发表机构
University of Turku（图尔库大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对扑翼飞行器逆气动建模，提出有界信道自适应谱残差门控循环单元（BCS-GRU），结合前向一致性目标，在长预测视界上提升逆预测精度和气动一致性。
AI 中文摘要
扑翼飞行器通过协调拍动、偏转和俯仰运动的变异性来调节气动力和力矩。由于由此产生的载荷既取决于瞬时机翼构型，又取决于其先前的运动历史，因此从期望的气动轨迹中恢复合适的机翼运动学是一个具有挑战性的逆问题。现有的序列模型能够捕捉时间依赖性，而谱方法可以利用扑翼运动的周期性结构。然而，不受限制的频域增强可能会干扰时间表示，并在运动学变量和预测视界之间产生不一致的修正。我们提出了有界信道自适应谱残差门控循环单元（BCS-GRU），它保留循环时间预测作为其主要表示，并将谱信息限制为受控的特定于输出的修正。我们进一步引入了一个因果对齐的前向一致性目标，该目标通过一个单独训练并冻结的气动代理来评估预测的运动学。在统一的情节级协议下的实验表明，BCS-GRU在逆预测方面优于循环和自适应谱基线，在更长的预测视界上具有更大的优势。前向一致性微调进一步提高了基于代理的气动一致性，同时保持了平均运动学精度。这些结果表明，受控谱修正和前向一致性学习为扑翼空气动力学的历史感知逆建模提供了一个有效的框架。
英文摘要
Flapping-wing vehicles regulate aerodynamic forces and moments through coordinated variations in stroke, deviation, and pitch motion. Because the resulting loads depend on both the instantaneous wing configuration and its preceding motion history, recovering suitable wing kinematics from a desired aerodynamic trajectory is a challenging inverse problem. Existing sequence models capture temporal dependencies, while spectral methods can exploit the periodic structure of flapping motion. However, unrestricted frequency-domain augmentation may interfere with temporal representations and produce inconsistent corrections across kinematic variables and prediction horizons. We propose the Bounded Channel-Adaptive Spectral Residual Gated Recurrent Unit (BCS-GRU), which retains recurrent temporal prediction as its primary representation and restricts spectral information to a controlled output-specific correction. We further introduce a causally aligned forward-consistency objective that evaluates predicted kinematics through a separately trained and frozen aerodynamic surrogate. Experiments under a unified episode-level protocol show that BCS-GRU improves inverse prediction over recurrent and adaptive-spectral baselines, with greater benefits at longer prediction horizons. Forward-consistent fine-tuning further improves surrogate-based aerodynamic consistency while maintaining mean kinematic accuracy. These results demonstrate that controlled spectral correction and forward-consistent learning provide an effective framework for history-aware inverse modelling of flapping-wing aerodynamics.
