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

Ngatini Ngatini(1*), Hendro Nurhadi(2)

(1) Universitas Internasional Semen Indonesia
(2) Institut Teknologi Sepuluh November
(*) Corresponding Author

Abstract


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.

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References


Yuh, J. (1994), “Learning Control for Underwater Robotic Vehicles”, IEEE Control Systems Magazine, Vol. 14 No. 2, hal. 39-46.

Von Alt, C. (2003). “Autonomous Underwater Vehicles”, The Autonomous Underwater Lagrangian Platforms and Sensors Workshop, Woods Hole Oceanographic Institution, United States.

Ermayanti, Z., Apriliani, E., and Nurhadi, H. (2014) Estimate and Control Positon on The Autonomous UnderwaterVehicle Based on Determined Trajectory. Thesis of Department of Mathematics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia.

Ngatini. 2017. Ensemble and Fuzzy Kalman Filter for Position Estimation of an Autonomous Underwater Vehicle Based on Dynamical System of AUV Motion. J. Expert Systems with Applications. Vol. 68. Issue 7, pp. 29-35.

Lewis, J. M., Laksmivarahan, S. and Dhall, S. (2006), Dynamic Data Assimilation, A Least Square Approach, Cambridge University Press, New York.

Ngatini, Apriliani, E., & Nurhadi, H. (2016). The Position Estimation of AUV Based on Non-Linear Ensemble Kalman Filter. Proceeding Basic 2016, Fakultas Matematika dan Ilmu Pengetahuan Alam: 6 (hal. 382-386). Indonesia: Universitas Brawijaya. 2338-0128.

Herlambang, T., Djatmiko, E. B. And Nurhadi, H. 2015. Ensemble Kalman Filter with a Square Root Scheme (EnKF-SR) for Trajectory Estimation of AUV SEGOROGENI ITS, International Review of Mechanical Engineering (I.RE.M.E), Vol. 9, No. 6 ISSN 1970-8734.

Yang, C. (2007), Modular Modelling and Control for Autonomous Underwater Vehicle (AUV). Thesis of Department of Mechanical Engineering, National University of Singapore.

Ataei, M., Koma, A. Y., 2014. Three-Dimensional Optimal Path Planning for Waypoint Guidance of an Autonomous Underwater Vehicle. J. Robotics and AutonomousSystems.

Apriliani, 2014. Metode Asimilasi Data: Salah Satu Penerapan Matematika dalam Bidang Lingkungan Hidup. Surabaya: Matematika ITS.

Apriliani, E., Arif, D. K., & Sanjoyo, B. A. (2010). The square root ensemble Kalman Filter to estimate the concentration of air pollution. Proceedings of the 2010 IEEE, international conference on mathematical application in engineering (ICMAE’10), kuala lumpur, Malaysia.

Lewis, L. F., 1986, “Optimal Estimation, with an introduction to stochastic control theory”, John Wiley and Sons, New York.

Heemink, A.W., 1986, “Storm surge prediction using Kalman filtering”, Thesis, Twente University, The Netherlands.

Verlaan, M., Heemink, A.W. 1997, “Tidal Flow Forecasting Using Reduced Rank Square Root Filters”, Stochastic Hydrologi and Hydraulics, No.11: pp. 349-368

Apriliani, E., 2001, “The Estimation of The Water Level by The Reduced Rank Square Root Information Filter”, Proceedings of the Asia – Pasific Vibration Conference, vol II, Jilin Science and Technology Press, China

Apriliani, E., Sanjoyo, B.A., Adzkiya, D., 2011a, “The Groundwater Pollution Estimation by the Ensemble Kalman Filter”, Canadian Journal on Science and Engineering Mathematics, Juni, 2011

Apriliani, E. Hanafi, L., Wahyuningsih, N.,2011b, “Metode Estimasi Penyebaran Polutan”, Jurnal Purifikasi, Vol 12. No 2, Desember 2011

Evensen, G. 2003. The Ensemble Kalman Filter: Theoretical Formulation and Practical Implementation. J. Ocean Dynamics. Vol.53. No.4, 343-367.




DOI: https://doi.org/10.35314/isi.v4i1.774

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