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Journal of Robotics and Control (JRC)
Published by Universitas Muhammadiyah Yogyakarta
ISSN : 27155056     EISSN : 27155072     DOI : https://doi.org/10.18196/jrc
Core Subject : Science, Engineering,
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering
Journal of Robotics and Control (JRC) is an international open-access journal published by Universitas Muhammadiyah Yogyakarta. The journal invites students, researchers, and engineers to contribute to the development of theoretical and practice-oriented theories of Robotics and Control. Its scope includes (but not limited) to the following: Manipulator Robot, Mobile Robot, Flying Robot, Autonomous Robot, Automation Control, Programmable Logic Controller (PLC), SCADA, DCS, Wonderware, Industrial Robot, Robot Controller, Classical Control, Modern Control, Feedback Control, PID Controller, Fuzzy Logic Controller, State Feedback Controller, Neural Network Control, Linear Control, Optimal Control, Nonlinear Control, Robust Control, Adaptive Control, Geometry Control, Visual Control, Tracking Control, Artificial Intelligence, Power Electronic Control System, Grid Control, DC-DC Converter Control, Embedded Intelligence, Network Control System, Automatic Control and etc.
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 40 Documents
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Search results for , issue "Vol. 6 No. 4 (2025)" : 40 Documents clear
Optimizing Resource Allocation and Link Reliability in IoT–Fog–Cloud Networks Using Machine Learning and Multi-Objective Algorithms Lakshmi, M. Sri; Kiran, Palakeeti; Suneetha, S; Sri, Buradagunta Swathi; Madala, Srinivasa Rao; Bhagavatham, Naresh Kumar; Bhavsingh, Maloth
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.v6i4.25026

Abstract

The internet of things (IoT) necessitates efficient real-time data transfer protocols to support its vast array of interconnected devices. This study presents an optimized framework for resource allocation and link reliability in IoT–fog–cloud networks by integrating an enhanced support vector machine (ESVM) for link stability prediction with a Communication and Energy Integration for latency improvement (CAELI) algorithm for multi-objective optimization. The proposed system improves the quality of service (QoS) by dynamically selecting energy-efficient, low-latency paths while accounting for network conditions and resource constraints. The ESVM leverages historical link characteristics to assess reliability, whereas CAELI minimizes communication delay and energy usage through adaptive optimization. The simulation results indicate that the model achieves consistent improvements across metrics such as link reliability, end-to-end delay, energy consumption, throughput, and packet delivery ratio (PDR), maintaining a PDR above 94%, which is particularly significant in real-time control systems where even minor packet loss can compromise operational integrity. A comparative analysis with existing baseline and recent optimization approaches demonstrated superior performance in both static and moderately dynamic network environments. However, the model’s effectiveness may be influenced by factors such as network scale, node mobility, and the complexity of parameter tuning in CAELI, which can affect the convergence rate and computational efficiency. These limitations suggest the need for further validation in large-scale heterogeneous IoT deployments. The proposed framework underscores the viability of combining predictive modeling with multi-objective optimization to enhance responsiveness, energy efficiency, and reliability in distributed fog-assisted architectures for time-sensitive IoT applications.
Improving Classification Accuracy of Breast Ultrasound Images Using Wasserstein GAN for Synthetic Data Augmentation Mas Diyasa, I Gede Susrama; Humairah, Sayyidah; Puspaningrum, Eva Yulia; Durry, Fara Disa; Lestari, Wahyu Dwi; Caesarendra, Wahyu; Dewi, Deshinta Arrova; Aryananda, Rangga Laksana
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.v6i4.25075

Abstract

Breast cancer remains one of the most prevalent cancers in Indonesia, and early detection plays a vital role in improving patient outcomes. Ultrasound imaging is a non-invasive and accessible technique used to classify breast conditions into normal, benign, or malignant categories. The advancement of deep learning, particularly Transfer Learning with Convolutional Neural Networks (CNNs), has significantly enhanced the performance of automated image classification. However, the effectiveness of CNNs heavily relies on large, balanced datasets—resources that are often limited and imbalanced in medical domains. To address this issue, this study explores the use of Wasserstein Generative Adversarial Networks (WGAN) for synthetic data augmentation. WGAN is capable of learning the underlying distribution of real ultrasound images and generating high-quality synthetic samples. The inclusion of the Wasserstein distance stabilizes training, with convergence observed around 2500–3000 epochs out of 5000. While synthetic data improves classifier performance, there remains a potential risk of overfitting, particularly when the synthetic images closely mirror the training data. Compared to traditional augmentation techniques such as rotation, flipping, and scaling, WGAN-generated data provides more diverse and realistic representations. Among the tested models, VGG16 achieved the highest accuracy of 83.33% after WGAN augmentation. Nonetheless, computational resource limitations posed challenges in training stability and duration. Furthermore, issues related to model generalizability, as well as ethical and patient privacy considerations in using synthetic medical data, must be addressed to ensure responsible deployment in real-world clinical settings.
Adaptive Sliding Mode Control for Trajectory Tracking in Three-Wheeled Mobile Robots: Experimental Validation and Performance Analysis Doan, Hoa Van
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.v6i4.25570

