Flight controller learning based on real-time model estimation of a quadrotor aircraft

Abstract

Aircraft prototyping and modeling is usually associated with resource expensive techniques and significant post-flight analysis. The NASA Learn-To-Fly concept targets the replacement of the conventional ground-based aircraft development and prototyping approaches with an efficient real-time paradigm. The work presented herein describes a learning paradigm of a quadcopter unmanned aircraft that utilizes real-time flight data. Closed-loop parameter estimation of a highly collinear model terms such as those found on a quadrotor is challenging. Using phase optimized orthogonal multisine input maneuvers, collinearity of flight data decreases leading to fast and accurate convergence of the Fourier transform regression estimator. The generated models are utilized to reconfigure a nonlinear dynamic inversion controller in normal, failure, and learning testing conditions. Results show highly accurate model estimation in different testing scenarios. Additionally, the nonlinear dynamic inversion controller easily integrates the identified model parameters without any need for gain scheduling or computationally expensive methods. Overall, the proposed technique introduces an efficient integration between real-time modeling and control adaptation utilizing the limited computational power of the quadcopter’s microcomputer.