Data-Driven Adaptive Disturbance Observers for Model-Free Trajectory Tracking Control of Maritime Autonomous Surface Ships

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)5584-5594
Journal / PublicationIEEE Transactions on Neural Networks and Learning Systems
Volume32
Issue number12
Online published13 Jul 2021
Publication statusPublished - Dec 2021

Abstract

In this article, we address the disturbance/uncertainty estimation of maritime autonomous surface ships (MASSs) with unknown internal dynamics, unknown external disturbances, and unknown input gains. In contrast to existing disturbance observers where some prior knowledge on kinetic model parameters such as the control input gains is available in advance, reduced- and full-order data-driven adaptive disturbance observers (DADOs) are proposed for estimating unknown input gains, as well as total disturbance composed of unknown internal dynamics and external disturbances. An advantage of the proposed DADOs is that the total disturbance and input gains can be simultaneously estimated with guaranteed convergence via data-driven adaption. We apply the proposed full-order DADO for the trajectory tracking control of an MASS without kinetic modeling and present a model-free trajectory tracking control law for the ship based on the DADO and a backstepping technique. We report the simulation results to substantiate the efficacy of the proposed DADO approach to model-free trajectory tracking control of an autonomous surface ship without knowing its dynamics.

Research Area(s)

  • Adaptation models, Data-driven adaptive disturbance observer (DADO), Disturbance observers, Kinetic theory, Marine vehicles, maritime autonomous surface ships (MASSs), ocean disturbances, Sea surface, Trajectory tracking, unknown input gains, unknown internal dynamics, Vehicle dynamics

Citation Format(s)

Data-Driven Adaptive Disturbance Observers for Model-Free Trajectory Tracking Control of Maritime Autonomous Surface Ships. / Peng, Zhouhua; Wang, Dan; Wang, Jun.
In: IEEE Transactions on Neural Networks and Learning Systems, Vol. 32, No. 12, 12.2021, p. 5584-5594.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review