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Alfian Ma'arif
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International Journal of Robotics and Control Systems
Published by Association for Scientific Computing Electronics and Engineering
ISSN : -     EISSN : 27752658     DOI : https://doi.org/10.31763/ijrcs
Core Subject : Engineering,
Control & Systems Engineering Electrical & Electronics Engineering
International Journal of Robotics and Control Systems is open access and peer-reviewed international journal that invited academicians (students and lecturers), researchers, scientists, and engineers to exchange and disseminate their work, development, and contribution in the area of robotics and control technology systems experts. Its scope includes Industrial Robots, Humanoid Robot, Flying Robot, Mobile Robot, Proportional-Integral-Derivative (PID) Controller, Feedback Control, Linear Control (Compensator, State Feedback, Servo State Feedback, Observer, etc.), Nonlinear Control (Feedback Linearization, Sliding Mode Controller, Backstepping, etc.), Robust Control, Adaptive Control (Model Reference Adaptive Control, etc.), Geometry Control, Intelligent Control (Fuzzy Logic Controller (FLC), Neural Network Control), Power Electronic Control, Artificial Intelligence, Embedded Systems, Internet of Things (IoT) in Control and Robot, Network Control System, Controller Optimization (Linear Quadratic Regulator (LQR), Coefficient Diagram Method, Metaheuristic Algorithm, etc.), Modelling and Identification System.
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 50 Documents
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Search results for , issue "Vol 5, No 2 (2025)" : 50 Documents clear
Optimizing Virtual Classrooms: Real-Time Emotion Recognition with AI and Facial Features Sakhi, Abdelhak; Mansour, Salah-Eddine
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1827

Abstract

Online education, especially post-COVID, faces the challenge of maintaining student engagement, particularly at the college level. A key factor in effective learning is understanding students’ emotional states, as they influence comprehension and participation. To address this, we propose an intelligent system that classifies students’ emotions by analyzing facial expressions, allowing teachers to adapt their methods in real-time. Our system utilizes the Learning Focal Point algorithm to improve emotion classification accuracy, focusing on key facial regions related to emotional expressions. The methodology involves preprocessing facial images, extracting features, and classifying emotions using the algorithm. Trained on a diverse dataset, the system performs well under various conditions, with a classification accuracy of 94% based on a well-known database. Although the system shows significant improvements over traditional methods, factors like image quality and internet connection can impact accuracy in realworld applications. Ultimately, our approach enhances remote learning by providing real-time emotional feedback, fostering a more responsive and student-centered environment.
Robust Multi-State EEG Cognitive Classification via Optimized Time-Domain Features and CatBoost Nassir, Layla M.; Ramadhan, Ali J.; Al-Sharify, Noor T.; Khalaf, Mohammed I.; Ogaili, Ahmed Ali Farhan; Jaber, Alaa Abdulhady; Al-Sharify, Zainab T.
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1799

Abstract

This study introduces a novel framework for classifying multi-state cognitive processes using electroencephalogram (EEG) signals. By integrating optimized time-domain feature extraction with ensemble learning techniques, the proposed method achieves exceptional accuracy in distinguishing eight distinct cognitive states. The preprocessing pipeline employs finite impulse response (FIR) bandpass filtering (0.5–45 Hz) and Independent Component Analysis (ICA) for artifact removal, while feature extraction leverages Hjorth parameters and statistical measures. A comparative analysis of classification algorithms reveals CatBoost as the top performer, achieving 93.4% accuracy, followed by Neural Network (91.3%), SVM (89.7%), and AdaBoost (88.9%). CatBoost excels in discriminating complex states with computational efficiency, processing times ranging from 18 ms (SVM) to 32 ms (CatBoost), supporting real-time applications. The framework demonstrates robustness under varying signal quality, maintaining 91% accuracy at 10 dB SNR. These advancements set new benchmarks for EEG-based cognitive monitoring, with implications for adaptive systems requiring real-time neural feedback.
Enhancement of Transient Stability and Power Quality in Grid-Connected PV Systems Using SMES Heroual, Samira; Belabbas, Belkacem; Elzein, I. M.; Diab, Yasser; Ma'arif, Alfian; Mahmoud, Mohamed Metwally; Allaoui, Tayeb; Benabdallah, Naima
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1760

