ABSTRACT
The memristor is also known as the fourth
fundamental passive circuit element. When current flows in one direction
through the device, the electric resistance increase and when current flows in
the opposite direction the resistance decrease. When the current is stopped,
the component retains the last resistance it had, and when the flow of charge
starts again, the resistance of the circuit will be what it was when it was
last active. It behaves as a non-linear resistor with memory.
Recently memristor have generated wide applications.
In this research work, give an introduction to memristor and propose a
mathematical model that is solved via open plus closed loop, the result of the
model is simulated using C++ software and Gnuplot. The research work concludes
with the uses of memristors.
TABLE OF CONTENTS
Front
Page i
Declaration ii
Certification
iii
Abstract
iv
Dedication
v
Acknowledgment vi
Table
of Contents vii
CHAPTER ONE: INTRODUCTION
1.1 Deterministic
Chaos
1
1.2
Differential Equations 3
1.3 Computer
Program 4
1.4 Memristor 4
1.5 Objective
of the Research 6
1.6 Research
Motivation 6
1.7 Research
Justification 6
1.8 Design
and Implementation 7
1.9 Definition
of Chaos 8
1.10 Lyapunov
Exponent 8
1.11 Phase Space 9
1.12 Poincare
Section 9
CHAPTER TWO
2.1 Literature Review 10
2.2 The historical concept of Chaos
Synchronization 10
2.3 Research background on Chaos control and
Synchronization 13
2.4 Description of open plus closed loops
controller
for general synchronization 16
2.5 Description of Memristor 17
2.6 Characteristic of a Memristor 20
CHAPTER THREE
3.1 Mathematical Model 26
3.2 OPCL
method of synchronization 26
CHAPTER FOUR
4.1 Numerical Result 32
4.2 Applications 32
4.3 Unidirectional coupled order 38
4.4 Bidirectional coupled order 44
CHAPTER FIVE
5.1 Conclusion and Discussion 56
References 57
CHAPTER ONE
INTRODUCTION
1.1 Deterministic Chaos
With the appearance of differential equations, the
three laws of motion and universal gravitation discovered by Newton in the
mid-1600s, dynamics has the become the most active research branch in Physics
and Mathematics. The basic problem of dynamics is to predict the future state
of a system given its initial state.
The system under consideration may be physical,
chemical, or biochemical. Regardless of the context, many systems are modeled mathematically
as differential equations with time as a continuous variable, or as difference
equations where time takes on discrete integer values. Systems described by
deterministic evolution equations are called deterministic dynamical systems. A
basic problem in astronomy, the three body gravitational system, in 1887
challenged the understanding of scientists when they could not demonstrate the stability
or any orbit of the solar system. A two hundred page paper written by the
mathematician H. Poincare showed that the problem is “impossible” to solve
because it may happen that small differences in the initial conditions produce
very great ones in the final state. Prediction (of the future states) becomes
impossible. The phenomenon Poincare discovered was an initial anticipation of
modern deterministic chaos. However, this discovery wasn’t widely appreciated
by the scientific society at the time because the mathematical works were
difficult to read, and the theories weren’t explicit and general enough to
convince scientists about the universality of chaos. This deterministic
aperiodic behaviour therefore remained in the background as a curiosity of
dynamical systems for the next 70 years, until high speed computer were
invented in the 1950s and gave scientist like the metrologist E.N. Lorenz opportunities
to work with differential equations in a way that was never before possible .
While working on modeling the weather system, Lorenz discovered that a set of
three first order, coupled and nonlinear differential equations could display
solutions in which trajectories could be strongly divergent if the simulation
is started from slightly different initial conditions.
This property is illustrated in the solution of the
equations which never settle down to an equilibrium or periodic state, instead
the solution continues to oscillate in an aperiodic fashion. Lorenz’s work
provided the strong foundation for chaos theory in the 1970s when the speed of
computer improved and refined experimental techniques were developed. With
discovery after discovery, it has become clear that chaos is ubiquitous in
nature and could appear in most branches of science. Besides the known example
of the solar and weather systems, chaos could be seen in turbulent fluids and
in population dynamics in biology, among others.
1.2 Differential
Equations
The wide range of possible applications of chaos
raised the interest in generating strong and well controlled chaotic dynamics.
When one seeks ways to create chaotic behaviors, a natural question to ask is:
Where is chaos coming from? And what are the
requirements for a dynamical system to exhibit such behaviour?
Analyzing the Lorenz equations, one can see that the
deterministic chaotic behaviour is neither due to external sources of noise
(there are none in the equations) nor to an infinite number of degree of
freedom (there only three degree of freedom in the equations). The equations
are purely classical. The erratic behaviour exhibited by the Lorenz system is
instead due to properties often seen in nonlinear systems. These include
exponentially fast separating initial close trajectories in a bounded region of
phase space. However, nonlinearity is a necessary but not sufficient condition
for the generation of chaotic motion since linear differential equations can be
solved by Fourier transformation and they do not lead to chaos. For a dynamical
system governed by a set of N first order autonomous, coupled, nonlinear,
ordinary differential equation, it is known that N must be equal to or greater
than three (3) for chaos to be possible.
1.3 Computer Program
Computers are idea tools for exploring nonlinear
systems and demonstrating the intricate and often expected features of chaotic dynamics.
