Kendali Haluan Kapal dengan Menggunakan Modifikasi Model Predictive Control-Kalman Filter

Syafii, Ahmad Maulana (2019) Kendali Haluan Kapal dengan Menggunakan Modifikasi Model Predictive Control-Kalman Filter. Masters thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Pada tesis ini dibahas algoritma Modifikasi Model Predictive ControlKalman Filter (MPC-KF) untuk menyelesaikan permasalahan kendali haluan kapal. Noise yang melekat pada model sistem dipertimbangkan sehingga permasalahan MPC menjadi permasalahan stokastik dengan ke ndala berbentuk probabilistik. Kalman Filter digunakan untuk menggantikan prediksi yang dilakukan oleh Model Predictive Control. Selanjutnya kendala probabilistik diubah menjadi deterministik sehingga dapat diselesaikan dalam metode Modifikasi MPC-KF. Pada permasalahan ini, rudder digunakan untuk mengendalikan sudut hadap kapal sehingga mencapai sudut hadap yang diinginkan. Hasil simulasi menunjukkan bahwa pengendali Modifikasi MPCKF mampu membuat sudut hadap kapal mendekati sudut referensi yang diberikan. Dari 10 kali percobaan didapatkan performansi terbaik pada saat Np adalah 15 yang ditinjau dari Root Mean Square Error (RMSE). ================================================================================================ In this thesis, modified Model Predictive Control-Kalman Filter (MPCKF) algorithm is proposed to solve the ship heading control problem by considering the presence of noise in the system. Noise attached to the system model is considered so that the MPC problem becomes a stochastic problem with probabilistic constraints. Kalman Filter is used to replace predictions made by the Model Predictive Control. Probabilistic constraints are changed to deterministic constraints so that they can be solved in the MPC-KF Modification method. In this problem, rudder angle is used to control the ship heading angle so that it reaches the desired angle. Simulation results show that the modified MPC-KF control ler is able to drive teh ship heading angle to the reference angle. From the 10 experiments, the best performance is obtained when Np is 15 according to Root Mean Square Error (RMSE) criteria.

Item Type: Thesis (Masters)
Additional Information: RTMa 519.2 Sya k-1 2019
Uncontrolled Keywords: Model Predicitve Control (MPC), Kalman Filter, Probabilistic Constrain, Ship Heading Control
Subjects: Q Science > QA Mathematics > QA402.3 Kalman filtering.
T Technology > TJ Mechanical engineering and machinery > TJ217.6 Predictive Control
Divisions: Faculty of Science and Data Analytics (SCIENTICS) > Mathematics > 44101-(S2) Master Thesis
Depositing User: Syafii Ahmad Maulana
Date Deposited: 17 Jan 2022 07:25
Last Modified: 17 Jan 2022 07:25
URI: https://repository.its.ac.id/id/eprint/62074

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