Aerodynamic State and Loads Estimation Using Bioinspired Distributed Sensing

Abstract

Flying animals exploit highly nonlinear dynamics to achieve efficient and robust flight control. It appears that the distributed flow and force sensor arrays found in flying animals are instrumental in enabling this performance. Using a wind-tunnel wing model instrumented with distributed arrays of strain and pressure sensors, we characterized the relationship between the distributed sensor signals and aerodynamic and load-related variables. Estimation approaches based on nonlinear artificial neural networks (ANNs) and linear partial least squares were tested with different combinations of sensor signals. The ANN estimators were accurate and robust, giving good estimates for all variables, even in the stall region when the distributed array pressure and strain signals became unsteady. The linear estimator performed well for load estimates but was less accurate for aerodynamic variables such as angle of attack and airspeed. Future applications based on distributed sensing could include enhanced flight control systems that directly use measurements of aerodynamic states and loads, allowing for increase maneuverability and improved control of unmanned aerial vehicles with high degrees of freedom such as highly flexible or morphing wings.