scholarly journals ESTIMASI GERAK TRANSLASI AUTONOMOUS UNDERWATER VEHICLE DENGAN ENSEMBLE KALMAN FILTER (EnKF)

2018 ◽  
Vol 2 (1) ◽  
pp. 41
Author(s):  
Teguh Herlambang ◽  
Subchan Subchan
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Degree Of Freedom

Penelitian dan pengembangan dari Autonomous Underwater Vehicle cukup banyak diantaranya terkait sistem kendali, navigasi dan hidrodinamika. Pada umunya persamaan gerak AUV adalah 6 derajat kebebasan/Degree of Freedom (DOF) yang terdiri dari gerak translasi (surge, sway, heave) dan gerak rotasi (roll, pitch, yaw). Pada paper ini dikembangkan metode estimasi gerak tranlasi dari ITSUNUSA AUV dengan metode Ensemble Kalman Filter. Pada paper ini juga dibandingkan berdasarakn pembangkian julah ensemble. Hasil simulasi menunjukkan bahwa yang terakurat adalah dengan membangkitkan 300 ensemble dengan error kecepatan untuk gerak surge adalah 0,082%, gerak sway 0.498% dan gerak heave 0.26%.

scholarly journals Discrete-Time Position Control for Autonomous Underwater Vehicle under Noisy Conditions

2021 ◽  
Vol 11 (13) ◽  
pp. 5790
Author(s):  
Qiang Liu ◽  
Muguo Li
Keyword(s):  
Kalman Filter ◽  
Discrete Time ◽  
Ensemble Kalman Filter ◽  
Position Control ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Control Law ◽  
Hydrodynamic Parameters ◽  
Time Position ◽  
Noisy Conditions

This paper deals with the discrete-time position control problem for an autonomous underwater vehicle (AUV) under noisy conditions. Due to underwater noise, the velocity measurements returned by the AUV’s on-board sensors afford low accuracy, downgrading its control quality. Additionally, most of the hydrodynamic parameters of the AUV model are uncertain, further degrading the AUV control accuracy. Based on these findings, a discrete-time control law that improves the position control for the AUV trajectory tracking is presented to reduce the impact of these two factors. The proposed control law extends the Ensemble Kalman Filter and solves the problem of the traditional Ensemble Kalman Filter that underperforms when the hydrodynamic parameters of the AUV model are uncertain. The effectiveness of the proposed discrete-time controller is tested on various simulated scenarios and the results demonstrate that the proposed controller has appealing precision for AUV position tracking under noisy conditions and hydrodynamic parameter uncertainty. The proposed controller outperforms the conventional time-delay controller in root-mean-square error by a percentage range of approximately 72.1–97.4% and requires at least 89.5% less average calculation time than the conventional model predictive control.

scholarly journals ESTIMASI GERAK SURGE, HEAVE DAN PITCH AUTONOMOUS UNDERWATER VEHICLE DENGAN ENSEMBLE KALMAN FILTER (EnKF)

2019 ◽  
Vol 3 (1) ◽  
pp. 51
Author(s):  
Teguh Herlambang ◽  
Subchan Subchan ◽  
Hendro Nurhadi
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle

Autonomous Underwater Vehicle (AUV) atau wahan air tanpa awak merupakan salah satu jenis robot bawah air yang relatif flexibel untuk eksplorasi bawah laut dan peralatan sistem pertahanan bawah laut. AUV dapat dikendalikan untuk bergerak dengan enam derajat kebebasan (6-DOF). Pengendalian AUV dibagi menjadi dua yaitu kendali kecepatan dan kendali agar mengikuti lintasan yang diinginkan.  Kendali AUV agar mengikuti trajectory yang diinginkan ini biasa disebut dengan sistem navigasi atau Estimasi trajectory.  Pada penelitian ini dikembangkan sistem navigasi untuk gerak menyelam dengan model 3-DOF yaitu gerak surge, heave dan pitch dengan metode Ensemble Kalman Filter (EnKF). Hasil simulasi menunjukkan bahwa metode EnKF dapat digunakan sebagai estimator gerak 3-DOF dengan menghasilkan error 0.011 m/s untuk gerak surge dan 0.009 m/s untuk gerak heave serta 0.004 rad/s untuk gerak pitch, sedangkan error posisi menyelam 0.01 m.

