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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
Efficient Detection Classifiers for Genetically-Modified Golden Rice Via Machine Learning Gutierrez, Joshua Balistoy; Arboleda, Edwin Romeroso
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.v5i1.1686

Abstract

Rice is a staple food for over half of the global population, especially in the Philippines. However, traditional rice lacks essential micronutrients like vitamin A, contributing to widespread Vitamin A Deficiency (VAD). Golden Rice was developed to combat VAD, and this is biofortified with beta-carotene, a precursor of Vitamin A. However, concerns about cross-contamination, food safety, and ethics have emerged. Current GMO detection methods, such as PCR and ELISA, are not ideal for large-scale or on-site use since these are intended to be performed inside laboratory and requires technical expertise.  This study presents a novel machine learning (ML)-based approach for the detection of genetically modified Golden Rice using RGB image data and several classification models as an efficient, rapid, non-destructive method to detect GMO Golden Rice. Two datasets of rice images (340 samples of GMO Golden Rice and 340 samples of Traditional Rice) were processed and split for training and testing (80-20 ratio). This study found that WEKA's Random Tree and MATLAB's Trilayered Neural Network achieved 100% accuracy in detecting GMO Golden Rice, with the fastest computational efficiency in their respective platforms. Additional metrics, such as Precision and Recall, further verified the robustness of these classifiers.  This research lays the foundation for developing portable, field-deployable detection tools to empower farmers and regulators while enhancing consumer trust in GMO labeling. Furthermore, the application of ML to GMO rice detection opens new possibilities for biofortified crop monitoring. Future work may explore integrating additional rice features and GMO varieties, validating the results, and expanding this methodology to other GMO rice variants and hybrid varieties to further enhance detection accuracy and scalability.
Fuzzy Dynamic Feedback Linearization for Efficient Mobile Robot Trajectory Tracking and Obstacle Avoidance in Autonomous Navigation Louda, Souhaib; Karkar, Nora; Seghir, Fateh; Boutalbi, Oussama
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.v5i2.1780

Abstract

Mobile robots are increasingly used in various applications that require precise trajectory tracking and efficient obstacle avoidance. Dynamic Feedback Linearization (DFL) is powerful method, however, it’s has limitations such as increased computational requirements, model dependency, inability to avoid obstacles, and reduced robustness. In this paper, we address the challenges of trajectory tracking and obstacle avoidance for non-holonomic mobile robots in certain static environments subjected to the challenge of the robot to follow the reference trajectory accurately while avoiding the known obstacle in the trajectory of the robot by switching the two behaviors. The proposed scheme leverages the adaptive performance control to minimize the error between the reference and actual trajectories and avoid the static obstacle successfully. Firstly, the Dynamic Feedback Linearization (DFL) concept is used to develop an efficient tracking control system. Secondly, a Fuzzy Logic Controller (FLC) is used to avoid obstacles in the reference trajectory of the robot . Finally, the simulations are conducted using MATLAB software and the TurtleBot2 mobile robot within the 3D Gazebo simulator. According to the simulation results, the proposed approach cuts tracking accuracy and obstacle avoidance success rate by 93% and 95%, respectively. Additionally, experimental validation is carried out with the Adapt Mobilerobots Pioneer-3DX mobile robot, the results obtained from the Robot Operating System (ROS) prove the efficacy of the proposed approach for efficiency and precision.
Hybrid Deep Learning Model for Hippocampal Localization in Alzheimer's Diagnosis Using U-Net and VGG16 Najjar, Fallah H.; Hassan, Nawar Banwan; Kadum, Salman Abd
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.v5i2.1739

Abstract

Alzheimer's disease (AD) is a complex neurodegenerative disease that involves considerable challenges in accurately diagnosing and locating the?affected brain regions. This paper?proposes a new fusion model based on VGG16 and U-Net to achieve accurate segmentation of hippocampus localization and improve AD diagnostic accuracy. Compared to previous techniques such as hierarchical fully?convolutional networks (FCNs) or LBP-TOP localization (an accuracy range of 68% to 95%), our approach achieved a superior accuracy (98.6%) with a mean Jaccard index of 97.3%, like the predicted accuracy range of conventional imaging analysis techniques. By utilizing pre-trained transfer learning models and sophisticated data augmentation methods,?generalization to different datasets greatly reduced over-fitting. Although existing approaches?usually require labor-intensive segmentation or employ handcrafted features, our model automates the hippocampus's localization, leading to improved efficiency and scalability. The effectiveness of our method is strongly supported by the performance metrics including Mean Squared Error (MSE) and Avg. error Standard Deviation which show that MSE values were 5 times lower than those produced using the Hough-CNN based?approach (0.0507 vs. 4.4%). Real-world demands include the need for minimal computational complexity and dependence?on pre-processed ADNI MRI datasets compromising generalizability in actual clinical frameworks. Our results?demonstrated that the fusion model yields superior hippocampal segmentation performance and a new standard for AD diagnostic scores, making a substantial impact on both academic and clinical domains.
Hand Keypoint-Based CNN for SIBI Sign Language Recognition Handayani, Anik Nur; Amaliya, Sholikhatul; Akbar, Muhammad Iqbal; Wiryawan, Muhammad Zaki; Liang, Yeoh Wen; Kurniawan, Wendy Cahya
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.v5i2.1745