Abstract

This paper presents an adaptive sliding mode control approach (ASMC) designed for trajectory tracking of a three-wheeled mobile robot (TWMR), accounting for external disturbances and wheel slippage effects. First, the TWMR system model is converted into a dynamic form of the tracking error, and then a SMC is designed for this error model. The synthetic disturbance is approximated through an adaptive law, which helps the system maintain high stability. The results from simulating the controller on Matlab/Simulink software, as well as implementing the algorithm on the experimental TWMR model, have demonstrated the accuracy and efficiency of the proposed method.
Advanced Sliding Mode Control with Disturbance Rejection Techniques for Multi-DOF Robotic Systems Basal, Mohamed Abdelhakim
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.v6i4.25779

Abstract

For the control of complex and non-linear systems such as robotic arms, especially in sensitive systems such as medical applications and chemical industries, it becomes necessary to improve the performance considering the balance between fast response and smooth, vibration-free, in addition to overcoming disturbances and model uncertainty. These and other reasons may be the reason for the failure of some linear and classical control systems. This research presents a hybrid control system that combines sliding mode control (SMC) with an active disturbance rejection controller (ADRC) for a three-degree-of-freedom (3-DOF) robotic arm. The research contributes to developing a robust control system that reduces the vibrations caused by the classical SMC and utilizes its advantages to achieve smooth, fast, high dynamic response. The proposed method combines the benefits of SMC stiffness for regulating the angular velocities and ADRC in disturbance compensation to regulate the angular positions, ensuring smooth and accurate control despite its relative complexity. The simulation results show that the classical SMC methodology provides superior performance compared to the traditional PIDC in terms of low settling time, but suffers from higher overshoot and large vibrations that sometimes cause a large value of tracking error. In contrast, the proposed control methodology contributes to the improvement of the robotic arm performance, achieving higher tracking accuracy, tracking error minimization, very low settling time, and clear vibration cancellation in both the output signals and the applied control signals. The proposed system has clear advantages, so it can provide a promising solution for robotic arms, particularly in industries demanding high performance, fast tracking and minimal vibrations.
Optimized Deep Learning Architecture for Pediatric Pneumonia Diagnosis in Chest Radiographs with Integration of EfficientNetB4, Topological Convolutional Layers, and Advanced Augmentation Strategies M, Inbalatha; N, Raghu
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.v6i4.25810

Abstract

Pediatric pneumonia diagnosis through chest X-ray analysis is complicated by subtle radiographic patterns and diagnostic subjectivity. A deep learning architecture integrating transfer learning with EfficientNetB4 as a feature extraction backbone is proposed, enhanced by a supplementary 3×3 convolutional layer (ReLU activation) and global average pooling to preserve localized pathological features. The dataset comprises 5,863 pediatric anterior-posterior chest radiographs curated from Guangzhou Women and Children’s Medical Center, rigorously validated by three board-certified radiologists to ensure diagnostic fidelity. Stratified sampling allocated 80% for training, 10% for validation, and 10% for testing, with stochastic augmentation (rotation: ±5°, width/height shift: ±10%, shear: 20%, horizontal flip) addressing class imbalance and enhancing model generalizability. Training employed Adam optimization (initial learning rate: 0.001) with binary cross-entropy loss, dynamically modulated via ReduceLROnPlateau (factor: 0.3, patience: 3). Independent test evaluation yielded 97.7% accuracy (95% CI: 96.8–98.5%), AUC-ROC of 0.9954, and F1-scores of 0.9842 (pneumonia) and 0.9573 (normal), supported by a Matthews correlation coefficient (MCC) of 0.9416 and Cohen’s Kappa of 0.9416. Precision-recall analysis demonstrated a 98.4% positive predictive value for pneumonia identification. The architecture’s robustness to imaging variability and high diagnostic precision positions it as a scalable triage tool in low-resource healthcare settings, potentially reducing diagnostic latency and improving pediatric outcomes.
Enhanced Xception Model for Deepfake Detection: Integrating CBAM, Contrastive Learning, and a Stacking Classifier Jyothi, B N; Jabbar, M 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.v6i4.25811

