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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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Revolutionizing Accessibility: Smart Wheelchair Robot and Mobile Application for Mobility, Assistance, and Home Management Jayasekara, Ninura; Kulathunge, Binali; Premaratne, Hirudika; Nilam, Insaf; Rajapaksha, Samantha; Krishara, Jenny
Journal of Robotics and Control (JRC) Vol 5, No 1 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i1.20057

Abstract

This research aims to advance accessibility and inclusivity for individuals with disabilities. We focus on specific daily challenges facing people with disabilities in communication, mobility, and daily task management and introduce AssistEase, a groundbreaking smart wheelchair solution designed to empower people with disabilities by improving mobility, communication capabilities, and daily task management. AssistEase will contribute to the disabled community around the world by allowing them to manage daily tasks and communicate more easily while ensuring mobility. AssistEase offers control options such as handsfree voice control, traditional manual control, smartphone-based Bluetooth control, or innovative gesture control, designed to cater to different user preferences and needs. This uses technologies such as speech recognition, computer vision, and haptic [92] feedback to help users navigate safely while avoiding obstacles. It integrates technologies like Flutter, TensorFlow, YOLOV8, Global Positioning System (GPS), Bluetooth, and Apple Home Kit, along with hardware components including Arduino and Raspberry PI. Preliminary trials have shown improvements in mobility, communication, and daily tasks for users in need. It achieves 95% precision in guiding wheelchair users while maintaining about 90% accuracy for the robotic arm and 89% for health monitoring and location tracking. Also, it provides a user-friendly app with 90% control accuracy. The communication device has 92% accuracy in facilitating user communication, while hand gesture control achieves 90% accuracy. To advance AssistEase smart wheelchair technology, further research, and development are required to enhance its adaptability for specific disabilities. AssistEase reflects a commitment to creating a more inclusive and thriving society, focusing on innovation and inclusion for individuals of all abilities.
EI-FRI: Extended Incircle Fuzzy Rule Interpolation for Multidimensional Antecedents, Multiple Fuzzy Rules, and Extrapolation Using Total Weight Measurement and Shift Ratio Alzubi, Maen; Almseidin, Mohammad; Kovacs, Szilveszter; Al-Sawwa, Jamil; Alkasassbeh, Mouhammd
Journal of Robotics and Control (JRC) Vol 5, No 1 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i1.20515

Abstract

Traditional fuzzy reasoning techniques demand a condensed fuzzy rule base to conclude a result. Still, due to incomplete data or a deficiency of expertise and knowledge, dense rule bases are not always available. Fuzzy interpolation methods have been widely explored to reasonably allow the interpolation of a fuzzy result using the closest current rules. Fuzzy rule interpolation is a type of fuzzy inference system in which conclusions can be obtained even with a few fuzzy rules. This benefit could be used to adapt the FRI to different application areas that suffer from a lack of knowledge. Alzubi et al. [17] offered a novel interpolative method that uses a weighted average based on the center point of the Incircle of the fuzzy sets. Nevertheless, the interpolated observation does not completely define the actual observation that is provided. In our offered extension to this method, a modification weight measure calculation and a shift technique are included to guarantee that the center point of the observation and the interpolated observation are mapped together. This weight measure calculation and shift technique enabled the capability of extrapolation to be conducted implicitly, which is also improves the performance results of the algorithm in the presence of multiple fuzzy rules and multidimensional priors.
A Review of Seaport Microgrids for Green Maritime Transportation: The Shore and the Seaside Almansor, Mohammed Jamal; Din, Norashidah Md; Baharuddin, Mohd Zafri; Alsayednoor, Huda Mohammed; Al-Shareeda, Mahmood A.; Ma, Maode; Ramly, Athirah Mohd
Journal of Robotics and Control (JRC) Vol 5, No 3 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i3.21723

