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
 61   62   63   64   65 
Application of Terminal Synergetic Control Based Water Strider Optimizer for Magnetic Bearing Systems Kadhim, Mina Q.; Yaseen, Farazdaq R.; Al-Khazraji, Huthaifa; Humaidi, Amjad J.
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.v5i6.23867

Abstract

Magnetic bearing (Magb) system is a modern and future electromagnetic device that has many advantages and applications. The open-loop dynamics of the Magb system has a nonlinear and unusable characteristic. In the present paper, a novel robust and advance terminal synergetic control (TSC) approach is developed to stabilize position of the Magb system. The controller is design based on the Magb model using the synergetic control associated with the terminal attractor method. The proposed control algorithm has the advantage of developing a control law which is continuous, chattering free, and allows for a more rapid system response. For further enhancement of the controller performance, a population-based algorithm named water strider optimizer (WSO) has been utilized to adjust the tunable coefficients of the control algorithm. In order to approve the ability and the performance of the proposed control approach, a simulation comparison results with the classic synergetic control (CSC) is conducted. Based on the simulation results, the TSC improves the settling time by 50% and the ITAE index by 45.3% as compared to the CSC. In addition, the recovery time under an external disturbance has been improved by 50% as compared to the CSC. These outcomes demonstrate that the proposed control algorithm allows for rapidly in the system response and more robustness.
Design of QazSL Sign Language Recognition System for Physically Impaired Individuals Zholshiyeva, Lazzat; Zhukabayeva, Tamara; Baumuratova, Dilaram; Serek, Azamat
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.v6i1.23879

Abstract

Automating real-time sign language translation through deep learning and machine learning techniques can greatly enhance communication between the deaf community and the wider public. This research investigates how these technologies can change the way individuals with speech impairments communicate. Despite advancements, developing accurate models for recognizing both static and dynamic gestures remains challenging due to variations in gesture speed and length, which affect the effectiveness of the models. We introduce a hybrid approach that merges machine learning and deep learning methods for sign language recognition. We provide new model for the recognition of Kazakh Sign Language (QazSL), employing five algorithms: Support Vector Machine (SVM), Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Networks (CNN) with VGG19, ResNet-50, and YOLOv5. The models were trained on a QazSL dataset of more than 4,400 photos. Among the assessed models, the GRU attained the highest accuracy of 100%, followed closely by SVM and YOLOv5 at 99.98%, VGG19 at 98.87% for dynamic dactyls, LSTM at 85%, and ResNet-50 at 78.61%. These findings illustrate the comparative efficacy of each method in real-time gesture recognition. The results yield significant insights for enhancing sign language recognition systems, presenting possible advancements in accessibility and communication for those with hearing impairments.
Robot-Assisted Upper Limb Rehabilitation Using Imitation Learning Auta, Ismail Ashiru; Fares, Ahmed; Iwata, Hiroyasu; El-Hussieny, Haitham
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.v6i1.23927

Abstract

Robotic rehabilitation offers an innovative approach to enhance motor function recovery in patients with upper-limb impairment. However, the primary challenge lies in the development of adaptive and personalized therapies to meet the unique needs of patients. In response to this challenge, this paper presents a Rehabilitation Learning from Demonstration (RLfD) framework, which integrates Dynamic Movement Primitives (DMP) for learning and generalizing movements, and a Model Reference Adaptive Controller (MRAC) for real-time adaptive control. This combination enables a two-link manipulator to accurately replicate and adapt therapist demonstrations specifically designed for upper-limb rehabilitation. Unlike conventional task-specific controllers, which are limited by poor adaptability, minimal feedback, and lack of generalization, our system dynamically adjusts robotic assistance in real time based on the subject’s tracking error to optimize therapy outcomes. The objective is to minimize assistance while maximizing patient participation in the rehabilitation process. To facilitate this, the framework employs visual tracking technology to capture therapist demonstrations accurately. Once captured, the DMP component of the framework learns from these movements and generalizes them to new goals, while maintaining the original motion patterns. Our evaluations with a simulated two-link manipulator demonstrated the framework’s precise trajectory tracking, robust generalization, and adaptability to disturbances mimicking patient impairments. These tests confirmed the system’s ability to follow complex trajectories and adapt to dynamic patient motor functions. The promising results from these evaluations highlight our approach’s potential to significantly enhance adaptability and generalization in variable patient conditions, marking a substantial improvement over conventional systems.
Design and Hardware Implementation of Combining PD with HSSC for Optimizing Behavior of Magnetic Levitation System Alrawi, Ali Amer Ahmed; Lilo, Moneer Ali; Al Mashhadany, Yousif
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.v6i1.23940

