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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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Reinforcement Learning-Based Trajectory Control for Mecanum Robot with Mass Eccentricity Considerations Nguyen, Minh Dong; Ngo, Manh Tien; Quang, Hiep Do; Phuong, Nam Dao
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.v5i5.22148

Abstract

This article presents a robust optimal tracking control approach for a Four Mecanum Wheeled Robot (FMWR) using an online actor-critic reinforcement learning (RL) algorithm to address the challenge of precise trajectory tracking problem in the presence of mass eccentricity and friction uncertainty. In order to handle these obstacles, a detailed dynamics model is derived using Lagrange’s equation, and the Hamilton–Jacobi–Bellman (HJB) equation is solved by iteration algorithm with policy evaluation and improvement. The training laws of optimal control law and value function are proposed after minimizing the modified Hamiltonian function. Moreover, to handle the time-varying property of tracking error model, a transform is given with the addition of time derivative term. Simulation Studies demonstrate the approach’s effectiveness, significantly improving trajectory tracking accuracy and robustness against disturbances. This research contributes to mobile robotics by enhancing control precision and reliability in dynamic environments.
Soft Tissue Compliance Detection in Minimally Invasive Surgery: Dynamic Measurement with Piezoelectric Sensor Based on Vibration Absorber Concept Hashem, Radwa; El-Hussieny, Haitham; Umezu, Shinjiro; El-Bab, Ahmed M. R. Fath
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.v5i5.22895

Abstract

Recent research in the medical field has increasingly focused on tissue repair, tumor detection, and associated therapeutic techniques. A significant challenge in Minimally Invasive Surgery (MIS) is the loss of direct tactile sensation by surgeons, as they cannot physically feel the organs they operate on. Tactile feedback enhances patient safety by tissue differentiation and reducing inadvertent damage risks. Addressing this challenge, this study introduces a novel tactile sensor designed for compliance detection to enhance tactile feedback in MIS. The sensor operates on a 2-Degree-of-Freedom (2-DOF) vibration absorber system, utilizing a piezoelectric actuator with a calibrated stiffness of 188 N/m. It interprets tissue stiffness regarding a spring constant, Ko, and measures changes in soft tissue stiffness by analyzing variations in the vibration absorber frequency, specifically at the frequency which causes the first mass to exhibit zero amplitude. The effectiveness of this sensor was evaluated through tests on polydimethylsiloxane (PDMS) specimens, which were engineered to replicate varying stiffness found in human organ tissues. Young's modulus of these specimens was determined using a universal testing machine, showing a range from 10.12 to 226.89 kPa. Additionally, the sensor was applied to measure the stiffness of various chicken tissues – liver, heart, breast, and gizzard with respective Young's moduli being 1.97, 9.47, 19.55, and 96.36 kPa. This sensor successfully differentiated between tissue types non-invasively, without requiring substantial deformation or penetration of the tissues. Given its piezoelectric nature, the sensor also holds significant potential for miniaturization through Micro-Electro-Mechanical Systems technology (MEMS), broadening its applicability in surgical environments.
System Identification and Control Strategy on Electric Power Steering DC Motor Arifin, Bustanul; Nugroho, Agus Adhi; Budisusila, Eka Nuryanto; Khosyi'in, Muhammad
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.v5i3.21614

Abstract

Power steering technology help human to control the car. The hydraulic power steering system now tends to be replaced by the electric power steering system (EPS). As the main driver that require precise control. The contribution of this research is to obtain system identification of EPS motor and novelty control strategy to achieve stable control better. Motor control require an appropriate mathematical model and up-down-up down signals of Pseudo Random Binary Signal Sequence (PRBS) were used. The modelling method used was the Numerical Algorithm for Subspace State Space System Identification (N4SID). The quality of the modeling needs to be measured to see whether it was close to the original signal. The validation of the model obtained tested using Variance Accounted For (VAC), Akaike Information Criterion (AIC), and Final Prediction Error (FPE). The best mathematical model was developed on the basis of these three criteria, which is 3rd order model. The control strategy carried out by means of the Ziegler Nichols, Tyreus Luyben and Haugen tuning technique. With these three tuning methods, the control parameters obtained were used for Proprotional-Integral (PI) and Proportional-Integral-Derivative (PID) control. Based on the study, the Haugen control shows the best results of the two other controls, namely with a rise time value of 11,361 ms, overshoot of 6,898%, and steady state at 1.3 s. This show that PI control using the Haugen tuning method able to control the motor well. Robustness tests have also been carried out because the steering system is operated in unpredictable environmental conditions. The control greatly influenced the performance and stability of EPS control in the car's steering system.
Current Trends in Incubator Control for Premature Infants with Artificial Intelligence Based on Fuzzy Logic Control: Systematic Literature Review Maharani Raharja, Nia; Suwarno, Iswanto; Sugiyarta, Sugiyarta
Journal of Robotics and Control (JRC) Vol 3, No 6 (2022): November
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v3i6.13341

