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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics Journal of Robotics and Control (JRC) Indonesian Journal of Electrical Engineering and Computer Science Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Scientific Journal of Engineering Research Jurnal Teknokes
Abdullayev, Vugar
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Aerospace Engineering Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Health Professions Materials Science & Nanotechnology Mechanical Engineering

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Journal : Journal of Robotics and Control (JRC)

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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.
Single Lead EMG signal to Control an Upper Limb Exoskeleton Using Embedded Machine Learning on Raspberry Pi Triwiyanto, Triwiyanto; Caesarendra, Wahyu; Abdullayev, Vugar; Ahmed, Abdussalam Ali; Herianto, Herianto
Journal of Robotics and Control (JRC) Vol 4, No 1 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i1.17364

Abstract

Post-stroke can cause partial or complete paralysis of the human limb. Delayed rehabilitation steps in post-stroke patients can cause muscle atrophy and limb stiffness. Post-stroke patients require an upper limb exoskeleton device for the rehabilitation process. Several previous studies used more than one electrode lead to control the exoskeleton. The use of many electrode leads can lead to an increase in complexity in terms of hardware and software. Therefore, this research aims to develop single lead EMG pattern recognition to control an upper limb exoskeleton. The main contribution of this research is that the robotic upper limb exoskeleton device can be controlled using a single lead EMG. EMG signals were tapped at the biceps point with a sampling frequency of 2000 Hz. A Raspberry Pi 3B+ was used to embed the data acquisition, feature extraction, classification and motor control by using multithread algorithm. The exoskeleton arm frame is made using 3D printing technology using a high torque servo motor drive. The control process is carried out by extracting EMG signals using EMG features (mean absolute value, root mean square, variance) further extraction results will be trained on machine learning (decision tree (DT), linear regression (LR), polynomial regression (PR), and random forest (RF)). The results show that machine learning decision tree and random forest produce the highest accuracy compared to other classifiers. The accuracy of DT and RF are of 96.36±0.54% and 95.67±0.76%, respectively. Combining the EMG features, shows that there is no significant difference in accuracy (p-value 0.05). A single lead EMG electrode can control the upper limb exoskeleton robot device well.
Co-Authors Abdulhamid, Mohanad Ahmadzada, Allahshukur Ahmed, Abdussalam Ali Alhaq, Elmira Rofida Amrinsani, Farid Andi Farmadi Anita Miftahul Maghfiroh Asgarova, Bahar Babayev, Nicat Djatmiko, Wahyu Dwi Kartini, Dwi Dyah Titisari, Dyah Dzira Naufia Jawza Fatma Indriani Fayyadh, Muhammad Naufaldi Friska Abadi Herianto Herianto Jafarov, Elvin Kholiq, Abd Kurniawan, Rozali Arsyad Lamidi, Lamidi Mahdy, Hafizh Aushaf Muhammad Alkaff Muhammad Amirul Mu'min Muhammad Itqan Mazdadi Muhammad Reza Faisal, Muhammad Reza Musvika, Syevana Dita Muzachim, Zuva Nani Sulistianingsih Nazila, Ragimova Nugraha, Priyambada C. Nur Astrif, Citra Rabani, Rifqi Ragimova, Nazila Rudy Herteno Sabarudin Saputra Saragih, Triando Hamonangan Sa’diah, Halimatus Setiani, Rahma Sumber, Sumber Triana Rahmawati Utama, Egan Graha Wahyu Caesarendra Wardhana, Farisy Azis Satria Yana Safitri, Yana