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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
A Comparative Study of Metaheuristic Optimization Algorithms in Solving Engineering Designing Problems Aribowo, Widi; Shehadeh, Hisham 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.v6i4.26410

Abstract

This paper presents a comprehensive comparative study of several metaheuristic optimization algorithms with the aim of identifying the most effective method for solving well-established engineering design problems. The algorithms selected for this study include Sperm Swarm Optimization (SSO), Chernobyl Disaster Optimizer (CDO), Bermuda Triangle Optimizer (BTO), Marine Predators Algorithm (MPA), and Particle Swarm Optimization (PSO). These algorithms are tested and evaluated through both qualitative and quantitative analyses.The first phase of testing involves applying the algorithms to a set of benchmark functions from the Congress on Evolutionary Computation (CEC) 2017 suite. Key performance indicators such as best fitness value, standard deviation, and mean are used to measure solution quality, while convergence curves are analyzed to assess optimization efficiency over iterations. This allows for a robust evaluation of each algorithm's ability to balance exploration and exploitation in the search space. In the second phase, the algorithms are implemented to solve real-world engineering design problems, including Speed Reducer Design, Pressure Vessel Design, Cantilever Beam Design, and Robot Gripper Optimization. These case studies further validate the practical applicability and versatility of the algorithms in handling complex, multidimensional, and constrained optimization tasks. The results indicate varying levels of performance across different problems, highlighting the strengths and limitations of each method. This comparative insight provides valuable guidance for researchers and practitioners in selecting suitable optimization techniques for specific engineering challenges.
Neural Network-Based Adaptive Robust Fractional PID Control for Robotic Systems Thi, Yen-Vu; Yao, Nan-Wang; Huu, Hai-Nguyen; Van, Cuong-Pham; Manh, Tung-Ngo
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.v6i4.26460

Abstract

This paper proposed an Adaptive Robust Fractional Oder PID controller based on neural networks (ARFONNs) in order to improve the trajectory tracking of Robotic systems. Robots are nonlinear objects with uncertain models, they are always affected by noise in the working process such as the payload variation, nonlinear friction, external disturbances, ect. To address this problem of robot, a proposed controller inherits the advantages of neural network, adaptive method and sliding mode controller to achieve fast and accurate control. The neural network controller has simple architecture, better approximation for the unknown dynamic of robotic systems, and fast training capability. Moreover, due to its robust nature, Sliding Mode Control (SMC) is a widely adopted nonlinear control approach. Furthermore, the quality of the robot control system is improved based on combining the flexibility of Fractional Order PID. The adaptive laws of the ARFONNs are defined by selecting a suitable Lyapunov function to the control system obtain global stability. In addition, Simulation and experimental results of the ARFONNs controller are conducted on a two-link Cleaning and Detecting Robot. The simulation and experimental results have compared with the Adaptive Robust Neural networks (ARNNs) and The neural networks controller (NNs) to demonstrate the stability and robustness as well as the performance of the ARFONNs controller.
Development of Euclidean Distance Algorithm for ANFIS Optimization in IoT-based Pond Water Quality Prediction Dahria, Muhammad; Defit, Sarjon; Yuhandri, Yuhandri
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.v6i4.26497

Abstract

Pond water quality is a pivotal factor that influences the productivity and health of biota in aquaculture systems. The monitoring and prediction of water quality parameters, including temperature, pH, and dissolved oxygen (DO) levels, are imperative for maintaining optimal environmental conditions. The objective of this research is to develop the Euclidean Distance algorithm as an optimization method in adaptive neuro-fuzzy inference system (ANFIS) modeling to enhance the accuracy of internet of things (IoT)-based pond water quality prediction. Water quality parameter data is collected in real-time using IoT sensors connected to an ESP32 microcontroller and transmitted to a cloud storage platform for analysis. Subsequently, the data undergoes a series of processing steps, including min-max normalization and feature selection based on Euclidean distance. This process aims to generate a more representative and relevant subset of data for the subsequent model training process. The ANFIS model was trained using the optimized data and evaluated using MSE, MAD, MRSE and MAPE metrics. The training process involving four data sharing scenarios demonstrated a reduction in error when compared to the model that lacked optimization, specifically: The following proportions were determined: 50% versus 50% (0.11824 versus 0.15536), 70% versus 30% (0.18666 versus 0.19454), 80% versus 20% (0.17843 versus 0.18833), and 90% versus 10% (0.22477 versus 0.22859). The findings indicate that the incorporation of the Weighted Euclidean Distance algorithm within the IoT-based prediction system can markedly enhance the efficiency and precision of the ANFIS model.
Adaptive Task-Space Control of Five-Bar Parallel Robot Dynamic Model with Fully Unknown Using Radial Basis Function Neural Networks for High-Precision Applications Tran, Thanh Hai; Ngo, Thanh Quyen; Uyen, Hoang Thi Tu; Nguyen, Van Tho; Duong, Tien Đoan
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.v6i4.26537

