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 35 Documents
 1   2   3   4 
Search results for , issue "Vol. 5 No. 6 (2024)" : 35 Documents clear
Performance Analysis of PID and SMC Control Algorithms on AUV under the Influence of Internal Solitary Wave in the Bali Deep Sea Wahyuadnyana, Kadek Dwi; Indriawati, Katherin; Darwito, Purwadi Agus; Aufa, Ardyas Nur; Tnunay, Hilton
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.v5i6.23800

Abstract

Autonomous Underwater Vehicles (AUVs) play a crucial role in deep-sea exploration, but their stability is often compromised by Internal Solitary Waves (ISWs) and nonlinear disturbances in stratified waters. This study aims to evaluate the performance of two control algorithms, Proportional-Integral-Derivative (PID) and Sliding Mode Control (SMC), in mitigating ISW effects on AUV trajectory tracking. Simulations were conducted in Simulink (MATLAB), modeling AUV dynamics under ISW disturbances with intensities ranging from 0% to 100%. The results reveal that both PID and SMC algorithms experience significant performance degradation as ISW intensity increases, with Root Mean Square Error (RMSE) values rising exponentially between 50% and 75% disturbance levels. While SMC offers better resilience to nonlinear disturbances than PID, neither algorithm fully compensates for high ISW intensities. These findings highlight the limitations of conventional control strategies and underscore the need for more robust, adaptive algorithms for reliable deep-sea AUV operations. Future work will explore Nonlinear Model Predictive Control (NMPC) for improved stability in complex marine environments.
Autonomous Robotic Systems with Artificial Intelligence Technology Using a Deep Q Network-Based Approach for Goal-Oriented 2D Arm Control Bashabsheh, Murad
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.v5i6.23850

Abstract

Accurate control robotic arms in two-dimensional environments present significant challenges, particularly in dynamic, real-time applications. Traditional model-based approaches require substantial system modeling, rendering them computationally extensive. This paper presents an adaptive Artificial Intelligence (AI)-driven approach through the use of Deep Q-Networks (DQN) control for a two–link robotic arm thus supporting better scalability. The DQN algorithm, a model-free Reinforcement Learning (RL) technique, allows the robotic arm to independently learn optimal control strategies by interaction with the environment and adapting to dynamic conditions. The task of the robot established reaches a specific target (red point) within a limited number of episodes. Key components of the methodology contain problem statement, DQN architecture, representation of the state and action spaces, a reward function, and the training process. Experimental results indicate that the DQN agent effectively learns to find optimal actions with high accuracy and robustness in guiding the arm to the target. The performance steadily improves during initial training, followed by stabilization, indicating an effective control policy. This study contributes to the knowledge of reinforcement learning in robotic control tasks and demonstrates, in particular, the potential of DQN for solving complex, goal-oriented tasks with minimal prior modeling. Compared to conventional control approaches, the DQN-driven one reveals higher flexibility, scalability, and efficiency. Although carried out in a simplified 2D environment, the novelty of this research lies in its emphasis on enabling the robotic arm to accomplish goal-oriented reaching tasks, lays a strong foundation for future applications in industrial automation and service robotics.
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.
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.

Page 4 of 4 | Total Record : 35

 1   2   3   4 

Filter by Year

2024 2024


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 (Forthcoming Issue) Vol 2, No 4 (2021): July Vol 2, No 3 (2021): May Vol 2, No 3 (2021): May (Forthcoming Issue) 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