The computer serves as a fast and efficient tool for generating numerical
solutions of the equations of motion of chaotic systems and allows us to
explore their behaviour and investigate details that are difficult or
impossible to analyse by pure analytical method. The basic features of dynamics
can only be imaged by a graphical plot of the iterated points in phase space.
Therefore the use of a computer is unavoidable in studying or teaching chaotic
dynamics. All programs used in this project were written with C++ under
windows.
1.4 Memristor
In 1971, Leon Chua postulated from symmetry argument
that a fourth passive element which links the fundamental quantities of charge
and magnetic flux must exit. This passive circuit element is named memristor,
and behaves as a resistor with a memory effect that is functionally dependent
upon charge.
L.Gmez-Guzman et al (2008) the HP lab showed the
basic
characteristics of the memristor in a
nanoscale device. These two terminal physical models have been investigated in
many applications, for example this new circuit element can be useful for low power
computation and storage for information or data without the need of power. In
addition, memristors can be used to implement programmable analog circuits.
Some of the research works on memristor are:
·
Experimental implementation
of the Memristor reported by Petrov et al. (1990);
·
Adaptive synchronization
of memristors-based Lorenz circuit reported by Wang et al. (1992);
·
Fuzzy modeling
and synchronization of different memistor-based chaotic circuits reported by
Abed et al.(1992) and
·
Modeling and
Fuzzy synchronization of memristor-based Lorenz circuits with memristor-based
Chua’s circuits reported by Kocarere et al. (1994).
We have discovered from part research works that
only few papers are reported on memristor systems despite its rich complex
dynamics and its importance in various fields of study like electronics, Physics, engineering, etc. This
research is aimed at generalized and synchronization of memristor using the
open plus closed loop method.
1.5 Objective of the Research
(i) To control chaotic dynamics of memristor
in a Lorenz circuit system through an open plus closed loop design (bidirectional
& unidirectional).
(ii) To synchronize chaotic dynamic in identical
Lorenz oscillator systems through the open plus closed loop design (bidirectional
& unidirectional coupling method).
1.6 Research Motivation
The motivations for this present research are as
follows:
(i)
The abundant and
complex dynamical system of memristor based Lorenz and Chua’s systems and it’s
application in various fields of study.
(ii)
memristor have
been widely used for modeling the behaviour of many engineering systems.
(iii)
The increasing
demand for memristors.
(iv)
Open plus closed
loop method has not been used to synchronize memristor-based Lorenz and Chua’s systems
1.7 Research Justification
From literature review most of the works reported on
chaotic oscillator, especially those with identical and non-identical Lorenz and
Chua’s systems on memristor using open plus closed loop are on the analysis of
dynamics.
This is one
of our motivation for this research since the the control and synchronization
of Lorenz and Chua’s system using the open
plus closed loop method with application to memristor has not been reported in the literature to the
best of our knowledge.
The use of chaos to study memristor offers several
advantages over conventional methods. For one, chaotic signals are much easier
and faster to generate using simple circuits. Also, the non-periodic and
bifurcation behaviour of the chaotic signal cannot easily be interpreted and
predicted, thus an increase in the system security is obtained. In addition,
large number of chaotic signals can be generated, which is useful in a multi
user environment.
1.8 Design and Implementation
We have designed the controllers for both unidirectional
and bidirectional synchronization via open plus closed loop techniques. The unidirectional
method is to control chaos in slave while the bidirectional method is to
control chaos in both drive and response. Having designed the controller for
both the control and synchronization via open closed loop techniques we then
solved the resulting equations using the 4th-order Runge-Kutta algorithm implemented
through the C++ program. Results obtained from unidirectional and bidirectional
were applied to the memristor. The effectiveness of the proposed systems was
validated through numerical simulation.
1.9 Definition of Chaos
The following definition for chaos comes from S.H
Strogatz (1994).
Chaos is aperiodic long-term behaviour in a
deterministic system that exhibit sensitive dependence on initial conditions
Aperiodic long term behaviour means that the trajectories do not settle down to a
periodic or quasi periodic motion.
A deterministic system is one whose time
evolution of the system is uniquely determined by a set of initial conditions
and not dependent on a random of noisy parameter.
Sensitive dependence on initial
conation means that already slight
differences in the initial conditions cause a strong divergence of the
trajectories.
1.10 Lyapunov Exponent
LYAPUNOV EXPONENT of a trajectory gives the exponential
rate of divergence of nearby trajectories. In this sense it provides a measure
of how chaotic a system is, i.e., it can be used to diagnose chaos. For a
chaotic motion, two trajectories with slightly different initial conditions
tend to diverge exponentially.
1.11 Phase Space
The phase space of a dynamical system is a mathematical
space with orthogonal co-ordinate directions representing each of the two
variables needed to specify the instantaneous state of the system Phase space
has two properties:
i)
Non-crossing
property: in phase space, two trajectories corresponding to similar energies may
pass very close to each other, but the orbits will not cross each other. This is
because past and future states of deterministic mechanical system are uniquely
prescribed by the system state at a given time.
ii)
Area
preservation property: volume preservation means that all the points found in a
given volume at one time move in such a way that at a later time the volume
occupied by these points remain the same.
1.12 Poincare Section
Complex chaotic phase diagrams are simplified by the
Poincare surface in which the phase portrait is sectioned or cut at some
appropriate periodic rate. In this way, a three dimensional system can be
reduced to a two dimensional system.
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