文献类型：期刊文献

英文题名：Cooperative formation control for multiple surface vessels based on barrier Lyapunov function and self-structuring neural networks

作者：Liu, Haitao[1,2];Chen, Guangjun[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 524000, Peoples R China

年份：2020

卷号：216

外文期刊名：OCEAN ENGINEERING

收录：SCI-EXPANDED(收录号：WOS:000596863000114)、、EI(收录号：20204109307042)、Scopus(收录号：2-s2.0-85091906256)、WOS

基金：This work was supported by the 2019 "Chong First-class" Provincial Financial Special Funds Construction Project [grant number 231419019], the Natural Science Foundation of Guangdong Province China [grant number 2018A0303130076], the Science and Technology Planning Project of Zhanjiang City [grant number 2020B01267, 2018A01019], the Fostering Plan for Major Scientific Research Projects of the Education Department of Guangdong Province -Characteristic Innovation Projects (grant number 2017KTSCX087), and the Fund of Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang) (grant number ZJW-2019-01).

语种：英文

外文关键词：Surface vessels; Cooperative formation control; Barrier Lyapunov function; Self-structure neural networks; Adaptive control

外文摘要：This paper studies the cooperative formation control problem of multiple surface vessels based on the barrier Lyapunov function and self-structuring neural networks with uncertainties and unknown disturbances. To constrain and prevent a consistency error that is too large, a tan-type barrier Lyapunov function is employed to dynamically restrain the formation of tracking errors. To handle the model uncertainties, self-structured neural networks are used to approximate the unknown parameters of the dynamics model and to avoid large computational burden. An adaptive law is developed to estimate and compensate for unknown disturbances and neural network approximation errors. Under the proposed distributed cooperative formation control law, formation behavior among vessels can be achieved through any directed communication topological network and an inaccurate model of each vessel. All signals in the closed-loop system are proven to be semi-globally uniformly ultimately bounded on the initial bounded conditions, and the formation tracking error converges to a small neighborhood of origin. The simulations evaluate the performance of the proposed controller, and verify the effectiveness of the proposed method.