Open AccessAdaptive neural network‐based fault‐tolerant trajectory‐tracking control of unmanned surface vessels with input saturation and error constraints
Author(s) -
Qin Hongde,
Li Chengpeng,
Sun Yanchao
Publication year - 2020
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2019.0221
Subject(s) - backstepping , control theory (sociology) , tracking error , fault tolerance , artificial neural network , actuator , trajectory , controller (irrigation) , lyapunov function , bounded function , computer science , control engineering , adaptive control , unmanned surface vehicle , fault (geology) , engineering , control (management) , nonlinear system , artificial intelligence , mathematics , mathematical analysis , distributed computing , physics , astronomy , quantum mechanics , seismology , marine engineering , geology , agronomy , biology
The unmanned surface vessel (USV) plays an important role in smart ocean. This study proposes an adaptive fault‐tolerant tracking control for USVs in the presence of input saturations and error constraints. A tan‐type barrier Lyapunov function is utilised for the error constraints and the neural networks are employed to treat the model uncertainty. Moreover, the adaptive technique combined with the backstepping method not only enables the actuator fault‐tolerant controller to address the fault effects but also handles the external disturbances and input saturations. The proposed control approach can track the desired trajectory with error constraints and the system is guaranteed to be uniformly bounded under certain actuator failure. Numerical simulation is carried out to verify the effectiveness of this control strategy.