Adaptive Neural Sliding Mode Control for Ship Via the Offset Estimating and the Bounded Triggering
Aiming at the lateral migration problem of ships affected by ocean environment such as wind and waves,an adaptive neural sliding mode control algorithm based on lateral migration estimation and boundary triggering is proposed in this paper.In the proposed algorithm,ship track control is transformed into course control problem by nonlinear sliding mode technology,and radial basis function(RBF)neural network is used to estimate and compensate ship's unknown lateral deviation distance online.At the same time,an event triggering rule based on boundary triggering is constructed for control signals,which can realize the step transmission of control signals within the predetermined rules,and reduce the communication load and actuator transition wear caused by frequent transmission of control signals.By means of Lyapunov stability theorem,it is proved that the proposed control algorithm satisfies the actual bounded stability.Finally,the effectiveness and robustness of the proposed algorithm are proved by numerical simulation experiments.
万方数据
维普
