cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Iswanto
Contact Email
-
Phone
+628995023004
Journal Mail Official
jrc@umy.ac.id
Editorial Address
Kantor LP3M Gedung D Kampus Terpadu UMY Jl. Brawijaya, Kasihan, Bantul, Yogyakarta 55183
Location
Kab. bantul,
Daerah istimewa yogyakarta
INDONESIA
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
 46   47   48   49   50 
Enhancing Multi-Robot Systems Cooperation through Machine Learning-based Anomaly Detection in Target Pursuit Khatib, Amine; Hamed, Oussama; Hamlich, Mohamed; Mouchtachi, Ahmed
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.v5i3.20333

Abstract

Effectively pursuing dynamically moving targets in the domain of multi-robot systems (MRS) poses a significant challenge. This paper proposes an innovative leader-follower strategy within the MRS framework, enabling robots to dynamically adjust their roles based on target proximity. This approach fosters coordination, allowing robots to act cohesively when pursuing diverse targets, from other robots to mobile objects. The centralized architecture of the MRS facilitates wireless communication, enabling robots to share sensor-derived data providing proximity cues rather than precise location information. However, data anomalies arising from sensor errors, transmission glitches, or encoding issues pose challenges, compromising the reliability of target-related information. To mitigate this, the paper introduces an advanced methodology integrating the leader-follower strategy with Discriminant Analysis (DA)-based anomaly detection. This novel approach validates and filters data, enhancing data integrity and supporting decision-making processes. The integration of DA methods within the leader-follower strategy is detailed, emphasizing steps in anomaly detection implementation, showcasing robustness in selecting high-quality information for decision-making in dynamic environments. The research's real-world relevance addresses the problem of the impact of sensor anomalies on the performance and reliability of MRS in dynamic environments. By integrating machine learning-based anomaly detection, this methodology enhances MRS adaptability and robustness, particularly in scenarios requiring precise target tracking and coordination. Numerical experiments and simulations demonstrate the efficacy of the DA-based anomaly detection and collaborative hunting strategy in MRS. This method contributes to improved target tracking, enhanced system coordination, and streamlined pursuit of dynamic targets, affirming its practical applicability in surveillance, search and rescue operations, and industrial automation.
Parameter Extraction of Triple-diode Photovoltaic Model via RIME Optimizer with Neighborhood Centroid Opposite Solution Izci, Davut; Ekinci, Serdar; Ma'arif, Alfian
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.v5i4.22256

Abstract

In this investigation, a novel application of the RIME optimizer with neighborhood centroid opposite solution is introduced to robustly estimate parameter values for an accurate photovoltaic triple-diode model. The suggested optimizer's performance is rigorously evaluated in comparison to other well-documented methods. The evaluation of the proposed optimizer is conducted using real data from the RTC France solar cell, and the results are assessed through various evaluation metrics, including root mean square error and statistical analyses for multiple independent runs. Specifically, the proposed optimizer demonstrates superior performance by achieving the lowest objective function values compared to other algorithms. Through a comprehensive quantitative and qualitative assessment, it can be inferred that the estimated parameters of the triple-diode model obtained using the proposed optimizer surpass the accuracy of those acquired through other optimization algorithms under consideration.
LICA-CS: Efficient Lossless Image Compression Algorithm via Column Subtraction Model Al Qerom, Mahmoud; Otair, Mohammad; Meziane, Farid; AbdulRahman, Sawsan; Alzubi, Maen
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.v5i5.21834

Abstract

Driven by the unprecedented amount of data generated in the last few decades, data storage and communication are becoming more challenging. Although many approaches in data compression have been developed to alleviate these challenges, more efforts are still needed, especially for lossless image compression, which is a promising technique when critical information loss is not allowed. In this paper, we propose a new algorithm called the Lossless Image Compression Algorithm using a Column Subtraction model (LICA-CS). LICA-CS is efficient, low in complexity, decreases the image bit-depth, and enhances state-of-the-art performance. LICA-CS first implements a color transformation method as a pre-processing phase, which strategically minimizes inter-channel correlations to optimize compression outcomes. After that, a novel subtraction method was developed to compress the image data column-wise. We tackle the similarity and proximity of pixel values within adjacent columns, which offers a distinct advantage in reducing image size observing a significant size reduction of 71%. This is achieved through the subtraction of neighboring columns. The conducted experiments on colored images show that LICA-CS outperforms existing algorithms in terms of compression rate. Moreover, our method exhibited remarkable enhancements in execution time, with compression and decompression processes averaging 1.93 seconds. LICA-CS advances the state-of-the-art in lossless image compression, promising enhanced efficiency and effectiveness in image compression technologies.
Plant Leaf Disease Detection Using Efficient Image Processing and Machine Learning Algorithms Kiran, S M; Chandrappa, D N
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.v4i6.20342

