Perancangan Sistem Kontrol Tug Boat Berbabsis Adaptive Neural Network Untuk Pengendalian Berthing Kapal Tongkang.

Ichsan, Farid Abdul Aziz (2022) Perancangan Sistem Kontrol Tug Boat Berbabsis Adaptive Neural Network Untuk Pengendalian Berthing Kapal Tongkang. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Industri kapal di Indonesia merupakan salah satu industri yang diandalkan karena wilayah perairan yang luas. Pada tugas akhir ini telah dirancang sistem pengendalian tug boat berbasis Adaptive Neural Network Brandt-Lin yang memungkinkan adaptasi tanpa fase oleh neuron untuk pengendalian berthing kapal tongkang. Hal ini bertujuan untuk membuat sistem kendali otomatis yang dapat meminimalisir error trayektori pada kapal saat melakukan berthing. Pada simulasi, dirancang sistem pengendalian gerak yaw dan pengendalian jarak antar kapal yang terintegrasi terhadap kedua kapal, agar hasil simulasi mendekati kondisi nyata, diberikan gangguan angin dengan variasi kecepatan dan sudut angin. Hasil menunjukkan bahwa simulasi sistem kontrol kapal tongkang dengan kapal tug boat dapat mengikuti trayektori yang telah ditentukan dan menjaga jarak sebesar 50 m. Hasil simulasi tanpa gangguan diperoleh nilai error lintasan maksimal untuk kapal tug boat dan tongkang berturut-turut sebesar 0.99062 m dan 0.859439 m, dengan nilai minimum sebesar 0.008 m dan 0.020785 m. Sedangkan hasil dengan gangguan angin diperoleh nilai error lintasan berturut maksimum 1.108355 m dan 1.1460 m
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Indonesia’s ship industry is one of the most relied fields. Therefore, Indonesia has the potential to develop a more modern water infrastructure. In this final project, a tug boat control system based on ANN Brandt-lin has been designed which allows phaseless adaptation to control the berthing of barge ships. This aims to create an automatic control system that minimize trajectory errors on ships when berthing occurs. An integrated yaw control system and distance control between the two ships are designed, to approach the real condition, wind disturbances with variations in wind speed and angle has been applied. The results show that the ships can follow a predetermined trajectory and maintain a distance of 50 m. Simulation results without interference obtained maximum trajectory error values for tug boats and barges of 0.99062 m and 0.859439 m, respectively, with a minimum value of 0.008 m and 0.020785 m. While the results with wind disturbance obtained a maximum successive path error value of 1.108355 m and 1.1460 m

Item Type:	Thesis (Other)
Additional Information:	RSF 629.836 Ich p-1 2022
Uncontrolled Keywords:	Adaptive Neural Network, Berthing, Brandt-Lin, Kapal, Sistem Pengendalian. ANN, Berthing, Brandt-Lin, Control system, Ships.
Subjects:	T Technology > TJ Mechanical engineering and machinery > TJ217 Adaptive control systems
Divisions:	Faculty of Industrial Technology and Systems Engineering (INDSYS) > Physics Engineering > 30201-(S1) Undergraduate Thesis
Depositing User:	Mr. Marsudiyana -
Date Deposited:	11 May 2026 08:17
Last Modified:	11 May 2026 08:17
URI:	http://repository.its.ac.id/id/eprint/133133

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