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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
Voltage Tracking of Bidirectional DC-DC Converter Using Online Neural Network for Green Energy Application Diana, Nor Farisha; Utomo, Wahyu Mulyo; Abu Bakar, Afarulrazi Bin; Salimin, Suriana; Priyandoko, Gigih; Widjonarko, Widjonarko
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.v6i1.22326

Abstract

In the current era, green energy systems like solar PV, wind energy, and battery storage critically rely on DC-DC converters to manage power flow and voltage conversion efficiently, ensuring optimal performance and reliability. Nevertheless, converters face multiple challenges, including efficiency losses, thermal management concerns, and electromagnetic interference, which can impact these green energy systems' overall performance and reliability. To overcome these challenges, it is necessary to utilize advanced control mechanisms, enhance heat management approaches, and optimize component design. Implementing these improvements will improve the effectiveness and durability of DC-DC converters in renewable energy applications. This research aims to analyze the performance characteristics of a three-phase interleaved half-bridge bidirectional (TPHB-Bi) converter. The research objective involves investigating the effectiveness of the proposed controller by rigorously assessing voltage tracking. This is done through comprehensive assessments of start-up procedures and reference voltage variations using MATLAB/Simulink. The study utilizes a neural network controller with an online learning algorithm based on backpropagation to enhance the converter's operational capabilities, ensuring a stable output voltage and improved transient response. The simulation results highlight the significant advantages of the proposed controller over a conventional PID controller. It exhibits a remarkable reduction in overshoot by 5.29%, considerably shorter rise times ranging from 0.01ms to 0.1ms, and faster settling times of 0.025s. The findings have great importance in promoting sustainable energy development and environmental protection. They demonstrate that implementing advanced control strategies for DC-DC converters can result in more efficient and reliable green energy systems.
Design of a Control System for Hybrid Quadcopter Tilt Rotor Based on Backward Transition Algorithm Darwito, Purwadi Agus; Agustina, Nilla Perdana; Ahnaf, Hudzaifa Dhiaul; Roosydi, Syahrizal Faried; Pratama, Detak Yan; Biyanto, Totok Ruki
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.v6i1.22594

Abstract

An Unmanned Aerial Vehicle (UAV) is an unmanned aerial vehicle that can be controlled using either automatic or manual control. UAVs are divided into two types: rotary-wing, which uses rotating propellers to fly the aircraft, and fixed-wing, which uses fixed wings to fly the aircraft. One of the advanced developments in UAV technology is the Hybrid Vertical Take-Off Landing Quadrotor Tiltrotor Aircraft (QTRA) system, which combines the quadrotor UAV system, classified under rotary-wing, with the fixed-wing UAV system. This allows for vertical takeoff and landing as well as the ability to cruise at maximum speed. In the transition between flight modes, from quadcopter to fixed-wing and vice versa, the transition is carried out by changing the thrust direction of the two front UAV rotors from horizontal to vertical and vice versa. The change in thrust angle on the rotor is referred to as a tilt rotor. The problem that arises from changing the aircraft mode from fixed-wing to quadcopter is controlling the UAV's transition mode, which must not lose its lift force. Therefore, the tilt angle must be changed as quickly as possible. To solve this issue, a Hybrid VTOL Quadrotor Tiltrotor aircraft concept was designed with fast response, controlled by a Proportional Derivative (PD) controller. The results of the PD control system response were tested in simulations by observing the X and Z positions of the UAV, which can stabilize the position during the transition. The success criteria targeted for a stable response include a tilting angle with a settling time of 7 seconds, an overshoot height of 16 meters, and a steady-state error approaching zero. From the transition simulation tests, the system response data showed performance with an X-axis settling time of 37 seconds, a steady-state error value of 0.1 meters, and an overshoot of 0.4%.
AI-Driven Classification of Children’s Drawings for Pediatric Psychological Evaluation: An Ensemble Deep Learning Approach Khlaif, Ali Ibrahim; Naceur, Mohamed Saber; Kherallahr, Monji
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.v6i1.23302