Abstract

India is often described as a country of villages, where a majority of the population depends on agriculture for their livelihood. The landscape of Indian agriculture is approximately 159.7 million hectares. Agriculture plays a pivotal role in India's Gross Domestic Product (GDP), accounting for about 18% of the nation's economic output. Diseases and pests can have detrimental effects on crops, leading to reduced yields. These challenges can include the spread of plant diseases, infestations by insects or other pests, and the overall degradation of crop health. Early detection of diseases in crops is crucial for several reasons. Detecting diseases at an early stage allows for prompt intervention, such as applying appropriate pesticides or taking preventive measures. The main aim of this study is to develop a highly effective method for plant leaf disease detection using computer vision techniques. Here, leaf disease detection comprises histogram equalization, denoising, image color threshold masking, feature descriptors such as Haralick textures, Hu moments, and color histograms to extract the salient features of leaf images. These features are then used to classify the images by training Logistic Regression, Linear Discriminant Analysis, K-nearest neighbor, decision tree, Random Forest, and Support Vector Machine algorithms using K-fold validation. K-fold validation is used to separate the validation samples from the training samples, and the K indicates the number of times this is repeated for the generalization. The training and validation processes are performed in two approaches. The first approach uses default hyperparameters with segmented and non-segmented images. In the second approach, all hyperparameters of the models are optimized to train segmented datasets. The classification accuracy improved by 2.19% by utilizing segmentation and hyperparameter tuning further improved by 0.48%. The highest average classification accuracy of 97.92% is achieved using the Random Forest classifier to classify 40 classes of 10 different plant species. Accurate detection of plant disease leads to the sustained growth of plants throughout the growing span of the plants.
Design and Implementation of Fuzzy Logic for Obstacle Avoidance in Differential Drive Mobile Robot Puriyanto, Riky Dwi; Mustofa, Ahmad Kamal
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.v5i1.20524

Abstract

Autonomous mobile robots based on wheel drive are widely used in various applications. The differential drive mobile robot (DDMR) is one type with wheel drive. DDMR uses one actuator to move each wheel on the mobile robot. Autonomous capabilities are needed to avoid obstacles around the DDMR. This paper presents implementing a fuzzy logic algorithm for obstacle avoidance at a low cost (DDMR). The fuzzy logic algorithm input is obtained from three ultrasonic sensors installed in front of the DDMR with an angle difference between the sensors of 45$^0$. Distance information from the ultrasonic sensors is used to regulate the speed of the right and left motors of the DDMR. Based on the test results, the Mamdani inference system using the fuzzy logic algorithm was successfully implemented as an obstacle avoidance algorithm. The speed values of the right and left DDMR wheels produce values according to the rules created in the Mamdani inference system. DDMR managed to pass through a tunnel-shaped environment and reach its goal without hitting any obstacles around it. The average speed produced by DDMR in reaching the goal is 4.91 cm/s.
Enhancing Multilevel Inverter Performance: A Novel Dung Beetle Optimizer-based Selective Harmonic Elimination Approach Taha, Taha A.; Neamah, Muthanna Ibrahim; Ahmed, Saadaldeen Rashid; Taha, Faris Hassan; Bektaş, Yasin; Desa, Hazry; Yassin, Khalil Farhan; Ibrahim, Marwa; Hashim, Abdulghafor Mohammed
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.v5i4.21722

