Airspeed information plays a crucial role in the takeoff, flight, and landing processes in animal flyers and aerial vehicles. Among the aerial vehicles, Flapping Wing Micro Air Vehicles (FWMAVs) represent the novel engineering approach of learning and mimicking animal flyers in the past two decades. A few prototypes of various sizes and functionalities of FWMAVs have been developed by MAVLab in the TU Delft Aerospace department since 2005. For these small and lightweight platforms, stable control under disturbances remains a challenge, and could further benefit from integrating effective airflow sensing into control system design.

Current commercial products are either not suitable due to size, weight, and power (SWaP) restrictions of the drone platform, or have very limited low-speed sensitivity. Therefore, to facilitate this, this thesis aims to develop a MEMS-based capacitive airflow sensor for a miniaturized and low-power implementation. Inspired by the filiform hair structure of arthropods, the sensor design comprises two key parts: the hair structure, whose displacement is governed by the drag force induced by the incident airflow, and the sensing base, located at the hair structure's base, which translates the structural displacement into a change in capacitance. The sensor exhibits the capability to sense airflow in one dimension and can be further adapted for omnidirectional sensing.

This thesis predominately focuses on the design and reliable fabrication of the sensing base. It is comprised of a suspended membrane supported by corner beams, supplemented by a pivoting dimple and anti-stitching dimples to facilitate robust hair movement and ensure optimal sensor functionality. The sensing base is fabricated at the TU Delft EKL cleanroom facility, equipped with sophisticated machinery that enables fabrication at micro and nano scales. Furthermore, the membrane suspension is achieved through the implementation of Vapor HF etching of a sacrificial layer beneath the membrane.

In conclusion, the devised sensors aspire to optimize flight control for FWMAVs within the constraints of SWaP, by drawing profound inspiration from the intricate workings of nature.

Flapping wing micro aerial vehicles (FWMAVs) are known for their flight agility and maneuverability. However, their in-gust flight performance and stability is still inferior to their biological counterparts. To this end, a simplified in-gust dynamic model, which could capture the main gust effects on FWMAVs, has been identified with real in-gust flights' data of a FWMAV, the Flapper Drone. Based on this model, an adaptive position and velocity controller was proposed with gain scheduling and implemented for in-gust flights under gust speeds up to 2.4 m/s. With this airflow-sensing based adaptive controller, the in-gust hovering stability of the Flapper Drone has been improved when the gust's intensity and frequency changes, comparing with the original fixed-gain cascaded PID controller case.