  The last few decades have led to the rise of research focused on propulsion
and control systems for bio-inspired unmanned underwater vehicles (UUVs), which
provide more maneuverable alternatives to traditional UUVs in underwater
missions. Recent work has explored the use of time-series neural network
surrogate models to predict thrust and power from vehicle design and fin
kinematics. We develop a search-based inverse model that leverages
kinematics-to-thrust and kinematics-to-power neural network models for control
system design. Our inverse model finds a set of fin kinematics with the
multi-objective goal of reaching a target thrust under power constraints while
creating a smooth kinematics 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, with
improvements in increasing thrust generation or reducing power consumption of
any given movement upwards of 0.5 N and 3.0 W in a range of 2.2 N and 9.0 W. As
propulsive efficiency is of utmost importance for flapping-fin UUVs in order to
extend their range and endurance for essential operations but lacks prior
research, we develop a non-dimensional figure of merit (FOM), derived from
measures of propulsive efficiency, that is able to evaluate different fin
designs and kinematics, and allow for comparison with other bio-inspired
platforms. We use the developed FOM to analyze optimal gaits and compare the
performance between different fin materials, providing a better understanding
of how fin materials affect thrust generation and propulsive efficiency and
allowing us to inform control systems and weight for efficiency on the
developed inverse gait-selector model.
