A Narwhal-Inspired Sensing-To-Control Framework for Small Fixed-Wing Aircraft

Abstract

Fixed-wing unmanned aerial vehicles (UAVs) offer endurance and efficiency but lack low-speed agility because its highly-coupled dynamical model. We present an end-to-end sensing-to-control pipeline that combines bio-inspired hardware instrumentation, physics-informed dynamics learning, and convex control allocation. Measuring oncoming flow on a small airframe is difficult as near-body aerodynamics, propeller slipstream, control surfaces actuation, and the present of gusts would distort pressures and make sensor signals input dependent variables. To raise the signal-to-noise ratio, and to gain invaluable response time, inspired by the Narwhal's tusk, we protrude our in-house developed multi-hole probes far ahead into the upstream, and complement it with sparse yet carefully placed wing pressure sensors for local flow measurement, with systematically-introduced gust of significant magnitude. A data-driven calibration maps pressures signal of the probes to airspeed and flow angles. We then learn a control-affine model of aerodynamic forces with a soft left/right symmetry regularizer that improves identifiability under partial observability and limits confounding between wing pressures and aileron inputs. Desired wrenches (forces output) are realized by a regularized least-squares optimizer that yields smooth, trimmed actuation. Wind-tunnel studies across multiple airspeeds and gust conditions show that adding wing pressures reduces force-estimation error by 25%–30%, the proposed model degrades far less under distribution shift (about 12% versus 44% for an unstructured baseline), and force tracking improves with smoother inputs, including a 27% reduction in normal-force RMSE relative to a plain affine model and 34% relative to an unstructured baseline.