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All Journal Jurnal Otomasi Kontrol dan Instrumentasi Journal of Robotics and Control (JRC)
Adinanta, Hendra
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Aerospace Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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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.
Kajian Eksperimen Teknik Kontrol Penerbangan Posisi Tinggal Landas Drone Bikopter dengan Metode PID Prakosa, Jalu Ahmad; Kurniawan, Edi; Adinanta, Hendra; Suryadi, Suryadi; Afandi, M. Imam
Jurnal Otomasi Kontrol dan Instrumentasi Vol 12 No 2 (2020): Jurnal Otomasi Kontrol dan Instrumentasi
Publisher : Pusat Teknologi Instrumentasi dan Otomasi (PTIO) - Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/joki.2020.12.2.1

Abstract

Pengembangan teknik kontrol penerbangan (flight control) untuk drone tidaklah sederhana disebabkan oleh nonlinearitas, ketidakpastian, dan karakteristik dinamis udara. Bahkan, ini lebih rumit dari sistem kontrol yang memiliki hanya satu input dan satu output, diakibatkan kendali penerbangan drone dapat memiliki lebih dari satu input dan output. Fasilitas 3 degree of freedom (DOF) helikopter dapat mengimplementasikan teknik penerbangan drone jenis bikopter yang memiliki dua rotor secara eksperimen. Posisi tinggal landas (take off) drone sangatlah penting berkaitan dengan keselamatan awal penerbangan. Metode kontrol Proportional Integral Derivatif (PID) merupakan teknik populer yang tidak hanya dipelajari di dunia pendidikan tetapi juga telah diterapkan oleh industri untuk mengontrol plant-nya. Tujuan penelitian ini adalah untuk mempelajari teknik kontrol penerbangan posisi take off drone bikopter dengan menerapkan metode PID baik kajian teori maupun eksperimennya. Hasil eksperimen menunjukkan bahwa koefisien penguatan (gain) berpengaruh terhadap kesalahan dan lineraritas posisi drone. Variasi komponen gain kendali proportional integral lebih buruk dari proportional derivatif. Untuk mendapatkan respon posisi yang paling akurat dan linear, seluruh koefisien PID harus diaplikasi pada teknik kontrol penerbangan posisi tinggal landas drone bikopter. 
Co-Authors Afandi, M. Imam Edi Kurniawan Jalu Ahmad Prakosa Maftukhah, Tatik Mahmudi Mahmudi Prakosa, Jalu A. Pratiwi, Enggar B. Purwowibowo Purwowibowo, Purwowibowo Rustandi, Dadang Suryadi Suryadi Wijonarko, Sensus