Abstract

One of the main issues with grid-connected distributed energy systems, including photovoltaic (PV) systems, is the DC bus voltage's instability during load fluctuations and power line short circuits. This paper attempts to address this problem and proposes to use superconducting magnetic energy storage (SMES) to stabilize the voltage of the DC link and improve the power quality and transient stability of the power system. The investigated configuration components are PV cells, boost converter, chopper, SMES, three level inverter (NPC), filter, grid, and load. MATLAB / Sim Power System is used to test the performance of a SMES in order to ensure the balance of the DC bus voltage of a PV system connected to the grid. Several scenarios were considered to show the performance and benefits of combining a SMES with the PV system. The outcomes of the examined scenarios (fault and load change) demonstrate the precision of the employed control systems, maintaining the DC voltage at acceptable levels (?500 V), enhances the structure stability, and improving power quality (GPV THD = 4.34). Finally, it can be concluded that the proposed configuration will help in achieving high penetration scenarios of PV systems.
Study and Analysis of Adaptive PI Control for Pitch Angle on Wind Turbine System Ibrahim, Luay G.; Shneen, Salam Waley
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1850

Abstract

In the current work, a study is proposed using the engineering program MATLAB through computer tests of a simulation model for modifying the tilt angle in wind turbines, with a study of the effect of changing the angle of the wind turbine on the mechanical energy resulting from changing wind speed. Variable wind speeds reduce turbine efficiency; pitch control mitigates this. A PI-based pitch controller adjusts blade angles to maintain optimal ?.20 kW model achieved 15% higher power output at variable speeds. ? (tip-speed ratio) and Cp, ? the ratio of blade tip speed to wind speed, determines turbine efficiency. Unlike prior fixed-speed models, our variable-speed design adapts to turbulent winds via real-time pitch adjustment. This approach aids in stabilizing grid integration for renewable energy systems. While pitch control improves turbine efficiency, existing studies lack real-time adaptive strategies for variable wind speeds. our work optimizes pitch angles dynamically using MATLAB simulations. We propose a data-driven pitch control model for 5 kW and 20 kW turbines, validated under turbulent wind conditions. This study aims to maximize power output by correlating pitch angle (?) and tip-speed ratio (?) via MATLAB simulations. As a research contribution, the turbine characteristic curve is examined, as changes occur with changes in lambda, and the Cp Max is obtained at the optimal lambda. Assuming that beta is chosen from the curves to determine how it changes and its effect on operation at a given Cp, a given lambda is determined from the curve. Torque can be recognized as the first variable, both mathematically and physically. A change in torque affects speed, and thus affects lambda. Since there is a relationship between turbine speed and wind speed with lambda, turbine speed also depends on mechanical speed. The aim of the study is to design and build a simulation model using a mathematical representation of a wind turbine to study the effect of tilt angle control on handling changes in wind speed. The research contributions include the design of two models: one with a capacity of 5 kW and the other with a capacity of 20 kW. The first model uses a constant speed, while the second uses a variable wind speed. To stabilize the output at rated power, the turbine is angled. Using the wind turbine simulation model and some proposed tests, we can determine the behavior of the system as speed changes.
Strategic Chess Algorithm-Based PI Controller Optimization for Load Frequency Control in Two-Area Hybrid Photovoltaic–Thermal Power Systems Obma, Jagraphon; Audomsi, Sitthisak; Ardhan, Kittipong; Sa-Ngiamvibool, Worawat; Chansom, Natpapha
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1844

