Application of a robust adaptive controller to autonomous diving control of an AUV

This paper presents a robust adaptive controller for autonomous diving control of an autonomous underwater vehicle (AUV). It is well known that the dynamics of underwater robotics vehicles (URVs) are highly nonlinear and the hydrodynamic coefficients of vehicles are difficult to be accurately determined a priori. In this paper, the smooth unknown dynamics of vehicle is approximated by neural network, and the remaining unstructured uncertainties, such as disturbances and unmodeled dynamics, are assumed to satisfy certain growth conditions characterized by 'bounding function' composed of known function multiplied by unknown constant. Under a certain relaxed assumptions on the control gain function, the proposed control scheme can guarantee that all the signals in the closed-loop system satisfy to be uniformly ultimately bounded (UUB). Simulation studies using ASUM AUV model, which is developing at KRISO, are included to illustrate the effectiveness of the proposed control scheme. Some practical features of the control laws are also discussed.