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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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On New Results of Stability and Synchronization in Finite-Time for Fitiz-Nagamo Model Using Grownal Inequality and Lyapunov Function Batiha, Iqbal M.; Bendib, Issam; Ouannas, Adel; Jebril, Iqbal H.; Alkhazaleh, Shawkat; Momani, Shaher
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.v5i6.23211

Abstract

Ionic diffusion across cytomembranes plays a critical role in both biological and chemical systems. This paper reexamines the FitzHugh-Nagumo reaction-diffusion system, specifically incorporating the influence of diffusion on the system’s dynamics. We focus on the system’s finite-time stability, demonstrating that it achieves and maintains equilibrium within a specified time interval. Unlike asymptotic stability, which ensures long-term convergence, finite-time stability guarantees rapid convergence to equilibrium, a crucial feature for real-time control applications. We prove that the equilibrium point of the FitzHugh-Nagumo system exhibits finite-time stability under certain conditions. In particular, we provide a criterion for finite-time stability and derive results using new lemmas and a theorem to guide the system’s design for reliable performance. Additionally, the paper discusses finite-time synchronization in reaction-diffusion systems, emphasizing its importance for achieving coherent dynamics across distributed components within a finite time. This approach has significant implications for fields requiring precise control and synchronization, such as sensor networks and autonomous systems. Practical simulations are presented to elucidate the theoretical principles discussed earlier, using the finite difference method (FDM) implemented in MATLAB.
Towards Resilient Machine Learning Models: Addressing Adversarial Attacks in Wireless Sensor Network Shihab, Mustafa Abdmajeed; Marhoon, Haydar Abdulameer; Ahmed, Saadaldeen Rashid; Radhi, Ahmed Dheyaa; Sekhar, Ravi
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.v5i5.23214

Abstract

Adversarial attacks represent a substantial threat to the security and reliability of machine learning models employed in wireless sensor networks (WSNs). This study tries to solve this difficulty by evaluating the efficiency of different defensive mechanisms in minimizing the effects of evasion assaults, which try to mislead ML models into misclassification. We employ the Edge-IIoTset dataset, a comprehensive cybersecurity dataset particularly built for IoT and IIoT applications, to train and assess our models. Our study reveals that employing adversarial training, robust optimization, and feature transformations dramatically enhances the resistance of machine learning models against evasion attempts. Specifically, our defensive model obtains a significant accuracy boost of 12% compared to baseline models. Furthermore, we study the possibilities of combining alternative generative adversarial networks (GANs), random forest ensembles, and hybrid techniques to further boost model resilience against a broader spectrum of adversarial assaults. This study underlines the need for proactive methods in preserving machine learning systems in real-world WSN contexts and stresses the need for continued research and development in this quickly expanding area.
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.
Momentum-Based Push Recovery Control of Bipedal Robots Using a New Variable Power Reaching Law for Sliding Mode Control Al-Tameemi, Ibrahim; Doan, Duc; Patanwala, Abizer; Agheli, Mahdi
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.v5i5.23379

Abstract

A significant challenge in deploying bipedal robots for human-oriented real-world applications is their ability to maintain balance when externally disturbed. Current momentum-based balance control strategies often exhibit inadequate robustness to disturbances due to reliance on simple proportional controllers and imprecise incorporation of desired angular momentum changes. Furthermore, the sequential activation of momentum and posture correction controllers compromises system stability when confronted with consecutive disturbances. This paper proposes and validates a new Variable Power Reaching Law for Sliding Mode Control (SMC) to enhance the regulation of linear momentum against disturbances. The proposed reaching law adjusts dynamically to the system's errors, ensuring fast convergence and minimal chattering. In this paper, we precisely define the desired angular momentum change in relation to the Center of Pressure (CoP), a crucial stability metric, as well as the desired linear momentum and ground reaction forces.  The null-space method, which allows for simultaneous task execution by using unused degrees of freedom, is employed to ensure effective balance and upright posture without interference. The posture correction control is projected onto the null-space of momentum control. Simulation results confirm that the proposed control system effectively stabilizes the robot against external disturbances, regulating momentum and restoring upright posture. The null-space method proves effective in maintaining balance under multiple disturbances by simultaneously controlling momentum and posture. Comparative evaluations show that our approach outperforms traditional momentum-based controls and nonadaptive reaching laws, reducing CoP fluctuations, managing disturbances up to 117 N, and minimizing chattering and steady-state error. These advancements underscore the potential for deploying bipedal robots in dynamic environments.
Computational Approaches to Two-Energy Group Neutron Diffusion in Cylindrical Reactors Batiha, Iqbal; Abdelnebi, Amira; Shqair, Mohammed; Jebril, Iqbal H.; Alkhazaleh, Shawkat; Momani, Shaher
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.v5i6.23392

