Surge Speed and Yaw Angle Control System Design On Unmanned Surface Vehicle (USV) Using PID-Fuzzy-Genetic Algorithm

Cendikiarani, Mutiara Tawakkal (2024) Surge Speed and Yaw Angle Control System Design On Unmanned Surface Vehicle (USV) Using PID-Fuzzy-Genetic Algorithm. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

In this era of increasing technological and communication advances, human work is increasingly helped by the presence of technology, one of which is the advancement of Unmanned Surface Vehicle (USV). This research focuses on the design and implementation of control systems for Unmanned Surface Vehicles (USVs), which specifically address surge speed and yaw angle. One of the types of thrusters used in USVs is the Azimuth Thruster which indicates that the USV does not use steering leaves. The controller development carried out in this study is optimizing the PID-Fuzzy controller from previous research. The optimization method used is a genetic algorithm. Fuzzy used to adjust gain K_p,τ_i,τ_d whose design uses two inputs, namely error and delta error and output in the form of gain in the form of K_p,τ_i,τ_d . The genetic algorithm works to optimize the membership function of PID-Fuzzy where the results of the optimization will show the smallest error values in the control of the surge speed and yaw angle. FIS (Fuzzy Inference System) of the best generation then carried out closed loop trials on both control systems. The simulation results presented show that this method succeeded in reducing cross-track error (XTE) at the yaw angle to 62022 compared to PID and PID-Fuzzy controllers of 62199 and 62192 respectively. The number of error areas at surge speeds also decreased in the PID-Fuzzy-Genetic Algorithm controller by as much. As shown, the intended method has the lowest error and can reduce until 7.1577% and reducing %Overshoot and speed up rise time. The best generation generated by genetic algorithms is able to make USVs cross the track quite well compared to other controllers.
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Di zaman kemajuan teknologi dan komunikasi yang terus meningkat ini, pekerjaan manusia semakin terbantu dengan hadirnya teknologi salah satunya berupa kemajuan Unmanned Surface Vehicle (USV). Penelitian ini berfokus pada desain dan implementasi sistem kontrol untuk Unmanned Surface Vehicles (USV), yang secara khusus menangani kecepatan surge dan sudut yaw. Tipe pendorong yang digunakan pada USV salah satunya adalah Azimuth Thruster yang menandakan USV tersebut tidak menggunakan daun kemudi. Pengembangan kontroler yang dilakukan pada penelitian ini adalah mengoptimisasi kontroler PID-Fuzzy dari penelitian sebelumnya. Metode pengoptimisasian yang digunakan adalah algoritma genetika. Fuzzy yang digunakan mengatur gain K_p,τ_i,τ_d . yang perancangannya menggunakan dua input yaitu error dan delta error dan output berupa gain berupa K_p,τ_i,τ_d . Algoritma genetika berkerja untuk mengoptimisasi membership function dari PID-Fuzzy dimana hasil dari optimisasi akan menunjukkan nilai error terkecil pada kontrol kecepatan surge dan sudut yaw. FIS (Fuzzy Inference System) dari generasi terbaik kemudian dilakukan uji coba closed loop pada kedua system kontrol. Hasil simulasi yang disajikan menunjukkan bahwa metode ini berhasil mereduksi cross-track error (XTE) pada sudut yaw menjadi sebesar 62022 dibandingkan dengan kontroler PID dan PID-Fuzzy yang masing-masing sebesar 62199 dan 62192. Jumlah area eror pada kecepatan surge juga mengalami penurunan pada kontroler yang dimaksud sebanyak 7.1577% . Seperti yang ditunjukkan, metode yang dimaksudkan memiliki kesalahan terendah dan dapat mengurangi %Overshoot dan mempercepat rise time. Generasi terbaik yang dihasilkan algoritma genetika mampu membuat USV melintasi lintasan dengan cukup baik dibandingkan pengontrol lainnya.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	USV, PID-Fuzzy, Genetic Algorithm, Surge Speed, Yaw Angle
Subjects:	T Technology > T Technology (General) > T57.8 Nonlinear programming. Support vector machine. Wavelets. Hidden Markov models. T Technology > T Technology (General) > T57.84 Heuristic algorithms. V Naval Science > VM Naval architecture. Shipbuilding. Marine engineering
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20201-(S1) Undergraduate Thesis
Depositing User:	Mutiara Tawakkal Cendikiarani
Date Deposited:	02 Feb 2024 08:32
Last Modified:	02 Feb 2024 08:32
URI:	http://repository.its.ac.id/id/eprint/105997

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