Perancangan Sistem Kendali Gerak Autonomous Underwater Vehicle (AUV) pada Kondisi Gangguan Arus Laut Menggunakan Artificial Neural Network untuk Self-Tuning PID

Firdausi, Farah Qatrunnada Naurah (2023) Perancangan Sistem Kendali Gerak Autonomous Underwater Vehicle (AUV) pada Kondisi Gangguan Arus Laut Menggunakan Artificial Neural Network untuk Self-Tuning PID. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

AUV (Autonomous Underwater Vehicle) merupakan kendaraan bawah air yang didesain untuk menjangkau kedalaman lautan. Sistem kendali gerak AUV yang banyak digunakan saat ini adalah Proportional, Integral, and Derivative (PID). Akan tetapi, pengendali PID memiliki kekurangan yaitu pada gain yang tidak dapat diubah setelah proses tuning sehingga kurang cocok untuk mengendalikan sistem yang non-linier seperti AUV. Logaritma Artificial Neural Network (ANN) merupakan salah satu metode pemrosesan sinyal yang dapat memprediksi nilai dari parameter dinamis sebuah sistem. Pada penelitian ini, dirancang logaritma ANN untuk mempelajari perubahan dinamika AUV dan melakukan self-tuning parameter PID selama proses path tracking sehingga dapat mencapai lintasan yang diinginkan. Variable arah gerak AUV yang diamati dalam sistem kontrol gerak berbasis ANN PID adalah surge (kecepatan maju/mundur), heave (kecepatan naik/turun), pitch (sudut inklinasi longitudinal), dan yaw (sudut heading). Keempat variable tersebut adalah parameter penting yang mempengaruhi pergerakan AUV di bawah air. Hasilnya menunjukkan bahwa sistem pengendalian ANN PID terbukti efektif dalam mempertahankan kestabilan gerak Autonomous Underwater Vehicle (AUV) dalam kondisi tanpa gangguan maupun dengan gangguan arus laut dengan kecepatan 0.31 m/s. Hal tersebut dibuktikan dengan hasil respon dinamik masing-masing subsistem yang memiliki error yang lebih kecil dari hasil respon dinamik sistem pengendali PID konvensional.
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AUV (Autonomous Underwater Vehicle) is an underwater vehicle designed to reach the depths of the ocean. The AUV motion control system that is widely used today is Proportional, Integral, and Derivative (PID). However, the PID controller has the disadvantage that the gain cannot be changed after the tuning process, so it is not suitable for controlling non-linear systems such as AUV. Logarithmic Artificial Neural Network (ANN) is a signal processing method that can capture the values of the dynamic parameters of a system. In this study, a logarithmic ANN was designed to study changes in AUV dynamics and perform self-tuning of PID parameters during the path tracking process so that it can reach the desired trajectory. The AUV motion direction variables observed in this ANN PID-based motion control system are surge (forward/backward speed), heave (up/down speed), pitch (longitudinal inclination angle), and yaw (heading angle). These four variables are important parameters that affect the movement of the AUV under the air. The results show that the ANN PID control system is proven to be effective in maintaining the stability of the Autonomous Underwater Vehicle (AUV) in conditions without disturbance or interference with ocean currents with a speed of 0.31 m/s. This is evidenced by the results of the dynamic response of each subsystem which has a smaller error than the results of the dynamic response of the conventional PID controller system.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	AUV (Autonomous Underwater Vehicle), Artificial Neural Network, Path Tracking Control, PID, AUV (Autonomous Underwater Vehicle), Artificial Neural Network, Path Tracking Control, PID
Subjects:	Q Science > QA Mathematics > QA76.87 Neural networks (Computer Science) T Technology > TJ Mechanical engineering and machinery > TJ217 Adaptive control systems T Technology > TJ Mechanical engineering and machinery > TJ223 PID controllers V Naval Science > VM Naval architecture. Shipbuilding. Marine engineering > VM365 Remote submersibles. Autonomous vehicles.
Divisions:	Faculty of Industrial Technology and Systems Engineering (INDSYS) > Physics Engineering > 30201-(S1) Undergraduate Thesis
Depositing User:	Farah Qatrunnada Naurah Firdausi
Date Deposited:	24 Jul 2023 02:44
Last Modified:	24 Jul 2023 02:44
URI:	http://repository.its.ac.id/id/eprint/99029

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