scholarly journals Estimasi Lintasan AUV 3 Dimensi (3D) Dengan Ensemble Kalman Filter

2019 ◽  
Vol 4 (1) ◽  
pp. 12
Author(s):  
Ngatini Ngatini ◽  
Hendro Nurhadi
Keyword(s):  
Kalman Filter ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Ensemble Kalman Filter ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Mean Square ◽  
Guidance Control ◽  
Non Linear

AUV (Autonomous Underwater Vehicle) merupakan kapal selam tanpa awak yang sistem geraknya dikemudikan (dikendalikan) oleh perangkat komputer. Sistem gerak dari AUV membutuhkan sebuah navigasi dan guidance control yang mampu mengarahkan gerak AUV, sehingga dibutuhkan sebuah estimasi posisi AUV sesuai dengan lintasan yang diberikan. Penelitian ini mengembangkan estimasi posisi dari AUV Segorogeni ITS menggunakan metode atau algoritma Ensemble Kalman Filter (EnKF) karena EnKF mampu mengestimasi persoalan berbentuk model sistem non linier dimana persamaan gerak dari AUV berbentuk non linear. Estimasi posisi dilakukan pada lintasan atau trayektori 3 dimensi (3D) yang dibangun dengan bantuan program Octave. Simulasi menampilkan hasil estimasi posisi AUV menggunakan algoritma EnKF dengan beberapa jumlah ensemble yang berbeda yaitu 50, 100, 200 dan 300 ensemble. Akurasi dari estimasi tersebut diukur dari nilai error hasil estimasi yaitu nilai RMSE (Root Mean Square Error). Hasil simulasi menunjukan rata-rata error estimasi yaitu 0.4 m posisi-x, 0.46 m posisi-y, 0.08 m posisi-z dan 0.1 m error sudut.

scholarly journals Observer Kalman filter identification of an autonomous underwater vehicle

2004 ◽  
Vol 37 (10) ◽  
pp. 269-272
Author(s):  
A. Tiano ◽  
R. Sutton ◽  
A. Lozowicki ◽  
W. Naeem
Keyword(s):  
Kalman Filter ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle

scholarly journals Stability and nonlinear controllability analysis of a quadrotor-like autonomous underwater vehicle considering variety of cases

2018 ◽  
Vol 15 (6) ◽  
pp. 172988141881940 ◽  
Author(s):  
Liwei Kou ◽  
Ji Xiang ◽  
Yanjun Li ◽  
Jingwei Bian
Keyword(s):  
Horizontal Plane ◽  
Autonomous Underwater Vehicle ◽  
Vertical Plane ◽  
Underwater Vehicle ◽  
Plane Motion ◽  
Small Time ◽  
Degree Of Freedom ◽  
Local Controllability ◽  
Actuation System ◽  
Nonlinear Controllability

A quadrotor-like autonomous underwater vehicle that is similar to, yet different from quadrotor unmanned aerial vehicles, has been reported recently. This article investigates the stability and nonlinear controllability properties of the vehicle. First, the 12-degree-of-freedom model of the vehicle deploying an X shape actuation system is developed. Then, a stability property is investigated showing that the vehicle cannot be stabilized by a time invariant smooth state feedback law. After that, by adopting a nonlinear controllability analysis tool in geometric control theory, the small-time local controllability of the vehicle is analyzed for a variety of cases, including the vertical plane motion, the horizontal plane motion, and the three-dimensional space motion. Finally, different small-time local controllability conditions for different cases are developed. The result shows that the small-time local controllability holds for vertical plane motion and horizontal plane motion. However, the full degree of freedom kinodynamics model (i.e. 12 states) of the vehicle does not satisfy the small-time local controllability from zero-velocity states.

Observer Kalman filter identification of an autonomous underwater vehicle

2007 ◽  
Vol 15 (6) ◽  
pp. 727-739 ◽  
Author(s):  
A. Tiano ◽  
R. Sutton ◽  
A. Lozowicki ◽  
W. Naeem
Keyword(s):  
Kalman Filter ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle
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