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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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Review of Intelligent and Nature-Inspired Algorithms-Based Methods for Tuning PID Controllers in Industrial Applications Patil, Ramakant S; Jadhav, Sharad P.; Patil, Machhindranath D.
Journal of Robotics and Control (JRC) Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i2.20850

Abstract

PID controllers can regulate and stabilize processes in response to changes and disturbances. This paper provides a comprehensive review of PID controller tuning methods for industrial applications, emphasizing intelligent and nature-inspired algorithms. Techniques such as Fuzzy Logic (FL), Artificial Neural Networks (ANN), and Adaptive Neuro Fuzzy Inference System (ANFIS) are explored. Additionally, nature-inspired algorithms, including evolutionary algorithms like Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Ant Colony Optimization (ACO), Simulated Annealing (SA), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Cuckoo Search (CS), Harmony Search (HS), and Grey Wolf Optimization (GWO), are examined. While conventional PID tuning methods are valuable, the evolving landscape of control engineering has led to the exploration of intelligent and nature-inspired algorithms to further enhance PID controller performance in specific applications. The study conducts a thorough analysis of these tuning methods, evaluating their effectiveness in industrial applications through a comprehensive literature review. The primary aim is to offer empirical evidence on the efficacy of various algorithms in PID tuning. This work presents a comparative analysis of algorithmic performance and their real-world applications, contributing to a comprehensive understanding of the discussed tuning methods. Findings aim to uncover the strengths and weaknesses of diverse PID tuning methods in industrial contexts, guiding practitioners and researchers. This paper is a sincere effort to address the lack of specific quantitative comparisons in existing literature, bridging the gap in empirical evidence and serving as a valuable reference for optimizing intelligent and nature-inspired algorithms-based PID controllers in various industrial applications. Keywords— PID controller; Intelligent and Nature-Inspired Algorithms; Fuzzy Logic; Artificial Neural Network; Adaptive NeuroFuzzy Inference System; Genetic Algorithm; Particle Swarm Optimization; Differential Evolution; Ant Colony Optimization; Simulated Annealing; Artificial Bee Colony; Firefly Algorithm; Cuckoo Search; Harmony Search; Grey Wolf Optimization.
Oil Palm USB (Unstripped Bunch) Detector Trained on Synthetic Images Generated by PGGAN Aji, Wahyu Sapto; bin Ghazali, Kamarul Hawari; Akbar, Son Ali
Journal of Robotics and Control (JRC) Vol 4, No 5 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i5.19499

Abstract

Identifying Unstriped Bunches (USB) is a pivotal challenge in palm oil production, contributing to reduced mill efficiency. Existing manual detection methods are proven time-consuming and prone to inaccuracies. Therefore, we propose an innovative solution harnessing computer vision technology. Specifically, we leverage the Faster R-CNN (Region-based Convolution Neural Network), a robust object detection algorithm, and complement it with Progressive Growing Generative Adversarial Networks (PGGAN) for synthetic image generation. Nevertheless, a scarcity of authentic USB images may hinder the application of Faster R-CNN. Herein, PGGAN is assumed to be pivotal in generating synthetic images of Empty Fruit Bunches (EFB) and USB. Our approach pairs synthetic images with authentic ones to train the Faster R-CNN. The VGG16 feature generator serves as the architectural backbone, fostering enhanced learning. According to our experimental results, USB detectors that were trained solely with authentic images resulted in an accuracy of 77.1%, which highlights the potential of this methodology. However, employing solely synthetic images leads to a slightly reduced accuracy of 75.3%. Strikingly, the fusion of authentic and synthetic images in a balanced ratio of 1:1 fuels a remarkable accuracy surge to 87.9%, signifying a 10.1% improvement. This innovative amalgamation underscores the potential of synthetic data augmentation in refining detection systems. By amalgamating authentic and synthetic data, we unlock a novel dimension of accuracy in USB detection, which was previously unattainable. This contribution holds significant implications for the industry, ensuring further exploration into advanced data synthesis techniques and refining detection models.
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.
Errors Detection Based on SDWT and BNN Applied for Position, Velocity and Acceleration Signals of a Wheeled Mobile Robot Saeed, Saad Zaghlul; Alobaidy, Muhamad Azhar Abdilatef; Yosif, Zead Mohammed
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.v5i5.21424

