Iraqi Journal for Electrical And Electronic Engineering

Navigational sensors are evolving
both on a commercial and research level.
However, the limitation still lies in the accuracy
of the respective sensors. For a navigation
system to reach a certain accuracy, multi sensors
or fusion sensors are used. In this paper, a
framework of fuzzy sensor data fusing is
proposed to obtain an optimised navigational
system. Different types of sensors without a
known state of inaccuracy can be fused using the
same method proposed. This is demonstrated by
fusing compass/accelerometer and GPS signal.
GPS has evolved as a choice of navigation
method for outdoor autonomous system. Despite
it emerging trend and application, one problem
remains is that it is still prone to inaccuracies
due to environmental factors. These factors are
available and evaluated in the GPS receiver
architecture. These inaccuracies are available in
the extracted NMEA(National Maritime
Electronics Association) protocols as SNR
(Signal to noise ratio) and HDOP (Horizontal
Dillusion of Precision). Dead reckoning sensors
on the other hand does not depend on external
radio signal coverage and can be used in areas
with low coverage. Unfortunately, the errors are
unbounded and has an accumulative effect over
time.

Fuzzy linear programming (FLP )
is an application of fuzzy set theory
in linear decision making problems
and most of these problems are
related to linear programming
contains fuzzy constrains or crisp
objectives function or contains crisp
constrains with fuzzy objectives
function, which called fuzzy linear
programming (FLP) with triplet
fuzzy numbers consist a hybrid
fuzzy. The crisp constrains used in
the problems of types (= or ≥) with a
proposed optimization fuzzy
objectives and fuzzy constrains. In
this paper proposed method for
solving fuzzy linear programming
problem by using Two-phase
technique to solve the problem and
to determine the optima crisp
objectives.

The main objective of designed the controller
for a vehicle suspension system is to reduce the discomfort
sensed by passengers which arises from road roughness
and to increase the ride handling associated with the
pitching and rolling movements. This necessitates a very
fast and accurate controller to meet as much control
objectives, as possible. Therefore, this paper deals with an
artificial intelligence Neuro-Fuzzy (NF) technique to
design a robust controller to meet the control objectives.
The advantage of this controller is that it can handle the
nonlinearities faster than other conventional controllers.
The approach of the proposed controller is to minimize
the vibrations on each corner of vehicle by supplying
control forces to suspension system when travelling on
rough road. The other purpose for using the NF controller
for vehicle model is to reduce the body inclinations that
are made during intensive manoeuvres including braking
and cornering. A full vehicle nonlinear active suspension
system is introduced and tested. The robustness of the
proposed controller is being assessed by comparing with
an optimal Fractional Order PIλDμ (FOPID) controller.
The results show that the intelligent NF controller has
improved the dynamic response measured by decreasing
the cost function.

In this paper, we evaluate the performance of UMTS
(Universal Mobile Telecommunication System) downlink system in
vicinity of UWB system. The study is achieved via simulating a
scenario of a building which is located within UMTS cell borders
and utilizes from both UMTS and UWB appliances. The
simulation results show that the UMTS system is considerably
affected by the UWB interference. However, in order to battle this
interference and achieve reasonable BER (Bit Error Rate) of 10-4,
we found that it is very necessary to carefully raise the UMTS base
station transmitted power against that of UWB interferer. So, the
minimum requirements for UMTS system to overcome UWB
interference are stated in this work

Induction Motors have been used as the workhorse
in the industry for a long time due to its easy build, high
robustness, and generally satisfactory efficiency. However, they
are significantly more difficult to control than DC motors. One
of the problems which might cause unsuccessful attempts for
designing a proper controller would be the time varying nature
of parameters and variables which might be changed while
working with the motion systems. One of the best suggested
solutions to solve this problem would be the use of Sliding Mode
Control (SMC). This paper presents the design of a new
controller for a vector control induction motor drive that
employs an outer loop speed controller using SMC. Several tests
were performed to evaluate the performance of the new
controller method, and two other sliding mode controller
techniques. From the comparative simulation results, one can
conclude that the new controller law provides high performance
dynamic characteristics and is robust with regard to plant
parameter variations

Control of Induction Motor (IM) is well known to be
difficult owing to the fact the models of IM are highly nonlinear
and time variant. In this paper, to achieve accurate control
performance of rotor position control of IM, a new method is
proposed by using adaptive inverse control (AIC) technique. In
recent years, AIC is a very vivid field because of its advantages.
It is quite different from the traditional control. AIC is actually
an open loop control scheme and so in the AIC the instability
problem cased by feedback control is avoided and the better
dynamic performances can also be achieved. The model of IM is
identified using adaptive filter as well as the inverse model of the
IM, which was used as a controller. The significant of using the
inverse of the IM dynamic as a controller is to makes the IM
output response to converge to the reference input signal. To
validate the performances of the proposed new control scheme,
we provided a series of simulation results

In this paper an integrated electronic system has
been designed, constructed and tested. The system utilizes an
interface card through the parallel port in addition to some
auxiliary circuits to perform fuzzy control operations for DC
motor speed control with load and no load. Software is written
using (C++ language Ver. 3.1) to display the image as control
panel for different types of both conventional and fuzzy control.
The main task of the software is to simulate: first, how to find
out the correct parameters for fuzzy logic controller
(membership’s function, rules and scaling factor). Second, how
to evaluate the gain factors (KP, KI and KD) by Ziegler-Nichols
method. When executing any type of control process the
efficiency is estimated by drawing the relative speed response
for this control

