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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. 1 (2025)" : 40 Documents clear
Integrated Radar and Missile System with Poisson-Prioritized Threat Management and PPN Guidance for Countering Multiple UAV Threats Hage, Giselle; Santoso, Ari; Sahal, Mochammad
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

Abstract

In military defense, critical infrastructure protection, and border and maritime surveillance, radar detection plays a critical role in neutralizing threats, since slight delays in detection can enable hostile UAVs to breach defenses and target critical objectives. This research proposes an air defense systems, consists of integrated radar and missile system, to detect and neutralize aerial threats. The radar detects UAVs, tracks their trajectories, and prioritizes threats according to the distance of UAVs within its detection range, incorporating Poisson-distributed probability to dynamically allocate missile resources, allowing the systems to cover broader threat zones, which is crucial for the real-time interception of multiple UAVs. Each UAV is equipped with a state feedback controller for accurate navigation, while the missile system consistently enhancing its trajectory to accurately track and intercept threats under PPN guidance. Simulated experiments indicate that the proposed system intercepted the aerial threats within its operational range and time constraints in various battlefield scenarios. The system’s effect within its operational radius has also been evaluated in an experiment designed to counter a swarm of 6 UAVs flies in a predefined formation. In this scenario, the air defense system successfully launched a missile towards UAV swarms that neutralized 83.33% of total identified threats. The proposed system can be an alternative air defense systems to confront UAV threats in battlefield situations, with potential application in disaster management, search and rescue, and early warning system.
Performance Optimization of BLDC Motor Control Using Sand Cat Swarm Algorithm and Linear Quadratic Regulator Abdulkareem, Hiba; Alsaif, Omar Ibrahim; Younis, Lujain; Khalid, Rana
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.v6i1.24958

Abstract

Brushless Direct Current (BLDC) motors are widely utilized in industrial applications due to their precision, efficiency, and ease of control. This study optimizes BLDC motor performance by enhancing the linear quadratic regulator (LQR) using the Matlab program's Sand Cat Swarm Optimization (SCSO) algorithm. The research evaluates key performance metrics, including settling time, overshoot, and cost function, to demonstrate the advantages of the proposed approach. Additionally, a comparative analysis was conducted using the butterfly optimization algorithm (BOA) and conventional LQR to validate the superiority of SCSO. Simulation results show that the LQR-SCSO method significantly improves performance, achieving a 77.2% reduction in settling time, a 91% reduction in overshoot, and a cost function of 0.3376. In comparison, the BOA method achieves reductions of 68.54% in settling time, 67.37% in overshoot, and a cost function of 0.8736, while the conventional LQR achieves reductions of 68% in settling time, 62.3% in overshoot, and a cost function of 1.8393. SCSO has excellent convergence and adaptability; however, the implementation is explored further in terms of computational cost adopted for industrial use in real time. The data are so highly processed that better controls are implemented to repeat simulations across defined parameters. The proposed LQR-SCSO approach is practical and potent in enhancing motor performance, which is a significant advancement and can applied in various fields in the industry, such as robotics and automated systems. However, the proposed method may face obstacles related to the higher computational complexity of higher-order applications, which can be a subject of future studies.
Leveraging LFP Architecture for Pneumothorax Detection in Chest X-rays Mansour, Salah-Eddine; Sakhi, Abdelhak
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.v6i1.25107

Abstract

The frequency of pneumothorax diagnoses has risen since the COVID-19 pandemic, leading to an increase in related research. This study presents a novel approach for pneumothorax detection using the Learning Focal Point (LFP) architecture, which is based on the LFP algorithm. The LFP architecture segments chest X-ray images into multiple zones, allowing for the effective extraction of critical regions associated with pneumothorax. By focusing on these essential zones, the method aims to enhance the accuracy and reliability of detection, optimizing both training and testing processes. Unlike traditional methods that process the entire image, the LFP architecture prioritizes the most relevant areas, improving the efficiency of the model. Our results demonstrate a significant improvement in detection accuracy, achieving an impressive score of 0.87. This advancement holds promise for aiding clinicians in making more accurate diagnoses and providing timely interventions for patients suffering from pneumothorax. The proposed LFP-based method can be a valuable tool in medical imaging, particularly in the context of emergency care, where rapid and reliable diagnosis is crucial. Overall, the study highlights the potential of the LFP architecture to improve pneumothorax detection and contribute to the advancement of medical diagnostic technologies.
Selective Harmonic Elimination in Reduced-Switch Multilevel Inverters for PV Systems Using the Sparrow Search Algorithm Baraa, Saif Mohamed; Desa, Hazry; Mohammed, Karar Saeed; Al-Malaisi, Taha Abdulsalam; Hussain, Abadal-Salam Taha; Majdi, Hasan S.
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

