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 24 Documents
 1   2   3 
Search results for , issue "Vol 5, No 4 (2024)" : 24 Documents clear
Investigation of Optimal Controllers on Dynamics Performance of Nonlinear Active Suspension Systems with Actuator Saturation Al-Ali, Mohammed A.; Lutfy, Omar F.; Al-Khazraj, Huthaifa
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.22139

Abstract

This study investigates designing optimal controllers on the dynamics performance of active suspension systems. The study incorporates nonlinearities and actuator saturation in the mathematical model of the suspension system for more reasonable representation of the real system. To improve ride comfort and stability performance in the presence of road disturbances, this study proposes two control frameworks including the Proportional-Integral-Derivative (PID) controller and the State Feedback (SF) controller. The focus of the study is to overcome the limitations of existing approaches in handling the actuator saturation in the controller design. To attain a better performance of the two proposed controllers including the input control constraint, a Grey Wolf Optimization (GWO) has been introduced to improve the searching process for the optimal values of the controllers’ adjustable parameters. The simulation results using MATLAB show that the proposed controllers exhibit a good performance in normal operation and in a robustness test involving system parameters’ changes. In terms of improving the response of the system, the GWO-PID controller shows a better response than that of the GWO-SF controller. Based on the Integral Square Error (ISE) index, the ISE is reduced by 16.67% using the GWO-PID controller compared to the GWO-SF controller.
Smartphone Sensor-based Development and Implementation of a Remotely Controlled Robot Arm Salah, Wael A.; Sneineh, Anees Abu; Shabaneh, Arafat A. A.
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.21987

Abstract

As a result of advances in both technology and science, it is now possible to carry out essential processes such as lifting objects and moving them by remote control of an arm. In this sense, it is much easier for a person to engage in potentially dangerous activities without running the risk of getting hurt. This article presents the development and design of a robot arm that is controlled by a smartphone device using gyroscope sensors integrated inside. Smartphones with built-in gyroscope sensors are used to operate robot arms in a flexible and affordable manner. The robot arm's movement is effectively controlled by the gyroscope sensors, which include proximity, orientation, and accelerometer sensors, to get it to the required position. The developed prototype found to capable of handling a variety of objects with a smooth movement and transporting them based on the movement of a mobile phone. The control of the arm imitates the movements of a human being, which results in the reduction of the amount of time and effort required by a person to carry out a certain process.
A Recurrent Deep Architecture for Enhancing Indoor Camera Localization Using Motion Blur Elimination Alam, Muhammad S.; Mohamed, Farhan B.; Selamat, Ali; Hossain, AKM B.
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.21930

Abstract

Rapid growth and technological improvements in computer vision have enabled indoor camera localization. The accurate camera localization of an indoor environment is challenging because it has many complex problems, and motion blur is one of them. Motion blur introduces significant errors, degrades the image quality, and affects feature matching, making it challenging to determine camera pose accurately. Improving the camera localization accuracy for some robotic applications is still necessary. In this study, we propose a recurrent neural network (RNN) approach to solve the indoor camera localization problem using motion blur reduction. Motion blur in an image is detected by analyzing its frequency spectrum. A low-frequency component indicates motion blur, and by investigating the direction of these low-frequency components, the location and amount of blur are estimated. Then, Wiener filtering deconvolution removes the blur and obtains a clear copy of the original image. The performance of the proposed approach is evaluated by comparing the original and blurred images using the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). After that, the camera pose is estimated using recurrent neural architecture from deblurred images or videos. The average camera pose error obtained through our approach is (0.16m, 5.61◦). In two recent research, Deep Attention and CGAPoseNet, the average pose error is (19m, 6.25◦) and (0.27m, 9.39◦), respectively. The results obtained through the proposed approach improve the current research results. As a result, some applications of indoor camera localization, such as mobile robots and guide robots, will work more accurately.
Sliding Mode Control based on Neural State and Disturbance Observers: Application to a Unicycle Robot Using ROS2 Nawress, Barhoumi; Gharbi, Asma Najet Lakhal; Braiek, Naceur Benhadj
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.21650

Abstract

The major problem dealing with mobile robots is the trajectory tracking control problem, in the presence of random disturbance and unmeasurable angular velocity. In this paper, we propose a Sliding Mode Control (SMC) based on a Nonlinear Disturbance Observer (NDO) and a Neural State Observer (NSO). The (SMC-NDO) controller displays limitations in mitigating external disturbances. Therefore, this research contribution suggests a novel approach that integrates a Neural State Observer (NSO) into the (SMC-NDO) controller, to significantly enhance the performance of a control system. The combined approach improves disturbance reduction while simultaneously estimating the unmeasurable angular velocity, ultimately leading to more accurate path tracking. Furthermore, the Lyapunov method is used to ensure the stability of the closed-loop control on the one hand, and the stability of the Neural State Observer based on the Backpropagation algorithm on the other hand. Numerical simulations and the implementation of the Simulator in ROS/Gazebo demonstrate better performance of our proposed approach (SMC-NSONDO) compared to the Sliding Mode control-based Disturbance Observer (SMC-NDO) and the Sliding Mode Control (SMC). The control proposal in this work is ready for use on most ROScompatible robots. This experiment should offer an enlightening perspective to robotics researchers.
Using Imperialist Competitive Algorithm Powered Optimization of Bifacial Solar Systems for Enhanced Energy Production and Storage Efficiency Hadi, Husam Ali; Kassem, Abdallah; Amoud, Hassan; Nadweh, Safwan; Ghazaly, Nouby M.; Abdulhasan, Mahmood Jamal
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.22100

