15

-10

-5

0

5Body Acceleration

Time(s)

Acc

eler

atio

n (m

/s2 )

PID

AFC-ILM

Figure 6.7: Body acceleration response with the sinusoidal disturbance

61

0 50 100 150 200 250 300 350 400-2

-1

0

1

2

3

4

5

6

7

8Suspension Deflection

Time(s)

Def

lect

ion

(cm

)

PID

AFC-ILM

Figure 6.8: Suspension deflection response with the sinusoidal disturbance

0 50 100 150 200 250 300 350 400-2

-1.5

-1

-0.5

0

0.5

1

1.5

2Tyre Deflection

Time(s)

Def

lect

ion

(cm

)

PID

AFC-ILM

Figure 6.9: Tyre deflection response with the sinusoidal disturbance

62

6.4 System Response with the Step Disturbance

The disturbance type step also generated by a specially design pneumatic

system controlled by PLC. Figure 6.10 shows the disturbance model of the step.

The shape of step is not so good due to some leaking at the pneumatic system. It

caused the pneumatic system cannot hold the load for a period of time to form a

good step. However the disturbance produce still can be used as long as we can put

some interruption to the system and observe the response. All the results observed

are shown in the Figures 6.11-6.14.

10 20 30 40 50 60 700

1

2

3

4

5

6

7

8Step Disturbance

Time(s)

Ste

p (c

m)

Figure 6.10: Disturbance model type step

63

0 50 100 150 200 250 300 350 400-4

-3

-2

-1

0

1

2

3

4Body displacement

Time(s)

Dis

plac

emnt

(cm

)

PID

AFC-ILM

Figure 6.11: Body displacement response with the step disturbance

0 50 100 150 200 250 300 350 400-30

-25

-20

-15

-10

-5

0

5Body Acceleration

Time(s)

Acc

eler

atio

n (m

/s2 )

PID

AFC-ILM

Figure 6.12: Body acceleration response with the step disturbance

64

0 50 100 150 200 250 300 350 4000

1

2

3

4

5

6

7

8Suspension Deflection

Time(s)

Def

lect

ion

(cm

)

PID

AFC-ILM

Figure 6.13: Suspension deflection response with the step disturbance

0 50 100 150 200 250 300 350 400-2

-1.5

-1

-0.5

0

0.5

1

1.5

2Tyre Deflection

Time(s)

Def

lect

ion

(cm

)

PID

AFC-ILM

Figure 6.14: Tyre deflection response with the step disturbance

65

The results for other conditions, i.e different value of parameter A and B,

different values of Initial Condition and the different type of disturbance, please

refer to Appendix C.

6.5 Conclusion

In order to get the best tune for the learning parameter, A and B, the

experiments were carried out without apply any disturbance to the suspension

system. The system with the set learning parameter then was applied the

disturbances. The result show that the active suspension system with pure PID

controller gives almost similar response with the AFC-ILM control scheme. AFC-

ILM suppose to give better response than pure PID. It means that AFC-ILM control

scheme was reached at the minimum level. Some tuning still has to be done to get

the better result for the AFC scheme but due to time constraint existing learning

parameters are remained for this project.

CHAPTER 7

CONCLUSION AND RECOMMENDATION

7.1 Conclusion

The implementation of the active force control (AFC) to the vehicle

suspension system has been successfully done in simulation study and in

experimental work. In simulation study the result shows that the use of AFC make

the system robust. It is because AFC can compensate any internal and external

disturbances that presence in the suspension system. In experimental work, it should

show the same result. However due to highly skill needed to tune the learning

parameter in the ILM to estimate initial mass for AFC, the result obtained is just

same with the pure PID controller.

The study in simulation demonstrate that AFC-CA and AFC-ILM give better

performance compare to pure PID controller. The most important thing in AFC is to

estimate the initial mass. If we get the accurate approximate initial mass, AFC will

give a better performance. In estimating the initial mass the method use is crude

approximation and iterative learning. Crude approximation method is easier than

iterative learning as we just have to directly change the value of initial mass. This

method however will take long time to get the right value of initial mass. Iterative

learning method is more intelligent to estimate the initial mass value as it will iterate

67

repetitively by decrease the error until it get the right value. But the problem in this

method is to tune the learning parameter.

Simulation study shows that by using AFC control scheme, the performance

of the system (active suspension in this case) will improve tremendously. AFC able

to compensate the presence of the known or unknown disturbances.

In experimental works, the experiment was run using the active suspension

rig. Two type of control schemes were used, those are PID and AFC-ILM and the

results from both were compared. From the experimental work, the results show that

the performance of the suspension system for both control scheme almost similar.

The theory says that AFC is better than PID. This condition was happen due to

learning parameter tuning for ILM is still not satisfy. Fine tune the learning

parameter to the right value will change to the better result.