Abstract

Incubator Control for Premature Babies has benefited greatly from the development of creative methods and uses of artificial intelligence. Due to the immaturity of the epidermis, premature infants lose fluid and heat early in life, which causes hyperosmolar dehydration and hypothermia. Water loss through the epidermis. Therefore, in order to maintain the baby's healthy temperature, an incubator is required. As a result, it is anticipated that the baby will maintain the same temperature as in the mother's womb. A temperature regulation system with good measurement and regulation quality is necessary due to the necessity of Incubator Control for Premature Infants with Artificial Intelligence Based on Fuzzy Logic in treating premature infants. The purpose of this research is to assess current trends in artificial intelligence-based fuzzy logic incubator control for preterm infants. The Preferred Reporting Items for Systematic Review (PRISMA) were used in this study's systematic literature review. 188 suitable articles that fit the inclusion requirements were found after the articles were screened and chosen. The outcomes demonstrated that the Incubator Control for Premature Infants offered the best environment for newborns with growth or disease-related issues (premature babies). An incubator is a sealed space free of dust and bacteria with the ability to regulate temperature, humidity, and oxygen to maintain a stable environment.
Human Activity Recognition Using Accelerometer & Gyroscope Smartphone Sensor by Extract Statistical Features Abdullah, Muthana Hmod; Ahmed, M. A.
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.v5i5.22443

Abstract

Understanding behavioral patterns and forecasting the bodily motions of persons heavily relies on detecting human activities. This has profound ramifications in several domains, including healthcare, sports, and security. This study sought to identify and classify 18 human actions recorded by 90 people using smartphone sensors using the KU-HAR dataset. The primary aim of this study is to examine statistical features such as (mean, mod, entropy, max, median …etc.) derived from time-domain sensory data collected using accelerometers and gyroscopes. Activity detection utilizes many machine learning methods such as Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LG), Naïve Bayes (NB), and AdaBoost. The RF model achieves the highest overall accuracy of 99%. While the DT model gets 95%, SVM receives 93%, and the KNN gets 82%. At the same time, the other model didn’t get good results. The research is evaluated using accuracy, recall, precision, and f1-scor. The research contribution is to extract the statistical feature from the raw file of the sensor to create a new dataset. This research recommends employing statistical features in time series. Future research is recommended to solve misclassification in certain activities, which could be achieved using feature selection to reduce the number of features.
Enhancing Security Mechanisms for IoT-Fog Networks Mansour, Salah-Eddine; Sakhi, Abdelhak; Kzaz, Larbi; Sekkaki, Abderrahim
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.v5i1.20745

Abstract

This study contributes to improving Morocco's fish canning industry by integrating artificial intelligence (AI). The primary objective involves developing an AI and image processing-based system to monitor and guarantee canning process quality in the facility. It commenced with an IoT-enabled device capable of capturing and processing images, leading to the creation of an AI-driven system adept at accurately categorizing improperly crimped cans. Further advancements focused on reinforcing communication between IoT devices and servers housing individual client's neural network weights. These weights are vital, ensuring the functionality of our IoT device. The efficiency of the IoT device in categorizing cans relies on updated neural network weights from the Fog server, crucial for continual refinement and adaptation to diverse can shapes. Securing communication integrity between devices and the server is imperative to avoid disruptions in can classification, emphasizing the need for secure channels. In this paper, our key scientific contribution revolves around devising a security protocol founded on HMAC. This protocol guarantees authentication and preserves the integrity of neural network weights exchanged between Fog computing nodes and IoT devices. The innovative addition of a comprehensive dictionary within the Fog server significantly bolsters security measures, enhancing the overall safety between these interconnected entities.
Application of Software Robots Using Artificial Intelligence Technologies in the Educational Process of the University Yeslyamov, Serik
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.v5i2.21083

