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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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CA-HBCA: A Software Engineering Framework for Secure, Scalable, and Adaptive Healthcare Blockchain Systems Qasim, Mustafa Moosa; Altmemi, Jalal M. H.; Ali, Akram Hussain Abd; 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.v6i4.26643

Abstract

Secure, scalable, and compliant solutions are becoming a requirement for healthcare systems handling sensitive medical data. Blockchain presents unique opportunities to create transparency and trust that is decentralized, yet has inherent challenges posed by scalability, sustainability and regulation. This study presents CA-HBCA, a Cognitive and Adaptive Software Engineering Framework for intelligent healthcare blockchain applications. The novel contribution of the research is the combination of four sledging modules, such as an AIbased cognitive security layer that triggers real-time anomaly detection, an adaptive sustainability engine that optimises energyperformance, a DevSecOps-based continuous delivery pipline, and a HL7/FHIR-compliant interoperability and consent management layer. Methodologically, the FEACAN was realized with Solidity, TensorFlow, and Ethereum/Hyperledger testnets, and tested by simulating healthcare scenarios such as EHR exchange, and adversary search. We obtained 93.2% precision of anomaly detection, 17.6% reduction of energy consumption, 42 transactions per second throughput in Hyperledger, and 98.7% of success rate of HL7-FHIR transformation, etc. The framework also demonstrated 100% smart contract–based consent compliance under test cases. The results indicate that CA-HBCA can be employed for the establishment of secure, sustainable and regulation-compliant blockchains in digital health infrastructures. In the future, we will also carry out validation with clinical real data sets and investigate the scalability in a variety of healthcare settings.
Multiple Targets Path Planning for Document Delivering Mobile Robot in Dynamic Environments Vu, Van-Phong; Nguyen, Dinh-Hieu; Nguyen, Thanh-Trung; Tran, Minh-Duc
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.v6i4.26660

Abstract

This paper proposes a method to control and make path planning for a delivering mobile robot that operates in a dynamic environment. The working environment is an office building that will appear both static obstacles and dynamic obstacles (such as such as people or other mobile robots). The mobile robot is designed to carry documents to multiple targets that are determined by users. Users can call the mobile robot and input the information of the documents and targets that need to be delivered via the website. The working environment map will be established by using LiDAR and SLAM technology. The path plaining is executed in two steps. Firstly, the ant colony algorithm (ACO) is employed to solve the indoor traveling salesman problem (ITSP), the TSP for indoor application, for determining the globally optimal moving schedule to multiple targets in this paper. Then, the shortest moving path between point to points for the delivering mobile robot is determined by using the Dijkstra algorithm. The shortest moving path for the delivering mobile robot is determined by using the Dijkstra algorithm. The ant colony algorithm (ACO) is employed to solve the inner traveling salesman problem (ITSP) to determine the optimal moving schedule to multiple targets in this paper. The dynamic window approach (DWA) methodology is applied to assist mobile robots in avoiding static and dynamic obstacles. In addition, the adaptive monte Carlo localization (AMCL) is used for positioning the mobile robot on the map. Finally, the simulation in MATLAB and Gazebo environment as well as the experiments, are presented to prove the superior success of the delivering mobile robot.
Noise-Reduced 3D Organ Modeling from CT Images Using Median Filtering for Anatomical Preservation in Medical 3D Printing Chotikunnan, Phichitphon; Chotikunnan, Rawiphon; Puttasakul, Tasawan; Khotakham, Wanida; Imura, Pariwat; Prinyakupt, Jaroonrut; Thongpance, Nuntachai; Srisiriwat, Anuchart
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.v6i4.26665

