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Journal : Jambura Journal of Electrical and Electronics Engineering

Design of Attitude Holding System for Prototype Autonomous Surface Vehicle Using the ANFIS Method Cahyadi, Nurahmad Hadi; Endrasmono, Joko; Putra, Zindhu Maulana Ahmad; Khumaidi, Agus; Adhitiya, Ryan Yudha; Riananda, Dimas Pristovani
Jambura Journal of Electrical and Electronics Engineering Vol 6, No 2 (2024): Juli - Desember 2024
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v6i2.26023

Abstract

Autonomous Surface Vehicle (ASV) is surface-controlled vessel without a crew, designed to explore waters autonomously without direct human intervention. In its development, ASV ships often experience waypoint navigation problems such as ship speed controls, ship steering angle direction, and ship holding attitude systems. This research aims to design an attitude control system for an ASV that focuses on a position control system for changes due to waves, currents and wind when the ASV is carrying out a mission. In developing an intelligent attitude control system, two controls are implemented, namely rotation control and translation control. This system uses a CMPS14 sensor to determine the ship's orientation and rotational speed which is used as a rotational control variable and is then synchronized with Zed F9P GNSS RTK GPS data readings to predict the ASV position when it encounters external disturbances for translational control variables which are processed using the ANFIS (Adaptive Neuro) algorithm. Fuzzy Inference System) to predict the actuator response in maintaining ASV heading and position. The ANFIS model designed in this research is able to predict the bowthruster speed for guarding the post with an RMSE of 1.6169%, while the ANFIS model for predicting ship Vx and Vy has an RMSE of 0,1857%. Although influenced by non-linear data variations and the choice of MF data type, the Vx and Vy prediction value produced by the ANFIS model is close to precise.