Abstract

Maintaining frequency stability in hybrid renewable-integrated power systems remains a critical challenge due to the inherent variability and uncertainty of photovoltaic–thermal (PV–T) energy sources. Traditional proportional–integral (PI) controllers, optimized using conventional metaheuristic algorithms such as the Whale Optimization Algorithm (WOA), Firefly Algorithm (FA), and Salp Swarm Algorithm (SSA), often suffer from limitations including slow convergence, premature convergence to local optima, and reduced robustness under severe load disturbances. The research contribution is the development and systematic evaluation of a chess algorithm (CA)-based PI controller tuning approach for enhancing load frequency control (LFC) in hybrid PV–T systems. Unlike population-based methods, the CA employs chess-inspired strategic decision-making processes, which improve the search efficiency and the ability to escape local optima in high-dimensional optimization problems. In this study, the proposed CA-based optimization method is applied to a two-area hybrid PV–T power system, where the system is subject to various operating conditions, including solar radiation fluctuations and step load perturbations. The tuning of PI controller parameters is performed using the integral of time-weighted absolute error (ITAE) as the objective function. Simulation results demonstrate that the CA-optimized PI controller achieves superior performance in minimizing overshoot, undershoot, and settling time when compared with controllers optimized by WOA, FA, and SSA. Specifically, the CA approach achieves faster stabilization and lower frequency deviations, highlighting its potential for real-time implementation and enhanced grid reliability. Future work will explore the scalability of the proposed method to multi-area power systems and evaluate its computational efficiency through hardware-in-the-loop validation.
Trend Analysis of Ergonomics in Improving Supply Chain Management Systematic Literature Review in Last Twenty Years: Knowledge Taxonomy Louah, Soulaiman; Sarir, Hicham
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1875

Abstract

The last ten years have seen a rise in scholarly interest in ergonomics in the supply chain management (SCM) discipline because of technological advancements, as it’s possible effects on productivity, worker satisfaction, and overall business success become more apparent. Nevertheless, there aren't many review studies on the subject at hand. To overcome the restriction, the study thoroughly examined the body of research on ergonomics in SCM that is currently available to enhance comprehension of the state of knowledge. This research article examines peer-reviewed works that have been published between 2000 and 2024 and are accessible through the Scopus database. After thoroughly searching the literature using the Scopus database, 84 papers published in 27 peer-reviewed international journals were found. The current study examines the trend of publications in the area of the analysis of ergonomics in sustaining SCM as well as the most well-known and prolific authors and articles. Then, to find topic clusters, a bibliometric analysis was done with VOSviewer as the primary metric tool in this study for visualizing and analyzing the major hotspots and the evolution of ergonomics in SCM research. The using of co-citation analysis and bibliographic coupling to construct the network map uncovers intriguing themes and patterns in the field of ergonomics in SCM and that points to the need for greater international cooperation in tackling this problem. That’s why our work has improved our understanding of ergonomics in SCM, and the results have led to recommendations for further research.
Smart Healthcare Framework: Real-Time Vital Monitoring and Personalized Diet and Fitness Recommendations Using IoT and Machine Learning Charfare, Ruwayd Hussain; Desai, Aditya Uttam; Keni, Nishad Nitin; Nambiar, Aditya Suresh; Cherian, Mimi Mariam
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1839

Abstract

Adopting a healthy lifestyle necessitates a well-balanced nutritional plan and personalized exercise routines aligned with an individual's health status. The healthcare system often lacks personalized care, leading to weak prevention and generic diets. This study presents an IoT-based framework for easy health monitoring without frequent doctor visits. The system integrates sensors to measure vital indicators like pulse rate, body temperature, SpO?, and BMI, with minimal assistance from healthcare personnel. Utilizing data gathered from individuals aged 16–25, ML algorithms like Logistic Regression, Random Forest, and KNN analyze the parameters to deliver personalized dietary and fitness recommendations. The dataset includes BMI, body temperature, pulse rate, and SpO2 measurements gathered via an integrated IoT unit. Before analysis, the data was refined and optimized through ML algorithms. This comprehensive approach moves beyond traditional diagnostic methods by incorporating personalized recommendations, including dietary plans and exercise routines, tailored based on the evaluated data. Among the evaluated algorithms, Random Forest demonstrated the highest accuracy (99%) in a 60:40 training-to-testing ratio. To improve accessibility, a user-friendly web platform is designed, facilitating seamless interaction and engagement. The framework unifies real-time monitoring, cardiovascular risk detection, and adaptive guidance, bridging fragmented digital health solutions for early intervention and better health outcomes.
Classifying Gait Disorder in Neurodegenerative Disorders Among Older Adults Using Machine Learning Rahman, Kazi Ashikur; Shair, Ezreen Farina; Abdullah, Abdul Rahim; Lee, Teng Hong; Ali, Nursabillilah Mohd; Zakaria, Muhammad Iqbal; Al Betar, Mohammed Azmi
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1722

