Article Per Year (5 Year)
p-Index From 2020 - 2025
2.288
P-Index
This Author published in this journals
All Journal Buletin Ilmiah Sarjana Teknik Elektro Journal of Robotics and Control (JRC) International Journal of Robotics and Control Systems Control Systems and Optimization Letters Signal and Image Processing Letters IJMST Scientific Journal of Engineering Research
Sharkawy, Abdel-Nasser
Unknown Affiliation
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

Published : 16 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 16 Documents
Search

 1   2 
Design and Implementation of a Reliable and Secure Controller for Smart Home Applications Based on PLC Khairullah, Shawkat Sabah; Sharkawy, Abdel-Nasser
Journal of Robotics and Control (JRC) Vol 3, No 5 (2022): September
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v3i5.15972

Abstract

Programmable logic controllers (PLCs) are increasingly being used to realize modern safety-critical instrumentation and control (IC) applications. Examples of these applications are industrial automation and control systems, plant process safety protection systems, smart home systems and digital IC systems embedded in nuclear power plants (NPPs) that require high levels of performance, reliability, and flexibility. The PLC is a flexible, programmable, and robust digital device that can execute all logical and mathematical runtime functions of the IC application and operate in harsh-critical environments. This paper proposes a PLC-based home security controller based on the ladder logic programming model. The design, analysis, and hardware implementation of this controller are presented in this paper. The designed system consists of three basic modules which are a sensing module used for reading the data of the input field devices for the smart home application, a computation-based decisional module used for executing the programming model, and an actuating module used for sending the control commands to the output field devices. The proposed home security system utilized different types of sensors such as a laser photoelectric sensor, a motion or proximity sensor, and a limit switch. In addition, a siren speaker, a light tower including three lights red, yellow, and green, two push-pull switches and emergency push-pull buttons were used as control inputs and output indicators in the implementation of this work This designed system is implemented on the Allen-Bradley CompactLogix PLC controller and Human Machine Interface (HMI) panel programmed as the graphical user interface. The experimental simulation results of the real hardware connection demonstrate that the proposed system is reliable, safe, and feasible for smart home security applications.
Design and Manufacturing Using 3D Printing Technology of A 5-DOF Manipulator for Industrial Tasks Sharkawy, Abdel-Nasser; Nazzal, Jamal Mahmoud
International Journal of Robotics and Control Systems Vol 4, No 2 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i2.1456

Abstract

Robotic manipulators have become very necessary in industrial applications all over the world. In this paper, a 5-DOF robotic manipulator is designed and manufactured to simulate a real industrial task. The manipulator is intended to transfer an object with a weight of 30 grams from a known place to another known one, which is a pick and place task. Firstly, all parts of the manipulator are designed using SolidWorks software. During the design, all parts’ dimensions are considered. The end-effector of the manipulator is designed based on gear system. Secondly, 3D printing technology is used to manufacture these designed parts. The manufacturing process is very accurate and efficient. Servo motors are considered to do the motion of the manipulator, which are easily and directly connected to the control circuit. As, 5-DOF manipulator is manufactured, five servo motors are used: one motor for every joint. The motion of the motors is controlled by Arduino Uno unit which is a cheap and easy programming unit. Experiments are executed with the developed robot to show its effectiveness and success by preparing three boxes which the robot effectively transfers from one place to another. Eventually, the challenges during the design and manufacturing of this robot are mentioned in this paper. 
Parametric Analysis of Climate Factors for Monthly Weather Prediction in Ghardaïa District Using Machine Learning-Based Approach: ANN-MLPs Dahmani, Abdennasser; Ammi, Yamina; Ikram, Kouidri; Kherrour, Sofiane; Hanini, Salah; Al-Sabur, Raheem; Laidi, Maamar; Ma’arif, Alfian; Sharkawy, Abdel-Nasser
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1651

