文献类型：期刊文献

英文题名：Predefined-time prescribed performance second-order sliding mode path following control for underactuated marine surface vehicles using self-structuring NN

作者：Liu, Haitao[1,2,3];Zhou, Xuecheng[1];Tian, Xuehong[1,2,3];Mai, Qingqun[1,2,3];Sun, Ning[1]

机构：[1]Guangdong Ocean Univ, Sch Mech Engn, Zhanjiang, Peoples R China;[2]Guangdong Ocean Univ, Shenzhen Inst, Shenzhen, Peoples R China;[3]Guangdong Engn Technol Res Ctr Ocean Equipment & M, Guangdong Prov Key Lab Intelligent Equipment South, Zhanjiang, Peoples R China

年份：2024

卷号：309

外文期刊名：OCEAN ENGINEERING

收录：SCI-EXPANDED(收录号：WOS:001254180900001)、、EI(收录号：20242416255366)、Scopus(收录号：2-s2.0-85195678233)、WOS

基金：This work was supported by the Key Project of the Science and Technology Program of Guangzhou [grant number 202002030243] , the Department of Education of Guangdong Province [grant numbers 2023ZDZX1005, 2021ZDZX1041] , the Shenzhen Science and Technol- ogy Program [grant number JCYJ20220530162014033] , the Guang- dong Basic and Applied Basic Research Foundation [grant number 2024A1515011345] , the National Natural Science Foundation of China [grant number 52171346] , and the Science and Technology Planning Project of Zhanjiang City [grant numbers 2021A05023 and 2021E05012] .

语种：英文

外文关键词：Underactuated MSV; Path following control; Predefined -time prescribed performance; Self -structuring neural network; Second -order sliding mode

外文摘要：This paper proposes a predefined time-prescribed performance second-order sliding mode path following controller for underactuated marine surface vehicles (MSVs) with unknown external environmental disturbances based on predefined time predictor line-of-sight (PTPLOS) guidance. First, a predefined time predictor is designed to address the problem of time-varying sideslip angles, and then a guidance law is designed. Second, a prescribed performance function with predefined times is proposed, which can not only set the rate of convergence and the final convergence range but also set an upper bound on the convergence time. Third, the predefined-time controller is designed in combination with second-order sliding mode control. Then, a selfstructuring neural network (SSNN) is developed to approximate external disturbances and uncertainties, and this approach can adaptively adjust the number of neurons, reducing the computational burden while maintaining high approximation accuracy. After that, the closed-loop system is proven to be predefined-time stable by using of the Lyapunov stability theory. Finally, the control method proposed in this paper is validated through numerical simulations, demonstrating its effectiveness.