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Faculty of Engineering & Technology
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Mohamed.A.Shamseldin

Basic information

Name : Mohamed.A.Shamseldin
Title: Lecturer
Google Schoolar Link
Personal Info: Dr. Shamseldin obtained the Bachelor of mechatronics engineering in 2010 from faculty of engineering, Helwan University, Cairo, Egypt. In 2016, he obtained the M.Sc. in system automation from faculty of engineering, Helwan University, Cairo, Egypt. In 2020, he obtained the Ph.D. in Mechatronics Engineering from faculty of engineering , Helwan University, Cairo, Egypt. Also, Mohamed was a member of mobility staff to teach in summer course in University of Central Lancashire, Preston, UK. View More...

Education

Certificate Major University Year
PhD Mechatronics Engineering Helwan University - Faculty of Engineering 2020
Masters System Automation and Management Engineering Helwan university- Faculty Of Engineering 2016
Bachelor Mechanical Department Helwan University - Faculty of Engineering 2010

Researches /Publications

Practical Implementation of an Enhanced Nonlinear PID Controller Based on Harmony Search for One-Stage Servomechanism System - 01/0

MOHAMED ABDELBAR SHAMSELDIN ALY

Mohamed Sallam , A.M. Bassiuny and A.M.Abdel Ghany

01/04/2020

This paper presents a practical implementation for a new formula of nonlinear PID (NPID) control. The purpose of the controller is to accurately trace a preselected position reference of one stage servomechanism system. The possibility of developing a transfer function model for experimental setup is elusive because of the lack of system data. So, the identified model has been developed via gathering experimental input/output data. The performance of the enhanced nonlinear PID (NPID) controller had been investigated by comparing it with linear PID controller. The harmony search (HS) tuning system had built to determine the optimum parameters for each control technique based on an effective objective function. The experimental and simulation results proved that the enhanced nonlinear PID (NPID) controller has better performance and more robust compared to linear PID controller. Both the simulation and the experimental results are identical significantly.

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Parallel distribution compensation PID based on Takagi-Sugeno fuzzy model applied on Egyptian load frequency control - 01/0

MOHAMED ABDELBAR SHAMSELDIN ALY

M. A. Abdel Ghany

01/04/2020

This paper presents a new technique for a Takagi-Sugeno (TS) fuzzy parallels distribution compensation-PID'S (TSF-PDC-PID'S) to improve the performance of egyptian load frequency control (ELFC). In this technique, the inputs to a TS fuzzy model are the parameters of the change of operating points. The TS fuzzy model can definite the suitable PID control for a certain operating point. The parameters of PID'S controllers are obtained by ant colony optimization (ACO) technique in each operating point based on an effective cost function. The system controlled by the proposed TSF-PDC-PID'S is investigated under different types of disturbances, uncertainty and parameters variations. The simulation results ensure that the TSF-PDC-PID'S can update the suitable PID controller at several operating points so, it has a good dynamic response under many types of disturbances compared to fixed optimal PID controller.

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Model reference self-tuning fractional order PID control based on for a power system stabilizer - 01/0

MOHAMED ABDELBAR SHAMSELDIN ALY

M. A. Abdel Ghany

01/04/2020

This paper presents a novel approach of self-tuning for a Modified Fractional Order PID (MFOPID) depends on the Model Reference Adaptive System (MRAS). The proposed self-tuning controller is applied to Power System Stabilizer (PSS). Takaji-Sugeno (TS) fuzzy logic technique is used to construct the MFOPID controller. The objective of MRAS is to update the five parameters of Takaji-Sugeno Modified FOPID (TSMFOPID) controller online. For different operating points of PSS, MRAS is applied to investigate the effectiveness of proposed controllers. The harmony optimization technique used to obtain the optimal parameters of TSMFOPID controllers and MRAS parameters. For different operating points with different disturbance under parameters variations the simulation results are obtained. This is to show that Self-Tuning of TSMFOPID based on (MRAS) have better performance than the fixed parameters TSMOFOPID controller. Keywords: Fractional order PID Harmony research (HS) Model reference adaptive control (MRAC) Power system stabilizer (PSS) Takaji-sugeno fuzzy This is an open access article under the CC BY-SA license. 1. INTRODUCTION Generator excitation control systems contain Automatic Voltage Regulators (AVR) for voltage regulation and conventional Power System Stabilizers (CPSS) for damping mechanical mode oscillations. The changes in operating conditions of PSS is challenge to update the controller parameters [1]. Therefore, the new studies seek to design advanced control techniques, which controllers adapt with the continuous change in operating points [2-4]. The conventional PID controller is common use in several of engineering applications. Due to the structure simplicity and easy parameter tuning, it is suitable for a certain operating point. In addition, its performance is good for linear and simple systems [5, 6]. Still, the behavior of PID control is linear and cannot deal with the high disturbance and high nonlinearity in complicated systems [5, 7, 8]. The current research directed to use the Fractional Order PID (FOPID) control where it presents the nonlinear face of PID control [9-11]. In FOPID controller, two additional parameters (the fractional integral and derivative gains) will be supplementary to increase the flexibility and reliability of controller [12-14]. Therefore, the dynamic performance of FOPID controller is enhanced compared to the conventional PID controller [15-17]. At different operating points for a certain system, adaptation online was used self-tuning using for the system. In this case, the fuzzy logic calculations need a long time and addition efforts by try and error is performed to obtain normalizing gains selection [18]. So, this study resort to the MRAS to self-tuning the TSMFOPID online where it has simple structure, easy to implement and fast calculations [19, 20].