Abstract

Accurate error detection in mobile robots is crucial for reliable operation and prevention of mechanical or electrical failures. Mechanical defects on the wheels of mobile robot make real path deviate from the desired path and trajectory. From the kinematics equations, error in the angular velocity of wheel affects the position, velocity, and acceleration. Each of these signals is fed to the Symelet discrete wavelet transform (SDWT) for the purpose of error's feature detection and extraction. The SDWT with 5-level for each component of the signal produce 10 inputs for the Bayesian Neural network (BNN). The BNN with single layer of 18 neurons classifies the inputs either no error case or specify the wheel(s) at which error had been happened. Straight line and circular paths were tested in the presence of errors in single wheel or both wheels. Two different path's time durations are tested to investigate robustness of the proposed methodology. The simulation’s results of two wheels mobile robot showed that acceleration's signal for a straight-line path has accuracy of 100%, MSE 3.05×10-23 and 9.81×10-17, training iterations are 15 and 23 for 4- and 2-seconds durations; respectively. While for a circular path, displacement's signal gave high accuracy 100%, MSE 8.86·10-16 and 3.76×10-18, training iteration 17 and 13 for 4- and 2-seconds durations; respectively. Acceleration signal can be used for detecting errors in real time by using accelerometer. Limitations such as amount of data besides to the sensor noise affects the proposed methodology.
Vicinity Monitoring of Military Vehicle Cabin to Improve Passenger Comfort with Fusion Sensors and LoRa RFM95W Fadillah, Wildan Muhammad Yasin; Mutiara, Giva Andriana; Periyadi, Periyadi; Alfarisi, Muhammad Rizqy; Meisaroh, Lisda
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.v5i5.21600

Abstract

The application and utilization of technology to measure the level of comfort in mass-produced vehicles, including military vehicles, is constantly evolving. Currently, the testing of comfort parameters is carried out manually through human-driven test drives. Thus, the range of variability in measurements is extensive as it depends on the subjective experiential indications of experts.  This research utilizes KY-037 sensor to measure noise level and BME280 sensor fusion to detect temperature, air pressure, humidity, and altitude.  These parameters have a significant impact on passenger comfort inside the passenger cabin of military vehicles. This project included involves the development of LoRa-based communication medium using RFM95W technology. The system has extensive performance testing inside the passenger cabin of a military vehicle on various test area tracks. The test results indicate that the system is capable of accurately reading the KY-037 sensor, with a range of 80 - 141 dB depending on the tracks. The BME280 sensor consistently measures a temperature of 36,98°C, altitude readings ranging from 667-677 meter above sea level, maintaining a stable air pressure of 955.35 hPa, and measuring the lowest humidity level in the vehicle cabin at 24.34%. The LoRa technology possesses remarkable to extend the communication range, even in challenging environments, reaching distances over 2 kilometers. The response time for data sent in web-based applications consistently remains below 1 second. Thus, this system can assist experts in enhancing cabin passenger comfort standards by narrowing the range and making it more measurable.
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.
LICA-CS: Efficient Lossless Image Compression Algorithm via Column Subtraction Model Al Qerom, Mahmoud; Otair, Mohammad; Meziane, Farid; AbdulRahman, Sawsan; Alzubi, Maen
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.v5i5.21834

Abstract

Driven by the unprecedented amount of data generated in the last few decades, data storage and communication are becoming more challenging. Although many approaches in data compression have been developed to alleviate these challenges, more efforts are still needed, especially for lossless image compression, which is a promising technique when critical information loss is not allowed. In this paper, we propose a new algorithm called the Lossless Image Compression Algorithm using a Column Subtraction model (LICA-CS). LICA-CS is efficient, low in complexity, decreases the image bit-depth, and enhances state-of-the-art performance. LICA-CS first implements a color transformation method as a pre-processing phase, which strategically minimizes inter-channel correlations to optimize compression outcomes. After that, a novel subtraction method was developed to compress the image data column-wise. We tackle the similarity and proximity of pixel values within adjacent columns, which offers a distinct advantage in reducing image size observing a significant size reduction of 71%. This is achieved through the subtraction of neighboring columns. The conducted experiments on colored images show that LICA-CS outperforms existing algorithms in terms of compression rate. Moreover, our method exhibited remarkable enhancements in execution time, with compression and decompression processes averaging 1.93 seconds. LICA-CS advances the state-of-the-art in lossless image compression, promising enhanced efficiency and effectiveness in image compression technologies.
Research Trends and Knowledge Taxonomy of Artificial Intelligence Applications in Supply Chain Management, Logistics, and Transportation: A Systematic Literature Review and Bibliometric Analysis Kriouich, Mohamed; Sarir, Hicham; Louah, Soulaiman
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.v5i5.21859