Abstract

In the rapidly developing field of smart cities, accurately predicting weather conditions plays a vital role in various sectors, including industry, tourism, agriculture, social planning, architecture, and economic development. Unfortunately, the instruments used (such as pyranometers, barometers, and thermometers) often suffer from low accuracy, high computational costs, and a lack of robustness. This limitation affects the reliability of weather predictions and their application across these critical areas. This study proposes artificial neural network-multilayer perceptrons (ANN-MLPs). A dataset of 480 data points was used, with 80% allocated for the training phase, 10% for the validation phase, and 10% for the testing phase. The best results were obtained with the structure 6-17-1 (6 inputs, 17 hidden neurons, and 1 output neuron) to predict weather condition data in the Ghardaïa district. Weather conditions parameters include air temperature, relative humidity, wind speed, and cumulative precipitation. Results showed that the most relevant input factors are, in order of importance: earth-sun distance (DT-S) with a relative importance (RI) of 31.10%, factor conversion (d) with an RI of 26.05%, and solar radiation (SR) with an RI of 16.26%. The contribution of the elevation of the sun (HI) has an RI of 13.29%. The optimal configuration includes seventeen neurons in the hidden layer with a logistic sigmoid activation function and a Levenberg–Marquardt learning algorithm, resulting in a root mean square error (RMSE) of 3.3043% and a correlation coefficient (R) of 0.9683. The proposed model can predict both short- and long-term climate factors such as solar radiation, air temperature, and wind energy in areas with similar conditions.
Impact of Inertial and External Forces on Joint Dynamics of Robotic Manipulator: Experimental Insights Sharkawy, Abdel-Nasser
Control Systems and Optimization Letters Vol 3, No 1 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i1.163

Abstract

In this paper, the effect of the inertial and external forces applied on the links of the robotic manipulator is studied and investigated on the manipulator joints’ parameters through experimental analysis. For this investigation and experiments, KUKA LWR manipulator is used and structured as a 2-DOF manipulator. Experimental work is carried out by commanding a sinusoidal joint motion to the two joints of the manipulator. Different scenarios are studied such as motion with free of collisions, motion with collision on the link between the two joints of the manipulator, motion with collision on the end-effector, and motions with different constant joint speeds. The diagrams of the position, velocity, acceleration, and torque of the manipulator joints are obtained and recorded from KUKA robot controller and then investigated and evaluated. The results reveal that during a motion free of collision, small spikes are found on the signals of the joint position, velocity, acceleration, and torques. These spikes resulted from the inertial forces applied on the joint. During a motion with collision, the signals of joint position, velocity, acceleration, and torque are highly affected due to the collision, inertial forces, and friction. During a collision on the end-effector, the torques of both joints are highly affected. During a collision on a link between the two joints, the torque of the first joint is highly affected, and the torque of the second joint is slightly affected. When the speed of the joint is increased, the torque signal is highly affected. These findings provide insights into the dynamic behavior of robotic manipulators under external forces, with implications for improving control algorithms and collision detection systems.
Ensuring Safety in Human-Robot Cooperation: Key Issues and Future Challenges Sharkawy, Abdel-Nasser; Mahmoud, Khaled H.; Abdel-Jaber, Gamal T.
Control Systems and Optimization Letters Vol 2, No 3 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i3.154

Abstract

Human-robot cooperation (HRC) is becoming increasingly essential in many different sectors such as industry, healthcare, agriculture, and education. This cooperation between robot and human has many advantages such as increasing and boosting productivity and efficiency, executing the task easily, effectively, and in a fast time, and minimizing the efforts and time. Therefore, ensuring safety issues during this cooperation are critical and must be considered to avoid or minimize any risk or danger whether for the robot, human, or environment. Risks may be such as accidents or system failures. In this paper, an overview of the safety issues of human-robot cooperation is discussed. The main key challenges in robotics safety are outlined and presented such as collision detection and avoidance, adapting to unpredictable human behaviors, and implementing effective risk mitigation strategies. The difference between industrial robots and cobots is illustrated. Their features and safety issues are also provided. The problem of collision detection or avoidance between the robot and environment is defined and discussed in detail. The result of this paper can be a guideline or framework to future researchers during the design and the development of their safety methods in human-robot cooperation tasks. In addition, it shapes future research directions in safety measures.
Modeling the Structural Dynamics of Carbon Fiber Composites for Robotic Systems Under Sinusoidal Load Al-Sabur, Raheem; Ameen, Yahya Muhammed; Khalaf, Hassanein I.; Mishra, Akshansh; Sharkawy, Abdel-Nasser
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1693

