Advanced neural control technique for autonomous underwater vehicles using modified integral barrier Lyapunov function

• Published in: Ocean engineering Vol. 266; p. 112842
• Main Authors: Thanh, Pham Nguyen Nhut, Anh, Ho Pham Huy
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.12.2022
• Subjects: Adaptive neural control technique; Autonomous underwater vehicles (AUV); Depth precision control; Extended disturbance observer (EDO); Input constraint; Modified integral barrier Lyapunov function (mIBLF); Extended disturbance observer (EDO); Depth precision control; Adaptive neural control technique; Input constraint; Autonomous underwater vehicles (AUV); Modified integral barrier Lyapunov function (mIBLF)
• ISSN: 0029-8018; 1873-5258
• DOI: 10.1016/j.oceaneng.2022.112842

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. •A new approach for depth control of AUV subject to model uncertainties, ocean currents, and input constraints is suggested.•A transform converts the input constraint into a state constraint problem. Then an EDO observer is constructed.•An adaptive neural learning law is designed using modified Lyapunov function (mIBLF) to ensure all states are confined.•A nonlinear disturbance observer is used to cope with external disturbances and neural-based approximation errors.•Comparative simulations are carried out to verify the effectiveness of the proposed neural control technique.