Abstract

In the wake of contemporary challenges such as the COVID-19 pandemic, understanding children’s mental health through non-verbal forms like drawing has become paramount. This study enhances pediatric psychological assessments by employing an ensemble of deep learning models to interpret children’s drawings, aiming for early detection of psychological states. Traditional drawing analysis methods are often subjective, variable and time consuming. To ddress these limitations, we developed an ensemble model that combines the strengths of VGG16, VGG19, and MobileNet architectures using a hard voting mechanism. This approach reduces bias and enhances reliability by integrating the unique capabilities of each model. Our methodology involved rigorous data collection through a custom Android application, followed by exploratory data analysis, data preprocessing, and comprehensive model valuation. The ensemble model was trained and validated on the diverse Kids’ Hand Movement Dataset (KHMD), demonstrating superior accuracy and robustness in classifying drawings that indicate various psychological conditions. It significantly outperformed individual models, achieving a 99% accuracy rate. These findings underscore the potential of advanced machine learning techniques in providing more accurate and bias-free insights into children’s psychological health, suggesting that ensemble learning can greatly improve the precision of pediatric psychological evaluations. Future work will explore expanding the dataset and employing more sophisticated ensemble methods to further enhance diagnostic accuracy.
Robust Power Management for Smart Microgrid Based on an Intelligent Controller Alsanad, Hamid R.; Al Mashhadany, Yousif; Algburi, Sameer; Abbas, Ahmed K.; Al Smadi, Takialddin
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.v6i1.23554

Abstract

A microgrid (MG) is an autonomous electrical system that can operate independently or link to the grid. It is usual practice to use a single grid organization to improve energy access and ensure a consistent supply of electricity. Microgrids (MGs) can be unstable if islanded given that they lack the predominant grid's high friction and are subject to large voltage and frequency swings. Standards, directions, and accessibility and interoperability criteria all address the dependability of a microgrid, the use of distributed local resources, and cybersecurity. This work presents a revolutionary intelligent controller, Adaptive. This study proposes a novel intelligent controller, the Adaptive Network-based Fuzzier Inference System - Drooping Controller (ANFISDC), with a drooping coefficient modification, to provide optimal power sharing while minimizing power overloading/curtailment. To provide the essential stability and lucrative power sharing for the islanded the microgrid, the dropping coefficient is changed to account for the power fluctuations of RES (renewable energy source) components as well as the relationship between electricity production and demand. Furthermore, secondary control is used to restore the frequency/voltage drop caused by the droop control. Simulations with load fluctuations in MATLAB/Simulink show that the proposed strategy improves the stability and economic viability of microgrids powered by energy from renewable sources based on droop. The outcomes of the simulation demonstrate how well the suggested ANFISDC approach works to keep the microgrid operating steadily and profitably.
Adaptive Intrusion Detection System with Ensemble Classifiers for Handling Imbalanced Datasets and Dynamic Network Traffic Almania, Moaad; Zainal, Anazida; Ghaleb, Fuad A; Alnawasrah, Ahmad; Al Qerom, Mahmoud
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.v6i1.23648

Abstract

Intrusion Detection Systems (IDS) are crucial for network security, but their effectiveness often diminishes in dynamic environments due to outdated models and imbalanced datasets. This paper presents a novel Adaptive Intrusion Detection System (AIDS) that addresses these challenges by incorporating ensemble classifiers and dynamic retraining. The AIDS model integrates K-Nearest Neighbors (KNN), Fuzzy c-means clustering, and weight mapping to improve detection accuracy and adaptability to evolving network traffic. The system dynamically updates its reference model based on the severity of changes in network traffic, enabling more accurate and timely detection of cyber threats. To mitigate the effects of imbalanced datasets, ensemble classifiers, including Decision Tree (DT) and Random Forest (RF), are employed, resulting in significant performance improvements. Experimental results show that the proposed model achieves an overall accuracy of 97.7% and a false alarm rate (FAR) of 2.0%, outperforming traditional IDS models. Additionally, the study explores the impact of various retraining thresholds and demonstrates the model's robustness in handling both common and rare attack types. A comparative analysis with existing IDS models highlights the advantages of the AIDS model, particularly in dynamic and imbalanced network environments. The findings suggest that the AIDS model offers a promising solution for real-time IDS applications, with potential for further enhancements in scalability and computational efficiency.
Design of QazSL Sign Language Recognition System for Physically Impaired Individuals Zholshiyeva, Lazzat; Zhukabayeva, Tamara; Baumuratova, Dilaram; Serek, Azamat
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.v6i1.23879

