文献类型：期刊文献

英文题名：Robust Adaptive Self-Structuring Neural Network Bounded Target Tracking Control of Underactuated Surface Vessels

作者：Liu, Haitao[1,2];Lin, Jianfei[1];Yu, Guoyan[1,2];Yuan, Jianbin[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 524000, Peoples R China

年份：2021

卷号：2021

外文期刊名：COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE

收录：SCI-EXPANDED(收录号：WOS:000738928100004)、、EI(收录号：20220311468960)、Scopus(收录号：2-s2.0-85122739702)、WOS

基金：This work was supported by the 2019 "Chong First-class" Provincial Financial Special Funds Construction Project (grant no. 231419019), the Natural Science Foundation of Guangdong Province, China (grant no. 2018A0303130076), the Key Project of Department of Education of Guangdong Province (grant no. 2021ZDZX1041), the Science and Technology Planning Project of Zhanjiang City (grant nos. 2020B01267 and 2021E05012), and the Fund of Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang) (grant no. ZJW-2019-01).

语种：英文

外文关键词：Computation theory - Uncertainty analysis - Adaptive control systems - Clutter (information theory) - Closed loop systems - Neural networks

外文摘要：This paper studies the target-tracking problem of underactuated surface vessels with model uncertainties and external unknown disturbances. A composite robust adaptive self-structuring neural-network-bounded controller is proposed to improve system performance and avoid input saturation. An extended state observer is proposed to estimate the uncertain nonlinear term, including the unknown velocity of the tracking target, when only the measurement values of the line-of-sight range and angle can be obtained. An adaptive self-structuring neural network is developed to approximate model uncertainties and external unknown disturbances, which can effectively optimize the structure of the neural network to reduce the computational burden by adjusting the number of neurons online. The input-to-state stability of the total closed-loop system is analyzed by the cascade stability theorem. The simulation results verify the effectiveness of the proposed method.