Abstract

This paper presents the design and implementation of a hybrid control system that uses Proportional-Derivative (PD) and High-Speed Switching Controller (HSSC) methods to enhance Maglev system performance. The goal is to design controllers that properly follow input references and improve system stability and reactivity. The PD controller is fast and easy to install, but it cannot handle system disturbances and nonlinearities, which might cause instability. HSSC integration addresses these issues. The HSSC makes the PD controller more resilient to external forces and nonlinear dynamics. The combined PD-HSSC approach ensures stable levitation, precise positioning control, and system reliability in various conditions. The hybrid system reduced steady-state error and maintained system stability under dynamic input conditions, although it over-shoot more than PD alone. The computer-aided real time simulation of system dynamics is done, and the control rules are formulated out of a combination of PD Control for normal control processes and the HSSC for enhanced robustness. The total control current is given by the algebraic addition of the PD control action going, the equivalent control going, and the switching control going. However, the proposed PD-HSSC technology is possible to provide a stable levitation state for the control of precise position, even in the nonlinear and disturbance conditions The experimental results showed an 89% enhancement in the efficiency of the hybrid control system. The integration of PID (Proportional-Integral-Derivative) and HSSC has been developed in this system using MATLAB Simulink. The real-time findings demonstrate that the PD-HSSC system is higher in stability for operating the maglev system. This is due to its much lower steady-state error compared to the PD system, regardless of the kind of step input or dynamically fluctuating sine and square wave inputs. However, PID_HSSC exhibited a greater degree of overshoot in comparison to the PD.
Design and Simulation of a 2-Degree of Freedom Energy Harvester from Blood Flow for Powering the Pacemaker Abdelmawgoud, Manar; Parque, Victor; Nada, Ayman; Fath El-Bab, Ahmed M. R.
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.v6i1.23953

Abstract

The pacemaker is a device that is used to treat different abnormal heart rhythms. It is usually powered using traditional batteries. These batteries run out of power after about 7 years, necessitating the replacement of either the pacemaker or its batteries for the patient’s survival. This means that the patient will need to undergo surgery for the replacement process which can compromise the patient’s life and increase the probability of being infected, not to mention the operation cost. To overcome this problem, energy harvesters can be a safer substitute for these traditional batteries since they can convert different forms of energy into electric energy, which can be stored and used when needed. In this paper, a 2-degree-of-freedom (DOF) piezoelectric energy harvester from blood flow is designed and modeled. The harvester is designed as a cut-out beam that is fixed on the pacemaker lead that passes through the Superior Vena Cava (SVC). To protect the harvester from being highly distorted by the blood flow, a plastic barrier is added in front of the harvester from the vein’s inlet side. The harvester consists of three layers, a PZT5A layer sandwiched between two plastic layers. The harvester is designed to have its first and second natural frequencies between 1Hz and 1.67Hz, the normal frequency range of the human heartbeat. The harvester harvests up to 3.8V which is considered satisfying since the pacemaker usually stimulates the heart using a voltage that ranges from 1V to 10V. This voltage can be used to power the pacemaker and extend its lifetime. The harvester was simulated using ANSYS Workbench Software 2020 R2. On the simulation level, the harvester obtained a maximum output power of 0.81µW at a load of 2.2MΩ.
Optimal Backstepping and Feedback Linearization Controllers Design for Tracking Control of Magnetic Levitation System: A Comparative Study Al-Ani, Fatin R.; Lutfy, Omar F.; Al-Khazraji, Huthaifa
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.v5i6.24073