Abstract

Gait disorders are a significant concern for older adults, particularly those with neurodegenerative diseases such as Parkinson’s disease, Huntington’s disease, and Amyotrophic Lateral Sclerosis. Accurately classifying these conditions using gait data remains a complex challenge, especially in older populations, due to age-related changes in gait patterns, comorbidities, and increased variability in mobility, which can obscure disease-specific characteristics. This study explicitly classifies neurodegenerative diseases in older adults by analysing age-specific gait force data. Continuous Wavelet Transform (CWT) was utilised for advanced feature extraction, capturing both temporal and spectral signal characteristics. Classifiers including Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Multilayer Perceptron (MLP) were employed. The results demonstrated that SVM achieved an accuracy of 87.5%, outperforming RF and MLP, which achieved 83.3% and 50.0%, respectively. These findings underscore the importance of using tailored machine learning approaches to improve the diagnosis and management of neurodegenerative diseases in older adults. The potential for real-world application includes integration into clinical settings, enabling early detection and personalized interventions for individuals with gait disorders.
Performance Enhancement of BLDC Motor Drive Systems Using Fuzzy Logic Control and PID Controller for Improved Transient Response and Stability Abdullah, Zainab B.; Shneen, Salam Waley; Dakheel, Hashmia Sharad
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1882

Abstract

Currently, systems generally need control units, which requires designing them to analyze the behavior of the system when there are suitable characteristics of the motor according to the required application. The electric motor is very important in many applications and is widely used because of the high-efficiency mechanical power, small sizes, and relatively high torques that these electrical machines have. Improving the performance of systems requires control units, which are of the types of traditional PID, expert Fuzzy, and intelligent control systems. Two systems were proposed, a system that relies on a traditional control unit and a system that relies on fuzzy logic to improve and raise the efficiency of performance and handle system fluctuations resulting from disturbances and different operating conditions. Simulation tests were conducted using MATLAB. The effectiveness of the proposed controllers is evaluated through measurement criteria including efficiency improvements, torque ripple reduction, or settling time. Simulation results for both the closed-loop system using the conventional controller and the expert controller showed that the improvement in system performance can be determined according to criteria that include response speed as well as the overshoot and undershoot rates. Specifically, the settling time using the conventional controller was 3.05 msec. The rise time using the conventional controller was 205.406 msec, while using the expert controller it was 205.406 msec. The overshoot rate (%) using the conventional controller was 18.452%, while using the expert controller it was 6.989%. The undershoot rate using the conventional controller was 6.633%, while using the expert controller it was 1.987%.
Real-Time Pose Estimation for Autonomous Vehicles Using Probabilistic Landmark Maps and Sensor Fusion Farag, Wael A.; Fayed, Mohamed
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1851

Abstract

This study introduces a robust and accurate method for estimating autonomous vehicle position, facilitating safe navigation in urban and highway settings. The proposed technique employs a probabilistic particle filter framework, which, unlike approaches constrained by Gaussian assumptions, represents probability densities as samples, enabling more flexible position estimation. A key innovation lies in integrating a finely tuned Unscented Kalman Filter (UKF) to fuse radar and lidar data specifically for robust detection of pole-like static landmarks, whose positions and associated uncertainties are probabilistically modeled within an offline reference map. The particle filter leverages Bayesian filtering, associating UKF-derived landmark observations with this probabilistic map to refine the vehicle's pose. Broad simulation tests validate the method's effectiveness, achieving a mean localization error of approximately 11 cm in both longitudinal and lateral directions. Furthermore, the system demonstrates robustness, maintaining localization accuracy below 30 cm even with landmark position uncertainties up to 2 meters, and confirms real-time capability exceeding 100 Hz. These findings establish the approach as a reliable and precise solution for autonomous vehicle localization across various scenarios.

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