Nonlinear and Machine-Learning-Based Station-Keeping Control of an Unmanned Surface Vehicle

Autor:	Manhar R. Dhanak, Alexandrea Barker, Armando J. Sinisterra, Siddhartha Verma
Rok vydání:	2020
Předmět:	Nonlinear system Unmanned surface vehicle Computer science business.industry Robustness (computer science) Control (management) Control engineering Robotics Control equipment Artificial intelligence Energy consumption Motion control business
Zdroj:	Volume 6B: Ocean Engineering.
DOI:	10.1115/omae2020-19276
Popis:	This study is part of ongoing work on situational awareness and autonomy of a 16’ WAM-V USV. The objective of this work is to determine the potential and merits of application of two different station-keeping controllers for a fixed-pose motion control of the USV. The assessment includes performance and power consumption metrics tested under harsh environmental disturbances to evaluate the robustness of the control methods. The first is a nonlinear trajectory-tracking control method based on the sliding-mode control technique, while the second method uses a machine-learning approach based on Deep Reinforcement Learning. Results from both the approaches are compared for various case studies.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::bbe44f4f56f20daa96671c7006ccb4a1 https://doi.org/10.1115/omae2020-19276 Zobrazit plný text záznamu