NDLI: Dynamic Neural Network-Based Adaptive Tracking Control for an Autonomous Underwater Vehicle Subject to Modeling and Parametric Uncertainties

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Dynamic Neural Network-Based Adaptive Tracking Control for an Autonomous Underwater Vehicle Subject to Modeling and Parametric Uncertainties

Content Provider	MDPI
Author	Filiberto, Muñoz Omar, Sandre-Hernández Cervantes-Rojas, Jorge Valdovinos, Jose Salazar, Sergio Romero, Hugo
Copyright Year	2021
Description	This research presents a way to improve the autonomous maneuvering capability of a four-degrees-of-freedom (4DOF) autonomous underwater vehicle (AUV) to perform trajectory tracking tasks in a disturbed underwater environment. This study considers four second-order input-affine nonlinear equations for the translational (x,y,z) and rotational (heading) dynamics of a real AUV subject to hydrodynamic parameter uncertainties (added mass and damping coefficients), unknown damping dynamics, and external disturbances. We proposed an identification-control scheme for each dynamic named Dynamic Neural Control System (DNCS) as a combination of an adaptive neural controller based on nonparametric identification of the effect of unknown dynamics and external disturbances, and on parametric estimation of the added mass dependent input gain. Several numerical simulations validate the satisfactory performance of the proposed DNCS tracking reference trajectories in comparison with a conventional feedback controller with no adaptive compensation. Some graphics showing dynamic approximation of the lumped disturbance as well as estimation of the parametric uncertainty are depicted, validating effective operation of the proposed DNCS when the system is almost completely unknown.
Starting Page	2797
e-ISSN	20763417
DOI	10.3390/app11062797
Journal	Applied Sciences
Issue Number	6
Volume Number	11
Language	English
Publisher	MDPI
Publisher Date	2021-03-21
Access Restriction	Open
Subject Keyword	Applied Sciences Marine Engineering Adaptive Identification Dynamic Neural Networks Tracking Control Underwater Vehicle
Content Type	Text
Resource Type	Article

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