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

TL;DR

This paper presents a neural network-based control system for flapping-fin underwater vehicles that optimizes fin kinematics for multiple objectives, enabling real-time adjustments for improved maneuverability.

Contribution

It introduces a search-based inverse model leveraging neural networks to optimize fin kinematics for multi-objective control in underwater vehicles.

Findings

Effective multi-objective control of fin kinematics demonstrated

Real-time cycle-to-cycle adjustments achieved

Neural network surrogate models accurately predict thrust

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.