Abstract

Interest in renewable energy has grown due to increased environmental awareness and concern about climate change. Among the various renewable energy technologies, grid-connected bifacial PV systems are particularly important due to their higher efficiency compared to conventional systems. However, maximizing energy harvesting and storage efficiency remains a challenge for these systems, requiring the use of an efficient charge controller and an appropriate battery. The process of setting charge controller parameters and selecting the best storage technology is complex and requires a thorough study of various operating conditions. The main research contribution of this paper is the development of an efficient optimization methodology to increase the energy production and storage efficiency of the studied systems using optimization algorithms. The imperialist competitive algorithm (ICA) is used in the system design to improve performance through optimal adjustment of charge controller parameters and selection of appropriate storage technology. This decision was based on factors such as energy production from PV panels, energy consumption from loads, and energy storage in batteries. Performance is also evaluated using both the flower pollination algorithm (FPA) and Gray Wolf optimization (GWO) algorithms. The study evaluated system performance by analyzing energy production, storage efficiency, and cost effectiveness. The results showed that the ICA algorithm is effective in improving energy production and storage, resulting in higher energy output, better battery efficiency, and lower system costs. In addition, lithium-ion batteries were identified as the best storage technology. This research demonstrates the potential of the ICA approach to increase efficiency and reduce costs in the PV systems.
Optimizing the Tuning of Fuzzy-PID Controllers for Motion Control of Friction Stir Welding Robots Marliana, Eka; Wahjudi, Arif; Nurahmi, Latifah; Batan, I Made Londen; Wei, Guowu
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.21697

Abstract

Friction stir welding (FSW) is defined as a solid-state welding method that is required to be accurate, especially for its motion. This requirement can be satisfied by implementing an accurate controller. The aim of this research was to develop an accurate control system based on a fuzzy-proportional integral derivative (PID) controller for parallel manipulator FSW robots. In order to achieve a higher accuracy in motion control, the tuning optimisation process for a fuzzy-PID controller was conducted using a genetic algorithm (GA) and particle swarm optimisation (PSO). The optimisation algorithms were applied to simultane-ously tune the fuzzy rules and output of the membership function from the fuzzy inference system (FIS). The PID controller was designed and tuned using a MATLAB® PID Tuner to obtain the desired response. It was then developed into a fuzzy-PID controller with Sugeno type-1 FIS with 2 inputs and 1 output. The tuning optimisation of the fuzzy-PID controller using GA and PSO was performed to achieve the global minimum integral absolute error (IAE) of the angular velocity. MATLAB® Simulink® was employed to test and simulate the controllers for three motors in the FSW robot model. The IAE values of the PID controller implemented for each motor were 0.03644, 0.04893, and 0.04893. The IAEs of the implemented fuzzy-PID-GA (output and rules) controller were 2.061, 2.048, and 2.048; of the implemented fuzzy-PID-GA (output) controller were 0.03768, 0.05059, and 0.05059; of the fuzzy-PID-PSO (output and rules) controller were 0.01886, 0.0253, and 0.02533; and of the fuzzy-PID-PSO (output) controller were 0.03767, 0.05059, and 0.05059. Therefore, the fuzzy-PID-PSO (output and rules) controller gave the most accurate results and outperformed the others. Keywords—Angular velocity, control system, friction stir welding, fuzzy-PID, genetic algorithm, motion, motor, parallel manipulator, particle swarm optimisation.
Analysis and Performance Comparison of Fuzzy Inference Systems in Handling Uncertainty: A Review Furizal, Furizal; Ma'arif, Alfian; Wijaya, Setiawan Ardi; Murni, Murni; Suwarno, Iswanto
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.22123

Abstract

Uncertainty is an inevitable characteristic in human life and systems, posing challenges in decision-making and data analysis. Fuzzy theory emerges to address this uncertainty by describing variables with vague or uncertain values, one of which is the Fuzzy Inference System (FIS). This research analyzes and compares the performance of FIS from previous studies as a solution to manage uncertainty. FIS allows for flexible and responsive representations of truth levels using human-like linguistic rules. Common FIS methods include FIS-M, FIS-T, and FIS-S, each with different inference and defuzzification approaches. The findings of this research review, referencing previous studies, indicate that the application of FIS in various contexts such as prediction, medical diagnosis, and financial decision-making, yields very high accuracy levels up to 99%. However, accuracy comparisons show variations, with FIS-M tending to achieve more stable accuracy based on the referenced studies. The accuracy difference among FIS-M studies is not significantly different, only around 7.55%. Meanwhile, FIS-S has a wider accuracy range, from 81.48% to 99% (17.52%). FIS-S performs best if it can determine influencing factors well, such as determining constant values in its fuzzy rules. Additionally, the performance comparison of FIS can also be influenced by other factors such as data complexity, variables, domain, membership functions (curves), fuzzy rules, and defuzzification methods used in the study. Therefore, it is important to consider these factors and select the most suitable FIS method to manage uncertainty in the given situation.
Improving the Productivity of Laying Hens Through a Modern Cage Cleanliness Monitoring System that Utilizes Integrated Sensors and IoT Technology Ishak, Fauzi; Wardhana, Ichlasul Amal Restu; Mutiara, Giva Andriana; Periyadi, Periyadi; Meisaroh, Lisda; Alfarisi, Muhammad Rizqi
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.21610

