文献类型：期刊文献

英文题名：Robust adaptive self-structuring neural networks tracking control of unmanned surface vessels with uncertainties and time-varying disturbances

作者：Liu, Haitao[1,2];Wang, Zhicheng[1];Tian, Xuehong[1]

机构：[1]Guangdong Ocean Univ, Sch Mech & Power Engn, Zhanjiang 524088, Peoples R China;[2]Southern Marine Sci & Engn Guangdong Lab Zhanjian, Zhanjiang, Peoples R China

年份：2022

卷号：32

期号：6

起止页码：3334

外文期刊名：INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL

收录：SCI-EXPANDED(收录号：WOS:000743076200001)、、EI(收录号：20220311471717)、Scopus(收录号：2-s2.0-85122723005)、WOS

基金：Science and Technology Planning Project of Zhanjiang City, Grant/Award Number: 2020B01267; the Fund of Southern Marine Science and Engineering Guangdong Laboratory, Grant/Award Number: ZJW-2019-01; 2019 "Chong First-class" Provincial Financial Special Funds Construction Project, Grant/Award Number: 231419019; the Key Project of DEGP, Grant/Award Number: 2021ZDZX1041; The Natural Science Foundation of Guangdong Province China, Grant/Award Number: 2018A0303130076; Zhanjiang innovation and entrepreneurship team lead "pilot plan" project, Grant/Award Number: 2020LHJH003

语种：英文

外文关键词：finite-time stability; high-gain observer; robust H-infinity; self-structuring neural networks; unmanned surface vessels

外文摘要：The trajectory tracking control problem of unmanned surface vessels (USVs) with uncertainties and time-varying disturbances is investigated. A robust adaptive trajectory tracking control scheme is proposed based on finite-time H-infinity control and a self-structuring neural networks (SSNN) identifier, which can obtain satisfactory performance with an L-2 norm-bounded, expected attenuation level within a finite time. The SSNN is developed to approximate USVs system uncertainties and external disturbances by online learning. Most importantly, a balance is achieved between the optimal number of neurons and the expected performance, which saves significant network resources. The Lyapunov stability analysis shows that the scheme ensures convergence of the tracking error to a small neighborhood around zero in finite time, while all the other closed-loop signals remain bounded. Moreover, the application of a high-gain observer effectively reduces the cost of velocity sensors. The feasibility and effectiveness of this control scheme are verified by theorem analysis and numerical simulations.