Abstract

SIBI is less widely adopted, and the lack of an efficient recognition system limits its accessibility. SIBI gestures often involve subtle hand movements and complex finger configurations, requiring precise feature extraction and classification techniques. This study addresses these issues using a Hand Keypoint-based Convolutional Neural Network (HK-CNN) for SIBI classification. The research utilizes Kinect 2.0 for precise data collection, enabling accurate hand keypoint detection and preprocessing. The optimal data acquisition distance between 50 and 60 cm from the camera is considered to obtain clear and detailed images. The methodology includes four key stages: data collection, preprocessing (keypoint extraction and image filtering), classification using HK-CNN with ResNet-50, EfficientNet, and InceptionV3, and performance evaluation. Experimental results demonstrate that EfficientNet achieves the highest accuracy of 99.1% in the 60:40 data split scenario, with superior precision and recall, making it ideal for real-time applications. ResNet-50 also performs well with 99.3% accuracy in the 20:80 split but requires longer computation time, while InceptionV3 is less efficient for real-time applications. Compared to traditional CNN methods, HK-CNN significantly enhances accuracy and efficiency. In conclusion, this study provides a robust and adaptable solution for SIBI recognition, facilitating inclusivity in education, public services, and workplace communication. Future research should expand dataset diversity and explore dynamic gesture recognition for further improvements.
Enhancing the Performance of Grid-Tied Renewable Power Systems Using an Optimized PI Controller for STATCOM Aljohani, Masnour
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.v5i2.1704

Abstract

Integrating electrical networks with renewable energy sources in hybrid systems may effectively meet increasing power demands while reducing reliance on traditional energy sources. Wind gusts in wind energy conversion systems (WECSs), along with variations in temperature and irradiance in photovoltaic (PV) systems, render these systems vulnerable. Three-phase faults at the point of common coupling (PCC) can disconnect renewable energy sources (RESs) from the grid, threatening system stability. This study enhances a hybrid PV-WECS system through the implementation of a static synchronous compensator (STATCOM) to mitigate wind gust effects and maintain RES connectivity during three-phase faults at the point of common coupling (PCC). STATCOM manages reactive power exchange between renewable energy sources and the grid through two PI controllers. The gains of the PI controller are optimized through elephant herding optimization (EHO), demonstrating superior performance compared to particle swarm optimization (PSO) in terms of PCC voltage stability and system efficiency. In three-phase faults, the EHO demonstrates superior performance over the PSO, achieving a PCC voltage of 0.7 in contrast to 0.37, thereby maintaining voltage levels within acceptable limits in the connecting zone according to grid codes. The EHO-optimized PI controllers for the STATCOM successfully reduce the SRG current during this fault, decreasing it from 155 (with PSO) to 111 (with EHO). Under wind gust conditions, the power profile obtained from the SRG is markedly enhanced when employing EHO in comparison to PSO.
Global Existence for Heat Equation with Nonlinear and Damping Piecewise Neumann Boundary Condition Batiha, Iqbal M.; Chebana, Zainouba; Oussaeif, Taki-Eddine; Abu-Ghurra, Sana; Al-Nana, Abeer; Bataihah, Anwar; Jebril, Iqbal 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.v5i2.1653

Abstract

The Columbia space shuttle catastrophe in 2003 served as the inspiration for this paper’s improved mathematical model, which includes a nonlinear damping Neumann boundary condition. By creating and examining a modified heat equation with piecewise nonlinear source terms and damping Neumann boundary conditions, the study seeks to investigate the incident’s heat transport dynamics. To ensure that the problem is well-posed, we provide strong mathematical arguments for the existence of solutions both locally and globally. In addition, we use numerical simulations to show how the nonlinear boundary conditions affect heat dissipation and to confirm the theoretical results. The findings advance our knowledge of thermal modeling in aircraft applications and offer greater insights into heat propagation under such conditions.
Reduction of Large Scale Linear Dynamic MIMO Systems Using Adaptive Network Based Fuzzy Inference System Oudah, Manal Kadhim; Shneen, Salam Waley; Aessa, Suad Ali
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.v5i2.1684

