Event-triggered adaptive neural tracking control for MSVs under input saturation: An appoint-time approach

Abstract

This paper addresses an event-triggered tracking control issue for marine surface vessels (MSVs), which suffers from uncertain dynamics, unknown external disturbances and input saturation. To facilitate the implementation of backstepping design procedure, the input saturation nonlinearity is replaced by a smooth Gaussian error function. Under the backstepping design framework, introducing a nonlinear transformation and integrating the indirect adaptive neural, single parameter learning and event-triggered control techniques, a novel event-triggered neuroadaptive appoint-time tracking control scheme is proposed. Compared with most existing results, the proposed control solution is of the following notable characteristics: (1) the convergence time and accuracy of the position and velocity errors are determined by designer offline; (2) only one unknown parameter needs to be updated, which reduces the computational burden of control system greatly; (3) it decreases mechanical wear of the MSV actuators by reducing the response frequency of actuators to the control command. The theoretical analyses validate that all signals of the closed-loop trajectory tracking control system are bounded. Simulation results illustrate the effectiveness of the developed scheme.