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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 21 Documents
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Search results for , issue "Vol 5, No 6 (2024)" : 21 Documents clear
Formation Control of Multiple Unmanned Aerial Vehicle Systems using Integral Reinforcement Learning Dang, Ngoc Trung; Duong, Quynh Nga
Journal of Robotics and Control (JRC) Vol 5, No 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23505

Abstract

Formation control of Unmanned Aerial Vehicles (UAVs), especially quadrotors, has many practical applications in contour mapping, transporting, search and rescue. This article solves the formation tracking requirement of a group of multiple UAVs by formation control design in outer loop and integral Reinforcement Learning (RL) algorithms in position sub-system. First, we present the formation tracking control structure, which uses a cascade description to account for the model separation of each UAV. Second, based on value function of inner model, a modified iteration algorithm is given to obtain the optimal controller in the presence of discount factor, which is necessary to employ due to the finite requirement of infinite horizon based cost function. Third, the integral RL control is developed to handle dynamic uncertainties of attitude sub-systems in formation UAV control scheme with a discount factor to be employed in infinite horizon based cost function. Specifically, the advantage of the proposed control is pointed out in not only formation tracking problem but also in the optimality effectiveness. Finally, the simulation results are conducted to validate the proposed formation tracking control of a group of multiple UAV system.
Active Vibration Isolation using Tilt Horizontal Coupling Immune Inertial Double Link Sensor for Low Frequency Applications Nair, Vishnu G.; Hegde, Navya Thirumaleswar; V., Dileep M.
Journal of Robotics and Control (JRC) Vol 5, No 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.22595

Abstract

Addressing the challenge of horizontal tilt coupling is crucial for using inertial sensors in precise applications, such as seismology and seismic isolation, including gravitational wave detection. Researchers have proposed various design solutions, with the Double Link (DL) sensor standing out for its sim- plicity, precision, and effectiveness. This paper explores the use of the DL sensor in an active vibration isolation system. We evaluated different control algorithms, including Proportional- Integral-Derivative (PID), Linear Quadratic Regulator (LQR), Linear Quadratic Gaussian (LQG), and H-infinity. Simulations conducted in the Simscape environment showed that the H-infinity controller performed best, achieving a significant reduction in vibration. While the current study is based on simulations, future work will focus on experimental validation to confirm the system’s practical applicability and robustness in real-world scenarios. Our results demonstrate the potential of the DL sensor and LQG controller to enhance vibration isolation in low-frequency applications. Additionally, we conducted a detailed literature review on various methods used in similar applications. This review highlights alternative approaches, such as other sensor designs and control strategies, and discusses their advantages and limitations.
A Scoping Review on Unmanned Aerial Vehicles in Disaster Management: Challenges and Opportunities Nair, Vishnu G.; D'Souza, Jeane Marina; C. S., Asha; Rafikh, Rayyan Muhammad
Journal of Robotics and Control (JRC) Vol 5, No 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.22596

Abstract

Unmanned Aerial Vehicles (UAVs), or drones, have recently become transformative tools in disaster management. This paper provides an overview of the role of drones in dis- aster response and recovery, covering natural disasters such as earthquakes, floods, and wildfires, as well as man-made incidents like industrial accidents and humanitarian crises. UAVs offer advantages including rapid data collection, real-time situational awareness, and improved communication capabilities. Notable examples include the use of drones in the 2015 Nepal earthquake for mapping and search operations, and during the 2017 Hurricane Harvey for flood assessment and resource distribution. Advanced technologies further enhance drone capabilities; AI algorithms were used in the 2019 Mozambique cyclone to prioritize rescue operations, while thermal sensors located survivors in the 2018 Mexico earthquake. Despite these benefits, challenges such as privacy concerns, regulatory issues, and community acceptance remain. For instance, privacy issues arose during Hurricane Harvey due to aerial surveillance, and regulatory barriers delayed responses in the 2018 Indonesia earthquake. Ethical dilemmas also surface, such as balancing response urgency with privacy rights and ensuring equitable access to UAV services. The paper discusses potential solutions, including establishing privacy protocols, engaging communities, and streamlining regulations. Collaboration between government agencies, NGOs, and the private sector is essential to develop standardized protocols and enhance community acceptance. By integrating AI, machine learning, and advanced sensors, drones can significantly improve disaster response efficiency. In conclusion, drones play a pivotal role in revolutionizing disaster management strategies. Ongoing advancements in drone technology offer unprecedented opportunities to enhance disaster response, ultimately mitigating human suffering and preserving critical infrastructure. This paper reviews the role of drones in disaster response and recovery efforts, covering various disaster types including natural and man-made incidents.
A Comprehensive Review of AI and Deep Learning Applications in Dentistry: From Image Segmentation to Treatment Planning Nambiar, Rajashree; Nanjundegowda, Raghu
Journal of Robotics and Control (JRC) Vol 5, No 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23056

