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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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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.
Design and Evaluation of Secure Software Architectures for 5G-Enabled Vehicular Driving System Saare, Murtaja Ali; Mattar, Ali K.; Sari, Sari Ali; Wong, Seng Yue
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.v6i4.26032

Abstract

Vehicular Ad-hoc Networks (VANETs) represent support for Intelligent Transport Systems (ITS) that allow vehicles and infrastructures to exchange real-time information. Nevertheless, the introduction of the 5G technology for the VANETs poses new security challenges, especially considering the emerging quantum computing threats. In response to this problem, we present a secure software architecture, Lattice Efficient Mutual Authentication (LEMA), designed to improve vehicular communication in 5G supported environments. The research novelty is the construction of LEMA—a lightweight and scalable framework for robust authentication in the fog, based on lattice-based postquantum cryptography, which is also resilient to classical and quantum-based attacks and provides low latency. The framework operates based on three core phases: initialization by a Trusted Authority, secure private key generation, and mutual authentication via LWE-based schemes. A testbed which is built on a Raspberry Pi is used for simulating OBUs to verify LEMA performance in a resource-constrained environment. We compare LEMA with the state of art and get the performance numbers for the computational overhead, communication cost and storage efficiency. Simulation results show that with LEMA, the computational time, the communication amount and the storage consumed can be decreased by at least 25%, 30% and 20% than the benchmark protocols, respectively, and it is secure against the man-in-themiddle and the key-compromise attacks. The authors’ use of fog servers for deployment of the system also significantly boosts real-time responsiveness. Finally, the LEMA model presents a promising quantum-secure authentication technique for 5G-based vehicular networks. In the future we plan to combine it with AIbased anomaly detection and blockchain, for better scalability, privacy and decentralization.
Q-RCR: A Modular Framework for Collision-Free Multi-Package Transfer on Four-Wheeled Omnidirectional Conveyor Systems Kautsar, Syamsiar; Aisjah, Aulia Siti; Arifin, Syamsul; Syai'in, Mat
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.v6i4.26050

Abstract

Modern logistics systems increasingly require high flexibility in handling simultaneous package transfers in compact, dynamic environments without collisions. Improper handling of multi-package transfers in omnidirectional conveyor systems can lead to deadlocks, congestion, or delivery delays, particularly in grid-based environments where routing complexity increases with package variability and layout density. This research addresses these challenges by introducing Q-RCR, a modular Q-Learning-based framework with Rule-Based Conflict Resolution (RCR) for intelligent path planning and collision handling in Four-Wheeled Omnidirectional Cellular Conveyor (FOCC) systems. The research contribution is decoupling path learning and collision handling, enabling independent agent training while minimizing computational burden and improving convergence in multi-agent scenarios. The proposed Q-RCR framework integrates Q-Learning for route optimization with a rule-based conflict resolution module, applying four adaptive strategies: Sequential Transfer, Insert Path, Reroute, and Hybrid. The method is implemented in a grid-based FOCC environment, supporting eight-directional movement and handling various package sizes. Experiments were conducted in four scenarios with grid dimensions ranging from 8×11 to 12×12 and involving up to four simultaneous packages. Results show that Q-RCR consistently outperforms Double Q-Learning, RRT, and A* regarding delivery time, path smoothness, and the number of activated cells. The hybrid mode demonstrated the most effectiveness in handling frequent collisions and maintaining operational flow continuity. The proposed framework demonstrates strong adaptability, scalability, and responsiveness, offering a practical and intelligent solution for real-time multi-package coordination in flexible manufacturing and warehouse automation environments.
Ensemble Voting Regressor for Enhanced Prediction in EMG-Based Prosthetic Wrist Control Karis, Mohd Safirin; Kasdirin, Hyreil Anuar; Abas, Norafizah; Zainudin, Muhammad Noorazlan Shah; Ali, Nursabilillah Mohd; Saad, Wira Hidayat Mohd; Razlan, Zarina
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.v6i4.26222

