Engineering and Technology Journal

For Orthogonal Frequency Division Multiplexing (OFDM) and other communication systems, many estimating approaches have been developed to estimate the channel state information and lower the Bit Error Rate (BER). These estimating methods, however, are still subject to the influence of large peak powers compared to average powers. Reduced computational complexity is one of the most significant factors to consider while developing a new estimate algorithm. This study aims to provide a novel design of the Packet-Discrete Wavelet Transform (P-DWT) algorithm for channel estimation in wireless OFDM instead of the fast Fourier transform (FFT). It is presented to retrieve the code of a spread spectrum signal and transmitted data bits, and it is compared to particle swarm optimization PSO and least mean square (LMS) optimization. The suggested approach reduces the computing cost of DWT by recognizing the Packet Wavelet Transform (PWT) coefficients and local points, findings utilizing P-DWT channels generated from both models and measurements show that the proposed technique outperforms pilot-based channel estimation in terms of bit error rate under sparseness conditions BER. Moreover, as compared to typical semi-blind approaches, the estimation accuracy is enhanced while computing cost is reduced.

Losses in the transmission line have a significant and growing impact on power systems around the world. Line losses overheat power lines, therefore electrical power systems require powerful processors and intelligent management methods. Flexible AC Transmission System (FACTS) device UPFC is one of the most important devices due to its ability to reduce total line losses that cause an increase in the transmission line capacity of the power system. In this paper, we used Particle Swarm Optimization (PSO) to determine the optimal location for the installation of UPFC device to minimize losses in the transmission line in the Iraqi international grid (ING) 400kV using a proportional-integral (PI) based UPFC controller. The potential solutions of PSO are called particles. All the particles selected in this controller depend on their parameter only, which keeps feasible solutions in their memory. The algorithm is coded in MATLAB and it is incorporated with the conventional Newton Raphson’s load flow analysis. The result shows that the proposed optimization method applied for two UPFC compensator parameters in the power system contributed to minimizing the active and reactive power losses under normal operating conditions using a modified version of the PSO algorithm.

This work presents a driving control for the trajectory tracking of four mecanum wheeled mobile robot (FMWMR). The control consists of Backstepping-Type 1 Fuzzy Logic-Particle swarm optimization i.e.,(BSC-T1FLC-PSO). The kinematic and dynamic models have been derived. Backstepping controller (BSC) is used for finding controlled torques that generated from robot motors while Type-1 fuzzy logic control (T1FLC) as well as particle swarm optimization (PSO) used for finding the appropriate values of gain parameters of BSC. Square trajectory has been selected to test the performance of the control system of FMWMR. MATLAB/ Simulink is used to simulate the results. It has been concluded from the results that obtained from this control system there is a good matching between the simulated and the desired trajectories.

This work aims to improve the voltage profile and reduce electrical network losses through optimal planning of distributed generators. A new search algorithm (Autoadd) along with the (PSO) are introduced to choose the best location and size for distributed generators. Two systems are implemented; a 33-bus test network and a 30-bus of a local community in the city of Al- Diwaniyah. At the power flow, a solution is implemented using a fixed-point iteration method within an OpenDSS environment to check the performance of both networks. Moreover, the optimal location and size of the distributed generators are determined using Autoadd and PSO methods. The Autoadd method is implemented within the OpenDSS environment, while the (PSO) method is implemented within the MATLAB-OpenDSS environment through the com-interface. The validity and effectiveness of the proposed methods are validated by comparison with the published researches. The results have proven that the fixed-point method has achieved high efficiency and accuracy in terms of analyzing the power flow, whereas the (Autoadd) algorithm has achieved a better effect in terms of improving the voltage profile and minimizing losses

In this paper, the control approach is used for achieving the desired performance and stability of the twin-rotor MIMO system. This system is considered one of the complex multiple inputs of multiple-output systems. The complexity because of the high nonlinearity, significant cross-coupling and parameter uncertainty makes the control of such systems is a very challenging task. The dynamic of the Twin Rotor MIMO System (TRMS) is the same as that in helicopters in many aspects. The Quantitative Feedback Theory (QFT) controller is added to the control to enhance the control algorithm and to satisfy a more desirable performance. QFT is one of the frequency domain techniques that is used to achieve a desirable robust control in presence of system parameters variation. Therefore, a combination between control and QFT is presented in this paper to give a new efficient control algorithm. On the other hand, to obtain the optimal values of the controller parameters, Particle Swarm Optimization (PSO) which is one of the powerful optimization methods is used. The results show that the proposed quantitative control can achieve more desirable performance in comparison to control especially in attenuating the cross-coupling and eliminating the steady-state error.

This paper presents a blood pump with a bearingless brushless DC motor, supported by speed, torque, and suspension force controllers. Simulation of the pump motor and its controllers tested by MATLAB/Simulink. Two Proportional plus Integral (PI) controllers are employed for controlling the rotational speed and torque of the motor. For controlling the suspension force a comparative study is presented between the Proportional plus Integral plus Derivative (PID) controller and two inputs PID-like Fuzzy Logic Controller (FLC). A particle swarm optimization technique is used to find the best values for the controller’s parameters. The results of the speed and torque controllers exhibit a good time response to reach the desired speed with a short period of time and to decrease the distorting effects of the load torque successfully. Under similar conditions, the PID-like FLC that controls the suspension forces shows a better time response compared to the PID controller. An enhancement in the responses is rated between 18% and 49%, measured using the absolute integral of error criteria on the x and y axes, and in the processing, time rated between 38% and 47%, very high oscillation suppression capability is observed in the PID-like FLC response.