Abstract

Designing a stable and accurate controller for nonlinear systems remains a significant challenge, mainly when the system contains uncertain factors or is affected by external disturbances. This study proposes an adaptive control method based on a Radial Basis Function Neural Network (RBFNN) to effectively estimate the uncertain components in nonlinear systems. The gradient descent algorithm updates the RBFNN parameters, and the control system's stability is rigorously proven based on the Lyapunov theory. The designed controller ensures accuracy under changing conditions and can adapt to nonlinear disturbances and system fluctuations flexibly. Through 45 consecutive test cycles, the system significantly improves precision and outperforms other control methods in comparative tests. This study opens up the potential for broad application in highly uncertain nonlinear MIMO systems, thanks to the effective combination of adaptive learning ability, stability, and simple implementation structure of the proposed controller.
Improving Short-Term Electricity Load Forecasting Accuracy Using the Ghost Convolutional Neural Network Model Tuan, Nguyen Anh; Toan, Nguyen 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.v6i4.26562

Abstract

Short-Term Load Forecasting (STLF) is essential for maintaining grid stability and optimizing operational efficiency in modern energy systems. While traditional Convolutional Neural Networks (CNNs) can extract local temporal features, they often struggle with capturing long-term dependencies and demand high computational resources. This study proposes a novel application of the Ghost Convolutional Neural Network (GhostCNN)—initially designed for image processing—to time-series electricity load forecasting. GhostCNN significantly reduces model complexity while preserving forecasting accuracy by generating redundant temporal features through lightweight linear operations. The model is trained and evaluated on a real-world electricity load dataset from Ho Chi Minh City, containing 13,440 hourly observations (~1.5 years). A comprehensive hyperparameter tuning strategy is applied, covering kernel size, Ghost ratio, sequence length, batch size, and learning rate. The model's performance is benchmarked against MLP, CNN, and LSTM architectures. GhostCNN achieves the lowest Mean Absolute Percentage Error (MAPE) of 1.15%, outperforming CNN (1.27%), MLP (1.67%), and LSTM (7.3%). Furthermore, GhostCNN reduces inference time by approximately 40% and decreases parameter count by ~45% compared to standard CNNs, affirming its suitability for real-time smart grid deployment. These results demonstrate that GhostCNN provides a robust, scalable, and efficient solution for accurate short-term electricity load forecasting in dynamic and resource-constrained environments.
Improved Tracking Accuracy of Par-4 Delta Parallel Robot Using Optimized FOPID Control with PSO Technique Mahdi, Shaymaa M.; Abdulkareem, Ahmed I.; Humaidi, Amjad J.
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.v6i4.26607

Abstract

The Par-4 Delta parallel robot is an excellent choice for most pick-and-place applications. The parallel robot has complex and high nonlinearities and the choice of control design is one key to improving the tracking performance and accuracy of parallel robots. This study proposes two structures of proportional-derivative-integral (PID) controller. The first scheme utilized Integer-order setting of controller's terms, while the second structure used integral and derivative terms with fractional orders and it is termed as fractional-order PID (FOPID) controller. The terms of FOPID controller are synthesized based on fractional calculus theorem. It has been shown that FOPID controller has high efficacy when applied to complex and nonlinear systems. However, the tuning of its terms is a critical issue in its design. As such, an algorithm-based particle swarm optimization (PSO) has been developed to tune the parameters of FOPID controller such as to achieve global minimum of tracking errors Par-4 Delta parallel robot. The effectiveness of optimized FOPID controller has been verified via numerical simulation and it is compared to integer PID (IPID) controller with the same PSO algorithm. The computer simulations have showed that better tracking errors have been obtained with FOPID controller compared to its counterpart. Using the root mean square of error (RMSE) as the metric of evaluation, the numerical results showed that PSO-FOPID achieved 60% and 62.9% improvement in terms of tracking accuracy along both the x-axis and the z-axis, respectively, as compared to IOPID applied controller techniques.
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.

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