Fault reconstruction and prescribed-time fault-tolerant control for dynamic positioning vessels based on iterative learning and sliding mode techniques

Considering thruster faults and unknown external environmental disturbances, this paper proposes a prescribed-time fault-tolerant control scheme for a dynamic positioning (DP) vessel based on the fault reconstruction strategy. Firstly, a sliding mode iterative learning observer (SMILO) is designed to reconstruct thruster faults by combining iterative learning strategy with sliding mode technique. Then, utilising the reconstructed fault information, a prescribed-time iterative learning sliding mode fault-tolerant controller is further developed, which adaptively compensates for unknown disturbances and reconstruction errors to improve tracking accuracy. Furthermore, the prescribed-time sliding mode surface ensures that the DP vessel's motion states converge at a preset time, allowing for more flexible adjustment of the system's convergence time based on actual operational requirements. The stability of the SMILO and control scheme is analysed by Lyapunov theory, and simulation results verify the effectiveness of the proposed methods.