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

arXiv: https://arxiv.org/abs/2209.06369 · HTML: https://mlsanity.com/paper/2209.06369/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 24 references that are in the map.
- First posted on arXiv 14 Sep 2022.

## Builds on (most cited first, 1 of 1)

- [Evaluation of Surrogate Models for Multi-fin Flapping Propulsion Systems](https://mlsanity.com/paper/1910.14194/) (2019)

## Built on by (most cited first, 1 of 1)

- [Underwater Soft Fin Flapping Motion with Deep Neural Network Based Surrogate Model](https://mlsanity.com/paper/2502.03135/) (2025)

## Abstract (arXiv)

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.