Data-Driven Machine Learning Models for a Multi-Objective Flapping Fin Unmanned Underwater Vehicle Control System

• URL: http://arxiv.org/abs/2209.06369v1
• Date: Wed, 14 Sep 2022 01:55:15 GMT
• Title: Data-Driven Machine Learning Models for a Multi-Objective Flapping Fin Unmanned Underwater Vehicle Control System
• Authors: Julian Lee and Kamal Viswanath and Jason Geder and Alisha Sharma and Marius Pruessner and Brian Zhou
• Abstract summary: We develop a search-based inverse model that leverages a kinematics-to-thrust neural network model for control system design. We demonstrate how a control system integrating this inverse model can make online, cycle-to-cycle adjustments to prioritize different system objectives.
• Score: 0.5522489572615558
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Flapping-fin unmanned underwater vehicle (UUV) propulsion systems provide high maneuverability for naval tasks such as surveillance and terrain exploration. Recent work has explored the use of time-series neural network surrogate models to predict thrust from vehicle design and fin kinematics. We develop a search-based inverse model that leverages a kinematics-to-thrust neural network model for control system design. Our inverse model finds a set of fin kinematics with the multi-objective goal of reaching a target thrust and creating a smooth kinematic transition between flapping cycles. We demonstrate how a control system integrating this inverse model can make online, cycle-to-cycle adjustments to prioritize different system objectives.

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