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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 708 Documents
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Type-2 Fuzzy Logic-Based Robot Navigation in Uncertain Environments: Simulation and Real-World Implementation Hachani, Soufiane; Nechadi, Emira
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i1.25553

Abstract

This study presents a type-2 fuzzy logic-based navigation system for mobile robots in uncertain environments, emphasizing both simulation and real-world implementation. The proposed system integrates two type-2 fuzzy logic controllers: one for path-following and another for handling uncertainty in dynamic surroundings. To evaluate the system’s effectiveness, numerical simulations are conducted in cluttered and unpredictable environments, followed by real-world tests. The evaluation considers success rates, path efficiency, and computational cost, demonstrating an improvement of up to 92% in navigation accuracy and 8% in handling environmental uncertainty compared to conventional fuzzy logic methods. Despite its robustness, the approach faces computational overhead and adaptability challenges in highly unstructured settings. The study highlights the scalability of the method, discussing its potential application to different robotic platforms and uncertain scenarios. The findings confirm that type-2 fuzzy logic enhances real-time decision-making in navigation while offering a resilient alternative to traditional path-planning methods.
Development of Method to Predict Career Choice of IT Students in Kazakhstan by Applying Machine Learning Methods Berlikozha, Bauyrzhan; Serek, Azamat; Zhukabayeva, Tamara; Zhamanov, Azamat; Dias, Oliver
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i1.25558

Abstract

The growing intricacy of IT education requires resources to aid students in choosing specialized pathways. This study investigates the prediction of specialization preferences among IT students at SDU University in Kazakhstan through the application of machine learning techniques. The research contribution is the development of a predictive model that enhances academic advising by incorporating multiple factors, including academic performance, personality traits, qualifications, and extracurricular involvement. The research examined 692 anonymized student profiles and evaluated the efficacy of five machine learning algorithms: Random Forest, K-Nearest Neighbors, Support Vector Machine, Gradient Boosting, and Naive Bayes. Stratified 10-fold cross-validation was utilized to reduce the risk of overfitting. Gradient Boosting attained a peak accuracy of 99.10% in validation; however, its performance decreased to 92.16% on an independent test set, suggesting overfitting. Naive Bayes exhibited the lowest accuracy, recorded at 35.26%. Logistic regression analysis indicated a statistically significant correlation (p < 0.05) among academic performance, extracurricular involvement, and specialization selection. Personality traits and certifications significantly influenced the prediction process. The findings suggest that although Gradient Boosting demonstrates high effectiveness, the associated risk of overfitting requires additional refinement for practical application. The notable impact of academic performance and extracurricular activities indicates that educational institutions ought to prioritize these elements in student guidance. The incorporation of machine learning-based recommendations into advising frameworks enhances the precision of specialization predictions, thereby improving student decision-making and career alignment.
Tracking Iterative Learning Control of TRMS using Feedback Linearization Model with Input Disturbance Danh, Hoang Dang; Van, Chi Nguyen; Van, Quy Vu
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i1.25579

Abstract

This paper presents a method for angular trajectory tracking control of the Twin Rotor Multi-Input Multi-Output System (TRMS) experimental model using linearized feedback control with nonlinear compensation and iterative learning-based angular trajectory tracking control. First, the dynamic model of the Twin Rotor MIMO System (TRMS) is developed in the form of Euler-Lagrange (ELF), including descriptions of uncertain parameters and input disturbances such as energy dependence related to the mass of components, friction forces, the effect of the TRMS flat cable, and the impact of the main rotor and tail rotor speeds on horizontal and vertical movements. Based on the nonlinear acceleration equations for the pitch and yaw angles of the TRMS, a compensator is designed to address the nonlinearity of the EL model. Notably, this compensator self-adjusts the compensation signal so that the closed-loop system, consisting of the TRMS and the compensator, becomes a predetermined linear model. Therefore, the structure of the compensator does not need to be designed based on the nonlinear model of the TRMS. After incorporating the compensator, the ELF becomes nearly linear with sufficient accuracy as designed. This system is then controlled using a predefined trajectory tracking controller based on iterative learning with proportional-type learning parameters. By adjusting a sufficiently small optional time parameter, the trajectory tracking error of the pitch and yaw angles of the closed-loop system can be reduced to a desired small-radius neighborhood. Simulation and experimental results demonstrate the trajectory-tracking capability of the closed-loop system. Although the convergence rate depends on the complexity of the TRMS dynamics, the robustness of this method with varying initial conditions is always ensured. The computational complexity is slightly higher compared to other methods, Still, this study contributes a straightforward yet effective trajectory control method under conditions of noise depending on the position, velocity, pitch and yaw angles and unmeasured kinematic model parameters for the TRMS system.
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.

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