7.2 Recommendation for Future Works

There are few number of future works could be considered as an extension to

the present study. They are as follows;

i) consider the use of the percentage AFC to the system.

ii) the use of self tuning method to estimate the estimated mass in AFC.

iii) study the effects to the performance of suspension system by increase

the sprung mass load.

68

REFERENCES

[1] P.G. Wright. (1984). The Application of Active Suspension to High

Performance Road Vehicles, Microprocessors in Fluid Engineering

IMechE Conference Publications.

[2] http://www.lanciamontecarlo.net/Scorpion/Technical_Suspension.html

[3] D. A. Crolla. (1988). Theoretical Comparisons of Various Active

Suspension Systems in Terms of Performance and Power Requirements. in

“Advanced Suspensions”, Suffolk : Mechanical Engineering Publications

Limited. pp 1 – 9.

[4] Alleyne, A., Neuhaus, P.D., Hedrick, J.K (1993). Application of Non

Linear Control Theory to Electronically Controlled Suspension, Vehicle

System Dynamics Vol. 22, No. 5-6, P.309-320.

[5] Gopalasamy, S,. et. al.. (1997). Model Predictive Control For Active

Suspension. Controller Design and Experimentally Study. Trans. of ASME,

Journal of Dynamic Systems and Control, Vol. 61, pp. 725-733.

[6] Hewit, J.R., (1998). Advances in Teleoperations, Lecture note on Control

Aspects, CISM.

[7] Mailah, M. and Yong, M.O., (2001). Intelligent Adaptive Active Force

Control of a Robot Arm With Embedded Iterative Learning Algorithms,

Jurnal Teknologi, UTM, No.35(A), pp. 85-98.

69

[ 8] Musa Mailah. (1999). A Simulation Study on the Intelligent Active Force

Control of A Robot Arm Using Neural Network, Jurnal Teknologi (D),

Universiti Teknologi Malaysia. pp 55 – 78.

[9] Arimoto, S., Kawamura, S., and Miyazaki, F. (1986). Convergence,

Stability and Robustness of Learning Control Schemes for Robot

Manipulators, Recent Trends in Robotics: Modelling, Control and

Education, ed. by Jamshidi M., Luh L.Y.S., and Shahinpoor M. 307 – 316.

[10] Zhang, Y. (2003). A hybrid adaptive and robust control methodology with

application to active vibration isolation, University of Illinois, Urbana-

Champaign, Ph.D. Thesis.

[11] Baillie, A.S. (1999). Development of a fuzzy logic controller for an active

suspension of an off-road vehicle fitted with terrain preview, Royal Military

Collage of Canada, Kingstone, Canada, Ph.D. Thesis.

[12] D’Amato, F. J. and Viassolo, D. E. (2000). Fuzzy Control for Active

Suspensions, Mechatronics, 10: 897-920.

[13] Omar, Z. (2002). Modelling and Simulation of an Active Suspension System

Using Active Force Control Strategy, MSc. Project Report, Universiti

Teknologi Malaysia.

[14] Mailah M., Priyandoko G. (2005). Simulation of a Suspension System with

Adaptive Fuzzy Active Force Control, International Journal of Simulation

Modelling, Vol.6 No.1, pp 25-36.

[15] G. Priyandoko, et al. (2007). Skyhook Adaptive Neuro Active Force Control

for an Active Suspension System, Procs. Of CIM07, Johor Persada

Convention Centre.

70

[16] Hyoun-Surk Roh and Youngjin Park. (1999). Preview Control of Active

Vehicle Suspension Based on a State and Input Estimator, in Ronald K.

Jurgen (Ed.).“Electronic Steering and Suspension Systems.” Warrendale :

Society of Automotive Engineers, Inc. pp 277 – 284.

[17] S. Roukieh and A. Titli. (1992). On the Model-Based Design of Semi-Active

and Active Suspension for Private Cars, in “Total Vehicle Dynamics.”

London : Mechanical Engineering Publications Limited. pp 305 – 318.

71

APPENDIX A

Simulation Result for Various Conditions

a) Passive suspension response for different value of step input

0 1 2 3 4 5 6 7 8 9 100

2

4

6

8

10

12

14

16

18

time (s)

step

inpu

t va

lue

(cm

)

Response for different step input for passive suspension

1.0

2.010.0

b) Active suspension response for different value of proportional gain (kp)

0 2 4 6 8 10-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

3

time, t

Am

plitu

de

zs response with PID controller (various kp, ki=5,kd=4)

kp=12

kp=20

kp=30

72

APPENDIX B

Experimental Results for Various Learning Parameter

The graphs show the response and their close-up for body displacement,

body acceleration, suspension deflection and tyre deflection respectively.