Abstract

Emerging from the field of microgrids is an efficient and persuasive transitional technology with great promise for easing energy crises, environmental worries, and economic limitations in seaports. When it comes to high-performance ports, this technology becomes even more important. One example is smart ports, which use state-of-the-art ICT applications to completely revamp container and vessel management. Strengthening national economic sustainability and global competitiveness are both impacted by this invention. Reducing the environmental impact of the maritime transport business is no easy feat. In this study, we take a look at how seaport microgrids are becoming more important in the quest for environmentally friendly marine transportation. We take a look at the major problems that contemporary seaports are facing, such as the ever-increasing need for energy, the contamination of both the air and water from ship emissions and the unpredictable cost of electricity. The goal is to bring together current information about smarter ports by giving examples and to encourage new ideas and research in this field. As part of our efforts to inspire new research into smart port development, we also outline certain open questions that need answering. This report could serve as a valuable resource for future research on seaport microgrids.
A Recurrent Deep Architecture for Enhancing Indoor Camera Localization Using Motion Blur Elimination Alam, Muhammad S.; Mohamed, Farhan B.; Selamat, Ali; Hossain, AKM B.
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.21930

Abstract

Rapid growth and technological improvements in computer vision have enabled indoor camera localization. The accurate camera localization of an indoor environment is challenging because it has many complex problems, and motion blur is one of them. Motion blur introduces significant errors, degrades the image quality, and affects feature matching, making it challenging to determine camera pose accurately. Improving the camera localization accuracy for some robotic applications is still necessary. In this study, we propose a recurrent neural network (RNN) approach to solve the indoor camera localization problem using motion blur reduction. Motion blur in an image is detected by analyzing its frequency spectrum. A low-frequency component indicates motion blur, and by investigating the direction of these low-frequency components, the location and amount of blur are estimated. Then, Wiener filtering deconvolution removes the blur and obtains a clear copy of the original image. The performance of the proposed approach is evaluated by comparing the original and blurred images using the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). After that, the camera pose is estimated using recurrent neural architecture from deblurred images or videos. The average camera pose error obtained through our approach is (0.16m, 5.61◦). In two recent research, Deep Attention and CGAPoseNet, the average pose error is (19m, 6.25◦) and (0.27m, 9.39◦), respectively. The results obtained through the proposed approach improve the current research results. As a result, some applications of indoor camera localization, such as mobile robots and guide robots, will work more accurately.
Three-Dimensional Coordination Control of Multi-UAV for Partially Observable Multi-Target Tracking Maynad, Vincentius Charles; Nugraha, Yurid Eka; Alkaff, Abdullah
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22560

Abstract

This research deals with multi-UAV systems to track partially observable multi-targets in noisy three-dimensional environments, which are commonly encountered in defense and surveillance systems. It is a far extension from previous research which focused mainly on two-dimensional, fully observable, and/or perfect measurement settings. The targets are modeled as linear time-invariant systems with Gaussian noise and the pursuers UAV are represented in a standard six-degree-of-freedom model. Necessary equations to describe the relationship between observations regarding the target and the pursuers states are derived and represented as the Gauss-Markov model. Partially observable targets require the pursuers to maintain belief values for target positions. In the presence of a noisy environment, an extended Kalman filter is used to estimate and update those beliefs. A Decentralized Multi-Agent Reinforcement Learning (MARL) algorithm known as soft Double Q-Learning is proposed to learn the coordination control among the pursuers. The algorithm is enriched with an entropy regulation to train a certain stochastic policy and enable interactions among pursuers to foster cooperative behavior. The enrichment encourages the algorithm to explore wider and unknown search areas which is important for multi-target tracking systems. The algorithm was trained before it was deployed to complete several scenarios. The experiments using various sensor capabilities showed that the proposed algorithm had higher success rates compared to the baseline algorithm. A description of the many distinctions between two-dimensional and three-dimensional settings is also provided.
A Passivity-based Control Combined with Sliding Mode Control for a DC-DC Boost Power Converter Huynh, Minh Ngoc; Duong, Hoai Nghia; Nguyen, Vinh Hao
Journal of Robotics and Control (JRC) Vol 4, No 6 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i6.20071

