文献类型：期刊文献

英文题名：Finite-Time Prescribed Performance Trajectory Tracking Control for Underactuated Autonomous Underwater Vehicles Based on a Tan-Type Barrier Lyapunov Function

作者：Liu, Haitao[1,2];Meng, Bingxin[1];Tian, Xuehong[1,2]

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：10

起止页码：53664

外文期刊名：IEEE ACCESS

收录：SCI-EXPANDED(收录号：WOS:000801976200001)、、EI(收录号：20222112149324)、Scopus(收录号：2-s2.0-85130436098)、WOS

基金：This work was supported in part by the 2019 "Chong First-Class'' Provincial Financial Special Funds Construction Project under Grant 231419019, in part by the Key Project of Department of Education of Guangdong Province under Grant 2021ZDZX1041, and in part by the Science and Technology Planning Project of Zhanjiang City under Grant 2020B01267 and Grant 2021E05012.

语种：英文

外文关键词：Trajectory tracking; Oceans; Vehicle dynamics; Uncertainty; Autonomous underwater vehicles; Lyapunov methods; Adaptive systems; Finite-time stability; trajectory tracking; tan-type barrier Lyapunov function; minimal learning parameter; underactuated autonomous underwater vehicles

外文摘要：This paper proposed a finite-time prescribed performance control scheme for underactuated autonomous underwater vehicles (AUVs) based on adaptive neural networks and a tan-type barrier Lyapunov function. Even in the presence of output constraints and environmental disturbances, the AUV can also precisely track the desired trajectory in a finite time. By introducing a tan-type barrier Lyapunov Function (TBLF), the singularity problem in process design is solved and all output errors are guaranteed to satisfy the prescribed performance specifications. Dynamic surface control (DSC) and the minimal learning parameter (MLP) are employed to greatly simplify the complexity of the algorithm and enhance the robustness of the control system, respectively. Lyapunov stability analysis proves that the proposed controller guarantees all signals in the closed-loop system to be uniformly ultimately bounded (UUB), and that the tracking errors converge to a small neighborhood near the origin in a finite time. Finally, the simulation results demonstrate the effectiveness and feasibility of the proposed controller.