Iraqi Journal for Electrical And Electronic Engineering

The control problem for a class of a nonlinear
systems that contain the coupling of unmeasured states and
unknown parameters is addressed. The system actuation is
assumed to suffer from unknown dead zone nonlinearity. The
parameters bounds of the unknown dead zone to be
considered are unknown. Adaptive sliding mode controller,
unmeasured states observer, and unknown parameters
estimators are suggested such that global stability is achieved.
Simulation for a single link mechanical system with unknown
dead zone and friction torque is implemented for proving the
efficacy of the suggested scheme

In this work, the collective behavior of Artemia
Salina is studied both experimentally and theoretically. Several
experiments have been designed to investigate the Artemia
motion under different environment conditions. From the
results of such experiments, a strategy to control the direction of
motion of an Artemia population, by exploiting their sensitivity
to light, has been derived and then implemented

Some engineering applications requires constant
engine speed such as power generators, production lines ..etc.
The current paper focuses on adding a new closed loop based on
engine torque. Load cells can be used to measure the torque of
load applied , the electrical signal is properly handled to
manipulate a special fuel actuator to compensate for the
reduction in engine speed. The speed loop still acts as the most
outer closed loop. This method leads to rapid speed
compensation and lead control action.

Soft computing control system have been
applied in various applications particularly in the
fields of robotics controls. The advantage of
having a soft computing controls methods is that it
enable more flexibility to the control system
compared with conventional model based controls
system. Firstly, soft computing methods enable a
transfer of human controls and thinking into the
machine via training. Secondly it is more robust to
error compared to conventional model based
system. In this paper, a UAV airship is controlled
using fuzzy logic for its propulsion and steering
system. The airship is tested on a simulation level
before test flight. The prototype airship has on
board GPS and compass for telemetry and
transmitted to the ground control system via a
wireless link

In this paper, explicit model predictive controller is
applied to an inverted pendulum apparatus. Explicit solutions
to constrained linear model predictive controller can be
computed by solving multi-parametric quadratic programs.
The solution is a piecewise affine function, which can be
evaluated at each sample to obtain the optimal control law. The
on-line computation effort is restricted to a table-lookup. This
admits implementation on low cost hardware at high sampling
frequencies in real-time systems with high reliability and low
software complexity. This is useful for systems with limited
power and CPU resources

A practical method of robust
generalized predictive controller (GPC)
application is developed using a
combination of Ziegler-Nichols type
functions relating the GPC controller
parameters to a first order with time delay
process parameters and a model matching
controller.
The GPC controller and the model
matching controller are used in a
master/slave configuration, with the GPC
as the master controller and the model
matching controller as the slave controller.
The model matching controller parameters
are selected to obtain the desired overall
performance.
The effectiveness of the proposed control
method is tested by simulation using a
mathematical model of the boiler super
heater temperature process

An accurate model for a permanent magnet synchronous
generator (PMSG) is important for the design of
a high-performance PMSG control system. The performance
of such control systems is influenced by PMSG parameter
variations under real operation conditions. In this paper, the
electrical parameters of a PMSG (the phase resistance, the phase
inductance and the rotor permanent magnet (PM) flux linkage)
are identified by a particle swarm optimisation (PSO) algorithm
based on experimental tests. The advantages of adopting the PSO
algorithm in this research include easy implementation, a high
computational efficiency and stable convergence characteristics.
For PMSG parameter identification, the normalised root mean
square error (NRMSE) between the measured and simulated data
is calculated and minimised using PSO.

The intergovernmental panel for climate change predicted
that the average temperature of our planet surface will
increase by 1.4–5.8 °C by the end of 21th century. Limited
resources of energy in joint to the effect of temperature on
the energy consumption together attract our attention to the
temperature changes phenomenon. Therefore, researches
have focused on the problem, which is the effect of climate
change on the energy consumption.
Zmeureanu and Renaud proposed a method for
estimation of the impact of climate change on heat energy
consumption in households sector [1]. Peirson and Henley
consider it as a dynamic problem and also shown that by
using autoregressive specification we can obtain a good
explanation of present load [2]. Pardo et al. studied the
relationship between weather and electricity demand in
Spain. They proposed a transfer function intervention model
to predict the electricity demand in the hot and cold days [3].
Bessec and Fouquau studied the relationship between
temperature and electricity consumption in 15 countries of
European Union. They showed the nonlinearity link between
electricity consumption and temperature found in more
limited geographical areas in previous studies; also they
showed that the sensitivity between electricity consumption
and temperature increases in summer [4]. Franco and

