Neural prescribed performance control for uncertain marine surface vessels without accurate initial errors
type: Item
instance of: scholarly article
title: Neural prescribed performance control for uncertain marine surface vessels without accurate initial errors
zbMATH Open document ID: 1426.93145
DOI: 10.1155/2017/2618323
author: Wenjie Si
author: Xunde Dong
published in: Mathematical Problems in Engineering
publication date: 2018-11-05T00:00:00Z
review text: Summary: This paper deals with the problems concerned with the trajectory tracking control with prescribed performance for marine surface vessels without velocity measurements in uncertain dynamical environments, in the presence of parametric uncertainties, unknown disturbances, and unknown dead-zone. First, only the ship position and heading measurements are available and a high-gain observer is used to estimate the unmeasurable velocities. Second, by utilizing the prescribed performance control, the prescribed tracking control performance can be ensured, while the requirement for the initial error is removed via the preprocessing. At last, based on neural network approximation in combination with backstepping and Lyapunov synthesis, a robust adaptive neural control scheme is developed to handle the uncertainties and input dead-zone characteristics. Under the designed adaptive controller for marine surface vessels, all the signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB), and the prescribed transient and steady tracking control performance is guaranteed. Simulation studies are performed to demonstrate the effectiveness of the proposed method.
Mathematics Subject Classification ID: 93C40
Mathematics Subject Classification ID: 93B52
zbMATH DE Number: 6971886
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/2618323
OpenAlex ID: W2578301990
Wikidata QID: Q59147415
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description: scientific article; zbMATH DE number 6971886