Abstract

This study offers a systematic approach to improving the reconstruction of three-dimensional anatomical models from CT imaging data. The main difficulty tackled is the maintenance of internal bone features during denoising, essential for producing clinically relevant models. A nonlinear filtering strategy was implemented, utilizing a 3×3 median filter alongside manual refinement to eliminate salt-and-pepper noise while preserving anatomical information. The study presents a reproducible image-processing pipeline that improves structural clarity and enables material-efficient 3D printing while preserving internal bone integrity. A publicly available dataset including 813 anonymized chest CT scans (512×512 pixels, 16-bit grayscale) from Zenodo was employed. Preprocessing included grayscale normalization, brightness adjustment, and the application of median filters with kernel sizes from 3×3 to 9×9, followed by artifact removal using FlashPrint software before STL conversion. The 3×3 median filter achieved an excellent balance between noise reduction and anatomical clarity, outperforming mean filtering and larger kernels in maintaining edge detail. Although statistical evaluation was not conducted, visual analysis validated an 18.07 percent decrease in print time and a 17.88 percent reduction in filament consumption. The technology exhibited actual efficacy in generating high-quality anatomical models. Future endeavors will incorporate automated segmentation and sophisticated denoising methodologies to enhance applicability in surgical simulation, clinical education, and personalized healthcare planning.
A Transformer-Enhanced CNN Framework for EEG Emotion Detection with Lightweight Gray Wolf Optimization and SHAP Analysis Sriwiboon, Nattavut; Phimphisan, Songgrod
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.v6i4.26725

Abstract

Emotion recognition from electroencephalogram (EEG) signals has been recognized as critical for enhancing human–computer interaction and mental health monitoring. In this paper, an explainable and real-time dual-stream deep learning framework has been proposed for EEG-based emotion classification. The model integrates a 1D convolutional neural network (1D-CNN) for local feature extraction and a transformer encoder for global dependency modeling, with multi-head attention used for feature fusion. Lightweight Gray Wolf Optimization (LGWO) has been employed for selecting optimal features, and an ensemble of lightweight classifiers has been applied to improve robustness. Experiments conducted on DEAP, SEED, BrainWave, and INTERFACE datasets have demonstrated superior performance, achieving accuracies of 96.90%, 94.25%, 93.70%, and 92.80%, respectively. An average inference delay of 5.2 milliseconds per trial has confirmed real-time applicability. Furthermore, SHAP analysis has been incorporated to interpret the model’s decision-making process by identifying influential EEG channels and frequency components. The results have validated the proposed model as a robust, accurate, and explainable solution for EEG-based emotion recognition, establishing a new benchmark for future research in affective computing and clinical applications.
Integration of PID-MRAC and Novel GCC-C2C for Developing Adaptive Deterministic MPPT Nurcahyo, Sidik; Suyono, Hadi; Hasanah, Rini Nur; Muslim, Muhammad Aziz
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.v6i4.26794

Abstract

This article proposes a new photovoltaic (PV) Maximum Power Point Tracker (MPPT) using PID-MRAC with a novel tracker of Gradual Capacitor-Charging (GCC) and Capacitor-to-Capacitor charge transfer (C2C). The research contribution is omitting the power fluctuation of optimisation-based MPPT and discontinuity or power loss of I-V sweep-based MPPT. GCC regularly and deterministically locates the maximum PV power voltage (Vmpp) by connecting a parallel capacitor to PV only when the PV is isolated from the converter. If one cycle of I-V sweeping is completed, C2C empties the capacitor by transferring its charge to a power supply capacitor to avoid the power-loss problem. A PID and non-inverting buck–boost converter was assigned to regulate the PV output voltage (Vpv) at Vmpp, thus enabling maximum energy harvesting. The Model Reference Adaptive Control (MRAC) adjusts the PID parameters to maintain the MPPT performance. Simulation results show that the MPPT worked well against load and irradiance changes, Iph=2.0A for 0.6s and Iph=3.8A for 1.4s. The GCC-C2C successfully locates Vmpp within 410ms. The PID could regulate Vpv to Vmpp with a settling time of 200ms at the initial stage or less than 10ms at the next stages. The MRAC also successfully tuned the PID parameters during operation. The superiority of this method over the P&O MPPT is its capability to deliver more power at various load power rates. Harvesting efficiency of the proposed MPPT at 5 ohm and 50 ohm loads is 96% and 82%, respectively, while P&O is only 84% and 21%.
Inverse Kinematics Optimization Using ACO, MOA, SPOA, and ALO: A Benchmark Study on Industrial Robot Arms El Mrabet, Aziz; Hihi, Hicham; Laghraib, Mohammed Khalil; Chahboun, Mbarek; Abouyaakoub, Mohcine; Ali, Ali Ait; Amalaoui, Aymane
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.v6i4.26809