Abstract

Deepfake detection has become increasingly vital in the era of sophisticated fake media generation techniques. Threats posed by these deep fakes make deep fake detection inevitable. Research on Deep fake detection has been conducted extensively. But problems like resource intensive models, generalizability across datasets are still existing. To overcome the above problems, we propose a framework which embraces the transfer learning and lightweight architecture of xception model. The framework consists of three major inherent steps for deep-fake detection. The first step involves a feature extractor that uses the pretrained Xception as the backbone. The feature extractor has two branches for global and local feature extraction. The global feature branch uses the pre-trained Xception for feature extraction, while the local feature branch uses the xception model enhanced through Convolutional Block Attention Module (CBAM) enhanced to effectively extract deepfake-specific features and contrastive learning to equip Xception with discriminative power for feature extraction. Once the local and global features are extracted, two separate Random Forest classifiers are trained on these features. Finally, the predicted probabilities from these two models are ensembled using a logistic regression meta-model. To avoid the effects of class imbalance on the model performance, care was taken to balance samples in each category through augmentations. The model is trained on Face Forensics++ dataset and evaluated for cross datasets on Celeb-Df and UADFV datasets. Given that generalization across datasets is a major challenge faced by deepfake detection models, we integrate domain adaptation where our model performs noticeably well minimal fine-tuning using 10 % data. The proposed framework showed significant improvements with a 5% increase in accuracy, a 1% increase in ROC, and a 2% increase in precision compared to state-of-the-art (SOTA) models.
A Comparative Analysis of Recent MPPT Algorithms (P&O\INC\FLC) for PV Systems Maamar, Yahiaoui; Elzein, I. M.; Benameur, Afif; Mohamed, Horch; Mahmoud, Mohamed Metwally; Mosaad, Mohamed I.; Shaaban, Salma Abdelaal
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.v6i4.25814

Abstract

Although solar (PV) power generators have been widely deployed, one important barrier to their effective energy capture is weather variability. It is a very challenging effort for these systems to operate at MPPT. Conventional MPPT methods still had an excessively long convergence period to the MPP. Because of their superior data processing, intelligent approaches are nevertheless given a reasonable length of time to reach the maximum point, beginning with the objective of keeping the PV generator in the MPP with outstanding performance. To accomplish MPPT, a comparison between intelligent (fuzzy control (FLC)) and conventional algorithms (perturb-and-observe (P&O) and the incremental conductance (INC)) is investigated. To do this, a mathematical model of PV cells based on two diodes with shunt and series resistors is created with MATLAB/Simulink. The model characteristics curves with the parameters listed in the MSR SOLAR datasheet are compared. Finally, we compared the results of the FLC with those of the P&O and the INC. The results obtained demonstrated the superiority of the FLC-MPPT controller.
Modeling and Control of an 8-Legged Stewart Platform Using Null-Space Control for Precise Motion Under Actuator Constraints Siradjuddin, Indrazno; Fitria, Ida Lailatul; Al Azhar, Gillang; Riskitasari, Septyana; Ronilaya, Ferdian; Wicaksono, Rendi Pambudi
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.v6i4.25920