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Modelling and control of solar‑driven humidification–dehumidification desalination plant - 01/1

MOHAMED ABDELBAR SHAMSELDIN ALY

Bahy Gabra, Mohamed Rady & A. M. Abdel Ghany

01/12/2019

This article reports on mathematical modelling and control of a solar-driven humidification–dehumidification desalination plant. Mathematical models for the components are constructed using CARNOT toolbox in MATLAB environment. Model validation has been shown by comparison with published experimental data. Solar collector outlet temperature control is a key parameter to optimize plant performance. In this study, solar field pump flow rate is controlled to maintain the collector outlet temperature at a predetermined set value. Three types of PID controllers are tested. These include PID, nonlinear PID and fractional-order PID. Controllers’ gains are optimized using genetic algorithm technique. The results show that FOPID controller offers a superior dynamic and static performance and can be automatically adjusted to compensate for weather changes.

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A New Model Reference Self-Tuning Fractional Order PD Control for One Stage Servomechanism System - 01/0

MOHAMED ABDELBAR SHAMSELDIN ALY

Mohamed Sallam, A. M. Bassiuny, A. M. Abdel Ghany

01/02/2019

This paper presents a new technique to adapt the fractional order PID (FOPID) control based on optimal Model Reference Adaptive System (MRAS). The proposed control technique has been subjected to motion control of one stage servomechanism system. This purpose should be achieved through different operating points and external disorders (friction and backlash). The parameters of MRAS have been obtained using the harmony search (HS) optimization technique to achieve the optimal performance. Also, the performance of proposed control technique has been investigated by comparing it with the PID and FOPID controllers. The practical results illustrate that the self-tuning FOPD control based on optimal model reference adaptive system can accommodate the tracking error rapidly respect to other control techniques

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Real-time implementation of an enhanced nonlinear PID controller based on harmony search for one-stage servomechanism system - 01/0

MOHAMED ABDELBAR SHAMSELDIN ALY

Mohamed Sallam, A. M. Bassiuny, A. M. Abdel Ghany

01/02/2019

This paper presents a real-time implementation of an enhanced nonlinear PID (NPID) controller to follow a preselected position profile of one stage servomechanism drive system. This purpose should be realized regardless the different operating points and external disorders (friction and backlash). In this study, the MATLAB Simulink used for purpose of controller design while the result from simulation will be executed in real time using LABVIEW software. There is not enough information about the servomechanism experimental setup so, the system identification techniques will be used via collecting experimental input/output data. The optimum parameters for the controllers have been obtained via harmony search optimization technique according to an effective cost function. Also, the performance of enhanced NPID controller has been investigated by comparing it with linear PID controller. The experimental and simulation results show that the proposed NPID controller has minimum rise time and settling time through constant position reference test. Also, the NPID control is faster than the linear PID control by 40% in case of variable position reference test.

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A Novel Self-Tuning Fractional Order PID Control Based on Optimal Model Reference Adaptive System - 01/0

MOHAMED ABDELBAR SHAMSELDIN ALY

Mohamed Sallam, A. M. Bassiuny, A. M. Abdel Ghany

01/01/2019

This paper presents a novel self-tuning fractional order PID (FOPID) control based on optimal Model Reference Adaptive Control (MRAC). The proposed control technique has subjected to a third order system case study (power system load frequency control). The model reference describes the requirements of designer. It can be first or second order system. The parameters of MRAC have obtained using the harmony search (HS) optimization technique to achieve the optimal performance. Sometimes, the tuning of the five parameters of FOPID control online at same moment consumes more calculation time and more processing. So, this study proposes three methods for self-tuning FOPID control. The first method has been implemented to tune the two integral and derivative parameters only and the rest of parameters are fixed. The second method has been designed to adjust the proportional, integral derivative parameters while the other fractional parameters are constant. The last method has developed to adjust the five parameters of FOPID control simultaneously. The simulation results illustrate that the third method of self-tuning FOPID control can accommodate the sudden disturbance compared to other techniques. Also, it can absorb the system uncertainty better than the other control techniques.

