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Abstract
In this paper, we solve the trajectory tracking control problem of autonomous underwater vehicles (AUVs) with actuator faults and model uncertainties. In order to achieve the output tracking with prescribed transient performance, we develop a novel tan-type barrier Lyapunov function (BLF)-based funnel tracking control strategy to constrain the position tracking error of the AUV within prespecified performance funnels. Furthermore, to maintain the predesigned transient tracking performance of the AUV when the actuator fault occurs, an event-triggered adaptive fuzzy fault-tolerant control (FTC) scheme is proposed to compensate actuator faults. Meanwhile, to handle the uncertainties caused by the system model, fuzzy logic systems (FLSs) are adopted to approximate the unknown hydrodynamic parameters and actuator fault parameters. Different from the existing results, the communication burden and the excess wear from the controller to the actuators are reduced utilising the event-triggered mechanism. By using the theory of Lyapunov stability, it is proven that all the signals of the closed-loop system are semiglobally uniformly bounded. Finally, the effectiveness of the proposed method is demonstrated by a simulation example.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability
All data generated or analysed during this study are included in this published article.
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Funding
Notes on contributors
Yuanbo Su
Yuanbo Su received the B.S. degree in electrical engineering and its automation from Zhonghuan Information College Tianjin University of Technology, Tianjin, China, in 2019. He is currently pursuing the M.S. degree in control theory and control engineering with Bohai University, Jinzhou, China. His current research interests include adaptive cooperative control, fixed-time control and autonomous underwater vehicle systems.
Hongjing Liang
Hongjing Liang received the B.S. degree in mathematics from Bohai University, Jinzhou, China, in 2009, the M.S. degree in fundamental mathematics from Northeastern University, Shenyang, China, in 2011, and Ph.D degree in control theory and control engineering from Northeastern University, Shenyang, China, in 2016. He was a Temporary Research Associate with the Science Program, Texas A&M University, Doha, Qatar. His research interests include adaptive control, fuzzy control, multi-agent systems and their applications. He was a recipient of the Best Paper Award in Theory from ICCSS 2017 and the Outstanding Reviewer Award of CAA/Automatica Sinica 2019, respectively. He has been in the editorial board of International Journal of Fuzzy Systems, Fluctuation and Noise Letters.
Yingnan Pan
Yingnan Pan received the B.S. degree in mathematics and applied mathematics and the M.S. degree in applied mathematics from Bohai University, Jinzhou, China, in 2012 and 2015, respectively, and the Ph.D. degree in navigation guidance and control from Northeastern University, Shenyang, China, in 2019. He is currently a Lecturer with Bohai University, Jinzhou, China. His current research interests include fuzzy control, robust control, event-triggered control and their applications.
Duxin Chen
Duxin Chen received the B.Sc. and Ph.D. degrees in control science and engineering from the Huazhong University of Science and Technology, Wuhan, China, in 2013, and 2018, respectively. He is currently a Lecturer with the School of Mathematics, Southeast University, Nanjing, China. His research interests include complex networks, distributed control, and intelligent transportation systems. He was a recipient of the 2016 Outstanding Reviewer Award of the Asian Journal of Control. He serves as an active reviewer for many journals.