Abstract

This paper introduces a novel approach for enhancing the performance of multilevel inverters by applying a dung beetle optimizer (DBO)-based Selective Harmonic Elimination (SHE) technique. Focusing on a 3-phase multilevel inverter (MLI) with a non-H-bridge structure, the proposed method offers advantages such as cost-effective hardware implementation and eliminating the traditional H-bridge inverter requirement. To assess its efficacy, we compare the presented DBO-based SHE technique (DBOSHE) with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), evaluating their ability to determine optimal switching angles for achieving low-distorted load voltage. Unlike methods reliant on time-consuming calculations or fixed solutions, DBO provides a flexible approach, considering multiple possibilities to yield accurate switching angles. Using Simulink, harmonic component values and Total Harmonic Distortion (THD) are obtained for each optimization technique, specifically emphasizing on 9-level and 11-level MLI topologies. Our study aims to identify the most effective optimization technique for achieving lower THD and THDe values while eliminating odd-order harmonics from the 3-phase load voltage. Finally, we demonstrate the effectiveness of employing DBO for THD and THDe optimization within the SHE technique.
Optimizing Solar Energy Production in Partially Shaded PV Systems with PSO-INC Hybrid Control Abboud, Sarah; Loulijat, Azeddine; Boulal, Abdellah; Semma, El Alami; Habachi, Rachid; Chojaa, Hamid; Ma'arif, Alfian; Suwarno, Iswanto; Mossa, Mahmoud A.
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.v5i2.20896

Abstract

Partial shading, from obstacles such as buildings or trees, is a major challenge for photovoltaic systems, causing unpredictable fluctuations in solar energy production and underlining the need for advanced energy management strategies. In this paper, we propose an innovative approach that combines hybrid metaheuristic optimization with maximum power point tracking control (MPPT), using particle swarm optimization (PSO) in conjunction with the incremental conductance (IC) algorithm. We compare the proposed method with the conventional Perturb and Observation (PO) algorithm. The choice of PO as a comparison method is due to its simplicity, its familiarity with the scientific literature, its low cost of implementation. The main objective of swarm optimization combined with the IC algorithm lies in its ability to overcome the challenges posed by partial shading, ensuring accurate and efficient tracking of the point of maximum power, thanks to dynamic adaptation to variations in solar irradiation, thus enhancing the performance and resilience of the photovoltaic system. This approach  is of crucial importance, offering considerable potential for solving the complex challenges associated with partial shading. Our results show that this hybrid MPPT algorithm offers superior tracking efficiency 98% , faster convergence 500ms , better stability and increased robustness compared to traditional MPPT approaches. The system is composed of a PV and a boost converter that connects the input to the resistive load. The algorithms were implemented with MATLAB/Simulink as the simulation tool. These results not only reinforce the viability of sustainable energy solutions, but also open the way for the development of more sustainable energy solutions.The perspectives of this work are oriented towards a practical and extended integration of the proposed hybrid approach in real photovoltaic systems, with a particular emphasis on experimental validation.
Robust Adaptive Trajectory Tracking Sliding Mode Control for Industrial Robot Manipulator using Fuzzy Neural Network Xuan, Quynh Nguyen; Cong, Cuong Nguyen; Ba, Nghien Nguyen
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.v5i2.20722

Abstract

This paper presents a control method for a two-link industrial robot manipulator system that uses Fuzzy Neural Networks (FNNs) based on Sliding Mode Control (SMC) to investigate joint position control for periodic motion and predefined trajectory tracking control. The proposed control scheme addresses the challenges of designing a suitable control system that can achieve the required approximation errors while ensuring the stability and robustness of the control system in the face of joint friction forces, parameter variations, and external disturbances. The control scheme uses four layers of FNNs to approximate nonlinear robot dynamics and remove chattering control efforts in the SMC system. The adaptive turning algorithms of network parameters are derived using a projection algorithm and the Lyapunov stability theorem. The proposed control scheme guarantees global stability and robustness of the control system, and position is proven. Simulation and experiment results from a two-link IRM in an electric power substation are presented in comparison to PID and AF control to demonstrate the superior tracking precision and robustness of the proposed intelligent control scheme.
Heart Disease Prediction Using Hybrid Machine Learning: A Brief Review Ahmed, Mohammed; Husien, Idress
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.v5i3.21606