Abstract

The demand for robotic systems employing composite materials is steadily improving due to their high bending stiffness, favorable strength-to-weight ratio, and durability under dynamic loading. It is still challenging to guarantee dynamic stability and precise frequency response in composite robotic components. This study addresses these issues by conducting a simulation-based 3D bending analysis and frequency response modeling of carbon/epoxy and carbon/PPS composites under sinusoidal loading. The remarkable mechanical and thermal properties of carbon/epoxy and carbon/PPS composites, such as their high specific strength, stiffness, and excellent fatigue resistance, align well with the requirements of robotic systems. The model comparison involved analyzing three-dimensional bending stresses, displacements, and free vibration dynamics for both materials under a sinusoidal load applied to their inner surfaces. The sinusoidal load was selected to simulate periodic dynamic forces commonly encountered in robotic applications, such as oscillating arms, vibrating components, and cyclic loading during operation. The thick shell (S=4) of axial length (L=4S) and circumferential span (α=45°) comprises cross-ply laminate [90°/0°/90°] with supported boundary conditions. The transverse displacement of the carbon PPS composite cylindrical shell was 0.719 nm, which was lower than that of the carbon epoxy composite (0.746 nm). The same behavior was observed for the stress values. Conversely, the PPS composite cylindrical shell yielded a higher natural frequency. The obtained eigenvalues indicated a similar behavior when comparing the shape modes with a relative increase in their values in the carbon PPS composite.
Transformer Models in Deep Learning: Foundations, Advances, Challenges and Future Directions Mangkunegara, Iis Setiawan; Purwono, Purwono; Ma’arif, Alfian; Basil, Noorulden; Marhoon, Hamzah M.; Sharkawy, Abdel-Nasser
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i2.13053

Abstract

Transformer models have significantly advanced deep learning by introducing parallel processing and enabling the modeling of long-range dependencies. Despite their performance gains, their high computational and memory demands hinder deployment in resource-constrained environments such as edge devices or real-time systems. This review aims to analyze and compare Transformer architectures by categorizing them into encoder-only, decoder-only, and encoder-decoder variants and examining their applications in natural language processing (NLP), computer vision (CV), and multimodal tasks. Representative models BERT, GPT, T5, ViT, and MobileViT are selected based on architectural diversity and relevance across domains. Core components including self-attention mechanisms, positional encoding schemes, and feed-forward networks are dissected using a systematic review methodology, supported by a visual framework to improve clarity and reproducibility. Performance comparisons are discussed using standard evaluation metrics such as accuracy, F1-score, and Intersection over Union (IoU), with particular attention to trade-offs between computational cost and model effectiveness. Lightweight models like DistilBERT and MobileViT are analyzed for their deployment feasibility. Major challenges including quadratic attention complexity, hardware constraints, and limited generalization are explored alongside solutions such as sparse attention mechanisms, model distillation, and hardware accelerators. Additionally, ethical aspects including fairness, interpretability, and sustainability are critically reviewed in relation to Transformer adoption across sensitive domains. This study offers a domain-spanning overview and proposes practical directions for future research aimed at building scalable, efficient, and ethically aligned. Transformer-based systems suited for mobile, embedded, and healthcare applications.
Temperature Monitoring System Internet of Things-based Electric Cars (IoT) Ardana, Regina Olivia Fitri; Ma'arif, Alfian; Marhoon, Hamzah M; Salah, Wael; Sharkawy, Abdel-Nasser
Signal and Image Processing Letters Vol 7, No 1 (2025)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v7i1.107

Abstract

Electric cars are a means of transportation that can meet the mobility needs of the community but are still environmentally friendly because they have no air pollution or exhaust emissions. Electric cars at Ahmad Dahlan University began to be made since 2019. In the effort to develop this electric car, there are several obstacles in monitoring the tools on the electric car during the race. So this research provides an Internet of Things-Based Battery and BLDC Motor Temperature Monitoring System on the ADEV 01 Monalisa Electric Car. which is made using several components including the DS18B20 Sensor, ADEV BLDC Motor, NodeMCU ESP32, LCD (Liquid Crystal Display). This research method develops a temperature monitoring system on an Internet of Things-based electric car using the Thinger.io platform. in this study tested the effectiveness of the DS18B20 temperature sensor in monitoring the temperature of the Battery and BLDC Motor on the ADEV 01 Monalisa electric car. the tests carried out were static testing, dynamic testing, testing data transmission to the Thinger.io platform, and distance testing. The results of testing the battery and BLDC motor on the ADEV 01 Monalisa Electric Car in a static state are good because the reading error is 0.60% and 0.50%. As for testing while running, namely 0.90% and 0.86%. Testing on the Internet of Things is successfully sent with a stable and fixed delay. therefore this parameter is good for monitoring electric vehicles. Researchers conducted distance testing for the Internet of Things using Thinger.io which aims to find out how far the internet connection can send sensor readings to Thinger.io. so the results obtained that a distance of 230 meters the internet connection is disconnected and cannot send data to Thinger.io.
Classification of Heart (Cardiovascular) Disease using the SVM Method Abidin, Minhajul; Munzir, Misbahul; Imantoyo, Adi; Bintang Grendis, Nuraqilla Waidha; Hadi San, Ahmad Syahrul; Mostfa, Ahmed A.; Furizal, Furizal; Sharkawy, Abdel-Nasser
Indonesian Journal of Modern Science and Technology Vol. 1 No. 1 (2025): January
Publisher : CV. Abhinaya Indo Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64021/ijmst.1.1.9-15.2025

