文献类型：期刊文献

英文题名：Fuzzy neural network adaptive AUV control based on FTHGO

作者：Yu, Guoyan[1,2];He, Feiyang[1,3,4,5];Liu, Haitao[1]

机构：[1]Guangdong Ocean Univ, Sch Mech Engn, Zhanjiang, Peoples R China;[2]Southern Marine Sci & Engn Guangdong Lab Zhanjiang, Zhanjiang, Peoples R China;[3]Guangdong Prov Marine Equipment & Mfg Engn Technol, Zhanjiang, Peoples R China;[4]Guangdong Ocean Univ, Sch Mech Engn, Zhanjiang 524088, Peoples R China;[5]Guangdong Prov Marine Equipment & Mfg Engn Technol, Zhanjiang 524088, Peoples R China

年份：2024

外文期刊名：SHIPS AND OFFSHORE STRUCTURES

收录：SCI-EXPANDED(收录号：WOS:001189414700001)、、EI(收录号：20241415844029)、Scopus(收录号：2-s2.0-85189180076)、WOS

基金：This work was supported by the Guangdong Inter-regional Collaborative Fund (No. 2019B1515120017).

语种：英文

外文关键词：AUV; trajectory tracking; fixed-time high gain state observer; fuzzy radial basis function neural network; fixed-time backstepping controller; first-order fixed-time filter

外文摘要：A fuzzy radial basis function neural network (Fuzzy RBFNN) adaptive control scheme, based on fixed-time high gain state observer (FTHGO), is proposed to address the unpredictability of real-time state and composite interference in the trajectory tracking of the fully driven Autonomous Underwater Vehicle (AUV), ensuring fixed-time system convergence regardless of initial conditions. Firstly, a fixed-time backstepping controller is designed and a first-order fixed-time filter is introduced to tackle the differential explosion issue. Secondly, an FTHGO is developed to observe the real-time states of the AUV without assuming global known state signal. Then, the composite interference in the AUV system is effectively compensated by integrating the Fuzzy RBFNN technique. Finally, the fixed-time stability of the entire closed-loop system is proven utilising the Lyapunov stability theory. The effectiveness of the proposed algorithm is proved by the simulation experiment.