Abstract

In this paper, a passivity-based control combined with sliding mode control for a DC-DC boost power converter is proposed. Moreover, a passivity-based control for a DC-DC boost power converter is also proposed. Using a co-ordinate transformation of state variables and control input, a DC-DC boost power converter is passive. A new plant is zero-state observable and the equilibrium point at origin of this plant is asymptotically stable. Then, a passivity-based control is applied to this plant such that the capacitor voltage is equal to the desired voltage. Additionally, the sliding mode control law is chosen such that the derivative of Lyapunov function is negative semidefinite. Finally, a passivity-based control combined with sliding mode control law is applied to this plant such that the capacitor voltage is equal to the desired voltage. The simulation results of the passivity-based control, the sliding mode control and the passivity-based control combined with sliding mode control demonstrate the effectiveness and show that the capacitor voltage is kept at the desired voltage when the desired voltage, the input voltage E and the load resistor R are changed. The results show that compared with the passivity-based control, the passivity-based control combined with sliding mode control has better performance such as shorter settling time, 8.5 ms when R changes and it has smaller steady-state error, which is indicated by the value of integral absolute error (IAE), 0.0679 when the desired voltage changes. The paper has limitations such as the assumed circuit parameters.
Adaptive Parallel Iterative Learning Control with A Time-Varying Sign Gain Approach Empowered by Expert System Chotikunnan, Phichitphon; Chotikunnan, Rawiphon; Minyong, Panya
Journal of Robotics and Control (JRC) Vol 5, No 1 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i1.20890

Abstract

This study explores the incorporation of time-varying sign gain into a parallel iterative learning control (ILC) architecture, augmented by an expert system, to enhance the performance and stability of a robotic arm system. The methodology involves iteratively tuning the learning control gains using time-varying sign gain guided by an expert system. Stability analysis, encompassing asymptotic and monotonic convergence, demonstrates promising results across multiple joints, affirming the effectiveness of the proposed control architecture. In comparison with traditional PID control, fixed gain ILC, and ILC with adaptive learning in the expert system, the analysis focuses on stability, precision, and adaptability, using root mean square error (RMSE) as a key metric. The results show that ILC with adaptive learning from the expert system consistently reduces RMSE, even in the presence of learning transients. This adaptability effectively controls the learning transients, ensuring improved performance in subsequent iterations. In conclusion, the integration of time-varying sign gain with expert system assistance in a parallel ILC architecture holds promise for advancing adaptive control in robotic systems. Positive outcomes in stability, precision, and adaptability suggest practical applications in real-world scenarios. This research provides valuable insights into the implementation of dynamic learning mechanisms for enhanced robotic system performance, laying the groundwork for future refinement in robotic manipulator control systems.
Power Management and Voltage Regulation in DC Microgrid with Solar Panels and Battery Storage System Mutlag, Ashraf Abdualateef; Abd, Mohammed Kdair; Shneen, Salam Waley
Journal of Robotics and Control (JRC) Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i2.20581

Abstract

Photovoltaics are one of the most important renewable energy sources to meet the increasing demand for energy. This led to the emergence of Microgrid s, which revealed a number of problems, the most important of which is managing and monitoring their operation, this research contributes mainly by using a maximum power tracking algorithm Which depends on artificial neurons and integrating it with a proposed algorithm for energy management in Standalone DC Microgrid, in order to control the distribution of power and maintain the DC bus voltage level.  Maximum Power Point Tracking (MPPT) algorithm based on ANN+PID is used. Where ANN tracks the maximum power point by estimating the reference voltage using real-time data such as temperature and solar radiation. The PI controller reduces the error between the measured voltage and the reference voltage and makes the necessary adjustments in order to control the boost converter connected to the photovoltaic panels. While the process of controlling the DC bus voltage level is done by controlling the battery charging and discharging process through the power management algorithm and controlling the Bidirectional converter switches according to the battery’s state of charge. The simulation results obtained by used MATLAB Simulink are shown that the used MPPT algorithm achieved the maximum power with the least amount of fluctuation, the method's efficiency was 99.92%, and its accuracy was 99.85%, as well as the success of the power management algorithm controlling the battery charging/discharging process and maintaining the DC voltage level at the specified value in different operating scenarios.
Ovarian Tumors Detection and Classification on Ultrasound Images Using One-stage Convolutional Neural Networks Le, Van-Hung; Pham, Thi-Loan
Journal of Robotics and Control (JRC) Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i2.20589

