Aerodynamic Moment Model Calibration from Distributed Pressure Arrays

Abstract

In recent years, studies on the role of distributed airflow mechanosensors in bird, bat, and insect flight behaviors have sparked a growing interest in integrating flow sensors, such as pressure or hair sensors, into flight control feedback systems of small unmanned aircraft. It is anticipated that a flight controller based on real-time distributed flow information feedback is more capable of rapidly responding to sudden airflow changes and may exhibit greater stability robustness properties than purely inertial feedback-based flight control laws. However, considering the uncertainty of state-of-the-art microflow sensor technologies, there is an urgent need for model calibration if flow information is to be used to calculate aerodynamic moments and/or forces for controlling aircraft motion. In this paper, a constrained least-squares estimator is developed to calculate the bias of distributed flow information to calibrate an aerodynamic moment model. Simulation and wind-tunnel experiment results demonstrate a significant improvement on the accuracy of the aerodynamic moment calculation after calibration.