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Brushless DC Motor Tracking Control Using Self-tuning Fuzzy PID control and Model Reference Adaptive Control - 01/0

MOHAMED ABDELBAR SHAMSELDIN ALY

Adel A. El-samahy

01/09/2018

This paper compares the performance of two different control techniques applied to high performance brushless DC motor. The first scheme is self-tuning fuzzy PID controller and the second scheme is model reference adaptive control (MRAC) with PID compensator. The purpose of the control algorithm is to force the rotor speed to follow the desired reference speed with good accuracy all time. This objective should be achieved for different speed/time tracks regardless load disturbance and parameter variations. The simulation results presented show that the second control scheme has better performance.

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A Novel Fuzzy Self Tuning Technique of Single Neuron PID Controller for Brushless DC Motor - 01/1

MOHAMED ABDELBAR SHAMSELDIN ALY

M.A. Abdel Ghany ; ; A.M. Abdel Ghany

01/12/2017

In this paper, a combination ANN/Fuzzy technique is used to design a Novel Fuzzy Single Neuron PID (NFSNPID) controller to achieve high performance brushless DC motor. The design steps include two parts. The first part uses the genetic algorithm (GA) to find the optimum parameters of Single Neuron PID (SNPID) controller, while the former deals with the design of fuzzy logic control to update the weights of SNPID control online. To demonstrate the designed controller effectiveness, a comparative study is made with between the NFSNPID, Conventional Fuzzy Single Neuron PID CFSNPID and SNPID. All controllers were used to drive, separately, the brushless DC motor against the sudden change of load and operating speed. The performed simulations show better results that motivate for further investigations.

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Implementation of Self-Tuning Fuzzy PID Control Applied on Brushless DC Motor - 01/1

MOHAMED ABDELBAR SHAMSELDIN ALY

W Refaey; Aml Eid; R Darwish; A Abdel Ghany

01/10/2016

this paper presents simple implementation for two different control techniques applied on brushless DC motor. The first technique is the conventional PID control while the second technique is the self-tuning fuzzy PID control. In the second technique the main role of fuzzy logic control adjusts the PID control output according to error and change of error. The Arduino microcontroller is used to send the control signal to real system and receive the speed feedback signal. The experimental results show that the superiority for self-tuning fuzzy PID control compared to conventional PID control.

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Practical Implementation of GA-Based PID Controller for Brushless DC Motor - 01/1

MOHAMED ABDELBAR SHAMSELDIN ALY

M. Abdullah Eissa, Adel. A. EL-Samahy

01/12/2015

This paper presents a practical implementation of a Genetic Algorithm (GA) based PID controller for high performance Brushless DC (BLDC) motor. The purpose is to test the ability of the proposed GA based PID controller to force the rotor speed of BLDC motor to follow a preselected speed track. Three different cost functions are used by GAoptimization method to find the proper PID controller parameters. The objective of the first cost function is to minimize the square error while the objective of the second cost function is to minimize rise time, steady state error, settling time and maximum over shoot according to the priority of the designer. Moreover, the objective of third cost function compromise between minimizing either the maximum over shoot and steady-state error or the rise time and settling time. The simulation and experimental results show that the performance of genetic PID controller based on third cost function has the best performance amongthese techniques.

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A Modified Model Reference Adaptive Controller for Brushless DC Motor - 01/1

MOHAMED ABDELBAR SHAMSELDIN ALY

M. Abdullah Eissa, Adel. A. EL-Samahy

01/12/2015

The model reference adaptive control (MRAC) are used extensively with high performance drives applications. This because of its ability to deal with external disturbances and parameter variation. This paper presents anexperimental implementation of two advanced control techniques to high performance brushless DC (BLDC) motor. The first technique is self-tuning fuzzy PID control algorithm in which the parameter of the PID is updated continuously according to the real time measurements of both error and change of error. The second technique is a modified MRAC (MRAC with PID compensator) in which the control action depends on MRAC and PID compensator.The experimental results presented show that the MRAC with PID compensator has better performance compared toanother technique.

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Speed Control of BLDC Motor By Using PID Control and Self-tuning Fuzzy PID Controller - 01/0

MOHAMED ABDELBAR SHAMSELDIN ALY

01/09/2014

This paper presents three different robust controller techniques for high performance brushless DC (BLDC) motor. The purpose is to test the ability of each control technique to force the rotor to follow a preselected speed/position track. This objective should be achieved regardless the parameter variations, and external disturbances. The first technique is conventional PID controller. The second controller technique use genetic algorithm to adjust the PID controller parameters based on three different cost functions. Finally a self-tuning fuzzy PID controller is developed and tested. These controllers are tested for both speed regulation and speed tracking. Results shows that the proposed self-tuning fuzzy PID controller has better performance.

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