Aerodynamic Modeling in Nonlinear Regions, including Stall Spins, for Fixed-Wing Unmanned Aircraft from Experimental Flight Data

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Virginia Tech


With the proliferation of unmanned aircraft designed for national security and commercial purposes, opportunities exist to create high-fidelity aerodynamic models with flight test techniques developed specifically for remotely piloted aircraft. Then, highly maneuverable unmanned aircraft can be employed to their greatest potential in a safe manner using advanced control laws. In this dissertation, novel techniques are used to identify nonlinear, coupled, aerodynamic models for fixed-wing, unmanned aircraft from flight test data alone. Included are quasi-steady and unsteady nominal flight models, aero-propulsive models, and spinning flight models. A novel flight test technique for unmanned aircraft, excitation with remote uncorrelated pilot inputs, is developed for use in nominal and nonlinear flight regimes. Orthogonal phase-optimized multisine excitation signals are also used as inputs while collecting gliding, aero-propulsive, and spinning flight data. A novel vector decomposition of explanatory variables leads to an elegant model structure for stall spin flight data analysis and spin aerodynamic modeling. Results for each model developed show good agreement between model predictions and validation flight data. Two novel applications of aerodynamic modeling are discussed including energy-based nonlinear directional control and a spin flight path control law for use as a flight termination system. Experimental and simulation results from these applications demonstrate the utility of high-fidelity models developed from flight data.



system identification, aerodynamic modeling, stall spin, remote uncorrelated pilot inputs, fixed-wing aircraft, unmanned aircraft, flight test