신경회로망을 이용한 자율무인잠수정의 적응제어

Abstract

Since the dynamics of autonomous underwater vehicles (AUVs) are highly nonlinear and their hydrodynamic coefficients vary with different vehicle’s operating conditions, high performance control systems of AUVs are needed to have the capacities of learning and adapting to the variations of the vehicle’s dynamics. In this paper, a linearly parameterized neural network (LPNN) is used to approximate the uncertainties of the vehicle’s dynamics, where the basis function vector of the network is constructed according to the vehicle’s physical properties. The network’s reconstruction errors and the disturbances in the vehicle’s dynamics are assumed be bounded although the bound may be unknown. To attenuate this unknown bounded uncertainty, a certain estimation scheme for this unknown bound is introduced combined with a sliding mode scheme. The proposed controller is proven to guarantee that all signals in the closed-loop system are uniformly ultimately bounded (UUB). Numerical simulation studies are performed to illustrate the effectiveness of the proposed control scheme.