Abstract

Cardiovascular disease is one of the leading causes of death worldwide, with a high mortality rate, especially in developing countries like Indonesia. This highlights the importance of developing systems to identify and detect heart disease at an early stage. In this study, the Support Vector Machine (SVM) algorithm was used to classify cardiovascular diseases by utilizing a dataset consisting of 303 patient records obtained from Kaggle. The dataset was divided into 70% for training and 30% for testing. Before optimization using GridSearchCV, the SVM model achieved an accuracy of 79%, precision of 79%, recall of 73%, and F1-score of 76%. However, after adjusting the hyperparameters with GridSearchCV, the model's accuracy slightly decreased to 77%, with precision remaining at 79%, recall dropping to 66%, and F1-score at 72%. Despite this decline in performance after optimization, the results indicate that although SVM has potential for classifying heart disease, its performance is highly influenced by data quality and the selection of appropriate hyperparameters. Even with the performance decrease postoptimization, the model still provides useful predictions, showing consistent results and a proportional class distribution.
Artificial Intelligence-Enhanced Sensorless Vector Control of Induction Motors Using Adaptive Neuro-Fuzzy Systems: Experimental Validation and Benchmark Analysis Bekhiti, Belkacem; Fragulis, George F.; Hariche, Kamel; Sharkawy, Abdel-Nasser
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.1950

Abstract

This study addresses the limitations of traditional Model Reference Adaptive Systems (MRAS) in sensorless induction motor (IM) control, particularly the degraded performance at low speeds and under dynamic load conditions. The main objective is to enhance speed and torque estimation accuracy by replacing the classical proportional-integral (PI) adaptation mechanism with an adaptive neuro-fuzzy architecture. The research contribution lies in developing and experimentally validating two intelligent adaptation schemes: one based on fuzzy logic and another combining fuzzy inference with a recurrent neural network (RNN) within a sensorless field-oriented control (FOC) framework. The proposed system integrates a fuzzy logic-based estimator and an RNN-driven torque predictor to improve tracking precision and robustness. Real-time implementation was carried out on a 1.1 kilowatt, 1430 revolutions per minute induction motor using a dSPACE DS1104 platform. Comparative experiments were conducted under two challenging benchmark profiles that include load disturbances, parameter mismatches, and full-speed reversals. Results showed that the hybrid neuro-fuzzy controller reduced the steady-state speed error by 91 %, from 0.65 rad/s to 0.08 rad/s, and improved torque estimation accuracy by 42%, reducing SMAPE from 45.2 % to 26.3 %, compared to the PI-based MRAS. It also outperformed the standalone fuzzy and neural MRAS controllers in rise time, tracking error, overshoot suppression, and adaptation quality. These findings confirm that the proposed method provides improved estimation fidelity, enhanced control robustness, and reliable sensorless operation suitable for real-time industrial applications. The study concludes that the integration of neuro-fuzzy intelligence into MRAS-based control structures offers a technically effective and scalable solution for advanced IM drives.
Co-Authors Abdel-Jaber, Gamal T. Abed, Ali A. Abidin, Minhajul Al-Sabur, Raheem Alfian Ma’arif Almtori, Safaa A. S. Ameen, Yahya Muhammed Ammi, Yamina Ardana, Regina Olivia Fitri Basil, Noorulden Bekhiti, Belkacem Bintang Grendis, Nuraqilla Waidha Dahmani, Abdennasser Driss, Zied Fragulis, George F. Furizal, Furizal Hadi San, Ahmad Syahrul Hamdan, I. Hamed, Ahmed Hassan Hanini, Salah Hariche, Kamel Ikram, Kouidri Imantoyo, Adi Khairullah, Shawkat Sabah Khalaf, Hassanein I. Kherrour, Sofiane Laidi, Maamar Listyantoro, Fiki Maghrabie, Hussein M. Mahmoud, Khaled H. Mangkunegara, Iis Setiawan Marhoon, Hamzah M Marhoon, Hamzah M. Mishra, Akshansh Mostfa, Ahmed A. Muhammad Amirul Mu'min Munzir, Misbahul Nazzal, Jamal Mahmoud Purwono Purwono, Purwono Salah, Wael Talib, Doaa Alaa Tri Stiyo Famuji Tristanti, Novi Wijaya, Setiawan Ardi Yana Safitri, Yana Zaibel, Ali Habel