Abstract

In this paper, the stabilization and trajectory tracking of the magnetic levitation (Maglev) system using optimal nonlinear controllers are considered. Firstly, the overall structure and physical principle represented by the nonlinear differential equations of the Maglev system are established. Then, two nonlinear controllers, including backstepping control (BSC) and feedback linearization (FL), are proposed to force the position of the ball in the Maglev system to track a desired trajectory. In terms of designing the control law of the BSC, the Lyapunov function is utilized to guarantee an exponential convergence of the tracking error to zero. For developing the control law of the FL, an equivalent transformation to convert the nonlinear system into a linear form is used, and then, the state feedback controller (SFC) method is utilized to track the ball to the desired position. In order to obtain a higher accuracy in motion control of the ball, the gains’ selection for the controllers to reach the desired response is achieved using the swarm bipolar algorithm (SBA) based on the integral time absolute error (ITAE) cost function. Computer simulations are conducted to evaluate the performance of the proposed methodology, and the results prove that the proposed control strategy is effective not only in stabilizing the ball but also in rejecting the disturbance present in the system. However, the BSC exhibits better performance than that of the FL-SFC in terms of reducing the ITAE index and improving the transit response even when the external disturbance is applied. The numerical results show that the settling time reduced to 0.2 seconds compared to 1.2 seconds for FL-SFC. Moreover, the ITAE index is reduced to 0.0164 compared to 0.2827 seconds for FL-SFC. In the context of external disturbance, the findings demonstrate that BSC reduced the recovery time to 0.05 seconds compared to 0.65 seconds for FL-SFC.
Concerns of Ethical and Privacy in the Rapid Advancement of Artificial Intelligence: Directions, Challenges, and Solutions Furizal, Furizal; Ramelan, Agus; Adriyanto, Feri; Maghfiroh, Hari; Ma'arif, Alfian; Kariyamin, Kariyamin; Masitha, Alya; Fawait, Aldi Bastiatul
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.v5i6.24090

Abstract

AI is a transformative technology that emulates human cognitive abilities and processes large volumes of data to offer efficient solutions across various sectors of life. Although AI significantly enhances efficiency in many areas, it also presents substantial challenges, particularly regarding ethics and user privacy. These challenges are exacerbated by the inadequacy of global regulations, which may lead to potential abuse and privacy violations. This study provides an in-depth review of current AI applications, identifies future needs, and addresses emerging ethical and privacy issues. The research explores the important roles of AI technologies, including multimodal AI, natural language processing, generative AI, and deepfakes. While these technologies have the potential to revolutionize industries such as content creation and digital interactions, they also face significant privacy and ethical challenges, including the risks of deepfake abuse and the need for improved data protection through platforms like PrivAI. The study emphasizes the necessity for stricter regulations and global efforts to ensure ethical AI use and effective privacy protection. By conducting a comprehensive literature review, this research aims to provide a clear perspective on the future direction of AI and propose strategies to overcome barriers in ethical and privacy practices.
Machine Learning Paradigms for UAV Path Planning: Review and Challenges Bacha, Anis Mahmoud; Zamoum, Razika Boushaki; Lachekhab, Fadhila
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.v6i1.24097

