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All Journal Journal of Robotics and Control (JRC) Indonesian Journal of Electrical Engineering and Computer Science
Shehadeh, Hisham A.
Unknown Affiliation
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Monitoring DC Motor Based On LoRa and IOT Suhermanto, Dimas Ahmad Nur Kholis; Aribowo, Widi; Shehadeh, Hisham A.; Rahmadian, Reza; Widyartono, Mahendra; Wardani, Ayusta Lukita; Hermawan, Aditya Chandra
Journal of Robotics and Control (JRC) Vol 5, No 1 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i1.19642

Abstract

Electrical energy efficiency is a dynamic in itself that continues to be driven by electrical energy providers. In this work, long-range (LoRa) technology is used to monitor DC motors. In the modern world, IoT is becoming increasingly prevalent. Embedded systems are now widely used in daily life. More can be done remotely in terms of control and monitoring. LoRa is a new technology discovered and developing rapidly. LoRa technology addresses the need for battery-operated embedded devices. LoRa technology is a long-range, low-power technology. In this investigation, a LoRa transmitter and a LoRa receiver were employed. This study employed a range of cases to test the LoRa device. In the first instance, there are no barriers, whereas there are in the second instance. The results of the two trials showed that the LoRa transmitter and receiver had successful communication. In this study, the room temperature is used to control DC motors. So that the DC motor's speed adjusts to fluctuations in the room's temperature. Additionally, measuring tools and the sensors utilised in this investigation were contrasted. The encoder sensor and the INA 219 sensor were the two measured sensors employed in this study. According to the findings of the experiment, the tool was functioning properly.
Novel modified Chernobyl disaster optimizer for controlling DC motor Aribowo, Widi; Shehadeh, Hisham A.
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1361-1369

Abstract

This article presents the modified Chernobyl disaster optimizer (CDO) method for DC motor control to find the optimal proportional integral derivative (PID) settings. DC motors are widely used machinery. DC motors are also simple to use. The detonation of the Chernobyl nuclear reactor core served as the inspiration for the idea and guiding principles of the CDO. CDO has limitations in the stability of exploration and exploitation areas. This research aims to obtain a new balance of exploration and exploitation. This study suggests incorporating the levy flight and chaotic algorithm (CA) techniques to enhance the CDO method. This study was conducted with the MATLAB/Simulink software. A comparative technique, which included the marine predator algorithm (MPA), golden jackal optimization (GJO), and CDO, was utilized to determine the performance of the MCDO method. According to the study’s findings, the MCDO method’s overshoot value outperformed all other approaches.
A Comparative Study of Metaheuristic Optimization Algorithms in Solving Engineering Designing Problems Aribowo, Widi; Shehadeh, Hisham A.
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.26410

Abstract

This paper presents a comprehensive comparative study of several metaheuristic optimization algorithms with the aim of identifying the most effective method for solving well-established engineering design problems. The algorithms selected for this study include Sperm Swarm Optimization (SSO), Chernobyl Disaster Optimizer (CDO), Bermuda Triangle Optimizer (BTO), Marine Predators Algorithm (MPA), and Particle Swarm Optimization (PSO). These algorithms are tested and evaluated through both qualitative and quantitative analyses.The first phase of testing involves applying the algorithms to a set of benchmark functions from the Congress on Evolutionary Computation (CEC) 2017 suite. Key performance indicators such as best fitness value, standard deviation, and mean are used to measure solution quality, while convergence curves are analyzed to assess optimization efficiency over iterations. This allows for a robust evaluation of each algorithm's ability to balance exploration and exploitation in the search space. In the second phase, the algorithms are implemented to solve real-world engineering design problems, including Speed Reducer Design, Pressure Vessel Design, Cantilever Beam Design, and Robot Gripper Optimization. These case studies further validate the practical applicability and versatility of the algorithms in handling complex, multidimensional, and constrained optimization tasks. The results indicate varying levels of performance across different problems, highlighting the strengths and limitations of each method. This comparative insight provides valuable guidance for researchers and practitioners in selecting suitable optimization techniques for specific engineering challenges.
Co-Authors Aditya Chandra Hermawan Ayusta Lukita Wardani Mahendra Widyartono Rahmadian, Reza Suhermanto, Dimas Ahmad Nur Kholis Widi Aribowo