Zakariya, M. Damar (2026) Integrasi Unscented Kalman Filter Dan Kontrol PID Cascade Untuk Navigasi Dan Manuver AUV Pada Lingkungan Laut Tidak Pasti. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Ketidakpastian lingkungan laut, seperti arus laut stokastik dan noise sensor, menjadi tantangan utama dalam sistem navigasi dan manuver Autonomous Underwater Vehicle (AUV). Gangguan tersebut menyebabkan penurunan akurasi estimasi keadaan (state estimation) sehingga berdampak pada meningkatnya tracking error dan menurunnya kestabilan sistem kontrol. Penelitian ini bertujuan merancang dan mengintegrasikan algoritma Unscented Kalman Filter (UKF) dengan kontrol PID Cascade untuk meningkatkan akurasi estimasi state serta performa navigasi dan manuver AUV pada lingkungan laut yang tidak pasti. Sistem dikembangkan menggunakan model AUV 5-Degree of Freedom (5-DOF) yang meliputi pemodelan kinematika, dinamika, dan hidrodinamika. UKF digunakan untuk mengestimasi posisi, orientasi, kecepatan translasi, dan laju sudut berdasarkan data pengukuran dari IMU, GPS, depth sensor, dan sensor kecepatan, sedangkan PID Cascade diterapkan sebagai pengendali dua tingkat (outer loop dan inner loop) untuk mengatur lintasan, kedalaman, heading, serta kecepatan AUV. Integrasi kedua metode diimplementasikan pada MATLAB/Simulink dengan skenario pengujian berupa gangguan arus laut stokastik dan noise sensor. Melalui pendekatan ini diharapkan diperoleh estimasi state yang lebih akurat, respons kontrol yang lebih stabil, serta kemampuan pelacakan lintasan yang lebih baik sehingga AUV mampu beroperasi secara andal pada kondisi lingkungan bawah laut yang tidak pasti.
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Uncertainties in the marine environment, such as stochastic ocean currents and sensor noise, pose major challenges for the navigation and maneuvering systems of Autonomous Underwater Vehicles (AUVs). These disturbances degrade state estimation accuracy, leading to increased tracking errors and reduced control system stability. This research aims to design and integrate an Unscented Kalman Filter (UKF) algorithm with Cascade PID control to enhance state estimation accuracy and improve AUV navigation and maneuvering performance in uncertain marine environments. The system is developed using a 5-Degree-of-Freedom (5-DOF) AUV model, encompassing kinematics, dynamics, and hydrodynamics. The UKF estimates position, orientation, translational velocity, and angular rate based on data from IMU, GPS, depth, and speed sensors, while Cascade PID control comprising outer and inner loops regulates the AUV's trajectory, depth, heading, and speed. The integration of these methods is implemented in MATLAB/Simulink, with testing scenarios incorporating stochastic ocean current disturbances and sensor noise. This approach is expected to yield more accurate state estimation, a more stable control response, and superior trajectory tracking capabilities, enabling the AUV to operate reliably under uncertain underwater conditions.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Autonomous Underwater Vehicle , Unscented Kalman Filter, Kontrol PID Cascade, Navigasi, Error Tracking, Autonomous Underwater Vehicle, Unscented Kalman Filter, PID Cascade Control, Navigation, Tracking Error. |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK6592.A9 Automatic tracking. |
| Divisions: | Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20201-(S1) Undergraduate Thesis |
| Depositing User: | M. Damar Zakariya Budi Setiawan |
| Date Deposited: | 28 Jul 2026 02:01 |
| Last Modified: | 28 Jul 2026 02:01 |
| URI: | http://repository.its.ac.id/id/eprint/138232 |
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