Abstract

Deep learning leverages multi-layered neural networks to analyze intricate data patterns, offering advancements beyond traditional methods. This review paper explores the significant impact of deep learning on diagnostic and treatment processes across various dental specialties. In restorative dentistry, deep learning algorithms enhance the detection of dental caries and optimize the design of restorations. Orthodontics benefits from automated cephalometric analysis and personalized treatment planning. Periodontics utilizes deep learning for accurate diagnosis and classification of periodontal diseases, as well as monitoring disease progression. In endodontics, these technologies improve root canal detection and treatment outcome predictions. Prosthodontics and oral surgery leverage deep learning for precise prosthesis design and surgical planning, enhancing patient-specific care. Despite the promising advancements, challenges such as data quality, model interpretability, and regulatory issues persist. To solve these problems and get the most out of deep learning in dentistry, the review stresses the need for ongoing research and collaboration between different fields. In our review, we discuss significant deep learning models such as Convolutional Neural Networks (CNNs) and their applications in dentistry, including tooth segmentation, lesion detection, and orthodontic treatment planning. We also examine the use of Generative Adversarial Networks (GANs) for generating synthetic data to enhance training datasets. This paper reviews recent research to provide a comprehensive overview of how deep learning is transforming dentistry, leading to improved patient outcomes, diagnostic accuracy, and treatment efficiency. The advancements in AI and 3D imaging herald a future of automated, high-quality dental diagnostics and treatments.
Optimization of Proportional Integral Derivative Controller for Omni Robot Wheel Drive by Using Integrator Wind-up Reduction Based on Arduino Nano Supriadi, Supriadi; Wajiansyah, Agusma; Zainuddin, Mohammad; Putra, Arief Bramanto Wicaksono
Journal of Robotics and Control (JRC) Vol 5, No 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.21807

Abstract

The experimental object used is a three-wheeled omni-robot frame, where the wheel axes have an angle difference of 120 degrees from each other. The Omni wheels have a diameter of 48 mm connected to the DC motor axis through a gearbox, which has a ratio of 80 to 1. Each wheel has been controlled using a proportional plus integral plus derivative (PID) controller embedded in a microcontroller, which is an Arduino nano board. The motor axis is equipped with a two-phase optical encoder that definitively generates four cycles per revolution for wheel speed acquisition as the controller input. The wheel speed control signal is distributed to the wheel through the H bridge as the controller output. The controller constants have been directly tuned to the robot frame's physical omni-wheel speed control system. The controller is tuned to minimize steady-state error, achieve fast settling times, and minimize overshoot. The best constants obtained are 1.5 (proportional), 0.012 (integral), and 10 (derivative). Using a tolerance band of +/- 2.5%, the system achieved a settling time of 1.1 seconds and a steady-state error of 0.3%. The control system is unstable when the actuator is saturated, which produces oscillations. Controller optimization has been successful by using integrator wind-up reduction. The steady-state average error was reduced to 9.95% without oscillation after optimization, compared to 46.37% with oscillations before optimization. The controller has been validated with speed-tracking tests on all velocity vector regions. The robot frame has been tested with basic maneuvers such as rotation, concerning, forward, and sideways.
Model-free Optimal Control for Underactuated Quadrotor Aircraft via Reinforcement Learning Duong, Quynh Nga; Dang, Ngoc Trung
Journal of Robotics and Control (JRC) Vol 5, No 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23585

Abstract

The control of Unmanned Aerial Vehicles (UAVs), especially quadrotor aircraft, has many practical applications such as transporting, mapping, rescue, and agricultural applications. This paper investigates solving the optimal tracking control problem for a quadrotor system. First, an underactuated quadrotor system is considered a highly nonlinear system with six degrees of freedom and four inputs. Second, a hierarchical control structure consisting of position and attitude controller is adopted to address the underactuated problem, the position controller to achieve the desired tracking and generates the references for the attitude controller, and the attitude controller to achieve the reference attitude tracking. Third, to achieve optimal trajectory tracking, two Data- based Reinforcement Learning (RL) algorithms are applied to both position and attitude controllers to find the optimal control input by using the input- output quadrotor system data. Compared with the traditional optimal algorithms which require directly solving the Algebraic Ricatti Equation (ARE) or the Hamilton-Jacobi-Bellman (HJB) equation. It is impossible or difficult to implement due to the high nonlinear dynamic nature of the quadrotor system. By using RL in the proposed method, optimal policies can be learned without the knowledge of quadrotor dynamic information. Applying the learning control policies to the quadrotor system, the vehicle achieves optimal trajectory tracking. Finally, a simulation result is conducted to verify the optimal trajectory tracking for quadrotor with the proposed controller.
Integration of Modbus-Ethernet Communication for Monitoring Electrical Power Consumption, Temperature, and Humidity Le, Long Ho; Ngo, Thanh Quyen; Toan, Nguyen Duc; Nguyen, Chi Cuong; Phong, Bui Hong
Journal of Robotics and Control (JRC) Vol 5, No 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.22456

