Metode Adaptive Extended Kalman Filter Untuk Estimasi Kesalahan Aktuator Pada Kapal Perang Extended Corvette SIGMA

Afifah, Haura (2026) Metode Adaptive Extended Kalman Filter Untuk Estimasi Kesalahan Aktuator Pada Kapal Perang Extended Corvette SIGMA. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Aktuator merupakan salah satu komponen penting dalam sistem kendali kapal karena berpengaruh terhadap kemampuan manuver dan stabilitas gerak kapal. Kerusakan pada aktuator, khususnya propeller, dapat menyebabkan perubahan respons gerak kapal yang berpotensi menyebabkan kecelakaan sehingga diperlukan metode estimasi untuk mendeteksi adanya perubahan tersebut. Penelitian ini membahas implementasi metode Adaptive Extended Kalman Filter (AEKF) untuk mengestimasi variabel keadaan kapal dan parameter kesalahan aktuator pada kapal perang Extended Corvette SIGMA. Model matematika gerak kapal 3-DOF yang digunakan mencakup gerak surge, sway, dan
yaw. Data yang digunakan merupakan data hasil uji Free Running Model (FRM) dengan skenario manuver turning dan zigzag, serta variasi kondisi propeller rusak kanan,
propeller rusak kiri, dan kedua propeller rusak. Dalam implementasinya, mekanisme stabilisasi numerik ditambahkan untuk meningkatkan kestabilan numerik selama proses estimasi. Implementasi AEKF dievaluasi menggunakan tiga variasi forgetting factor, yaitu ξ = 0.999, ξ = 0.995, dan ξ = 0.991, yang berperan dalam mengatur respons adaptasi estimator terhadap perubahan parameter kesalahan aktuator. Data pengukuran yang digunakan meliputi posisi x, posisi y, dan sudut yaw, sedangkan kecepatan u, v, r, serta parameter kesalahan aktuator θ diperoleh sebagai hasil estimasi. Pada variabel x,y,ψ dilakukan perhitungan Root Mean Square Error (RMSE) untuk mengetahui performa AEKF. Hasil penelitian menunjukkan bahwa metode AEKF mampu menghasilkan estimasi besaran kesalahan aktuator. Hasil estimasi kesalahan aktuator θ oleh AEKF cenderung stabil, sementara RMSE pada variabel keadaan menunjukkan nilai yang cenderung kecil.
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Actuators are one of the main components in modern ship control systems, such as the Extended Corvette SIGMA warship. Malfunctions in the actuator can lead to degraded performance and system stability in navigation. Therefore, a fault diagnosis method is required to help detect faults occurring in the ship’s actuators. This study applies the Adaptive Extended Kalman Filter (AEKF) approach to estimate ship state variables and actuator fault parameters in the Extended Corvette SIGMA warship. This research focuses on diagnosing actuator faults in the Extended Corvette SIGMA warship using the AEKF approach based on a 3-DOF ship motion model consisting of surge, sway, and yaw. The experimental data were obtained from Free Running Model (FRM) testing with turning and zigzag maneuver scenarios under right propeller fault, left propeller fault, and both propellers fault conditions. In its implementation, numerical stabilization mechanisms were incorporated to enhance numerical stability throughout the estimation process. The AEKF implementation was evaluated using three forgetting factor values: ξ = 0.999, ξ = 0.995, and ξ = 0.991, to adjust the estimator adaptation response to actuator fault parameter changes. The measurement data consisted of x-position, y-position, and yaw angle, while the velocities u, v, and r, along with the actuator fault parameter θ, were obtained through estimation. The estimation results were evaluated by calculating the Root Mean Square Error (RMSE) of the x, y, and ψ variables to evaluate the performance
of the AEKF. The results show that the AEKF method is able to estimate actuator fault magnitude. The actuator fault estimation parameter θ obtained by the AEKF tends to be stable, while the RMSE values of the state variables are relatively small.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Adaptive Extended Kalman Filter, Estimasi kesalahan aktuator, Free Running Model, Kerusakan propeller, model 3-DOF. Adaptive Extended Kalman Filter, Actuator fault diagnosis, 3-DOF motion model, Free Running Model, Propeller damage.
Subjects:	Q Science > Q Science (General) > Q180.55.M38 Mathematical models Q Science > QA Mathematics > QA371 Differential equations--Numerical solutions Q Science > QA Mathematics > QA401 Mathematical models. Q Science > QA Mathematics > QA402.3 Kalman filtering. T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL152.8 Vehicles, Remotely piloted. Autonomous vehicles.
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Mathematics > 44201-(S1) Undergraduate Thesis
Depositing User:	Haura Afifah
Date Deposited:	29 Jul 2026 01:18
Last Modified:	29 Jul 2026 01:18
URI:	http://repository.its.ac.id/id/eprint/139292

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