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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 361 Documents
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Study and Analysis of PWM with DC-DC Converter for Inverting Buck-Boost Inverter Topology Gaber, Rajaa Khalaf; Shneen, Salam Waley; Jiaad, Suaad Makki
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.v5i2.1823

Abstract

The simulation aims to study and analyze the effect of the duty cycle on the output voltage and signal reflection. This type of simulation is important for many practical applications of inverter boost converters, such as renewable energy systems or portable electronics. A voltage converter is being developed to generate a negative voltage output, i.e., it has the ability to invert the output signal. The converter's input is connected to a DC voltage source, and is intended to generate a higher or lower voltage, depending on the application requirements, while maintaining the inverting output signal. This converter is used in many fields, most notably those powered by batteries, such as portable devices, where the required voltage varies depending on the load. Converters regulate and provide a stable and suitable voltage for the batteries. A study and analysis of these converters will address these challenges by building and designing a simulation model to generate a voltage suitable for covering the load or charging the batteries, operating efficiently and reliably under various operating conditions. Its effectiveness can be verified through proposed tests covering operating conditions suitable for real-time operation. The first contribution is to verify the possibility of changing the converter output signal to the same value as the converter output voltage during the pulse generator duty cycle (50%). The second contribution is to verify the possibility of increasing the value of the converter output voltage in the pulse generator duty cycle (70%) or decreasing the value of the converter output voltage in the pulse generator duty cycle (20%). The results demonstrated the effectiveness of the proposed model and the possibility of changing the output voltage value with changing the output signal.
Optimizing Small-Scale Wind Energy Generation: Site-Specific Wind Speed Analysis and Turbine Placement Strategies Ahmed, Shouket A.; Çiçek, Adem; Bektas, Enes; Yassin, Khalil Farhan; Radhi, Ahmed Dheyaa; Awad, Raad Hamza; Almalaisi, Taha Abdulsalam; Itankar, Nilisha; Sekhar, Ravi; Ahmed, Ahmed H.
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.v5i2.1792

Abstract

Wind is an effective renewable power source suitable for localized electricity production when regional environmental factors have substantial impact on system output. The research studies the best wind turbine placement through wind speed variability studies conducted with calibrated anemometers and data loggers that assess site conditions. A data-based assessment method creates the research's main contribution which facilitates the optimization of wind power potential measurement for enhanced energy efficiency. The research methodology includes continuous Vantage Pro2 equipment together with anemometers at different heights for wind speed observation while performing accuracy-based calibration analysis. The research shows that elevating the turbine from seven meters to ten meters leads to a 12 percent growth in the amount of power produced. The power output of wind energy decreases as wind speed changes because of environmental conditions so proper installation locations become essential. Energy performance increases best when selecting sites which feature reliable and elevated wind speeds. This research provides useful knowledge about enhancing decentralized power generation through wind energy but it cannot be easily scaled up to bigger systems. The study demonstrates that specific site assessments together with practical recommendations will enhance the efficiency of small-scale wind energy systems.
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.
Third-Order Sliding Mode Control of Five-Phase Permanent Magnet Synchronous Motor Using Direct Torque Control Based on a Modified SVM Algorithm Mehedi, Fayçal; Bouyakoub, Ismail; Yousfi, Abdelkader; Reguieg, Zakaria
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.v5i3.1899

Abstract

Direct Torque Control (DTC) is a powerful method for multiphase drive systems, offering significant performance and efficiency gains, but its implementation is challenged by complexities like uncertainties and disturbances. This research addresses these issues, particularly the variable switching frequencies of hysteresis controllers with switching table and the limitations of conventional proportional-integral (PI) controllers in the outer loop, to enhance DTC for superior control in multiphase drives. The study proposes an improved DTC technique for a five-phase permanent magnet synchronous motor (5Ph-PMSM). This strategy integrates a robust nonlinear third-order super-twisting sliding mode control (TOSMC) with a modified space vector modulation (MSVM) algorithm. The MSVM is based on calculating the minimum and maximum of the five-phase voltages, contributing to optimized performance. This proposed DTC-TOSMC-MSVM approach significantly outperforms conventional DTC (DTC-Conv). It achieves tighter control, substantially reducing flux and torque ripple, and minimizing response time. Furthermore, it lowers the total harmonic distortion (THD) and improves disturbance rejection. The merits of the proposed strategy of 5Ph-PMSM are demonstrated through various tests. MATLAB simulations confirm these benefits, showing an 88.88% reduction in speed response time compared to DTC-Conv. Additionally, the proposed method reduces flux ripple by 51.85%, torque ripple by 63.15%, and stator current THD by 61.08%. In addition, the proposed method demonstrates robust performance when faced with changes in machine parameters and load disturbances, making it superior to traditional DTC approaches.
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.

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