Kinematics of the robot is divided into two parts: the forward
kinematics, which evaluates the end-effector’s position from joint angles, and the
inverse kinematics, which demonstrates the joint angles from the end-effector's
position. The solution of the inverse kinematics problem is too difficult and
complicated for the redundant robot arm manipulator. A Particle Swarm
Optimization (PSO) algorithm is an effective method to solve global optimization
problems. This paper presents the solution of inverse kinematics problem of a
three-link redundant manipulator robot arm using PSO without using the inverse
kinematics equations. The circle, square and triangle generated trajectories using
PSO are enhanced as compared with the trajectories of other works. The
enhanced PSO algorithm is successfully found the best generating three joint
angles and the best generating end-effector's position of a three-link robot arm.
Then according to these joints and positions the circle, square and triangle path
trajectories, results are smoother than the path trajectories of other work. This
enhanced solution of inverse kinematics using PSO algorithm is too fast due to
the short elapsed time in every iteration of trajectory. Besides that, these
velocities results have been given evaluated and give an indication that the threelink robot is moving fast during the PSO algorithm. The elapsed time of circle
trajectory equals to 20.903981 seconds, the elapsed time of square trajectory
equals to 11.747171 seconds and the elapsed time of triangle trajectory equals to
15.729663 seconds. MATLAB R2015b program is used in order to simulate all
results. The main benefit of this work is to solve two problems: 1) inverse
kinematics is too complex equations of the three-link robot. The solutions of best
joint angles using PSO are computed within joint limits without using inverse
kinematics equations. 2) Another problem, this work is enhanced three
trajectories with respect to the best joint angles and reaches 96% percent as
compared with another work. The error is too small according to the start and
goal PSO generated points for each trajectory.

Cryptanalysis is a complex and mathematically challenging field of study. It takes some data or message, which is called cipher text and attempt to restore its plaintext. This paper attempts to use an improved particle swarm optimization (PSO) to obtain the plaintext from the transposition cipher. This improved method gives a good performance for the PSO algorithm by generating best solution from the best to avoid stability to reach to solution (key). This key is used for breaking transposition cipher.

This paper presents a robust design of the four parameters PI-PD controller for systems with parametric uncertainties. The Particle swarm Optimization (PSO) method is applied to tune the controller parameters such that the robust specifications are satisfied. For the robust stability and performance to be guaranteed, the Kharitonov's theorem of interval polynomials is combined with the time domain performance index. The effectiveness of the proposed controller is illustrated by two examples of uncertain systems.

The Controller design is considered as the important part in the IC engines, to get a stable operation which is the main objective for engine generator set, through controlling the throttle angle to get constant engine rotation speed at different load conditions.The Model has been taken from previous research, considering the throttle angle as an input while the output is the rotation speed, then the controllers have been designed to adjust the rotational speed with the help atMatlab and Simulink techniques. Two main types of controllers have been used in this work which are; PID and Fuzzy PD like controllers. The Proportional-Integral-Derivative parameters have been tuned by particle swarm optimization technique and for the first controller and validated by Integral Square Error (ISE), Integral Time Absolute Error (ITAE) and Integral Absolute Error(AE). While, Fuzzy PD like consisted of seven membership function and forty nine rules. Finally, the results showed the superiority of PID based on Particle Swarm Optimization (PSO) compared with Fuzzy PD like controller.

The needs for increasing the power generation make the use of solar cells plays an important role in the daily life. For this reason, it is important to use solar tracking system to increase or getting almost optimum amount from solar cells. In this paper, proposed intelligent controllers were designed and used to make solar cells facing the sun over the year. The proposed controller was trained by two ways; the first was trained by supervised feed forward neural network and the second by Particle Swarm Optimization (PSO) the results obtained for both designs are then compared. The controller was trained using MATLAB and then converted to SIMULINK model in order to test it, and convert it to a Very high speed integrated circuit Hardware Description Language (VHDL) language using MATLAB tool box in order to download it on Spartan 3A Field Programmable Gate Arrays (FPGAs) card. This makes the implementation of the intelligent controller more efficient and easy to use because of its reprogram-ability and the high speed performance. The controller was designed to a fully controlled DC motor driver which is used to rotate two DC motors in X-axis and Y-axis directions respectively.
The experimental results show that tracking sun increases the efficiency of the system to produce energy from solar cell about 44.3778 % more energy than the solar cell without tracking system.

Field oriented control space vector pulse width modulation (FOC- SVPWM) is one of the effective and modern methods for speed control of Permanent magnet synchronous motor (PMSM). A mathematical model and theoretical analysis of (FOC-SVPWM) driven a PMSM are presented .In this work, a control methods for PMSM using Model reference adaptive system (MRAS) are utilized to compare the performance behavior under conventional PI, Fuzzy PI, and Particle swarm optimization (PSO) control methods.
Extensive simulation results are presented using MATLAB/SIMULINK program which including (SVPWM generation, inverter, PMSM, the reference frame transformation and different PI controllers) as well as the estimation method using MRAS.
This work presents a comparative study to investigate the performance of PMSM based on MRAS when different load conditions are applied to PMSM and under three different controllers: the first controller is the Proportional-Integral (PI) based on classical trial and error method, the second controller is PI controller based on PSO technique for optimal gains tuning and thus improve the performance of the system. The obtained results show that an improvement in motor performance when using PI-PSO compared to classical PI controller. The third controller is Fuzzy-PI with scaling factor (gains) tuned by PSO technique. This method can improve the performance of the system compared with PI-PSO in terms of reducing steady state error, rising time, overshoot and smoother response to make this controller more robust to variation in load other than the rest motor controllers.