Dynamic event-triggered control for MSVs via composite-learning-based adaptive neural approach

Abstract

This article investigates the control issue of marine surface vehicles (MSVs) subject to uncertainties and input saturation. To ensure the smooth implementation of backstepping design framework, the non-smooth saturation model is replaced by a smooth function, which is analogous to the hyperbolic tangent function. To exact reconstruct the uncertainties including unknown internal nonlinear dynamic and external disturbance, an idea of separation reconstruction is proposed. That is, the adaptive neural network (NN) is utilized to online reconstruct the unknown internal nonlinear dynamic, and a NN-based disturbance observer is proposed to timely recover the external disturbance plus the reconstruction error of adaptive NN. To enhance the control accuracy, a serial-parallel estimation model (SPEM) is involved to capture the prediction error of MSVs’ velocity. Based on this, a novel adaptive law used to update the NN weight is designed by involving the velocity error, prediction error and estimation value of disturbance. Furthermore, a composite-learning-based adaptive neural control (CLBANC) law is designed. To decrease the physical wear of actuator, a dynamic event-triggered mechanism is established between the control law and actuator. Finally, a dynamic event-triggered (DET) CLBANC (DET-CLBANC) scheme is developed for MSVs. Theoretical analysis indicates that the proposed control scheme is endowed with the ability to ensure the boundedness of all signals in the whole closed-loop system of MSVs. Moreover, the effectiveness of DET-CLBANC scheme is verified by simulation and comparison.