Abstract

This paper investigates the modeling, control, and redundancy resolution of an 8-legged Stewart platform, emphasizing the use of null-space control to achieve precise trajectory tracking while adhering to actuator constraints. The proposed control framework combines a Proportional-Integral-Derivative (PID) controller with null-space projection to exploit the platform’s inherent redundancy for secondary objectives, such as singularity avoidance, energy optimization, and enhanced fault tolerance. A clamping strategy ensures that actuator lengths remain within operational limits, thereby preventing mechanical failures. Simulation results demonstrate significant error reduction in both position and orientation, even under strict actuator constraints. Specifically, the system achieved exponential convergence to the desired pose within 3 s, with a maximum position error of less than 1 × 10−3 m and orientation error below 5 × 10−4 rad. Actuator efficiency was also enhanced, as the algorithm dynamically redistributed efforts among actuators to avoid overloading any single leg. While energy consumption was not explicitly optimized in this study, the framework provides a foundation for future work in minimizing energy usage through advanced secondary objectives. Stability is analyzed rigorously using Lyapunov’s direct method. Compared to traditional six-legged platforms, the 8- legged design offers superior flexibility and adaptability, making it particularly suitable for applications in flight simulators, robotic surgery, and industrial automation where precision and reliability are critical. However, the proposed approach has certain limitations. For instance, the current implementation assumes ideal actuator dynamics and does not account for uncertainties such as friction, backlash, or external disturbances. Additionally, the clamping strategy may introduce computational overhead, potentially impacting real-time performance in highly dynamic scenarios. Future research could address these limitations by incorporating adaptive or robust control techniques and optimizing computational efficiency. This work advances the design and control of redundant parallel manipulators, offering practical insights into dealing with physical limitations and providing a foundation for future innovations in high-performance motion control systems.
Early Detection of Short Circuit Faults Between Windings in Distribution Transformers Using Finite Element Method Aljammal, Mustafa Thaer; Alyozbaky, Omar Sh.
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.v6i4.26004

Abstract

The primary aim and contribution of this study is the presentation of a non-intrusive early diagnosis method based on finite element simulation (FEM). The focus was on a 1000 kVA distribution transformer based on manufacturing data and field tests conducted in Mosul, Iraq. An accurate two-dimensional model of the transformer was developed using ANSYS Maxwell software, simulating normal operation and various internal fault scenarios (such as single-phase or double-phase short-circuits and ground faults) at varying rates. The resulting changes in magnetic flux distribution, core losses, currents, and voltages were analyzed as indicators to determine the presence, type, and severity of faults. A representation of internal faults in the three-phase transformer windings was performed to detect and diagnose faults early. The results clearly show that small short-circuit faults (up to 1.2% of the windings) are distinguishable by specific changes in transformer parameters. These faults lead to a localized temperature increase and the onset of insulation deterioration. It was also observed that an increase in the fault percentage (5% to 25%) causes a significant increase in magnetic flux and total losses. These effects are significantly exacerbated by ground faults or faults involving two phases. These results confirm that computational analysis provides a powerful tool for proactive monitoring, enabling preventive maintenance scheduling based on initial fault indications. This contributes to extending transformer life, enhancing network reliability, and avoiding costly catastrophic failures. Continuous monitoring and effective ground protection remain critical elements for maintaining transformer safety and efficiency.
SECRE-MEN: A Lightweight Quantum-Resilient Authentication Framework for IoT-Edge Networks Faleh, May Adnan; Abdulsada, Ali M.; Alaidany, Ali A.; Al-Shareeda‬‏, ‪Mahmood A.; Almaiah, Mohammed Amin; Shehab, Rami
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.v6i4.26006

Abstract

The wide 6G-IoT and Mobile Edge Computing (MEC) deployments give rise to severe concerns in authentication, revocation and protection against quantum-post and side channel attacks. In this paper, SECRE-MEN (Secure and Efficient Cryptographic Revocable Authentication for MEC enabled Networks) is presented to be a lightweight and scalable authentication architecture specifically designed for the resource limited IoT systems. SECRE-MEN consists of three main parts: (1) Masked Cryptographic Techniques that are used to randomise elliptic curve operations, thereby mitigate side-channel attacks, (2) VCs, providing support for digitally-signed, lightweight authentication, without requiring the use of bulky certificates, and (3) a Bloom filter-based RDB, which is distributed across multiple MEC nodes, to allow for fast, memory-efficient revocation checks. To enable future-proof security post-quantum cryptography (PQC) is included in SECRE-MEN by lattice-based schemes, such as Kyber and Dilithium, which may incur additional computational cost on ultra-low-power platforms according to the trade-off introduced in this paper. Effort experiments show that the proposed RAM-MENAMI decreases 29.3% the computation cost, and reduces 21.8% the communication budget and improve 20.3% of power efficiency in comparison with the RAM-MEN. In addition, SECRE-MEN is resistant against impersonation, MITM, replay and quantum attacks, as well as allows for dynamic revocation and secure synchronization among MEC nodes. This places SECREMEN as an effective toolkit for cybersecurity of massive IoT-MEC networks in the era of the evolving 6G.

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