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.

翻译：过去几十年间，针对仿生无人水下航行器推进与控制系统研究日益兴起，这类系统为水下任务提供了比传统无人水下航行器更具机动性的替代方案。近期研究探索了使用时序神经网络代理模型，根据航行器设计和鳍肢运动学预测推力和功率。我们开发了一种基于搜索的逆模型，该模型利用运动学-推力与运动学-功率神经网络模型进行控制系统设计。我们的逆模型能够寻找到一组鳍肢运动学参数，在功率约束下以实现目标推力为多目标，同时在扑翼周期之间创建平滑的运动学过渡。我们展示了集成该逆模型的控制系统如何进行在线、周期到周期的调整以优先满足不同系统目标，在2.2N至9.0W的范围内，可将任意给定运动的推力提升至少0.5N或功耗降低至少3.0W。由于推进效率对延长扑翼式无人水下航行器的航程与续航时间至关重要，但现有研究匮乏，我们基于推进效率度量推导出一个无量纲品质因数，该参数能够评估不同鳍肢设计和运动学特性，并实现与其他仿生平台的性能比较。我们运用该品质因数分析最优步态，比较不同鳍材的性能表现，从而更深入理解鳍材如何影响推力生成与推进效率，并以此指导控制系统设计及在已开发的逆步态选择模型中实现效率优化的权重配置。