Abstract

Large Scale Multiple Input Multiple Output (MIMO) technology is a promising technology in wireless communications, and it is already at the heart of many wireless standards. MIMO technologies provide significant performance improvements in terms of data transfer rate and reduction the interference. However, MIMO techniques face large-scale linear dynamic problems such as system stability and it will be possible to overcome this problem by tuning the proportional integral derivative (PID) in continuous systems. The aim of this paper is to design an efficient model for MIMO based on Adaptive Neural Inference System (ANFIS) controller and compare it with a traditional PID controller. and evaluated by objective function as integral time absolute error (ITAE). ANFIS is used to train fuzzy logic systems according to the hybrid learning algorithm. The training involves the fuzzy logic parameters through simulating the validation data to represent a model to know the correctness and effectiveness of the system. It is optimizes the system performance in real time, however, to avoid potential problems such as easy local optimality. In the proposed approach stability is guaranteed as the initial steady-state scheme. ITAE is combined with ANFIS to minimize the steady-state transient time responses between the high-order initial pattern and unit amplitude response. The proposed ANFIS self-tuning controller is evaluated by comparing with the conventional PID. MATLAB simulink is used to illustrate the results and demonstrate the possibility of adopting ANFIS controller. The simulation results showed that the performance of ANFIS controller is better than the PID controller in terms of settling time, undershoot and overshoot time.
Improving TCP/AQM Network Stability Using BBO-Tuned FLC Nadhim, Rasha F.; Oudah, Manal Kadhim; Aziz, Ghada Adel; 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.v5i2.1761

Abstract

One of the modern technologies used to improve the performance of various systems, including communications networks and the Internet, is the technology based on biogeography (BBO) that many researchers in the field of automation and control have shed light on. Fuzzy logic is one of the expert systems that has dealt with its use in control systems by many researchers within different applications. The current work has shed light on the mechanism of using The Biogeography Based-Optimization (BBO) technique for adjusting FLC parameters is called (BBO-FLC). The simulation was performed using Matlab program and the researchers adopted the technique as part of the stability of TCP network. The performance of the techniques used in the optimization process can be identified by comparing the results of each case, such as the proposed technique, with other types represented by the traditional control type Proportional–integral–derivative controller (PID). The possibility of using modern and intelligent optimization techniques for the optimal controller is tested using a tuning process for the parameters of the fuzzy type expert controller with the help of the biogeography-based optimization (BBO) technique. The contributions of the research are to verify the possibility of improving the performance by comparing the behavior of the system for the proposed test and simulation cases by obtaining the prescribed level and without exceeding the permissible values.
Optimized Selective Harmonic Elimination in CHB-MLI Using Red-Tailed Hawk Algorithm for Unequal DC Sources Yahia, Elaf Hamzah; Hamad, Hasan Salman; Ahmed, Shouket A.; Almalaisi, Taha Abdulsalam; Majdi, Hasan S.; Ahmed, Omer K.; Solke, Nitin; Sekhar, Ravi
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.v5i2.1777

Abstract

The study develops an optimized SHE procedure to regulate a CHB-MLI powered by PV modules which use unequal DC sources. The main goal involves finding suitable switching angles that produce minimal low-order harmonics during steady output voltage operation under variable input scenarios. The Red-Tailed Hawk Algorithm (RTHA) serves as a recent bio-inspired metaheuristic optimization method to solve effectively the nonlinear transcendental SHE equations. The MATLAB/Simulink environment implements a validation of the proposed method by modeling a three-phase 7-level CHB-MLI system. A performance evaluation of the proposed algorithm occurs against established optimization methods consisting of Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA). Total Harmonic Distortion reduction, computational efficiency and convergence rate serve as the three main performance indicators for evaluation. The experimental findings show RTHA accomplishes higher harmonic reduction while offering improved speed and stability when dealing with unequal DC voltage issues when contrasted against traditional optimization methods. RTHA operates better than analytical approaches in real-world inverter applications through its flexible and adaptable approach despite needing complex calculations and preset conditions. The scale-up of RTHA applications requires additional research because excessive computational requirements and initial value dependencies must be addressed. The research shows that RTHA-based SHE optimization represents a viable and implementable solution for power quality advancement in renewable energy systems.
Design of a Small Wind Turbine Emulator for Testing Power Converters Using dSPACE 1104 Boutabba, T.; Benlaloui, Idriss; Mechnane, F.; Elzein, I. M.; Ma'arif, Alfian; Hassan, Ammar M.; Mahmoud, Mohamed Metwally
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.v5i2.1685

Abstract

Interest in wind turbine emulators (WTE) has increased due to the growing need for wind power generation as a low-maintenance, more effective substitute for conventional models. This paper presents the design of a small WTE utilizing a dSPACE 1104 system. The setup includes a DC motor, driven by a buck converter, coupled to a permanent magnet synchronous generator, all managed through a hardware-in-the-loop configuration using the dSPACE 1104 board. The DC motor simulates the rotational motion generated by wind energy, accurately replicating the characteristics of an actual WT. This control system enables the simulation of various wind speeds and torque values in MATLAB/Simulink software, providing a valuable tool for analyzing and developing power converters. The results obtained confirmed the effectiveness of the proposed emulator, as the experimental outcomes closely matched the theoretical calculations.

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