用于高效多鳍拍打运动的基于斯特劳哈尔数感知的模型预测控制
Strouhal-Aware Model Predictive Control for Efficient Multi-Fin Flapping Locomotion
AI总结：
研究将生物启发的经验法则转化为实时控制，用基于斯特劳哈尔数感知的模型预测控制增强准稳态水动力模型，经两阶段采样和梯度优化求解非凸问题，实现节能并提高速度。
摘要：
高效拍打推进取决于在狭窄的斯特劳哈尔数窗口内运行。我们将这一受生物启发的经验法则转化为实时控制，并在由四个软鳍驱动的自主水下航行器上进行了演示。所提出的基于斯特劳哈尔数感知的模型预测控制（MPC）通过对斯特劳哈尔偏差的显式惩罚增强了准稳态水动力模型，通过两阶段采样和梯度优化解决了由此产生的非凸问题，该优化在船上以25Hz运行。水池和现场试验表明，该控制器将每个鳍保持在最佳斯特劳哈尔走廊（0.25 - 0.35）内，同时精确跟踪指令力。这导致在0.1至0.3m/s的巡航范围内机械功率平均降低8.8%至32%。所提出的方法还允许达到0.4m/s的速度，这对于传统逆模型的基线来说是无法实现的。结果证实，将第一原理流动物理嵌入MPC目标可在不牺牲敏捷性的情况下实现显著的续航能力提升，为下一代多鳍机器人的能量感知运动提供了一条通用途径。
英文摘要：
Efficient flapping propulsion hinges on operating within a narrow Strouhal number window, a principle nature has converged upon for maximum thrust-to-power ratio. We translate this bioinspired empirical rule into real-time control, demonstrating it on an autonomous underwater vehicle driven by four soft fins. The proposed Strouhal-aware Model Predictive Control (MPC) enhances a quasi-steady hydrodynamic model with an explicit penalty for Strouhal deviation, solving the resulting nonconvex problem via a two-stage sampling and gradient optimization that runs onboard at 25 Hz. Pool and field trials show that the controller keeps each fin within the optimal Strouhal corridor (0.25-0.35) while precisely tracking commanded forces. This results in a mean reduction in mechanical power of 8.8\% to 32\% throughout the cruising range of 0.1 to 0.3 m/s. The proposed method also allows for a velocity of 0.4 m/s, which is unattainable for a baseline of the conventional inverse model. The results confirm that embedding first-principle flow physics into an MPC objective yields tangible endurance gains without sacrificing agility, offering a generic pathway to energy-aware locomotion in next-generation multifin robots.
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