Adaptive neural path-following control of under-actuated AUV subject to completely unknown dynamic and input constraints

Abstract

This paper investigates a path-following control for autonomous underwater vehicles that are under-actuated and are subject to completely unknown dynamics and input constraints in the vertical plane. Initially, the-line-of sight guidance is adopted to generate the desired pitch angle and the updated law for the path variable to guide the vehicle toward the desired path. Subsequently, a transformation is applied to turn the input constraints into a constraint on new states. The state constraint problem, unknown dynamics, and disturbances are then addressed with the proposal of an innovative integral barrier Lyapunov function and adaptive law. Through the Lyapunov theory, all errors are shown to be uniformly ultimately bounded. Eventually, a simulation via MATLAB is implemented to illustrate the feasibility and efficiency of the designed controller.