자율무인잠수정의 자율다이빙을 위한 강인한 적응제어기 응용

Abstract

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 underwa-ter robotics vehicles (URVs) are highly nonlinear and time- varying, and the hydrodynamic coefficients of vehicles are hard to be determined accurately in priori. In this paper, the smooth unknown dynamics of vehicle is approximated by neural net-work, 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 func-tion, the proposed control scheme can guarantee that all the signals in the closed-loop system satisfy to be uniformly ulti-mately bounded (UUB). Simulation studies on ASUM AUV model, which is developing at KRISO, are included to illustrate the effectiveness of the proposed control scheme, and some practical features of the control laws are also discussed.