Flight Test Approach for Modeling and Control Law Validation for Unmanned Aircraft

A nonlinear energy-based control law was flight tested on a small, fixed-wing unmanned aircraft. This paper summarizes the selected aircraft, instrumentation system, data processing techniques, system identification methods, and the control laws that were implemented. The flight test campaign used a build up approach with increasingly complex computer generated system identification excitations and then increasingly complex control laws. This build up approach allowed the team to overcome technical challenges in a progressive manner and then finally test an advanced nonlinear control law in flight. Automated multistep, frequency sweep, and multisine excitation inputs were implemented for system identification. System identification methods were leveraged to develop linear and nonlinear aerodynamic models. A servoactuator model was developed from ground test data and data processing techniques were used to condition the flight test data for analysis. A proportional-derivative attitude commanded system was implemented and tuned using pilot comments, without the use of an a priori model. A linear quadratic regulator was tuned using the linear aerodynamic model, and refined during flight tests to improve handling qualities. A port-Hamiltonian energy-based nonlinear control law was tuned in simulation using the nonlinear model, and gains refined during flight tests to improve directional tracking and perturbation response. Implementation techniques for automated system identification maneuvers, as well as feedback control using a Pixhawk and Raspberry Pi co-computer are documented and made available by means of a publicly accessible web repository. Flight test results illustrate the utility of the experimental data collection and analysis methods for testing advanced flight control schemes.