A Learn-To-Fly Approach for Adaptively Tuning Flight Control Systems

Abstract

A method is presented for adaptively tuning feedback control gains in a ight control sys- tem to achieve desired closed-loop performance. The method combines efficient parameter estimation for identifying closed-loop dynamics models, with online nonlinear optimization for sequentially perturbing and updating control gains to improve performance. Prior in- formation on stability and control derivatives is not needed, nor is any knowledge about the control system architecture. Following convergence, the optimized control gains (with uncertainties), the open-loop dynamics model, and the closed-loop dynamics model are available. The method is demonstrated for tuning a longitudinal stability augmentation system using a realistic nonlinear ight dynamics simulation of the NASA FASER airplane. Convergence was attained using five piloted maneuvers that spanned approximately one minute of ight test time. Although demonstrated for a relatively simple case, the method is general and can be applied to other aircraft, axes, performance metrics, and control systems.