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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 23 Documents
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Search results for , issue "Vol 5, No 3 (2024)" : 23 Documents clear
Development of Adaptive PD Control for Infant Incubator Using Fuzzy Logic Kholiq, Abd; Lamidi, Lamidi; Amrinsani, Farid; Triwiyanto, Triwiyanto; Mahdy, Hafizh Aushaf; Nazila, Ragimova; Abdullayev, Vugar
Journal of Robotics and Control (JRC) Vol 5, No 3 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i3.21510

Abstract

This research aims to design an innovative fuzzy logic auto-tuning PD algorithm to control the temperature in a baby Incubator. The proposed Fuzzy-PD method combines fuzzy logic with PD control using the Arduino Mega 2560 microcontroller. The Proportional and Derivative parameters are adjusted by fuzzy logic based on feedback of error values and rate of change of error. The temperature setting range used in data collection is 32-37°C. When the temperature setting is higher, the time required to reach the specified temperature setting becomes longer. The overshoot tends to be low, as the system is designed to respond to temperature changes with high precision. The temperature inside the baby Incubator can be maintained with a low steady-state error value. The adaptive fuzzy-PD system can restore the temperature inside the baby Incubator to the set temperature after a disturbance. Compared to the x device, the average error value is 0.0013%. Independent sample t-tests show no significant difference between the baby Incubator and the Incu analyzer device. It can be concluded that the combination of fuzzy logic and PD control system works well in maintaining temperature stability with low error values. The results are better than previous research focusing on designing a PD algorithm with a maximum rise time of 480 seconds. Furthermore, there is potential for further development with a fuzzy logic auto-tuning PID algorithm to achieve better results.
Using Grey Wolf Optimization Algorithm and Whale Optimization Algorithm for Optimal Sizing of Grid-Connected Bifacial PV Systems Hadi, Husam Ali; Kassem, Abdallah; Amoud, Hassan; Nadweh, Safwan; Ghazaly, Nouby M.
Journal of Robotics and Control (JRC) Vol 5, No 3 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i3.21777

Abstract

The shift towards renewable energies is driven by the shortage of fossil fuels for electricity generation and the associated harmful impacts. Grid-connected PV systems are a reliable and effective choice for power production across different uses, making them a key player in the global renewable energy landscape. Consequently, the careful selection of components for these systems is a crucial and widely studied aspect in this area of research. This paper introduced using gray wolf optimization algorithm GWO whale optimization algorithm WOA for determining the optimal number of grid - connected bifacial photovoltaic PV systems in Babylon Hilla. The considered factors included available space, desired energy production, radiation, dihedral factor, budget constraints, and grid connectivity requirements. The mathematical formulation of the problem and implementation details of the algorithms are presented. In addition, two cases studied are performed one for a residential area, and the other for a single house. The results demonstrated the efficiency and effectiveness of both algorithms in identifying optimal solutions for determining the size of systems in the area under study. However, the WOA surpassed the GWO in meeting the optimization criteria. The proper selection of these systems resulted in higher power generation, lower costs, improved energy management, and the advancement of sustainable solar energy solutions.
System Identification and Control Strategy on Electric Power Steering DC Motor Arifin, Bustanul; Nugroho, Agus Adhi; Budisusila, Eka Nuryanto; Khosyi'in, Muhammad
Journal of Robotics and Control (JRC) Vol 5, No 3 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i3.21614

Abstract

Power steering technology help human to control the car. The hydraulic power steering system now tends to be replaced by the electric power steering system (EPS). As the main driver that require precise control. The contribution of this research is to obtain system identification of EPS motor and novelty control strategy to achieve stable control better. Motor control require an appropriate mathematical model and up-down-up down signals of Pseudo Random Binary Signal Sequence (PRBS) were used. The modelling method used was the Numerical Algorithm for Subspace State Space System Identification (N4SID). The quality of the modeling needs to be measured to see whether it was close to the original signal. The validation of the model obtained tested using Variance Accounted For (VAC), Akaike Information Criterion (AIC), and Final Prediction Error (FPE). The best mathematical model was developed on the basis of these three criteria, which is 3rd order model. The control strategy carried out by means of the Ziegler Nichols, Tyreus Luyben and Haugen tuning technique. With these three tuning methods, the control parameters obtained were used for Proprotional-Integral (PI) and Proportional-Integral-Derivative (PID) control. Based on the study, the Haugen control shows the best results of the two other controls, namely with a rise time value of 11,361 ms, overshoot of 6,898%, and steady state at 1.3 s. This show that PI control using the Haugen tuning method able to control the motor well. Robustness tests have also been carried out because the steering system is operated in unpredictable environmental conditions. The control greatly influenced the performance and stability of EPS control in the car's steering system.

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