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Advanced neural control technique for autonomous underwater vehicles using modified integral barrier Lyapunov function
Ocean Engineering Số , năm 2022 (Tập 266, trang -)
ISSN: 298018
ISSN: 298018
DOI:
Tài liệu thuộc danh mục:
Article
English
Từ khóa: Autonomous underwater vehicles; Autonomous vehicles; Integral equations; Ocean currents; Uncertainty analysis; Adaptive neural control; Adaptive neural control technique; Autonomous underwater vehicle; Autonomous underwater vehicles]; Control techniques; Depth precision control; Disturbance observer; Extended disturbance observer; Input constraints; Lyapunov's functions; Modified integral barrier lyapunov function; Precision control; autonomous underwater vehicle; comparative study; computer simulation; control system; numerical model; oceanic current; Lyapunov functions
Tóm tắt tiếng anh
This paper presents a novel approach for depth precision control of under-actuated autonomous underwater vehicles (AUV) subject to model uncertainties, ocean currents, and input constraints. Specifically, a transformation is made to convert the input constraint problem into a state constraint problem. Subsequently, an observer-based guidance law is developed to deal with the drift affected by unknown ocean currents by using an extended disturbance observer (EDO). An adaptive neural controller is then designed using the DSC technique and an advanced modified integral barrier Lyapunov function (mIBLF) to guarantee that all states are confined within the given constraint. Besides, a novel nonlinear disturbance observer is introduced to cope with external disturbances and neural network approximation errors. It is proved that all closed-loop signals are uniformly ultimately bounded by Lyapunov stability theory. Finally, comparative simulations are carried out to verify the effectiveness and outstanding characteristics of the proposed method. 2022 Elsevier Ltd