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

This paper develops a robust controller for automatic ship berthing subjected 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, the 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 are carried out to highlight the advantages of the proposed controller.