Neural network-based output feedback control for reference tracking of underactuated surface vessels. (March 2017)

• Record Type: Journal Article
• Authors: Park, Bong Seok; Kwon, Ji-Wook; Kim, Hongkeun
• Is Part Of: Automatica. Volume 77(2017)
• Page Start: 353
• Page End: 359
• Publication Date: 2017-03
• Subjects: Underactuated surface vessel -- Adaptive observer -- Neural network -- Input saturation; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2016.11.024
• Languages: English
• ISSNs: 0005-1098

Abstract

Abstract: This paper proposes an adaptive output feedback control for trajectory tracking of underactuated surface vessels (USVs). For the realistic dynamical model of USVs, we consider the USV model, where the mass and damping matrices are not diagonal. Moreover, except the mass matrix, the system parameters and nonlinearities of the USV are all assumed to be unknown. Despite this uncertain circumstance, we develop an adaptive observer based on the neural networks to estimate the velocity data of USVs. Then, an output feedback control law is designed by simultaneously considering the input saturation and underactuated problems.