Abstract

Cardiovascular disease is a widespread and potentially fatal condition that requires proactive preventive measures and efficient screening approaches on a global scale. To tackle this issue, recent studies have investigated novel machine-learning frameworks that propose to diagnose and forecast cardiovascular disease by capitalizing on enormous datasets and predictive patterns linked to this condition. The research contribution is a thorough examination and implementation of ensemble learning and other hybrid machine-learning techniques for heart disease prediction. By employing ensemble learning on datasets including The Cleveland heart disease dataset and The IEEE Dataport heart diseases dataset such as age, chest pain type, blood pressure, blood glucose level, ECG in rest, heart rate, and four types of chestpain. To predict heart disease, our methodology integrates numerous machine learning models. By capitalising on the merits of specific algorithms while addressing their drawbacks, this approach yields a predictive model that is more resilient. The findings of our research exhibit encouraging outcomes in the realm of heart disease prediction, attaining enhanced precision and dependability in contrast to discrete algorithms. Through the utilisation of ensemble learning, we successfully discerned predictive patterns that are linked to heart disease, thereby augmenting the capabilities of diagnostics. In summary, the findings of our study emphasise the considerable potential of ensemble techniques within the realm of machine learning for the advancement of cardiac disease prediction. By providing a more dependable method for rapid diagnosis and prognosis of cardiac disease, this strategy has substantial ramifications for healthcare practices.
Modeling and Designing of Active Vibration Isolation Platform Mikhailov, Valery; Kopylov, Alexey; Kazakov, Alexander
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.v5i4.22103

Abstract

The most important task of ensuring the quality of operation of technological and research equipment is its effective protection from external vibration effects in the area of low frequencies, at which resonance phenomena are manifested. For this purpose, various types of vibration isolation systems are used, which are divided into passive and active. Passive systems effectively suppress vibrations at frequencies above 40-50 Hz, at lower frequencies such systems are ineffective as they cannot compensate for resonance phenomena. In this case, active vibration isolation systems are used. The active dampers and platform based on magnetorheological (MR) elastomers presented in the paper demonstrated higher vibration isolation efficiency in the frequency range of 0.3-3 Hz compared to the piezoelectric system and in the frequency range of 0.3-20 Hz compared to the platform based on electromagnetic power drive. At these frequencies, the vibration displacement amplitude transfer coefficient was less than 0.075.  The use of MR effect makes it possible to regulate the stiffness and deformation of the elastic active element made of MR elastomer by changing the magnitude of magnetic induction. In this case it is possible to control dynamic and precision characteristics of the active damper, as well as to increase the efficiency of vibration isolation.  During dynamic modelling of the active MP damper the differential equations and transfer functions of its elements were determined. Modelling in Simulink MATLAB software environment allowed to determine the transient process of damper movement under the influence of harmonic oscillations, select the type of regulator and calculate its tuning parameters. Experimental studies were carried out on a vibration test bench and confirmed the modelling results at frequencies of 0.3-50 Hz. At frequencies higher than 50 Hz passive vibration isolation begins to prevail due to absorption of vibration energy in the MR elastomer, which is not taken into account in the modelling. The experimental amplitude-frequency response of the platform showed high vibration isolation efficiency at frequencies of 0.3-100 Hz with vibration displacement amplitude transfer coefficient less than 0.075.

Page 48 of 71 | Total Record : 708

 46   47   48   49   50 

Filter by Year

2020 2025


Reset
Filter By Issues
All Issue Vol. 6 No. 5 (2025) Vol. 6 No. 4 (2025) Vol. 6 No. 2 (2025) Vol. 6 No. 1 (2025) Vol 5, No 6 (2024) Vol. 5 No. 6 (2024) Vol. 5 No. 5 (2024) Vol 5, No 5 (2024) Vol 5, No 4 (2024) Vol 5, No 3 (2024) Vol 5, No 2 (2024) Vol 5, No 1 (2024) Vol 4, No 6 (2023) Vol 4, No 5 (2023) Vol 4, No 4 (2023) Vol 4, No 3 (2023) Vol 4, No 2 (2023) Vol 4, No 1 (2023) Vol 3, No 6 (2022): November Vol 3, No 5 (2022): September Vol 3, No 4 (2022): July Vol 3, No 3 (2022): May Vol 3, No 2 (2022): March Vol 3, No 1 (2022): January Vol 2, No 6 (2021): November (Forthcoming Issue) Vol 2, No 6 (2021): November Vol 2, No 5 (2021): September Vol 2, No 5 (2021): September (Forthcoming Issue) Vol 2, No 4 (2021): July Vol 2, No 4 (2021): July (Forthcoming Issue) Vol 2, No 3 (2021): May Vol 2, No 3 (2021): May (Forthcoming Issue) Vol 2, No 2 (2021): March (Forthcoming Issue) Vol 2, No 2 (2021): March Vol 2, No 1 (2021): January Vol 1, No 6 (2020): November Vol 1, No 5 (2020): September Vol 1, No 4 (2020): July Vol 1, No 3 (2020): May Vol 1, No 2 (2020): March Vol 1, No 1 (2020): January More Issue