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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 35 Documents
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Search results for , issue "Vol. 5 No. 6 (2024)" : 35 Documents clear
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.
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.
A Low-Cost High Performance Electric Vehicle Design Based on Variable Structure Fuzzy PID Control Shamseldin, Mohamed A.; Araby, Medhat; El-khatib, 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.v5i6.22071

Abstract

This paper introduces the design steps and implementation of Electric Vehicle (EV) based on variable structure fuzzy PID control. The role of fuzzy logic is making change in the membership function to tune the fuzzy action according to the error and change of error. The control implementation was executed using a low-cost Arduino mega 2560 and had been programed by MATLAB SIMULINK.  Also, a nonlinear model for the EV was built and validated by the actual performance of the EV experimental setup. The overall EV closed loop implemented on the MATLAB SIMULINK to select the proper control parameters. The proposed variable structure fuzzy PID control had been compared to the traditional PID control to ensure robustness and reliability. The results show that the proposed control technique can deal with the EV disturbances and continuous change in the operating points.
A Model of Proactive-Reactive Job Shop Scheduling to Tackle Uncertain Events with Greedy Randomized Adaptive Search Procedure Nisar, Muhammad Usman; Ma'ruf, Anas; Cakravastia, Andi; Halim, Abdul Hakim
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.v5i6.22208

Abstract

Despite substantial research on job shop scheduling (JSS), there is a gap owing to the lack of a unified framework that considers exact, heuristic, and metaheuristic methods for JSS. This study addressed this gap by presenting a comprehensive approach. The study offered following contributions in this regard: analyzed the exact optimization method for benchmarking, investigated a greedy algorithm (G_r A) for faster solutions, and implemented a novel Greedy Randomized Adaptive Search Procedure (GRASP) to achieve high-quality solutions with computational effectiveness. Additionally, this study considered serious dynamic events (SDE) such as new job arrivals (NJA), rush order (RO), machine failures (MF), and scheduled machine maintenance (SMM), as scheduling disruptions and proposed a proactive-reactive rescheduling strategy, with right-shift (RF) and regeneration (Reg) methods using a hybrid (periodic and event-driven) policy to tackle them. Results showed that the exact methods are optimal but computationally intensive, G_r A are faster but suboptimal, and GRASP strike a balance, delivering high-quality solutions with only a 3.43% gap from exact methods while maintaining computational efficiency. Additionally, RF method effectively handled MF, while Reg efficiently integrated NJA, RO, and SMM. Overall, this study offered a comprehensive approach to JSS, enhancing applicability in manufacturing environments.
Integration of Modbus-Ethernet Communication for Monitoring Electrical Power Consumption, Temperature, and Humidity Le, Long Ho; Ngo, Thanh Quyen; Toan, Nguyen Duc; Nguyen, Chi Cuong; Phong, Bui Hong
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.v5i6.22456

Abstract

Effective management of electrical energy requires monitoring, controlling, and storing parameters gathered from power measurement devices including voltage, current, temperature, and humidity. This assessment of the quality of electrical energy is essential for management organizations, power companies, and individual consumers to develop efficient electricity usage plans. Based on the requirement, we proposed a hardware implementation for data collection and online communication software integrated with a system for collecting data on consumption of electrical energy. The EM115-Mod CT multifunction industrial meters by FINECO, the KLEA 220P three-phase multifunction meter by KLEMSAN, and the ME96SS–ver.B by MITSUBISHI are involved. Finally, the collected data of electrical consumption, temperature, and humidity can be stored on an SD card, transmitted to the cloud for real-time monitoring on mobile devices, and facilitated by the ESP-WROOM-32 microcontroller from Espressif system.
Voltage Regulation and Power Management of DC Microgrid with Photovoltaic/Battery Storage System Using Flatness Control Method Mutlag, Ashraf Abdualateef; Abd, Mohommed Kdair; Shneen, Salam Waley
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.v5i6.22530

Abstract

This research aims to propose a power management strategy (PMS) based on the flatness control method for a stand-alone DC microgrid system. The goal of the proposed strategy is to create an efficient PMS using nonlinear flatness theory in order to provide a constant DC bus voltage and the best possible power-sharing mechanism between the battery and the PV array. A maximum power point tracking (MPPT) technique based on an artificial neural network (ANN) to optimize the PV's power. Moreover, the suggested PMS technique was tested in a simulation environment based on MATLAB®/Simulink. The obtained results demonstrate that the proposed PMS method can stabilize the bus voltage under variations in load or solar radiation. Additionally, the PMS method reduced bus voltage spikes and guaranteed good power quality, which extended the battery's lifespan and increased its efficiency. Also, the proposed approach outperforms the standard PI approach in terms of tracking efficiency and has a lower rate of overshoot in the bus voltage under different load scenarios. Therefore, the method is effective when compared with the classical PI approach. The overshoot in the PI method is 58 V, while the overshoot in the DC voltage is 5 V in the proposed method. The tracking speed of the proposed system is very low, and the slower speed was observed in the classical method, and the rise time of PI was 7.9ms, while the proposed method equals 2.2ms.
Application of Sentiment Analysis as an Innovative Approach to Policy Making: A review Firdaus, Asno Azzawagama; Saputro, Joko Slamet; Anwar, Miftahul; Adriyanto, Feri; Maghfiroh, Hari; Ma'arif, Alfian; Syuhada, Fahmi; Hidayat, Rahmad
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.v5i6.22573

