Disturbance observer-based adaptive neural control for underactuated surface vehicle with constraint of input saturation

• Published in: Ocean engineering Vol. 287; p. 115744
• Main Authors: Liu, Ruilin, Zhang, Wenjun, Zhang, Guoqing, Zhang, Xianku
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2023
• Subjects: Adaptive disturbance observer; Dynamic surface control; Input saturation; Path following; Underactuated surface vehicle; Input saturation; Underactuated surface vehicle; Dynamic surface control; Adaptive disturbance observer; Path following
• ISSN: 0029-8018; 1873-5258
• DOI: 10.1016/j.oceaneng.2023.115744

In this paper, a robust adaptive neural control algorithm is provided to solve the problem of path-following control for underactuated surface vehicle (USV) subject to input saturation, in the presence of model uncertainties and unknown marine environmental disturbance. In the algorithm, through the application of neural network (NN), an adaptive disturbance observer (ADO) is proposed to observe the unknown disturbance, which does not need the precise information of the upper bound of the disturbance, and the model uncertainty can be appropriated simultaneously. Specially, since the ADO and control law share the same set of NN, the number of adaptive parameters of the controller can be greatly reduced. After that, dynamic surface control (DSC) method is used to avoid repeated derivative in the process of back-stepping, which solve the “calculation explosion” problem. An auxiliary system is designed to compensate errors caused by input saturation, which solve the input saturation problem of actuators. Through Lyapunov stability analysis, it is proved that all signals of the closed-loop system are semi-globally ultimately uniformly bounded (SGUUB). Finally, some experiments are conducted to demonstrate the effectiveness of the algorithm. •This paper proposes a disturbance observer-based adaptive neural control scheme for underactuated surface vehicle (USV) with the constraint of input saturation.•The neural networks are used to observe the unknown disturbance and tackle with the model uncertainty.•The minimum learning parameter (MLP) and dynamic surface control (DSC) technique are applied to release the computing memory.•The auxiliary system is designed to compensate the input saturation phenomenon.