Evaluation of autonomous underwater vehicle motion trajectory optimization algorithms

Abdullah Hussein Abdullah Al-Amoodi

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Type :	article
Subject :	T Technology (General)
ISSN :	0950-7051
Main Author :	Abdullah Hussein Abdullah Al-Amoodi
Title :	Evaluation of autonomous underwater vehicle motion trajectory optimization algorithms

Place of Production :	Tanjung Malim
Publisher :	Fakulti Komputeran dan Meta Teknologi
Year of Publication :	2023
Notes :	Knowledge-Based Systems
Corporate Name :	Universiti Pendidikan Sultan Idris
HTTP Link :	Click to view web link

Abstract : Universiti Pendidikan Sultan Idris

The operation of autonomous underwater vehicles (AUVs) relies on three major motions: yaw, theta, and depth, each requiring its own set of proportional integral derivative (PID) controller gain criteria. Thus, different issues arise, including the availability of multiple criteria for optimization algorithm evaluation, the importance of these criteria, the trade-off between criterion performance, and criterion critical values. These issues make the evaluation of optimization algorithms for AUV motion control a complex multicriteria decision-making (MCDM) problem. This research proposes a novel selection-integrated approach for AUV optimization algorithms in different motions using two MCDM methods: fuzzy-weighted zero-inconsistency (FWZIC) for criteria weighting and fuzzy decision by opinion score method (FDOSM) for optimization algorithm selection. The approach comprises three main phases: development of PID, FWZIC-based criteria weighting, and FDOSM-based optimization algorithm selection. In all three motion types depth, yaw, and theta Kp_? had the highest weight value, with respective weights of 0.143, 0.149, and 0.142. In contrast, Ki_depth consistently received the lowest weight value across all three motion types, with respective weights of 0.057, 0.0598, and 0.057. Regarding the depth motion, the Archimedes optimization algorithm (AOA) was the highest-performing alternative with a score of 0.077, while the eagle strategyparticle swarm optimization algorithm was the worst alternative with a score of 0.022. The Cuckoo optimization algorithm was identified as the best alternative for the yaw motion with a score of 0.072, whereas black hole optimization had the lowest score of 0.036. The best alternative for the theta motion was AOA with a score of 0.067, and the worst algorithm was sunflower optimization with a score of 0.028. This developed approach was evaluated based on systematic ranking and sensitivity analysis, which confirmed the validity of the proposed work. 2023 The Author(s)

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