Abstract

Currently, the advent of CNN (Convolutional Neural Network) has brought very convincing results to computer vision problems. One-stage CNNs are a suitable choice for research and development to have an overview of the current results of the process of detecting and classifying OTUM from ovarian ultrasound images. In this paper, we have performed a comprehensive study on one-stage CNNs for the problem of detecting and classifying OTUM on ovarian ultrasound images. The OTUM datasets we tested were two popular OTUM datasets: OTU and USOVA3D. The one-stage CNNs we tested and evaluated belong to the YOLO (You Only Look Once) family (YOLOv5, YOLOv7, YOLOv8 variations, and YOLO-NAS), and the SSD (Single Shot MultiBox Detector) family (VGG16-SSD, Mb1-SSD, Mb1-SSDLite, Sq-SSD-Lite, and Mb2-SSD-Lite). The results of detecting OTUM (with or without OTUM on ovarian ultrasound images) are high (with Mb1-SSD of Acc = 98.90%, P = 98.58%, R = 98.9% on “USOVA3D 2D f r1 80 20” set; with Mb2-SSD-Lite of Acc = 97.87%, P = 97.16%, R = 97.87% on “USOVA3D 2D f r2 80 20” set). The results of detecting and classifying OTUM into 8 classes are low (the highest is Acc = 92.04%, P = 74.81%, R = 92.04% on the OTU-2D dataset). Regarding computation time, CNNs of the YOLO family have faster computation times than networks of the SSD family. The above results show that the problem of classifying ovarian tumors on ultrasound images still contains many challenges that need to be resolved in the future.
Effectiveness of CNN Architectures and SMOTE to Overcome Imbalanced X-Ray Data in Childhood Pneumonia Detection Pamungkas, Yuri; Ramadani, Muhammad Rifqi Nur; Njoto, Edwin Nugroho
Journal of Robotics and Control (JRC) Vol 5, No 3 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i3.21494

Abstract

Pneumonia is a disease that causes high mortality worldwide in children and adults. Pneumonia is caused by swelling of the lungs, and to ensure that the lungs are swollen, a chest X-ray can be done. The doctor will then analyze the X-ray results. However, doctors sometimes have difficulty confirming pneumonia from the results of chest X-ray observations. Therefore, we propose the combination of SMOTE and several CNN architectures be implemented in a chest X-ray image-based pneumonia detection system to help the process of diagnosing pneumonia quickly and accurately. The chest X-ray data used in this study were obtained from the Kermany dataset (5216 images). Several stages of pre-processing (grayscaling and normalization) and data augmentation (shifting, zooming, and adjusting the brightness) are carried out before deep learning is carried out. It ensures that the input data for deep learning is not mixed with noise and is according to needs. Then, the output data from the augmentation results are used as input for several CNN deep learning architectures. The augmented data will also utilize SMOTE to overcome data class disparities before entering the CNN algorithm. Based on the test results, the VGG16 architecture shows the best level of performance compared to other architectures. In system testing using SMOTE+CNN Architectures (VGG16, VGG19, Xception, Inception-ResNet v2, and DenseNet 201), the optimum accuracy level reached 93.75%, 89.10%, 91.67%, 86.54% and 91.99% respectively. SMOTE provides a performance increase of up to 4% for all CNN architectures used in predicting pneumonia.

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