Abstract

Effective management of electrical energy requires monitoring, controlling, and storing parameters gathered from power measurement devices including voltage, current, temperature, and humidity. This assessment of the quality of electrical energy is essential for management organizations, power companies, and individual consumers to develop efficient electricity usage plans. Based on the requirement, we proposed a hardware implementation for data collection and online communication software integrated with a system for collecting data on consumption of electrical energy. The EM115-Mod CT multifunction industrial meters by FINECO, the KLEA 220P three-phase multifunction meter by KLEMSAN, and the ME96SS–ver.B by MITSUBISHI are involved. Finally, the collected data of electrical consumption, temperature, and humidity can be stored on an SD card, transmitted to the cloud for real-time monitoring on mobile devices, and facilitated by the ESP-WROOM-32 microcontroller from Espressif system.
Enhanced Stacked Ensemble-Based Heart Disease Prediction with Chi-Square Feature Selection Method Sarra, Raniya R.; Gorial, Ivan Isho; Manea, Reham Raad; Korial, Ayad E.; Mohammed, Mustafa; Ahmed, Yousif
Journal of Robotics and Control (JRC) Vol 5, No 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23191

Abstract

Heart disease (HD) is the primary cause of death globally, requiring more accurate, affordable diagnostic technologies. Traditional HD diagnostic methods are adequate but expensive and limited, creating a need for creative alternatives. Machine learning (ML) is one of the many sophisticated technologies healthcare systems use to predict diseases. This work aims to enhance the accuracy and efficiency of HD diagnosis by developing a stacked ensemble classifier that combines predictions from different ML classifiers and uses chi-square feature selection to prioritize significant features. Combining predictions from three basic ML classifiers—decision trees (DT), support vector machines (SVM), and multilayer perceptron (MLP)—the paper creates a stacked ensemble classifier. To raise diagnostic accuracy, this stacked ensemble classifier maximizes the strengths of base classifiers and reduces their errors. Furthermore, applying the chi-square feature selection approach, the study finds five important features for training the classifiers on the Cleveland dataset with thirteen (13) features. Selecting only important features through feature selection minimizes dimensionality, simplifies the classifier, and improves computational performance. This also reduces overfitting, increases generalizability, and speeds up diagnosis, making it more viable for real-time clinical applications. Before and following the feature selection procedure, the ensemble classifier performance is assessed against the base classifiers concerning the accuracy, recall, precision, and f1-score. These metrics are chosen for their ability to validate the effectiveness of the proposed diagnostic tool.  With an accuracy of 85.5%, the stacked ensemble classifier exceeded base classifiers before feature selection. After feature selection, the stacked ensemble classifier’s accuracy improved to 90.8%. These results underline the proposed method as an inexpensive and more efficient diagnostic tool for HD as compared to current methods, enabling earlier HD detection and lowering healthcare costs. In conclusion, this creative method could alter healthcare systems by providing a highly accurate and affordable diagnostic tool for clinical use.
Landing Control Based on Energy Prediction for a Quadcopter Under External Disturbances Nguyen, Cuong V.
Journal of Robotics and Control (JRC) Vol 5, No 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23074

Abstract

Unmanned aerial vehicles (UAVs) have recently become one of the most popular research topics. The high diversity in its uses has attracted research attention regarding structure or control capabilities. However, if the energy consumed in each mission cannot be predicted, the available flight time will pose many risks to the UAV and data security. This paper proposes a control algorithm based on predicting the remaining flight time to determine a safe landing station. Suppose the UAV cannot reach the desired destination station. In that case, it will find the nearest landing station to recharge its energy until the SoC (State of Charge) 90%, then the UAV will continue to perform the mission until the UAV reaches the destination station. In addition, the paper uses a marker-based landing method to improve landing accuracy. The sliding mode controller (SMC) is designed to consider external disturbance factors and consider a solution to reduce chattering.
Work Fatigue Detection of Search and Rescue Officers Based on Hjorth EEG Parameters Pamungkas, Yuri; Indriani, Ratri Dwi; Crisnapati, Padma Nyoman; Thwe, Yamin
Journal of Robotics and Control (JRC) Vol 5, No 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23511

Abstract

Work fatigue can cause a decrease in cognitive function, such as decreased thinking ability, concentration, and memory. A tired brain cannot work optimally, interfering with a person's ability to perform tasks that require complex thinking. In general, to evaluate work fatigue in a person, self-assessment activities using the Perceived Stress Scale (PSS) are the method most often used by researchers or practitioners. However, this method is prone to bias because sometimes people try to hide or exaggerate their tiredness at work. Therefore, we propose to evaluate people's work fatigue based on their EEG data in this study. A total of 25 participants from SAR officers recorded their EEG data in relaxed conditions (pre-SAR operations) and fatigue conditions (post-SAR operations). Recording was performed on the brain's left (fp1 t7) and right (fp2 t8) hemispheres. The EEG data is then processed by filtering, artifact removal using ICA method, signal decomposition into several frequency bands, and Hjorth feature extraction (activity, mobility, and complexity). The main advantage of Hjorth parameters compared to other EEG features is its ability to provide rich information about the complexity and mobility of the EEG signal in a relatively simple and fast way. Based on the results of activity feature extraction, feature values will tend to increase during the post-SAR operation conditions compared to the pre-operation SAR conditions. In addition, the results of the classification of pre-and post-operative SAR conditions using Bagged Tree algorithm (10-fold cross validation) show that the highest accuracy can be obtained is 94.8%.

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