Event-triggered adaptive predefined-time sliding mode control of autonomous surface vessels with unknown dead zone and actuator faults

In this paper, an adaptive predefined-time fault tolerant sliding mode control (SMC) method for a tracking problem of autonomous surface vessels (ASVs) subject to unknown input dead zones and actuator faults is developed. First, a predefined-time sliding variable is constructed to ensure that the tracking errors of the ASV can converge to a bounded region in a predefined time. Second, an event-triggered mechanism is designed to reduce the actuator wear and energy consumption of the ASV. Subsequently, the neural network (NN) is employed to approximate the lumped uncertainties. A novel updated law for weights of the NN is proposed to guarantee an accurate approximation of the lumped uncertainties. In addition, an adaptive predefined-time SMC law is proposed according to the designed update law. The practical predefined-time stability of the closed-loop system is proven by using the Lyapunov method. Finally, the performance and superiority of the proposed control strategy are demonstrated by numerical simulation results.