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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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Artificial Intelligence-Driven and Secure 5G-VANET Architectures for Future Transportation Systems Saare, Murtaja Ali; Abdulhamed, Mohamed Abdulrahman; 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.v6i4.26295

Abstract

The advent of 5G has opened a new era of intelligent, adaptive and secure VANETs that is envisaged to serve as the backbone network architecture for next generation of vehicular transportation systems. In this work, we present a connected 5G VANETs-to-Edge Computing systems with Artificial Intelligence (AI) infrastructure to improve system adaptability, anomaly detection, trust management, and real-time decisionmaking. Crucial enabling technologies like Software-Defined Networking (SDN). Mobile Edge Computing (MEC), and millimeterwave communication are investigated in detail. We examine key security threats such as identity forgery, data interception, and denial-of-service attacks, and assess the AI-enhanced defense measures such as intrusion detection systems and blockchainbased trust models. Applications, like autonomous platooning, and collaborative vehicle authentication provide additional examples of AI technologies’ added value in the context of vehicular communications and safety. The paper concludes by providing open issues and future directions, including quantum-resistant protocols, lightweight AI models and cognitive networking in the context AI-driven 5G-VANET ecosystems.
Robust Velocity Control for a Launch Vehicle Erection System Saber, Ahmed K.; Maged, Shady A.; Abdelaziz, M.; Mohamed, Mostafa S.
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.v6i4.26385

Abstract

The design of a launch vehicle erection system requires careful consideration of factors such as load capacity, pressure requirements, actuator type, safety mechanisms, and control strategy. Ensuring precise velocity control is critical, as the system’s changing geometry and dynamic behavior influence its loading conditions, stability, and overall performance. This study investigates the velocity control of a hydraulic erection beam using a proportional directional control valve (PDCV). Four control techniques are examined: a classical PID controller, a sliding mode controller (SMC), a model predictive controller (MPC), and a PID controller optimized using the Particle Swarm Optimization (PSO) method. The controllers are evaluated through MATLAB/SIMULINK simulations under both undisturbed and disturbed conditions. Simulation results indicate that the classical PID controller struggles with stability under disturbances, while the MPC exhibits slow response times and fails to reach the desired position. The integration of PSO further degrades performance by introducing instability. In contrast, the SMC demonstrates superior robustness, achieving minimal response variation across all conditions. Comparative experiments validate these findings, confirming that SMC offers the best balance of precision, reliability, and disturbance rejection. These results highlight that SMC is the most effective control technique for real-world hydraulic erection systems, ensuring high stability, accuracy, and operational reliability.
Non-Intrusive Real-Time Tourist Crowd Monitoring for Overtourism Mitigation using YOLOv8-Based Head Detection and Tracking Wijayanti, Kurnia; Mutiara, Giva Andriana; Suryawardani, Bethani; Ervina, Ersy; Kusuma, Guntur Prabawa
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.v6i4.26396

Abstract

Overtourism has emerged as a critical issue in popular tourist destinations, often leading to environmental strain, reduced visitor satisfaction, and safety concerns. Traditional methods such as ticket counts, or vehicle estimation fail to provide real-time insights or adapt effectively to dynamic outdoor environments. This study proposes a privacy-aware, real-time visitor capacity monitoring system for smart tourism, utilizing YOLOv8-based head detection and Centroid Tracking to ensure accurate, non-intrusive people counting in dense and complex crowd scenarios. Head detection is employed specifically to preserve personal privacy without compromising on detection performance. The system was trained on a custom dataset comprising over 3,000 annotated frames with diverse lighting conditions, occlusion levels, and viewing angles. Deployment at Wana Wisata Kawah Putih, an open-air tourist destination in Indonesia, demonstrated strong performance with 94.2% accuracy, 95.1% precision, and 90.6% recall, while sustaining >60 FPS for real-time execution. The integration of Centroid Tracking enables lightweight, frame-to-frame identity association with minimal computational overhead, making the system suitable for deployment on moderate-performance hardware. Despite its robustness, the system's performance slightly degrades under extreme weather (e.g., fog, direct glare) and rapid lighting transitions, which remain challenges for visual models. Moreover, the current model requires further evaluation for cross-location generalizability. Future research will explore the integration of predictive analytics for visitor flow forecasting, and further optimization of energy efficiency and adaptive detection under environmental uncertainty. This work contributes a scalable, ethical solution for real-time crowd monitoring to support informed, sustainable tourism management.
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.
An Explainable CNN–LSTM Framework for Monthly Crude Oil Price Forecasting Using WTI Time Series Data Thongjamroon, Joompol; Phimphisan, Songgrod; Sriwiboon, Nattavut
Journal of Robotics and Control (JRC) Vol. 6 No. 5 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i5.26609

Abstract

Crude oil price forecasting has posed significant challenges due to its volatility and nonlinear dynamics. This study has proposed an explainable CNN–LSTM framework to predict monthly West Texas Intermediate (WTI) crude oil prices. The model has captured both local and sequential patterns without using external inputs or decomposition. Trained over 50 epochs across three data splits, it has been evaluated using RMSE, MAE, MASE, SMAPE, and directional accuracy. A classification accuracy of 92.4% and directional accuracy of up to 87.4% have been achieved. The model has consistently outperformed classical and hybrid baselines, with statistical significance confirmed by the Friedman–Nemenyi test. Saliency-based interpretability has further enhanced transparency, making the framework suitable for real-world energy forecasting.

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