Abstract

Path planning is a crucial step in robotic navigation to satisfy: tasks safety, efficiency requirements and adapt to the complexity of environments. Path planning problem is particularly critical for Unmanned Aerial Vehicles (UAV), being increasingly involved within important tasks in diverse military and civil fields such as: inspection, search and rescue and communication, taking advantage of their high flexibility, maneuverability and cost-effective solutions. This continuous growth made the solution of UAV path planning problem an interesting research topic in recent years. In this scope, machine learning algorithms were a promising tool due to their continuous data-driven selfimprovement to adapt with the high dynamicity of environments where conventional programming fails. This paper provides a review on recent developments in machine learning-based UAV path planning issued from credible databases like: IEEE, Elsevier, Springer Links and MDPI. The main contribution of this paper is to delve through these recent works providing a taxonomy of algorithms into the fundamental paradigms: supervised, unsupervised and reinforcement, evaluating their efficiency and limitations under distinct scenarios. Despite the relative generalization of deep reinforcement learning to different environments, this study highlighted some active challenges about computational cost and real-time applications that remain open.
A Review on Comparative Analysis of Generative Adversarial Networks’ Architectures and Applications Bhat, Ranjith; Nanjundegowda, Raghu
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.v6i1.24160

Abstract

Generative Adversarial Networks (GANs) are a major advancement in generative modeling, surpassing traditional machine learning models in tasks such as image generation, super-resolution, and image-to-text translation. A GAN consists of two neural networks: a Generator (G), which creates data from noise or a latent vector, and a Discriminator (D), which determines whether the data is real or generated. These networks train competitively, improving each other iteratively to produce increasingly realistic outputs. However, GANs face challenges like mode collapse, unstable training, and convergence issues, leading to the adoption of strategies such as instance normalization and enhanced loss functions. Future research can focus on improving stability, developing novel loss functions, and applying GANs in unsupervised learning. Performance metrics like Inception Score, Fréchet Inception Distance (FID), and Structural Similarity Index (SSIM) are essential for evaluating and comparing GAN architectures. Additionally, ethical concerns, including the misuse of GANs for deepfakes and synthetic data, underscore the importance of transparency, accountability, and ethical standards in research and deployment. This review provides an accessible introduction to GANs for novice researchers, along with a detailed analysis of their limitations, applications, and future prospects, offering valuable insights and direction for advancing this field.
Optimization of Hierarchical Sliding Mode Control Parameters for a Two-Wheeled Balancing Mobile Robot Using the Firefly Algorithm Tao, Ngoc-Linh; Pham, Dinh-Hieu; Pham, Minh-Khoi; Nguyen, Thi-Van-Anh
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.v6i1.24192

Abstract

Two-wheeled balancing Mobile Robots (2WBMRs) are inherently unstable, posing significant challenges in control. This paper addresses the problem of optimizing control parameters for such systems to improve stability and overall performance. The proposed solution integrates Hierarchical Sliding Mode Control (HSMC) with the Firefly Algorithm, which is a stochastic algorithm inspired by the flashing behavior of fireflies, to optimize control performance. The research contribution is the development of an optimized control system where the Firefly Algorithm is used to fine-tune HSMC parameters, ensuring improved stability and responsiveness. Additionally, the integration of Sliding Mode Control (SMC) within the HSMC framework provides precise yaw angle stabilization, contributing to comprehensive robot control. In this approach, the Firefly Algorithm is applied to optimize the HSMC parameters due to its capability to optimize multidimensional variables and its robust optimization abilities, aiming to enhance the stability of the vehicle in the best possible way. Simulations were conducted to compare the proposed method before and after applying the optimization algorithm, evaluating key performance metrics such as response time and stability. The results indicate a (10%) improvement in stability, demonstrating that the Firefly Algorithm significantly enhances control performance. These findings suggest that the optimized control system not only improves the stability of 2WBMRs but also has potential applications in broader dynamic control systems. In conclusion, based on the research results, we can conclude that the use of the HSMC-SMC controller for nonlinear systems like 2WBMRs is feasible and can be applied to many other nonlinear systems. Furthermore, the Firefly Algorithm has proven to be a powerful tool for optimizing parameters in control systems and can be applied in robotics and automation systems.

Page 63 of 71 | Total Record : 708

 61   62   63   64   65 

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 Vol 2, No 6 (2021): November (Forthcoming Issue) 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 (Forthcoming Issue) Vol 2, No 3 (2021): May Vol 2, No 2 (2021): March Vol 2, No 2 (2021): March (Forthcoming Issue) 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