Abstract

Accurately capturing user motion intention is crucial for effective wrist control in myelectronic prosthetic hands. While various regression models have been explored to improve prediction performance, each presents specific limitations when used independently. This study proposes a novel ensemble learning approach that utilizes a Voting Regressor to combine the strengths of several regression models ANN, ANFIS, fuzzy logic, and their combinations (ANN-ANFIS, ANN-Fuzzy, ANFIS-Fuzzy, and ANN-ANFIS-Fuzzy) to improve predictive performance. Surface EMG signals were collected from the FCR and ECRL muscles at five contraction levels: 20%, 40%, 60%, 80%, and 100% MVC. These signals were used to predict wrist velocity, which was then validated using a SimMechanics based prosthetic hand model in MATLAB 2017a. The ensemble model outperformed all individual and combination models at four MVC levels; 20%, 40%, 60%, and 100%. However, at 80% MVC, a single model achieved superior performance. Based on the average performance gain at the four winning MVC levels, the ensemble method achieved an overall improvement of 11.38%. When applied to the prosthetic hand simulation, the ensemble model showed slight additional improvements in RMSE at each MVC level, highlighting the practical applicability of the approach. To assign optimal and objective weights to the contributing models, MCDM-WSM approach was applied. This method combined multiple evaluation metrics (RMSE, %NRMSE, MAE, R², and p-value) into a single composite score, leading to the final weighted regression equation: YVR-HG-wrist = (0.5163)YANN + (0.2367)YANFIS + (0.2470)YFuzzy. Furthermore, the ensemble model reduced reliance on additional control strategies such as PID tuning, as its improvements in RMSE were comparable to those typically achieved through PID-based compensation. These findings highlight the potential of a performance-weighted ensemble approach to provide more accurate, robust, and practical EMG-based prosthetic wrist control especially in real-time applications.
Unveiling the Predictive Power of Machine Learning and Deep Learning: A Comparative Study on Disease Diagnosis, Detection, and Mortality Risk in Healthcare Santoso, Daniel; Firdaus, Asno Azzawagama; Yunus, Muhajir; Pangri, Muzakkir
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.v6i4.26223

Abstract

This study compares the roles of machine learning (ML) and deep learning (DL) in healthcare, focusing on their applications, challenges, and prospects. It addresses the increasing relevance of AI in public health systems and contributes a structured analysis of how ML and DL process different healthcare data types. A systematic literature review was conducted using sources from Google Scholar, Elsevier, Springer, IEEE, and MDPI, applying inclusion criteria based on relevance, publication quality, and recency (2018–2024). Article selection and synthesis using meta-analysis followed the PRISMA framework. The review identified four key application areas: (1) disease outbreak prediction, (2) disease forecasting, (3) disease diagnosis and detection, and (4) disease hotspot monitoring and mapping. ML techniques such as Random Forest and ensemble methods show high performance in handling structured data like patient records, whereas DL architectures like convolutional neural network (CNN) and long-short term memory (LSTM) are superior for unstructured data, including medical imaging and bio signals. Challenges common to both approaches include data quality issues, dataset bias, privacy concerns, and integration into existing healthcare infrastructures. Looking forward, promising directions include explainable AI (XAI), transfer learning, federated learning, and real-time data use from wearable and internet of things (IoT) devices. The study concludes that while ML and DL can significantly improve diagnosis, response to health threats, and resource allocation, maximizing their impact requires continuous cross-sector collaboration, transparency, and ethical governance.
The Effect of Eye Shape and the Use of Corrective Glasses on the Spatial Accuracy of Eye-Gaze-Based Robot Control with a Static Head Pose Suryadarma, Engelbert Harsandi Erik; Laksono, Pringgo Widyo; Priadythama, Ilham; Herdiman, Lobes; Suhaimi, Muhammad Syaiful Amri Bin
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.v6i4.26229

