Work

Adaptive neural network fault-tolerant sliding mode control for ship berthing with actuator faults and input saturation

Year: 2025

Type: article

Abstract: This paper develops a robust controller for automatic ship berthing subject to actuator faults, input saturation, modeling uncertainties, and external disturbances. First, sliding mode control (SMC) is used as a core controller to provide robust features for the ship berthing system. Second, fault-tolerant control (FTC) is combined with the controller to face the actuator faults. Third, radial basis function (RBF) neural network is employed to approximate the modeling uncertainties while the effect of external disturbances is compensated by an adaptive control technique. In addition, an anti-saturation auxiliary system is conducted to deal with the input saturation for physical limitations of the actuators. Finally, numerical simulation and comparison of the results with the other control approaches, such as sliding mode control (SMC), adaptive neural network sliding mode control (ANNSMC), are carried out to highlight the advantages of the proposed controller.

Source: International Journal of Naval Architecture and Ocean Engineering

DOI: 10.1016/j.ijnaoe.2025.100644

Authors: Son Tung Vu, Thai Duong Nguyen, Hai Van Dang, Van Suong Nguyen

Institution: Vietnam Maritime University

Language: English

Topic: Adaptive Control of Nonlinear Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,200

Funder: Trường Đại học Hàng hải Việt Nam