A neural network adaptive controller design for free-pitch-angle diving behavior of an autonomous underwater vehicle

This paper presents a neural network adaptive controller for diving control of an autonomous underwater vehicle (AUV). In general, while deriving the diving equations of an AUV, the pitch angle of the vehicle is often assumed to be small in the diving motion. This is a somewhat strong restricting condition in many practical applications, and would be broken in this paper. Furthermore, because the dynamics of AUVs are highly nonlinear and the hydrodynamic coefficients of the vehicles are difficult to be accurately estimated a priori, the smooth unknown dynamics in the pitch motion of an AUV is approximated by a neural network, and the remaining unstructured uncertainties, such as disturbances and unmodeled dynamics, are assumed to be unbounded, although they still satisfy certain growth conditions. Under a certain relaxed assumptions on the control gain functions, proposed control scheme can guarantee that all the signals in the closed-loop system satisfy to be uniformly ultimately bounded (UUB). Simulation studies are included to illustrate the effectiveness of the proposed control scheme, and some practical features of the control laws are also discussed.