Abstract

This study investigates the application of metaheuristic algorithms to solve the inverse kinematics (IK) problem in robotic manipulators, which is often challenging for high-degree-of-freedom systems. The research contribution is the comparative evaluation of four recent metaheuristic algorithms—Ant Colony Optimization, Mayfly Optimization Algorithm, Stochastic Paint Optimizer, and Ant Lion Optimizer—across different robot configurations. A kinematic analysis was conducted on three robotic arms: a 4-DOF SCARA, a 6-DOF ABB IRB 1600, and the dual-arm 15-DOF Motoman SDA20D/12L. For each manipulator, the end-effector pose was optimized by solving the IK problem using the selected algorithms. A total of 30 random target positions were tested within the operational space to ensure diversity in pose challenges; while not exhaustive, this sampling provides statistically informative trends. We evaluate each algorithm based on the number of optimal solutions obtained, the precision of the computed joint configurations, and execution time. The results indicate that the Mayfly Optimization Algorithm consistently delivered the highest precision with relatively low execution time across all robot types. In contrast, the Ant Lion Optimizer showed inconsistent performance in higher-DOF settings. Unlike traditional Jacobian-based or analytical IK methods, metaheuristics offer flexibility in handling complex, nonlinear systems without requiring gradient information. These findings provide practical insight for selecting suitable algorithms in real-world robotic applications.
Adaptive Particle Swarm and Ant Colony Optimization Path Planning for Autonomous Robot Navigation Essaadoui, Alami; Baba, Youssef; Hamed, Oussama; Hamlich, Mohamed; Guemimi, Chafik; EL Kebch, Ali
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.v6i4.26853

Abstract

Path planning in cluttered and uncertain environments remains a significant challenge in robotics, autonomous navigation, and logistics optimization. This paper proposes a novel Adaptive Hybrid PSO-ACO Planner, which synergistically combines Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) to compute efficient paths in grid-based environments with static obstacles. Unlike traditional fixed-phase hybrids, our approach features a dynamic switching strategy between PSO and ACO based on real-time convergence behavior, allowing the algorithm to maintain progress and escape local minima. Additionally, adaptive parameter tuning is integrated to enhance the balance between global exploration and local exploitation throughout the search. The switching logic is governed by two criteria: a stagnation threshold that triggers phase transitions and a progress-dependent adaptation mechanism that adjusts search intensities over time. PSO dominates the early search phase, rapidly exploring the solution space, while ACO refines promising paths through pheromone-guided optimization in later stages. The proposed planner also includes a path reconstruction module to ensure solution completeness and robustness. Experimental evaluations on grid-based environments demonstrate that the proposed method consistently achieves higher path quality and faster convergence compared to standalone PSO and ACO approaches. Quantitative results demonstrate notable improvements in path efficiency and overall success rate across a range of obstacle densities. These advancements establish the Adaptive Hybrid PSOACO Planner as a robust and efficient tool for real-time and practical deployment in autonomous robot navigation systems.
A Hybrid Transformer-MLP Approach for Short-Term Electric Load Forecasting Nguyen, Tuan Anh; Tran, Thanh Ngoc
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.v6i4.26960

