Adaptive formation control for autonomous surface vessels with prescribed-time convergence. (11th July 2022)

• Record Type: Journal Article
• Authors: Huang, Ziyang; Li, Jun; Huang, Bing
• Is Part Of: International journal of advanced robotic systems. Volume 19:Number 4(2022)
• Publication Date: 2022-07-11
• Subjects: Autonomous surface vessels -- predefined-time cooperative control -- dynamic surface control -- neural network -- minimum-learning-parameter; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/17298806221105722
• Languages: English
• ISSNs: 1729-8806

Abstract

This article is dedicated to solving the problem of predefined-time cooperative control for autonomous surface vessels encountering model uncertainties and external perturbations. By virtue of the prescribed-time stable theory, a robust formation controller is constructed, with which the settling time of the cooperative system can be prescribed in advance. The controller is developed under the backstepping framework, where the dynamic surface control is applied to generate the real-time command. Considering the unmodeled autonomous surface vessel dynamics, the neural network-based nonlinear approximator is incorporated with minimum-learning-parameter technique. Under this scenario, the real-time control can be pursed with one parameter being estimated. Finally, comparative simulation examples are provided to exhibit the effectiveness and advantages of designed control strategies.