Abstract

The integration of eye-gaze technology into robotic control systems has shown considerable promise in enhancing human–robot interaction, particularly for individuals with physical disabilities. This study investigates the influence of eye morphology and the use of corrective eyewear on the spatial accuracy of gaze-based robot control under static head pose conditions. Experiments were conducted using advanced eye-tracking systems and multiple machine learning algorithms—decision tree, support vector machine, discriminant analysis, naïve bayes, and K-nearest neighbor—on a participant pool with varied eye shapes and eyewear usage. The experimental design accounted for potential sources of bias, including lighting variability, participant fatigue, and calibration procedures. Statistical analyses revealed no significant differences in gaze estimation accuracy across eye shapes or eyewear status. However, a consistent pattern emerged: participants with non-monolid eye shapes achieved, on average, approximately 1% higher accuracy than those with monolid eye shapes—a difference that, while statistically insignificant, warrants further exploration. The findings suggest that gaze-based robotic control systems can operate reliably across diverse user groups and hold strong potential for use in assistive technologies targeting individuals with limited mobility, including those with severe motor impairments such as head paralysis. To further enhance the inclusiveness and robustness of such systems, future research should explore additional anatomical variations and environmental conditions that may influence gaze estimation accuracy.
Adaptive Strategies for Dynamic Obstacle Avoidance and Formation Control in Multi-Agent Drone Systems: A Review Argiliana, Shania; Ekawati, Estiyanti; Mukhlish, Faqihza
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.v6i4.26243

Abstract

Obstacle avoidance in multi-agent systems is a critical area of research driven by advancements in autonomous technology and artificial intelligence. This review examines various approaches to path planning, formation control, and communication architectures, focusing on their effectiveness in static and dynamic environments. The research contribution is a comprehensive analysis of current techniques based on a structured selection process evaluating peer-reviewed studies through computational efficiency, real-time adaptivity, and scalability. The findings highlight the strengths and limitations of classical methods, such as the Improved Artificial Potential Field (IAPF), and modern techniques like Reinforcement Learning (RL) and Model Predictive Control (MPC). Comparative analysis reveals that while these approaches improve adaptivity, they also introduce challenges such as high computational loads, difficulties in large-scale multi-agent coordination, and sensitivity of parameter tuning. Additionally, existing formation control strategies depend highly on stable inter-agent communication, making them vulnerable to delays and failures in decentralized networks. This review identifies key research gaps and suggests future directions, including hybrid RL-MPC formation control, adaptive path planning algorithms, and scalable communication protocols to enhance multi-agent system performance in real-world applications.
AI-Enhanced High-Speed Data Encryption System for Unmanned Aerial Vehicles in Fire Detection Applications Moldamurat, Khuralay; Spada, Luigi La; Zeeshan, Nida; Bakyt, Makhabbat; Kuanysh, Absalyam; Zhanibek, Kazybek bi; Tilenbayev, Alzhan
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.v6i4.26275

Abstract

Small unmanned aerial vehicles (UAVs) are increasingly used for wildfire detection, where they must not only identify fire events rapidly but also transmit large volumes of sensor data securely to ground stations. Achieving both fast on-board analysis and high-speed encrypted data transmission within the size, weight, and power limits of UAV platforms remain a major technical challenge. In this study, we introduce a compact, FPGA-based system that simultaneously performs real-time fire detection and high-throughput data encryption. Our system integrates a programmable logic chip (FPGA), deep-learning models for visual recognition, and AES-256 cryptographic cores onto a single hardware module. A key innovation is a shared scheduling mechanism that coordinates these two functions efficiently. Furthermore, we demonstrate how artificial intelligence contributes beyond image classification: a lightweight neural network monitors input data streams and dynamically adjusts encryption key parameters, thereby improving security without compromising performance. The hardware supports encrypted data transfer rates of 800 megabits per second at a latency of just 2 microseconds, while identifying fire signatures at 30 frames per second. Extensive testing, including cross-validation on a 50,000-frame dataset and environmental stress testing from –20 °C to 55 °C, confirms robust performance under real-world conditions. While the current memory footprint limits multi-camera input, this work offers a foundational design for future systems that aim to combine edge computing, secure communications, and AI-driven perception in autonomous aerial platforms.

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