Data-Driven Machine Learning Models for a Multi-Objective Flapping Fin Unmanned Underwater Vehicle Control System
This work addresses control system design for naval UUVs, but it is incremental as it builds on existing neural network surrogate models.
The authors tackled the problem of controlling flapping-fin unmanned underwater vehicles by developing a search-based inverse model that uses a neural network to find fin kinematics achieving target thrust and smooth transitions, demonstrating online adjustments for multi-objective control.
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.