Abstract

This literature review comprehensively explains the role of sentiment analysis as a policymaking solution in companies, organizations, and individuals. The issue at hand is how sentiment analysis can be effectively applied in decision making. The solution is to integrate sentiment analysis with the latest NLP trends. The contribution of this research is the assessment of 100-200 recent studies in the period 2020-2024 with a sample of more than 5,000 data, as well as the impact of the resulting policy recommendations. The methods used include evaluation of techniques such as Deep Learning, lexicon-based, and Machine Learning, using evaluation matrices such as F1-score, precision, recall, and accuracy. The results showed that Deep Learning techniques achieved an average accuracy of 93.04%, followed by lexicon-based approaches with 88.3% accuracy and Machine Learning with 83.58% accuracy. The findings also highlight the importance of data privacy and algorithmic bias in supporting more responsive and data-driven policymaking. In conclusion, sentiment analysis is reliable in areas such as e-commerce, healthcare, education, and social media for policy-making recommendations. However, special attention should be paid to challenges such as language differences, data bias, and context ambiguity which can be addressed with models such as mBERT, model auditing, and proper tokenization.
Active Vibration Isolation using Tilt Horizontal Coupling Immune Inertial Double Link Sensor for Low Frequency Applications Nair, Vishnu G.; Hegde, Navya Thirumaleswar; V., Dileep 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.v5i6.22595

Abstract

Addressing the challenge of horizontal tilt coupling is crucial for using inertial sensors in precise applications, such as seismology and seismic isolation, including gravitational wave detection. Researchers have proposed various design solutions, with the Double Link (DL) sensor standing out for its sim- plicity, precision, and effectiveness. This paper explores the use of the DL sensor in an active vibration isolation system. We evaluated different control algorithms, including Proportional- Integral-Derivative (PID), Linear Quadratic Regulator (LQR), Linear Quadratic Gaussian (LQG), and H-infinity. Simulations conducted in the Simscape environment showed that the H-infinity controller performed best, achieving a significant reduction in vibration. While the current study is based on simulations, future work will focus on experimental validation to confirm the system’s practical applicability and robustness in real-world scenarios. Our results demonstrate the potential of the DL sensor and LQG controller to enhance vibration isolation in low-frequency applications. Additionally, we conducted a detailed literature review on various methods used in similar applications. This review highlights alternative approaches, such as other sensor designs and control strategies, and discusses their advantages and limitations.
A Scoping Review on Unmanned Aerial Vehicles in Disaster Management: Challenges and Opportunities Nair, Vishnu G.; D'Souza, Jeane Marina; C. S., Asha; Rafikh, Rayyan Muhammad
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.v5i6.22596

Abstract

Unmanned Aerial Vehicles (UAVs), or drones, have recently become transformative tools in disaster management. This paper provides an overview of the role of drones in dis- aster response and recovery, covering natural disasters such as earthquakes, floods, and wildfires, as well as man-made incidents like industrial accidents and humanitarian crises. UAVs offer advantages including rapid data collection, real-time situational awareness, and improved communication capabilities. Notable examples include the use of drones in the 2015 Nepal earthquake for mapping and search operations, and during the 2017 Hurricane Harvey for flood assessment and resource distribution. Advanced technologies further enhance drone capabilities; AI algorithms were used in the 2019 Mozambique cyclone to prioritize rescue operations, while thermal sensors located survivors in the 2018 Mexico earthquake. Despite these benefits, challenges such as privacy concerns, regulatory issues, and community acceptance remain. For instance, privacy issues arose during Hurricane Harvey due to aerial surveillance, and regulatory barriers delayed responses in the 2018 Indonesia earthquake. Ethical dilemmas also surface, such as balancing response urgency with privacy rights and ensuring equitable access to UAV services. The paper discusses potential solutions, including establishing privacy protocols, engaging communities, and streamlining regulations. Collaboration between government agencies, NGOs, and the private sector is essential to develop standardized protocols and enhance community acceptance. By integrating AI, machine learning, and advanced sensors, drones can significantly improve disaster response efficiency. In conclusion, drones play a pivotal role in revolutionizing disaster management strategies. Ongoing advancements in drone technology offer unprecedented opportunities to enhance disaster response, ultimately mitigating human suffering and preserving critical infrastructure. This paper reviews the role of drones in disaster response and recovery efforts, covering various disaster types including natural and man-made incidents.
Comprehensive Study on Detecting Multi-Class Classification of IoT Attack Using Machine Learning Methods Zhukabayeva, Tamara; Zholshiyeva, Lazzat; Ven-Tsen, Khu; Adamova, Aigul; Karabayev, Nurdaulet; Mardenov, Erik
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.v5i6.22819

Abstract

The proliferation of IoT devices has heightened their susceptibility to cyberattacks, particularly botnets. Conventional security methods frequently prove inadequate because of the restricted processing capabilities of IoT devices. This paper suggests utilizing machine learning methods to enhance the detection of attacks in Internet of Things (IoT) environments. The paper presents a novel approach to detect different botnet assaults on IoT devices by utilizing ML methods such as XGBoost, Random Forest, LightGBM, and Decision Tree. These algorithms were examined using the N-BaIoT dataset to classify multi-class botnet attacks and were specifically designed to accommodate the limitations of IoT devices. The technique comprises the steps of data preparation, preprocessing, classifier training, and decision-making. The algorithms achieved high detection accuracy rates: XGBoost (99.18%), Random Forest (99.20%), LGBM (99.85%), and Decision Tree (99.17%). The LGBM model demonstrated exceptional performance. The incorporation of the attack evaluation model greatly enhanced the identification of botnets in IoT networks. The paper displays the efficacy of machine learning techniques in identifying botnet assaults in IoT networks. The models generated exhibit exceptional accuracy and can be seamlessly integrated into existing cybersecurity systems.

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