In this paper, the power system stabilizer (PSS) and
Thyristor controlled phase shifter(TCPS) interaction is investigated .
The objective of this work is to study and design a controller
capable of doing the task of damping in less economical control
effort, and to globally link all controllers of national network in an
optimal manner , toward smarter grids . This can be well done if a
specific coordination between PSS and FACTS devices , is
accomplished . Firstly, A genetic algorithm-based controller is
used. Genetic Algorithm (GA) is utilized to search for optimum
controller parameter settings that optimize a given eigenvalue based
objective function.
Secondly, an optimal pole shifting, based on modern
control theory for multi-input multi-output systems, is used. It
requires solving first order or second order linear matrix Lyapunov
equation for shifting dominant poles to much better location that
guaranteed less overshoot and less settling time of system
transient response following a disturbance.

Minimization of active power losses is one of the
essential aims for any electric utility, due to its importance in
improvement of system properties towards minimum
production cost and to support increase load requirement. In
this paper we have studied the possibility of reducing the value
of real power losses for (IEEE-14- Bus bar) global system
transmission lines by choosing the best location to install shunt
capacitor depending on new algorithm for calculate the optimal
allocation, which considering the value of real power losses
derivative with injection reactive power as an indicator of the
ability of reducing losses at load buses. The results show the
validity of this method for application in electric power
transmission lines

Network reconfiguration in distribution system is
realized by changing the status of sectionalizing switches, and is
usually done for the purpose of loss reduction. Loss reduction can
result in substantial benefits for a utility. Other benefits from loss
reduction include increased system capacity, and possible
deferment or elimination of capital expenditures for system
improvements and expansion. There is also improved voltage
regulation as a result of reducing feeder voltage drop. Research
work included by this paper focuses on using branch exchange
method to minimize losses and solve the problems over different
radial configuration. Solution’s algorithm for loss minimization
has been developed based on two stages of solution methodology.
The first stage determines maximum loss-reduction loop by
comparing the size of circles for every loop. In a distribution
system, a loop is associated by a tie-line and hence there are
several loops in the system. To obtain the maximum lossreduction
loop, size of modified zero loss-change circles are
compared, and the loop within the largest circle is identified for
maximum loss-reduction. The second stage determines the
switching operation to be executed in that loop to reach a
minimum loss network configuration by comparing the size of
the loop circle for each branch-exchange. The smallest circle is to
be identified for the best solution; the size of the loop circle is
reduced when the losses are minimized. The performance of the
proposed branch exchange method is tested on 16-bus
distribution systems

This paper describes the capability of
artificial neural network for predicting the critical
clearing time of power system. It combines the
advantages of time domain integration schemes with
artificial neural network for real time transient
stability assessment. The training of ANN is done using
selected features as input and critical fault clearing
time (CCT) as desire target. A single contingency was
applied and the target CCT was found using time
domain simulation. Multi layer feed forward neural
network trained with Levenberg Marquardt (LM)
back propagation algorithm is used to provide the
estimated CCT. The effectiveness of ANN, the method
is demonstrated on single machine infinite bus system
(SMIB). The simulation shows that ANN can provide
fast and accurate mapping which makes it applicable to
real time scenario

In this paper we use fuzzy set method to solve one of
the important problems in mechanical engineering (the residual
service life). The residual service life for rolling stock can change
depending on it’s using conditions. The Paper offer’s a new
method depending on fuzzy set method by using the material
mechanical and chemical corrosion mathematical model

This paper presented an investigation into the performance of system identification using an
Adaptive Neuro-Fuzzy Inference System (ANFIS) technique for the dynamic modelling of a twodimensional
flexible plate structure. It is confirmed experimentally, using National Instrumentation
(NI) Data Acquisition System (DAQ) and flexible plate test rig that ANFIS can be effectively used for
modelling the system with highly accurate results. The accuracy of the modelling results is
demonstrated through validation tests including training and test validation and correlation tests.