Abstract

The use of artificial intelligence (AI) in education has gained interest due to its increasing application in various fields. This study explores the potential of AI-based software robots in higher education and their ability to revolutionize educational methodologies. The research purpose is to examine the positive impact of the use of software robots in educational settings. The study focuses on evaluating the prospects of expanding the use of AI-based software robots in higher education. The research uses a combination of observational techniques and practical case studies. It includes an experimental investigation of the basic principles of developing an AI-based robot teacher, with the aim of eventually implementing it in educational processes. The research findings indicate that integrating AI-driven software robots into university education can provide substantial benefits and significant improvements over traditional teaching models. These robots can enhance the educational process and address various developmental challenges. The study highlights the transformative impact of AI-based software robots in modernizing university education. The findings demonstrate the potential of these technologies to reshape the current higher education system.
Efficient Path Planning Algorithm for Mobile Robots Performing Floor Cleaning Like Operations Nair, Vishnu G
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.v5i1.20035

Abstract

In this paper, we introduce an efficient path planning algorithm designed for floor cleaning applications, utilizing the concept of Spanning Tree Coverage (STC). We operate under the assumption that the environment, i.e., the floor, is initially unknown to the robot, which also lacks knowledge regarding obstacle positions, except for the workspace boundaries. The robot executes alternating phases of exploration and coverage, leveraging the local map generated during exploration to construct a STC tree, which then guides the subsequent coverage (cleaning) phase. The extent of exploration is determined by the range of the robot's sensors. The path generation algorithms for cleaning fall within the broader category of coverage path planning (CPP) algorithms. A key advantage of this algorithm is that the robot returns to its initial position upon completing the operation, minimizing battery usage since sensors are only active during the exploration phase. We classify the proposed algorithm as an offline-online scheme. To validate the effectiveness and non-repetitive nature of the algorithm, we conducted simulations using VRep/MATLAB environments and implemented real-time experiments using Turtlebot in the ROS-Gazebo environment. The results substantiate the completeness of coverage and underscore the algorithm's significance in applications akin to floor cleaning.
Predicting SI Engine Performance Using Deep Learning with CNNs on Time-Series Data Hofny, Mohamed S.; Ghazaly, Nouby M.; Shmroukh, Ahmed N.; Abouelsoud, Mostafa
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.v5i5.22558

Abstract

In this study, deep learning (DL) model is used to predict brake power (BP) of GX35-OHC 4-stroke, air-cooled, single-cylinder gasoline engine. The engine uses E15 (85% gasoline + 15% ethanol) as a fuel due to its high performance and low emissions. A convolutional neural networks (CNN) model is used on time-series data due to their ability to capture temporal patterns and relationships in sequential data, such as engine BP. While studying the performance of the network, it is found that the root mean squared error (RMSE) is 0.0007, explained variance score (EVS) is 0.9999, and mean absolute percentage error (MAPE) is 0.22%. Compared to traditional machine leaning methods, these metrics demonstrate the high accuracy and reliability of the model, confirming its effectiveness in predicting BP. Various performance curves are plotted such as comparing target and predicted values, regression plots (to indicate the generalization capability),  learning curve (to demonstrate the model's effective training progress and convergence), Bland-Altman plot (to show the convergence between the actual and predicted values), histogram and density plot (to show a close fit between predicted and actual values), density plot of actual and predicted outputs, and residual plot (to show randomly distributed errors). This high accuracy and reliability of this DL model help in effective real-time engine performance monitoring, and reducing emission levels, especially for the adoption and use of renewable fuels like E15.
Explainable Ensemble Learning Models for Early Detection of Heart Disease Laftah, Raed Hassan; Al-Saedi, Karim Hashim Kraidi
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.v5i5.22448

Abstract

Coronary diseases (CVD) are a major global health concern, and timely, accurate diagnosis is crucial for effective treatment and management. As machine learning is, has steadily been on the improvement way, and it's there where we find the transformative potential for enhancing the diagnostic accuracy for their predictive accuracy using the Local Interpretable Model-agnostic Explanations technique to ensure the explainability of our models. With the advancement of machine learning, we aim to enhance diagnostic accuracy by developing a high-precision prediction tool for heart disease using various ML models. We utilized a Kaggle dataset to implement several ML models, including Random Forest, Gradient Boosting, CatBoost, K-Nearest Neighbor, Naive Bayes, Support Vector Machine, and AdaBoost, with appropriate data preprocessing. The soft voting ensemble method, combining various models, achieved a notable 98.54% accuracy and 99% precision, recall, and f1-score, with Random Forest, CatBoost, and the Voting Classifier outperforming others. These results indicate that our model is highly reliable and sets a new standard for CVD prediction. Future research should focus on validating this model with larger datasets and exploring deep learning approaches.

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