Abstract

This study addresses the critical need for accurate neutron diffusion modeling in cylindrical reactors, focusing on the two-energy groups neutron diffusion system. Such modeling is essential for optimizing reactor design and safety in nuclear engineering. The research primarily aims to enhance computational methods by transitioning from a traditional integer-order model to a more sophisticated fractional-order model, which can capture complex physical phenomena with greater precision. The study employs the Laplace Transform Method (LTM) to first solve the integer-order system and then extends this approach to a fractional-order system using the Caputo derivative, a method well-suited for systems with memory effects. To efficiently solve the resulting fractional-order model, we introduce the Modified Fractional Euler Method (MFEM), designed to improve numerical accuracy and stability. The effectiveness of this approach is demonstrated through specific numerical applications, such as simulating neutron flux distributions, which validate the model’s accuracy and its potential impact on advancing reactor physics. These applications showcase the practical relevance of the proposed methods and their contribution to improving nuclear reactor simulations.
Enhancing Voice Authentication with a Hybrid Deep Learning and Active Learning Approach for Deepfake Detection Ahmed, Ali Saadoon; Khaleel, Arshad 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.v5i6.23502

Abstract

This paper explores the application of active learning to enhance machine learning classifiers for spoofing detection in automatic speaker verification (ASV) systems. Leveraging the ASVspoof 2019 database, we integrate an active learning framework with traditional machine learning workflows, specifically focusing on Random Forest (RF) and Multilayer Perceptron (MLP) classifiers. The active learning approach was implemented by initially training models on a small subset of data and iteratively selecting the most uncertain samples for further training, which allowed the classifiers to refine their predictions effectively. Experimental results demonstrate that while the MLP initially outperformed RF with an accuracy of 95.83% compared to 91%, the incorporation of active learning significantly improved RF's performance to 94%, narrowing the performance gap between the two models. After applying active learning, both classifiers showed enhanced precision, recall, and F1-scores, with improvements ranging from 3% to 5%. This study provides valuable insights into the role of active learning in boosting the efficiency of machine learning models for dynamic spoofing scenarios in ASV systems. Future research should focus on designing advanced active learning techniques and exploring their integration with other machine learning paradigms to further enhance ASV security.
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.
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%.
Enhancing Autonomous Navigation in GNSS-Denied Environment: Obstacle Avoidance Observability-Based Path Planning for ASLAM Elahian, Samaneh; Atashgah, Mohammad Ali Amiri; Suwarno, Iswanto
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.v5i6.23519

Abstract

Obstacle avoidance (OA) is necessary for any path planning in outdoor environment to prevent any collision with the obstacles in natural environment. In this paper, a quadrotor navigates using Active Simultaneously Localization and Mapping (ASLAM) in GNSS-denied outdoor environment. In ASLAM, the quadrotor path is defined using real-time Observability Based Path Planning (OBPP) method, autonomously. To prepare using of the OBPP in outdoor environment, it is necessary to add the ability of OA to it. So, the OA-OBPP method is introduced which defines the path based on terrestrial landmarks while preventing any collision with the obstacles. This approach is developed by redefining a dataset of in range landmarks while all of the landmark in the vicinity of the obstacles are removed from the in-range landmarks dataset.  To evaluate the performance of the proposed method, simulations of the OA-OBPP algorithm are conducted for a simplified 6-Degree of Freedom (DOF) quadrotor using MATLAB. The simulations evaluate the efficiency, accuracy and robustness of the proposed method. Results across various scenarios show that the method effectively avoids collisions with obstacles while simultaneously determining a path to the goal. Additionally, a comparison of the position estimation RMSE with Monte Carlo PP highlights the accuracy of the OA-OBPP. The robustness of the method, tested with varying initial positions, demonstrates its success in real-time path planning (PP) from any starting point to the destination without collisions. The results confirm that the OA-OBPP enhances the robot's capability to perform real-time, autonomous, and robust path planning in outdoor environments, even in the absence of GNSS signals, through visual navigation.
Enhanced Total Harmonic Distortion Optimization in Cascaded H-Bridge Multilevel Inverters Using the Dwarf Mongoose Optimization Algorithm Salih, Sinan Q.; Mejbel, Basim Ghalib; Ahmad, B. A.; Taha, Taha A.; Bektaş, Yasin; Aldabbagh, Mohammed M; Hussain, Abadal-Salam T.; Hashim, Abdulghafor Mohammed; Veena, B. S.
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.v5i6.23548

Abstract

Total harmonic distortion (THD) is one of the most essential parameters that define the operational efficiency and power quality in electrical systems applied to solutions like cascaded H-bridge multilevel inverters (CHB-MLI). The reduction of THD is crucial due to the fact that improving the system’s power quality and minimizing the losses are key for performance improvement. The purpose of this work is to introduce a new DMO-based approach to optimize the THD of the output voltage in a three-phase nine-level CHB-MLI. The proposed DMO algorithm was also subjected to intense comparison with two benchmark optimization techniques, namely Genetic Algorithm and Particle Swarm Optimization with regards to three parameters, namely convergence rate, stability, and optimization accuracy. A series of MATLAB simulations were run to afford the evaluation of each algorithm under a modulation index of between 0.1 and 1.0. The outcome of the experiment amply proves that in comparison with THD minimization for the given OP, the DMO algorithm was significantly superior to both RSA-based GA and PSO algorithms in their ability to yield higher accuracy while requiring lesser computational time. Consequently, this work could expand the application of the DMO algorithm as a reliable and effective means of enhancing THD in CHB-MLIs as well as advancing the overall quality of power systems in different electrical power networks.

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