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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.

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Unmanned aircraft systems (UAS) have experienced tremendous growth through both commercial (i.e., toys and videography) and defense avenues. The rapid expansion, particularly in the consumer market, has outpaced regulatory bodies. Certification to commercially operate such vehicles currently requires the successful completion of a knowledge examination, without the need to physically operate a vehicle. The focus of the work presented herein is on quantifying the pilot and multi-rotor performance in an attempt to provide quantitative metrics that can be used to establish training and certification for pilots and aircraft. Test pilots were categorized based on their experience level, and the quadrotor unmanned aircraft was categorized based on the flight control mode. Cross-track command (CTC) and path error (PE) were calculated as potential time-domain metrics to quantify pilot and quadcopter performance. Individual binary logistic regression models were developed to identify the pilot experience level (PEL) and UAV control level (UCL) from the decision tree outcomes. A verification test case was included to evaluate the established regression models. Results show that the models can evaluate pilot and quadcopter performance individually, which can be used to develop the pilot training curriculum and/or evaluate pilot effectiveness in specific flight scenarios.