Abstract

Animal husbandry plays a crucial role in the Indonesian economy. One example is layer farming. The cage's environmental conditions can have an impact on the health of laying hens, including factors like temperature, humidity, and the presence of ammonia gas. This research aims to support chicken farmers in identifying and monitoring the environmental conditions surrounding their chicken coops, with the goal of enhancing the productivity of laying hens. This study is organized using a prototype development approach. The proposed system utilizes Arduino UNO as a microcontroller, ESP32 as a connecting node from hardware to software, MQ-135 sensor as an ammonia gas sensor, DHT-22 sensor as a temperature and humidity sensor, and 16x2 I2C LCD to display the collected data. WIFI connected web monitoring system built with Laravel, MySQL, and Bootstrap. An improvement to the existing system is the integration of an ammonia gas odor sensor calibrated against clean air as a reference. Testing was conducted for a continuous period of 7 days. Comparison of test results is performed with existing devices to observe the difference in measured values. The measurement result demonstrates a remarkable ability to accurately measure temperature, humidity, and ammonia levels in the air. The difference with the comparable device was about 2%.  Meanwhile, the monitoring dashboard for IoT functional monitoring operates effectively, allowing chicken farmers to efficiently analyze the cleanliness of their chicken coops. All measurement parameters are conveniently recorded in the form of tables and graphs, providing valuable information.
Integration of Convolutional Neural Networks and Grey Wolf Optimization for Advanced Cybersecurity in IoT Systems Jaddoa, Israa Ahmed
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.22178

Abstract

The rapid integration and application of the Internet of Things in daily life have significantly improved connectivity and intelligent control to various devices. However, it has exposed such systems to increased susceptibility to cyber challenges, such as infiltration, data sovereignty, and cyber-attacks. There is a need for an efficient and secure solution to these apparent security concerns which require complex social structures to adapt to various learning lessons quickly. The purpose of this study is to provide an inventive evolutionary operation to enhance the security of IoT networks and by integrating Convolutional Neural Networks and items of Grey Wolf Optimization algorithms – Standard GWO, Modified GWO and Advanced modified GWO. The GWOs were used to include surveillance accuracy layout, hence boosting detection accuracy. The action Lloyd testing found that smaller OWG intelligence (which achieved initially) unlimited interpretations which increased the percentage and was 97.4 %. This approach was further increased with FGWE, achieving 97.7 percentage, and 97.8 2.02% errors. The performance of both was 98.4 and 97.5 for the two classes, respectively. The current study’s results reveal the effectiveness of computational development to enhancing secure IoT networks and offer a secure prototype for potential study to optimize the security structure. effet for keynote curricular scenarios due to the system cause and trusty security solutions.
Evaluating Security Mechanisms for Wireless Sensor Networks in IoT and IIoT Zhukabayeva, Tamara; Buja, Atdhe; Pacolli, Melinda
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.21683

Abstract

In the era of interconnected digital ecosystems, the security of Wireless Sensor Networks (WSN) emerges as a pivotal concern, especially within the domains of the Internet of Things (IoT) and the Industrial Internet of Things (IIoT). However, the very nature of WSNs—being distributed, resource-constrained, and often deployed in unattended environments—poses unique cybersecurity challenges.  A main issue and challenge remains their Cybersecurity in communication. In this paper, we provide a systematic review focused on three themes including 1) techniques for secure communication in WSN; 2) algorithms and methods for intrusion detection in WSN; and 3) IoT and IIoT security concerning WSN. It has provided the results of its own for the publications made in the data analysis of three themes. The paper also has a simulation experiment to investigate the behavior of WSNs under sinkhole attacks—one of the prevalent threats to network integrity. Utilizing the Contiki OS Cooja simulator, the experiment carefully evaluates the performance of existing detection algorithms and introduces a novel method for identifying and neutralizing malicious nodes. Our simulation discloses unconventional communication patterns during sinkhole attacks running RPL protocol, emphasizing the effectiveness of our detection mechanisms against cyber threats. Particularly, the introduction of a malicious node (Node 13) significantly disrupted network communication, with traditional security mechanisms failing to immediately detect and isolate the threat. The scope of future research work will include the broader spectrum of cyber threats beyond sinkhole attacks, exploring advanced detection mechanisms, and machine learning-based security protocols for enhanced trust and transparency in WSN communications.

Page 1 of 3 | Total Record : 24

 1   2   3 

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