Abstract

International Medium voltage and high-power systems use MLIs with low harmonic distortion voltage wave forms in medium voltage systems. Nevertheless, implementation of conventional MLI topologies appears to face various issues such as enhanced system complexity, costs, and conduction losses for specific switching frequencies as well as increased switching frequency leading to impractical solutions in RE systems. Based on the above analysis, this work introduces a three-phase, seven-level RS MLI topology applicable to photovoltaic (PV) systems. The proposed RS MLI has fewer switch devices than a typical topology to achieve cost optimizations without compromising the features of precise topologies. In an attempt to improve on the design of the RS MLI, the Selective Harmonic Elimination (SHE) method is implemented to minimize THD and switching losses. Iterative solutions can be delicate depending on the configuration of the SHE’s and more so for higher level configurations. Thus, for solving the problem the Sparrow Search Algorithm (SSA), is developed to serve as the new optimization method. SSA is thus compared with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) using MATLAB/SIMULINK simulations with modulation indices of 0.1, 0.5 and 1.0. It is established from the result that proposed strategic swarm approach (SSA) yields better accuracy, fast convergence speed and improves the THD of the system compared to GA and PSO. However, there is still the question of computational complexity, which seems to entail studying the RS MLI in different conditions as an open problem for future work. The innovation made by this work can help to enhance RS MLI designs to better feasible for use in renewable energy systems.
Design of Adaptive Synergetic Controller for One Degree of Freedom Robotic ARM Under External Disturbance Hamoudi, Ahmed Khalaf; Husain, Suha S.
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.v6i1.25207

Abstract

In order to manage a one-link robot arm, this research proposes a unique control architecture based on the Synergetic Control (SC) principle. The synergetic control design is initially developed using known system parameters and subjected to external disturbances. However, in practical robotic systems, uncertainties are inherent in the system parameters. As a result, an algorithm known as Adaptive Synergetic Control (ASC) is presented and developed for a robot arm that encounters parameters uncertainty. To estimate disturbances and guarantee the asymptotic stability of the monitored system, adaptive synergetic laws are developed. The adaptive laws and control of the ASC were established to ensure the stability of the controlled robotic arm. The recommended controller addresses the tracking problem of a single-degree-of-freedom (SDOF) robot arm, and disturbance control scenario was conducted and simulated. Additionally, the paper compares the ASC method with the adaptive backstepping control technique to evaluate the effectiveness of ASC, this comparison demonstrated the efficiency of the recommended strategy in terms of maximum tracking error and maximum control effort. The performance of both SC, ASC is demonstrated through computer simulations, showing that the adaptive controller can handle uncertainties as well as disturbance and maintain system stability.
Enhancing Diabetic Retinopathy Classification in Fundus Images using CNN Architectures and Oversampling Technique Pamungkas, Yuri; Triandini, Evi; Yunanto, Wawan; Thwe, Yamin
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.v6i1.25331

Abstract

Diabetic Retinopathy (DR) is a severe complication of diabetes mellitus that affects the retinal blood vessels and is a leading cause of blindness in productive-age individuals. The global increase in diabetes prevalence requires an effective DR classification system for early detection. This study aims to develop a DR classification system using several CNN architectures, such as EfficientNet-B4, ResNet-50, DenseNet-201, Xception, and Inception-ResNet-v2, with the application of the SMOTE oversampling technique to address data class imbalance. The dataset used is APTOS 2019, which has an unbalanced class distribution. Two scenarios were tested, the first without data balancing and the second with SMOTE implementation. The test results show that in the first scenario, Xception achieved the highest accuracy at 80.61%, but model performance was still limited due to majority class dominance. The application of SMOTE in the second scenario significantly improved model accuracy, with EfficientNet-B4 achieving the highest accuracy of 97.78%. Additionally, precision and recall increased dramatically in the second scenario, demonstrating SMOTE's effectiveness in enhancing the model's ability to detect minority classes and reduce prediction errors. DenseNet-201 achieved the highest precision at 99.28%, while Inception-ResNet-v2 recorded the highest recall at 98.57%. Overall, this study proves that the SMOTE method effectively addresses class imbalance in the fundus dataset and significantly improves CNN model performance. Although data balancing can help improve model quality by dealing with data imbalances, it comes at a higher computational cost. Using data balancing techniques with SMOTE significantly increased the iteration time per round on all tested CNN architectures.
Investigating Quantum-Resilient Security Mechanisms for Flying Ad-Hoc Networks (FANETs) Abbood, Abdulnasser AbdulJabbar; AL-Shammri, Faris K.; Alzamili, Zainab Marid; Al-Shareeda‬‏, ‪Mahmood A.; Almaiah, Mohammed Amin; AlAli, Rommel
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.v6i1.25351

Abstract

Flying Ad Hoc Networks (FANETs) are indispensable in applications such as Surveillance, Disaster response missions, and Military operations. Both security and communication efficiency must meet certain requirements. However, their effectiveness is hobbled by dynamic topologies, resource constraints, and cyber threats. Therefore, Post-Quantum Cryptography (PQC) is necessary. Classical algorithms and current PQC schemes for FANETs have been discussed in this thesis, including cryptographic solutions that are lightweight enough for resourceconstrained environments. The numerical results of the experiment show that while lattice-based cryptography involves minimal risk of breaches, its power consumption is 25% higher than that for other systems and its processing time 30% slower. In contrast, multivariate polynomial cryptography is better on metrics like usage of electricity: only 10% more power consumed energywise and 15% more CPU cycles needed for processing. The introduction of PQC algorithms and architectures resulted in a 5–10% reduction in network throughput and increased latency to 20% in some scenarios. The results show that hybrid cryptographic systems—combining classical with PQC techniques— have the potential to achieve both high efficiency and long-term security. Case studies have validated the feasibility of tailored quantum-safe algorithms in FANETs, which can offer considerable security benefits while standing rigorous scrutiny in terms of scalability and computational performance on dynamic, missioncritical operations.
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.

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