Abstract

Due to industrialization and globalization, supply chains (SC) have become more and more in need of artificial intelligence (AI), which has sparked conversations on how to use it to improve SC performance globally. Using both quantitative and qualitative methodologies, this study provides a thorough examination of the trends, gaps, and knowledge structure in the literature on AI in SC. Scientific mapping was used to summarize 140 important publications published between 1998 and 2022. Publication years, attribution, journal co-citations, partnerships between countries and institutions, significant papers, related keywords, and historical study groups were all included in the bibliographic analysis. A thematic categorization of the data produced 22 sub-branches of AI application in SC that are covered in five domains: environment, planning and risk management, SC areas, technology, logistics and transportation, and planning and environment. The study identifies current knowledge gaps and recommends future research directions due to limited international cooperation and inadequate platforms for advancing technology research. these findings aid academics and practitioners by providing a coherent intellectual outlook on AI's involvement in SC.
ESPNow Protocol-Based IIoT System for Remotely Monitoring and Controlling Industrial Systems Hailan, Maryam Abdulhakeem; Ghazaly, Nouby M.; Albaker, Baraa Munqith
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.v5i6.21925

Abstract

The shift from conventional manufacturing facilities to intelligent manufacturing facilities is a topic of significant interest due to its profound and enduring implications for the evolution of manufacturing practices on a global scale. The advent of Industry 4.0 is geared toward advancing the manufacturing sector by facilitating the production of goods with brief product life spans and tailored to individual customer preferences in a financially efficient manner. This paper introduces an Industrial Internet of Things system that powers the ESP32 microcontroller, the Blynk platform, and the ESP-Now protocol for remote monitoring and control of industrial processes. The system aims to improve operational efficiency and data management in industrial settings by addressing challenges associated with communication protocols and user interfaces. The implementation of the system comprises configuring the ESP32 to collect data from several sensors dispersed across factory sites. Integration with the Blynk platform enables real-time data visualization and device management, while the ESP-Now protocol facilitates efficient communication among IoT devices for seamless monitoring and control functionalities. The developed system shows significant advancements in industrial monitoring and control by offering enhanced scalability, interoperability, and adaptability to diverse industrial environments. Monitoring capabilities include weather conditions, motion detection, gas levels, and water quality assessment, with control functionalities extending to regulating water pumps and lamps. Metrics for assessing GUI performance include response time, data visualization accuracy, and user interaction efficiency. Robust encryption protocols and authentication mechanisms are implemented to ensure data security and privacy, enhancing the system's reliability and trustworthiness in industrial applications. The integrated system provides a comprehensive solution for industrial monitoring and control, offering efficient communication, scalability, and data security measures to optimize operational efficiency in diverse industrial environments. The system's advanced features and capabilities position it as a valuable tool for enhancing industrial processes and ensuring seamless data management and control.
Enhanced Transformer Protection Using Fuzzy-Logic-Integrated Differential Relays: A Comparative Study with Rule-based Methods Hussein, Raad Ibrahim Hussein; Gökşenli, Nurettin; Bektaş, Enes; Teke, Mustafa; Tümay, Mehmet; Yaseen, Ethar Sulaiman Yaseen; Bektaş, Yasin; Taha, Taha A.
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.v5i5.21937

Abstract

The power transformers are the important part of electrical networks where transformer reliability and operational lifetime depends on sufficiently accurate and reliable protective means. Other traditional forms of differential protection that were developed initially also suffer from the inability to distinguish between a fault and normal operation such as inrush currents in transformers and CT saturation. This paper presents the development of an improved differential relay augmented by Fuzzy-Logic Control System (FLC), to improve (a) dependability, (b) performance of the existing transformer protection systems, and (c) accuracy in fault identification possible due to uncertainty and non-linearity in transformer operation. They include the proposed methodology compared to the traditional Rule-based current differential method in outlining the protection settings. MATLAB/Simulink model of the power transformer and protection methods suggested in the study form a part of the investigation. Computer simulations show that the presented scheme provides a substantial increase in the speed and resolution of fault detection and fault types identification relating to current differential method based on the Rule. The system’s accuracy rate is the average of 98% for internal faults and 95% for external faults while its response time is 25ms for internal faults and 30ms for external faults. Furthermore, the Fuzzy-Logic-based system has an 90% efficiency in detect the defect and 85% efficiency in identify the inrush currents. The findings of this research prove that the differential relay based on Fuzzy-Logic enhances the flexibility and reliability of transformer protection and opens the road to the introduction of further improvements in the intelligent protection systems in the future.

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