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Flapping-fin unmanned underwater vehicle (UUV) propulsion systems providehigh maneuverability for naval tasks such as surveillance and terrainexploration. Recent work has explored the use of time-series neural networksurrogate models to predict thrust from vehicle design and fin kinematics. Wedevelop a search-based inverse model that leverages a kinematics-to-thrustneural network model for control system design. Our inverse model finds a setof fin kinematics with the multi-objective goal of reaching a target thrust andcreating a smooth kinematic transition between flapping cycles. We demonstratehow a control system integrating this inverse model can make online,cycle-to-cycle adjustments to prioritize different system objectives.

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