文献类型：期刊文献

英文题名：Neural network-based prescribed performance adaptive finite-time formation control of multiple underactuated surface vessels with collision avoidance

作者：Lin, Jianfei[1];Liu, Haitao[1,2];Tian, Xuehong[1]

机构：[1]Guangdong Ocean Univ, Sch Mech & Power Engn, Zhanjiang 524088, Peoples R China;[2]Guangdong Ocean Univ, Shenzhen Inst, Shenzhen 518120, Peoples R China

年份：2022

卷号：359

期号：11

起止页码：5174

外文期刊名：JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS

收录：SCI-EXPANDED(收录号：WOS:000899870800003)、、EI(收录号：20222612297185)、Scopus(收录号：2-s2.0-85132857492)、WOS

基金：This work was supported by the 2019 "Chong First-classd Provincial Financial Special Funds Construction Project [grant number 231419019], the Key Project of DEGP [grant number 2021ZDZX1041], the Science and Technology Planning Project of Zhanjiang City [grant number 2020B01267, 2021E05012], and the Zhanjiang innovation and entrepreneurship team lead "pilot plan" project [grant number 2020LHJH003].

语种：英文

外文关键词：Adaptive control systems - Closed loop systems - Errors - Uncertainty analysis

外文摘要：In this paper, a leader-follower formation control scheme of multiple underactuated surface vessels (USVs) is proposed for trajectory tracking, which not only solves the line of sight (LOS) and angle tracking errors within the prescribed performance, but also avoids collisions and maintains the communication connection distance. To achieve the prescribed performance and converge the tracking errors in finite time, a tan-type barrier Lyapunov function (TBLF) is introduced into the designed control strategy. In the process of formation control design, the measured values of the LOS range and angle are available, and the velocity of the leader is estimated using a high-gain observer. Next, a novel self-structuring neural network (SNN) is proposed to estimate the uncertain dynamics induced by the model uncertainties and environmental disturbances, and the computation amount is reduced by optimizing the number of neurons. Combining coordinate transformation and dynamic surface control (DSC), an adaptive NN controller with prescribed performance is proposed. The Lyapunov analysis shows that, although uncertain dynamics exist, the tracking errors can converge to a small region in finite time while achieving the prescribed performance, avoiding collisions, and maintaining the communication distance. In the closed-loop system, all signals are practical finite-time stable (PFS). Finally, the effectiveness of the proposed scheme is illustrated through a numerical simulation. (c) 2022 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.