0 50 100 150 200 250 300 350 400-5

-4

-3

-2

-1

0

1

2

3

4

5Body Displacement

Time(s)

Dis

plac

emen

t (c

m)

A=10

A=20A=50

50 60 70 80 90 100-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

Body Displacement

Time(s)

Dis

plac

emen

t (c

m)

A=10

A=20A=50

73

0 50 100 150 200 250 300 350 400-30

-25

-20

-15

-10

-5

0

5

10Body Acceleration

Time(s)

Acc

eler

atio

n (m

/s2 )

A=10

A=20A=50

0 10 20 30 40 50 60 70 80 90 1000

1

2

3

4

5

6Body Acceleration

Time(s)

Acc

eler

atio

n (m

/s2 )

A=10

A=20A=50

74

0 50 100 150 200 250 300 350 400-6

-4

-2

0

2

4

6

8Suspension Deflection

Time(s)

Def

lect

ion

(cm

)

A=10

A=20A=50

0 10 20 30 40 50 60 70 80 90 1000

1

2

3

4

5

6

7Suspension Deflection

Time(s)

Def

lect

ion

(cm

)

A=10

A=20A=50

75

0 50 100 150 200 250 300 350 400-2

-1.5

-1

-0.5

0

0.5

1

1.5

2Tyre Deflection

Time(s)

Def

lect

ion

(cm

)

A=10

A=20A=50

0 10 20 30 40 50 60 70 80 90 100-1

-0.5

0

0.5

1

1.5Tyre Deflection

Time(s)

Def

lect

ion

(cm

)

A=10

A=20A=50

76

0 50 100 150 200 250 300 350 400-5

-4

-3

-2

-1

0

1

2

3

4

5Body Displacement

Time(s)

Dis

plac

emen

t (c

m)

B=15

B=20B=50

40 50 60 70 80 90 100-2

-1.5

-1

-0.5

0

0.5

1

1.5

2Body Displacement

Time(s)

Dis

plac

emen

t (c

m)

B=15

B=20B=50

77

0 50 100 150 200 250 300 350 400-30

-25

-20

-15

-10

-5

0

5

10Body Acceleration

Time(s)

Acc

eler

atio

n (m

/s2 )

B=15

B=20B=50

0 10 20 30 40 50 60 70 80-5

-4

-3

-2

-1

0

1

2

3

4

5

6Body Acceleration

Time(s)

Acc

eler

atio

n (m

/s2 )

B=15

B=20B=50

78

0 50 100 150 200 250 300 350 400-6

-4

-2

0

2

4

6

8Suspension Deflection

Time(s)

Def

lect

ion

(cm

)

B=15

B=20B=50

10 20 30 40 50 60 70 80 90 100 110 120-2

-1

0

1

2

3

4

5

6

7Suspension Deflection

Time(s)

Def

lect

ion

(cm

)

B=15

B=20B=50

79

0 50 100 150 200 250 300 350 400-2

-1.5

-1

-0.5

0

0.5

1

1.5

2Tyre Deflection

Time(s)

Def

lect

ion

(cm

)

B=15

B=20B=50

0 10 20 30 40 50 60 70 80 90 100-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1Tyre Deflection

Time(s)

Def

lect

ion

(cm

)

B=15

B=20B=50

80

APPENDIX C

Experimental Results for Different Conditions

Data 1 : A=100, B=90, IC=250

Data 2 : A=150, B=200, IC=500

Disturbance type : High Sin and high speed ( ≈1.8Hz)

0 50 100 150 200 250 300 350 400-5

-4

-3

-2

-1

0

1

2

3

4

5Body displacement

Time(s)

Dis

plac

emnt

(cm

)

Data1

Data2

PID

AFC-ILM

81

0 50 100 150 200 250 300 350 400-5

-4

-3

-2

-1

0

1

2

3

4

5Body Acceleration

Time(s)

Acc

eler

atio

n (m

/s2 )

Data1

Data2

PID

AFC-ILM

0 50 100 150 200 250 300 350 4000

1

2

3

4

5

6

7

8Suspension Deflection

Time(s)

Def

lect

ion

(cm

)

Data1

Data2

PID

AFC-ILM

82

0 50 100 150 200 250 300 350 400-5

-4

-3

-2

-1

0

1

2

3

4

5Tyre Deflection

Time(s)

Def

lect

ion

(cm

)

Data1

Data2

PID

AFC-ILM

83

APPENDIX D

The Sketch of the Experimental Rig

Load

Tyre

Body

Pneumatic to generate

disturbanceMotor

Pneumatic actuator

84

APPENDIX E

The LVDT

The LVDT used in this project is AML/IEU+/-75mm-X-10.

85

86

APPENDIX F

The Pressure Sensor

The pressure sensor used in this project is model DP2-22.

87

88

APPENDIX G

The Data Acquisition System Card DAS 1602

89

90

APPENDIX H

The Accelerometer

The accelerometer used in this project is ADXL-105EM-1.

91

92

93

94

95

96

97