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All Journal Journal of Robotics and Control (JRC) Journal of Technomaterial Physics
Wijonarko, Sensus
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Aerospace Engineering Astronomy Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Materials Science & Nanotechnology Mechanical Engineering Physics

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A Performance Evaluation of Repetitive and Iterative Learning Algorithms for Periodic Tracking Control of Functional Electrical Stimulation System Kurniawan, Edi; Pratiwi, Enggar B.; Adinanta, Hendra; Suryadi, Suryadi; Prakosa, Jalu A.; Purwowibowo, Purwowibowo; Wijonarko, Sensus; Maftukhah, Tatik; Rustandi, Dadang; Mahmudi, Mahmudi
Journal of Robotics and Control (JRC) Vol 5, No 1 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i1.20705

Abstract

Functional electrical stimulation (FES) is a medical device that delivers electrical pulses to the muscle, allowing patients with spinal cord injuries to perform activities such as walking, cycling, and grasping. It is critical for the FES to generate stimuli with the appropriate controller so that the desired movements can be precisely tracked. By considering the repetitive nature of the movements, the learning-based control algorithms are utilized for regulating the FES. Iterative learning control (ILC) and repetitive control (RC) are two learning algorithms that can be used to accomplish accurate repetitive motions. This study investigates a variety of ILC and RC designs with distinct learning functions; this constitutes our contribution to the field. The FES model of ankle angle, which is in a class of discrete-time linear systems is considered in this study. Two learning functions, i.e., proportional, and zero-phase learning functions, are simulated for the second-order FES model running at a sampling time of 0.1 s. The results indicate the superior performance of the ILC and RC in terms of convergence rate using the zero-phase learning function. ILC and RC with a zero-phase learning function can reach a zero root-mean-square error in two iterations if the model of the plant is correct. This is faster than proportional-based ILC and RC, which takes about 40 iterations. This indicates that the zero-phase learning function requires two iterations to ensure that the patient's ankle angle precisely tracks the intended trajectory. However, the tracking performance is degraded for both control methods, especially when the model is subject to uncertainties. This specific problem can lead to future research directions.
Dissipation of Electrical Energy into Heat Energy in Web-Based Rainfall Meter Microcontrollers Wijonarko, Sensus
Journal of Technomaterial Physics Vol. 3 No. 1 (2021): Journal of Technomaterial Physics
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jotp.v3i1.5541

Abstract

The heat dissipation of a system has been observed. In this study, the Arduino Nano as a microcontroller of web based rainfall gauge calibrator will be analyzed through the application of an automatic pump, the DHT22 sensor as a detector for temperature change on the microcontroller on standby for 5 minutes and operate for 55 minutes. Electrical energy can be obtained from voltage and current measurements using a multimeter and heat energy can be obtained from temperature changes detected by the DHT 22 sensor. The temperature sensor readings are displayed from the microcontroller to the PC into the PLX-DAQ application as an interface. From the results of observations and calculations, the data obtained on the percentage of electrical energy dissipation into heat energy has 4 stages, that is on standby 8.9%, from the end of standby to operate at 1.0%, transition 0.2%, and ideal stable 0.1%-0% . After 20 minutes until finished operating shows an ideal stable state. This is due to the microcontroller heat dissipation and energy absorbed by the ambient is the same.
Co-Authors Adinanta, Hendra Edi Kurniawan Maftukhah, Tatik Mahmudi Mahmudi Prakosa, Jalu A. Pratiwi, Enggar B. Purwowibowo Purwowibowo, Purwowibowo Rustandi, Dadang Suryadi Suryadi