文献类型：期刊文献

英文题名：Barrier function-based adaptive fixed-time ADLESN prescribed performance tracking control for UUVs with input saturation

作者：Qi, Zhenghong[1,2];Liu, Haitao[1,2,3];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 Engn Technol Res Ctr Ocean Equipment & M, Guangdong Prov Key Lab Intelligent Equipment South, Zhanjiang 524088, Peoples R China

年份：2024

卷号：302

外文期刊名：OCEAN ENGINEERING

收录：SCI-EXPANDED(收录号：WOS:001222152200001)、、EI(收录号：20241515861997)、Scopus(收录号：2-s2.0-85189495041)、WOS

基金：Acknowledgments This work was supported by the Shenzhen Science and Technology Program [grant number JCYJ20220530162014033] , the Key Project of Department of Education of Guangdong Province [grant number 2021ZDZX1041,2023ZDZX1005] , the National Natural Science Foun-dation of China [grant number 62171143] , Guangdong Basic and Applied Basic Research Foundation [grant number 2024A1515011345] , and the Science and Technology Planning Project of Zhanjiang City [grant numbers 2021A05023 and 2021E05012] .

语种：英文

外文关键词：Adaptive practical fixed-time control; ADLESN; Input saturation; Prescribed performance; Unmanned underwater vehicle

外文摘要：This work addresses the adaptive fixed -time trajectory tracking problem for an unmanned underwater vehicle (UUV) under asymmetric input saturation, modeling uncertainty, ocean current disturbances, and output constraints. A novel fixed -time performance function is designed, and a control framework based on an asymmetric barrier function and prescribed performance control (PPC) is constructed. The tracking performance is ensured based on this framework, and the error caused by the error transformation necessary for the PPC strategy is also considered. Then, a practical fixed -time theorem is introduced, based on which a fixed -time adaptive filter is designed to avoid the " complexity explosion " problem. Then, an adaptive double -layer echo state network (ADLESN) that enhances the sparsity of the output weights is proposed, and the parameters of the activation function of the network can be adjusted adaptively. The ADLESN is employed to estimate the upper bounds on the system concentration of uncertainty, and an adaptive error compensation system is designed for the network to improve the approximation accuracy. Furthermore, the convergence of all errors in fixed time is verified by employing Lyapunov stability analysis, and the potential singularity problem is avoided. Finally, comparative experiments based on numerical simulations are presented to demonstrate the benefits of the algorithm.