Title: 3D trajectory tracking control of an underactuated AUV based on adaptive neural network dynamic surface

Authors: Xiao Liang; Zhao Zhang; Xingru Qu; Ye Li; Rubo Zhang

Addresses: School of Naval Architecture and Ocean Engineering, Dalian Maritime University, Dalian, 116026, China ' School of Naval Architecture and Ocean Engineering, Dalian Maritime University, Dalian, 116026, China ' School of Naval Architecture and Ocean Engineering, Dalian Maritime University, Dalian, 116026, China ' Science and Technology on Underwater Vehicle Technology, Harbin Engineering University, Harbin, 150001, China ' Key Laboratory of Intelligent Perception and Advanced Control of State Ethnic Affairs Commission, Dalian Minzu University, Dalian, 116600, China

Abstract: This paper addresses the 3D trajectory tracking control of an underactuated autonomous underwater vehicle (AUV) under uncertain model parameters and unknown external disturbances. A dynamic surface control scheme based on neural network and adaptive technique is proposed. In controller design, the first-order integral filters are employed to estimate derivative of virtual control, which avoid repeated derivative of virtual control. To deal with the effect of unknown external disturbances and uncertain model parameters, the neural network and adaptive technique are combined to approximate unknown nonlinear functions. All of the error signals in the closeloop system are uniformly ultimately bounded based on Lyapunov stability theory. Simulation studies and comparisons with adaptive dynamic surface control scheme illustrate the effectiveness and superiority of the proposed control scheme.

Keywords: underactuated autonomous underwater vehicle; trajectory tracking; dynamic surface control; integral filters; RBF neural network; adaptive technique.

DOI: 10.1504/IJVD.2020.115864

International Journal of Vehicle Design, 2020 Vol.84 No.1/2/3/4, pp.203 - 218

Received: 15 Feb 2020
Accepted: 27 Nov 2020
Published online: 25 Jun 2021 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article