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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
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Journal : International Journal of Robotics and Control Systems

 1 
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.
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.
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.
Automated Water Cooling and Solar Tracking for Efficiency Improvement of PV Systems: A Systematic Review Hamed, Ahmed Hassan; Sharkawy, Abdel-Nasser; Hamdan, I.; Maghrabie, Hussein M.
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1642

Abstract

This article presented previous efforts for overcoming low photovoltaic (PV) solar panel electrical efficiencies resulted from excess heat problem reached in hot climates. Utilizing water cooling, temperature-controlled water cooling and solar tracking solar systems are discussed in this paper. Water is a perfect medium can be used for absorbing excess heat due to its high thermal capacity, availability and low cost. In addition to, utilizing control systems for water cooling systems based on Arduino unit and microcontroller chip which can be interfaced with Bluetooth, WIFI, and Internet of Things (IOT) enhances saving time and effort in large PV solar plants and PV performance. Solar tracking systems, depend on light-dependent resistors (LDRs) which are resistors operated by incident light, or ultraviolet (UV) sensors which are detectors based on incident ultraviolet radiation sensing enhances PV performance. Solar tracking systems enhances PV electrical efficiency compared to fixed PV panels. PV efficiencies of latest studies were presented and compared. Utilizing water cooling systems enhances PV electrical efficiency up to 30%, using an ON-OFF temperature-controlled water-cooling systems increased overall efficiency up to 51.4% and can reduce consumption of water up to 29.28%. In addition to, using two solar tracking systems enhances PV solar panel efficiency up to 65%. The increase in PV installation faces challenges includes millions of solar waste tons that harms environment and human health. However, it can be eliminated utilizing recycling technologies. Artificial intelligence (AI), machine learning techniques would enhance PV performance analyzing and data collection.
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