Learning Periodic Patterns and Developing Internal Models for Avian-Inspired Flapping-Wing State Estimation

This article presents a novel learning-based approach for online state estimation in flapping-wing aerial vehicles (FWAVs). Leveraging low-cost magnetic, angular rate, and gravity (MARG) sensors, the proposed method effectively mitigates the adverse effects of flapping-induced oscillations that challenge conventional estimation techniques. By employing a divide-and-conquer strategy grounded in cycle-averaged aerodynamics, the framework decouples the slow-varying components from the high-frequency oscillatory components, thereby preserving critical transient behaviors while delivering a smooth internal state representation. The complete oscillatory state of FWAV can be reconstructed based on above two components, leading to substantial improvements in state estimation accuracy. Experimental validations on an avian-inspired FWAV demonstrate that the estimator enhances accuracy and smoothness, even under complex aerodynamic disturbances. These encouraging results highlight the potential of learning algorithms to overcome issues of flapping-wing induced oscillation dynamics.