  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.
