Kiki, Mayga (2020) Perbandingan Metode Kalman Filter Dan Ensemble Kalman Filter Untuk Estimasi Momentum Air Dan Kecepatan Baling-Baling Pada Model Bow Thruster Autonomous Surface Vehicle. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Autonomous Surface Vehicle (ASV) merupakan sebuah wahana berbentuk kapal dipermukaan air yang dapat bergerak tanpa awak secara otomatis. Sistem bow thrutser berfungsi sebagai kendali alat gerak pendorong dalam operasi kerja kapal. Pada penelitian ini, dilakukan estimasi momentum air dan kecepatan baling-baling berdasarkan model dinamik bow thrutser. Metode estimasi yang digunakan adalah metode Kalman Filter dan Ensemble Kalman Filter. Hasil estimasi pada kondisi tunnel thruster menunjukkan bahwa RMSE oleh metode Ensemble Kalman Filter relative lebih kecil yaitu 0,7920 dan 0,1352, sedangkan hasil estimasi pada kondisi open-bladed thruster menunjukkan bahwa RMSE oleh metode Kalman Filter relative lebih kecil yaitu 1,9957 dan 2,0609.
Kata kunci: Autonomous Surface Vehicle(ASV), Bow Thruster, Kalman Filter, Ensemble Kalman Filter
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Autonomous Surface Vehicle(ASV) is a boat-shaped-vehicle that can move automatically, so control system is required. Bow thruster system have a function an actuator for ship operation. In this research, we estimate motor rotation rate and section average flow velocity based on thruster dynamic model. The methods of estimation will use in this research is Kalman Filter and Ensemble Kalman Filter method. The estimation result based on tunnel thruster test have RMSE 0,7920 and 0,1352 using Ensemble Kalman Filter method, the estimation result on open-balded thruster have RMSE 1,9957 and 2,0609 using Kalman Filter method
Keywords:Autonomous Surface Vehicle(ASV), Bow Thruster, kalman Filter, Ensemble Kalman Filter.
| Item Type: | Thesis (Other) |
|---|---|
| Additional Information: | RSMa 519.544 Kik p-1 • Kiki, Mayga |
| Uncontrolled Keywords: | Autonomous Surface Vehicle (ASV), Bow thruster, Kalman Filter, Ensemble Kalman Filter. |
| Subjects: | Q Science > QA Mathematics > QA402.3 Kalman filtering. |
| Divisions: | Faculty of Science and Data Analytics (SCIENTICS) > Mathematics > 44201-(S1) Undergraduate Thesis |
| Depositing User: | Mayga Kiki |
| Date Deposited: | 23 Aug 2020 06:35 |
| Last Modified: | 30 Oct 2023 08:18 |
| URI: | http://repository.its.ac.id/id/eprint/80027 |
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