Abstract

Automating real-time sign language translation through deep learning and machine learning techniques can greatly enhance communication between the deaf community and the wider public. This research investigates how these technologies can change the way individuals with speech impairments communicate. Despite advancements, developing accurate models for recognizing both static and dynamic gestures remains challenging due to variations in gesture speed and length, which affect the effectiveness of the models. We introduce a hybrid approach that merges machine learning and deep learning methods for sign language recognition. We provide new model for the recognition of Kazakh Sign Language (QazSL), employing five algorithms: Support Vector Machine (SVM), Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Networks (CNN) with VGG19, ResNet-50, and YOLOv5. The models were trained on a QazSL dataset of more than 4,400 photos. Among the assessed models, the GRU attained the highest accuracy of 100%, followed closely by SVM and YOLOv5 at 99.98%, VGG19 at 98.87% for dynamic dactyls, LSTM at 85%, and ResNet-50 at 78.61%. These findings illustrate the comparative efficacy of each method in real-time gesture recognition. The results yield significant insights for enhancing sign language recognition systems, presenting possible advancements in accessibility and communication for those with hearing impairments.
Robot-Assisted Upper Limb Rehabilitation Using Imitation Learning Auta, Ismail Ashiru; Fares, Ahmed; Iwata, Hiroyasu; El-Hussieny, Haitham
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.v6i1.23927

Abstract

Robotic rehabilitation offers an innovative approach to enhance motor function recovery in patients with upper-limb impairment. However, the primary challenge lies in the development of adaptive and personalized therapies to meet the unique needs of patients. In response to this challenge, this paper presents a Rehabilitation Learning from Demonstration (RLfD) framework, which integrates Dynamic Movement Primitives (DMP) for learning and generalizing movements, and a Model Reference Adaptive Controller (MRAC) for real-time adaptive control. This combination enables a two-link manipulator to accurately replicate and adapt therapist demonstrations specifically designed for upper-limb rehabilitation. Unlike conventional task-specific controllers, which are limited by poor adaptability, minimal feedback, and lack of generalization, our system dynamically adjusts robotic assistance in real time based on the subject’s tracking error to optimize therapy outcomes. The objective is to minimize assistance while maximizing patient participation in the rehabilitation process. To facilitate this, the framework employs visual tracking technology to capture therapist demonstrations accurately. Once captured, the DMP component of the framework learns from these movements and generalizes them to new goals, while maintaining the original motion patterns. Our evaluations with a simulated two-link manipulator demonstrated the framework’s precise trajectory tracking, robust generalization, and adaptability to disturbances mimicking patient impairments. These tests confirmed the system’s ability to follow complex trajectories and adapt to dynamic patient motor functions. The promising results from these evaluations highlight our approach’s potential to significantly enhance adaptability and generalization in variable patient conditions, marking a substantial improvement over conventional systems.
Design and Hardware Implementation of Combining PD with HSSC for Optimizing Behavior of Magnetic Levitation System Alrawi, Ali Amer Ahmed; Lilo, Moneer Ali; Al Mashhadany, Yousif
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.v6i1.23940

