文献类型：期刊文献

英文题名：Adaptive intelligent formation control for multiple underactuated surface vessels with prescribed performance

作者：Liu, Haitao[1,2,3];Huang, Xiuying[1];Tian, Xuehong[1,2,3];Yuan, Jianbin[1,2]

机构：[1]Guangdong Ocean Univ, Sch Mech Engn, Zhanjiang 524088, Peoples R China;[2]Guangdong Ocean Univ, Shenzhen Inst, Shenzhen 518120, Peoples R China;[3]Guangdong Res Ctr Reprod Control & Breeding Techn, Zhanjiang 524088, Peoples R China

年份：2024

卷号：296

外文期刊名：OCEAN ENGINEERING

收录：SCI-EXPANDED(收录号：WOS:001180015100001)、、Scopus(收录号：2-s2.0-85184342098)、WOS

基金：This work was supported by the Key Project of Department of Edu- cation of Guangdong Province [grant number 2021ZDZX1041, 2023ZDZX1005] , the Shenzhen Science and Technology Program [grant number JCYJ20220530162014033] , and the Science and Technology Planning Project of Zhanjiang City [grant numbers 2021A05023 and 2021E05012] .

语种：英文

外文关键词：Fuzzy wavelet cerebellar model articulation; controller; Bidirectional fuzzy wavelet brain emotional; learning controller; Wavelet function; Underactuated surface vessels; Formation control

外文摘要：To control a leader-follower formation in underactuated surface vessels (USVs) with model uncertainty and external disturbances, a control strategy combining a fuzzy wavelet cerebellar model articulation controller (FWCMAC) and bidirectional fuzzy wavelet brain emotional learning controller (BFWBELC) is proposed in this paper to maintain connections between the USVs. Combined with the barrier Lyapunov function performance constraint method, a performance function is introduced as the boundary function of the formation error to ensure the transient and steady-state performance of the formation control system. FWCMAC is taken as the formation guidance controller, and the online learning law is derived using the gradient descent method. The BFWBELC is considered as the main controller, and the online learning rules are constructed by combining the bidirectional reward mechanism. The wavelet function is introduced to improve the processing ability of the controller for the stimulus signal. In addition, an adaptive compensator is proposed to handle the residual error of the adaptive intelligence controller with respect to the ideal controller. Based on Lyapunov stability theory, it is proven that all signals in the formation control system are ultimately bounded. The numerical simulation results demonstrate the effectiveness of the proposed formation control system.