Abstract

Short-term electric load forecasting plays a vital role in ensuring the stability and efficiency of smart grid operations. However, accurately predicting demand remains challenging due to nonlinearity, volatility, and long-term temporal dependencies in consumption patterns. The research proposes a lightweight hybrid deep learning model that integrates a Transformer encoder with a multi-layer perceptron (MLP) to enhance prediction accuracy and robustness for short-term load forecasting. The proposed model employs a Transformer to extract long-range temporal features through self-attention mechanisms, while the MLP captures complex nonlinear mappings at the output stage. A real-world electricity load dataset collected from three Australian states (NSW, QLD, VIC) between 2009 and 2014 is used for evaluation. To assess model performance, mean absolute percentage error (MAPE), mean squared error (MSE), and Root Mean Squared Error (RMSE) are used. Experimental results demonstrate that the proposed transformer-MLP model consistently achieves the lowest forecasting error across all regions. MAPE ranges from 0.69% to 0.95%, outperforming standard deep learning models, including LSTM, CNN, and MLP. Despite its shallow architecture and reduced computational complexity, the hybrid model effectively captures both temporal dependencies and nonlinear variations. This study provides a practical, deployable forecasting solution for smart grids. Future work will extend the model to multi-step forecasting, incorporate exogenous variables such as weather and calendar effects, and explore deeper Transformer variants further to enhance prediction accuracy and generalization across diverse load conditions.
Real-Time Prohibited Item Detection in X-ray Security Screening via Adaptive Multi-scale Feature Fusion and Lightweight Dynamic Convolutions Nguyen, Hoanh; Ha, Chi Kien
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.v6i4.27030

Abstract

Prohibited item detection in X-ray security screening is a challenging task due to the diverse shapes, sizes, and materials of concealed objects. In this paper, we propose a novel end-to-end framework, integrating adaptive multiscale convolution blocks (AMC Block) and an adaptive lightweight convolution module (ALCM), to address these challenges with high accuracy and efficiency. The AMC block leverages parallel convolutional paths with varying kernel sizes and dilation rates, enabling the capture of both fine-grained and large-scale features. This multiscale strategy ensures that small items like wires and larger objects such as bags or metallic weapons are equally well-detected. Building on top of multi-stage features extracted by the AMC block, we introduce the ALCM to refine and fuse feature maps at different pyramid levels. The ALCM employs a dynamic weight generator (DWG), which adaptively assigns importance to multiple convolutional kernels based on local content, followed by multi-scale depthwise convolutions (MSDC), a lightweight mechanism that enriches features across scales using parallel convolutions with different receptive fields. This approach enhances spatial context while keeping the parameter overhead minimal. Experimental results on two public large-scale X-ray datasets, OPIXray and HiXray, demonstrate that our method achieves state-of-the-art performance while maintaining real-time inference speed. Specifically, our model achieves 91.2% mAP@0.5 and 78.4% mAP@0.5:0.95 on OPIXray, and 87.3% mAP@0.5 and 73.5% mAP@0.5:0.95 on HiXray, outperforming strong baselines including YOLOv9 and Faster R-CNN. Despite competitive accuracy, our model remains efficient with 92.0 GFLOPs and 42 FPS. Furthermore, we examine the generalizability of our system across varied X-ray imaging settings and discuss failure cases such as false negatives in cluttered environments. These findings highlight the practical applicability of our approach for deployment in real-world security checkpoints, striking a strong balance between detection accuracy and computational efficiency.
Improving PMSM Control Based on Optimizing the Output Membership Function of Fuzzy PI Controller with Weighted Modified Jaya Algorithm Khanh, Pham Quoc; Anh, Ho Pham Huy; Thuyen, Chau Minh
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.v6i4.27080

Abstract

Electric vehicle manufacturers prefer permanent magnet synchronous motors for their drive systems because of their high efficiency and power density. PI controllers are ineffective in variable speed drives because they are only linearized to operate within a specific range. One of the solutions applied is to use fuzzy PI controllers instead of PI controllers. When using fuzzy controllers, the designer must be able to adjust the controller parameters to ensure the required control efficiency. There are many ways to improve the control quality of a fuzzy PI controller, such as changing the fuzzification and defuzzification coefficients, the fuzzy rules, the type of membership function, and the shape of the output membership function. In this paper, the method of changing the shape of the output membership function of the speed controller based on the fuzzy PI controller is used to improve the speed control quality of the PMSM motor. The Jaya algorithm and its variants have proven effective in recent publications. In this paper, the Jaya algorithm is supplemented with hybrid weights when targeting the best and worst values in the proposed population to determine the optimal shape of the output membership function implemented by the proposed weighted modified Jaya optimization algorithm. Experimental results on the physical model show the effectiveness of improving the quality of motor speed control when applying the proposed optimization algorithm compared with the basic fuzzy controller based on error assessment criteria such as integral time absolute error (ITAE), integral square error (ISE), and integral absolute error (IAE).

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