This paper presents a method for improving
the speed profile of a three phase induction motor in
direct torque control (DTC) drive system using a
proposed fuzzy logic based speed controller. A
complete simulation of the conventional DTC and
closed-loop for speed control of three phase induction
motor was tested using well known Matlab/Simulink
software package. The speed control of the induction
motor is done by using the conventional proportional
integral (PI) controller and the proposed fuzzy logic
based controller. The proposed fuzzy logic controller
has a nature of (PI) to determine the torque reference
for the motor. The dynamic response has been clearly
tested for both conventional and the proposed fuzzy
logic based speed controllers. The simulation results
showed a better dynamic performance of the
induction motor when using the proposed fuzzy logic
based speed controller compared with the
conventional type with a fixed (PI) controller

This Paper presents a novel hardware design
methodology of digital control systems. For this, instead of
synthesizing the control system using Very high speed
integration circuit Hardware Description Language (VHDL),
LabVIEW FPGA module from National Instrument (NI) is used
to design the whole system that include analog capture circuit to
take out the analog signals (set point and process variable) from
the real world, PID controller module, and PWM signal
generator module to drive the motor. The physical
implementation of the digital system is based on Spartan-3E
FPGA from Xilinx. Simulation studies of speed control of a D.C.
motor are conducted and the effect of a sudden change in
reference speed and load are also included

This paper presents a new strategy for controlling
induction motors with unknown parameters. Using a simple
linearized model of induction motors, we design robust adaptive
controllers and unknown parameters update laws. The control
design and parameters estimators are proved to have global
stable performance against sudden load variations. All closed
loop signals are guaranteed to be bounded. Simulations are
performed to show the efficacy of the suggested scheme

This paper deals with the application of
Fuzzy-Neural Networks (FNNs) in multi-machine
system control applied on hot steel rolling. The
electrical drives that used in rolling system are a
set of three-phase induction motors (IM)
controlled by indirect field-oriented control
(IFO). The fundamental goal of this type of
control is to eliminate the coupling influence
though the coordinate transformation in order to
make the AC motor behaves like a separately
excited DC motor. Then use Fuzzy-Neural
Network in control the IM speed and the rolling
plant. In this work MATLAB/SIMULINK models
are proposed and implemented for the entire
structures. Simulation results are presented to
verify the effectiveness of the proposed control
schemes. It is found that the proposed system is
robust in that it eliminates the disturbances
considerably

In a human–robot interface, the prediction of
motion, which is based on context information of a task,
has the potential to improve the robustness and reliability
of motion classification to control human-assisting
manipulators. The electromyography (EMG) signals can
be used as a control source of artificial arm after it has
been processed. The objective of this work is to achieve
better classification with multiple parameters using KNearest
Neighbor for different movements of a prosthetic
arm. A K- Nearest Neighbor (K-NN) rule is one of the
simplest and the most important methods in pattern
recognition. The proposed structure is simulated using
MATLAB Ver. R2009a, and satisfied results are obtained
by comparing with conventional method of recognition
using Artificial Neural Network(ANN), that explains the
ability of the proposed structure to recognize the
movements of human arm based EMG signals. Results
show the proposed technique achieved a uniformly good
performance with respect to ANN in term of time which is
important in recognition systems, better accuracy in
recognition when applied to lower SNR signal .

This is a design of an expert system in the
focused ion beam optical system by using fuzzy logic
technique to build an intelligent agent. Present software
has been designed as an interpretation expert system,
written in Visual C# for optimizing the calculation of
three electrostatic lenses column. By using such rule
based engine, the axial potential distributions for
electrostatic fields undergo the constraints have been
used to find spot size focusing for ions in the image
plane which have values are very useful for getting and
designing FIB model, over ranges of ion beam angles (5,
10,30,50,75 and 100) mrad

This paper aims to give an overview of the European
Rail Traffic Management System (ERTMS) which is currently
being implemented in Europe and other parts of the world. It
provides some background on this system, the objectives behind
the development of the ERTMS, its architecture, different
application levels of the ERTMS and brief information on its
implementation in Europe and worldwide. The paper assumes
the readers have a little or no knowledge of the ERTMS

Self-organizing systems arise in many different fields.
In this work we analyze data from social and biological systems.
A central question is to demonstrate the presence of the
determinism in time-series extracted from such systems that
appear apparently not correlated but that are two good
benchmarks for the study of complexity in real systems. We will
apply the Kaplan test and we will define an order parameter for
the biological data to characterize the complexity of the system

The objective in Data Grids is to reduce access and
file (replica) transfer latencies, as well as to avoid single site
congestion by the numerous requesters. To facilitate access and
transfer of the data, the files of the Data Grid are distributed
across the multiple sites. The effectiveness of a replica
selection strategy in data grids depends on its ability to serve
the requirement posed by the users' jobs. Most jobs are
required to be executed at a specific execution time. To achieve
the QoS perceived by the users, response time metrics should
take into account a replica selection strategy. Total execution
time needs to factor latencies due to network transfer rates and
latencies due to search and location. Network resources affect
the speed of moving the required data and searching methods
can reduce scope for replica selection. This paper presents a
replica selection strategy that adapts its criteria dynamically so
as to best approximate application providers’ and clients’
requirements. We introduce a new selection technique (EST)
that shows improved performance over the more common
algorithms