Abstract

This paper presents the design and implementation of a hybrid control system that uses Proportional-Derivative (PD) and High-Speed Switching Controller (HSSC) methods to enhance Maglev system performance. The goal is to design controllers that properly follow input references and improve system stability and reactivity. The PD controller is fast and easy to install, but it cannot handle system disturbances and nonlinearities, which might cause instability. HSSC integration addresses these issues. The HSSC makes the PD controller more resilient to external forces and nonlinear dynamics. The combined PD-HSSC approach ensures stable levitation, precise positioning control, and system reliability in various conditions. The hybrid system reduced steady-state error and maintained system stability under dynamic input conditions, although it over-shoot more than PD alone. The computer-aided real time simulation of system dynamics is done, and the control rules are formulated out of a combination of PD Control for normal control processes and the HSSC for enhanced robustness. The total control current is given by the algebraic addition of the PD control action going, the equivalent control going, and the switching control going. However, the proposed PD-HSSC technology is possible to provide a stable levitation state for the control of precise position, even in the nonlinear and disturbance conditions The experimental results showed an 89% enhancement in the efficiency of the hybrid control system. The integration of PID (Proportional-Integral-Derivative) and HSSC has been developed in this system using MATLAB Simulink. The real-time findings demonstrate that the PD-HSSC system is higher in stability for operating the maglev system. This is due to its much lower steady-state error compared to the PD system, regardless of the kind of step input or dynamically fluctuating sine and square wave inputs. However, PID_HSSC exhibited a greater degree of overshoot in comparison to the PD.
Design and Simulation of a 2-Degree of Freedom Energy Harvester from Blood Flow for Powering the Pacemaker Abdelmawgoud, Manar; Parque, Victor; Nada, Ayman; Fath El-Bab, Ahmed M. R.
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.v6i1.23953

Abstract

The pacemaker is a device that is used to treat different abnormal heart rhythms. It is usually powered using traditional batteries. These batteries run out of power after about 7 years, necessitating the replacement of either the pacemaker or its batteries for the patient’s survival. This means that the patient will need to undergo surgery for the replacement process which can compromise the patient’s life and increase the probability of being infected, not to mention the operation cost. To overcome this problem, energy harvesters can be a safer substitute for these traditional batteries since they can convert different forms of energy into electric energy, which can be stored and used when needed. In this paper, a 2-degree-of-freedom (DOF) piezoelectric energy harvester from blood flow is designed and modeled. The harvester is designed as a cut-out beam that is fixed on the pacemaker lead that passes through the Superior Vena Cava (SVC). To protect the harvester from being highly distorted by the blood flow, a plastic barrier is added in front of the harvester from the vein’s inlet side. The harvester consists of three layers, a PZT5A layer sandwiched between two plastic layers. The harvester is designed to have its first and second natural frequencies between 1Hz and 1.67Hz, the normal frequency range of the human heartbeat. The harvester harvests up to 3.8V which is considered satisfying since the pacemaker usually stimulates the heart using a voltage that ranges from 1V to 10V. This voltage can be used to power the pacemaker and extend its lifetime. The harvester was simulated using ANSYS Workbench Software 2020 R2. On the simulation level, the harvester obtained a maximum output power of 0.81µW at a load of 2.2MΩ.
Machine Learning Paradigms for UAV Path Planning: Review and Challenges Bacha, Anis Mahmoud; Zamoum, Razika Boushaki; Lachekhab, Fadhila
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.v6i1.24097

Abstract

Path planning is a crucial step in robotic navigation to satisfy: tasks safety, efficiency requirements and adapt to the complexity of environments. Path planning problem is particularly critical for Unmanned Aerial Vehicles (UAV), being increasingly involved within important tasks in diverse military and civil fields such as: inspection, search and rescue and communication, taking advantage of their high flexibility, maneuverability and cost-effective solutions. This continuous growth made the solution of UAV path planning problem an interesting research topic in recent years. In this scope, machine learning algorithms were a promising tool due to their continuous data-driven selfimprovement to adapt with the high dynamicity of environments where conventional programming fails. This paper provides a review on recent developments in machine learning-based UAV path planning issued from credible databases like: IEEE, Elsevier, Springer Links and MDPI. The main contribution of this paper is to delve through these recent works providing a taxonomy of algorithms into the fundamental paradigms: supervised, unsupervised and reinforcement, evaluating their efficiency and limitations under distinct scenarios. Despite the relative generalization of deep reinforcement learning to different environments, this study highlighted some active challenges about computational cost and real-time applications that remain open.

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