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Browsing by Author "Cotting, Malcolm Christopher"

Now showing 1 - 3 of 3
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    Aerodynamic Modeling in Nonlinear Regions, including Stall Spins, for Fixed-Wing Unmanned Aircraft from Experimental Flight Data
    Gresham, James Louis (Virginia Tech, 2022-06-28)
    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.
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    Evolution of Flying Qualities Analysis: Problems for a New Generation of Aircraft
    Cotting, Malcolm Christopher (Virginia Tech, 2010-03-29)
    A number of challenges in the development and application of flying qualities criteria for modern aircraft are addressed in this dissertation. The history of flying qualities is traced from its origins to modern day techniques as applied to piloted aircraft. Included in this historical review is the case that was made for the development of flying qualities criteria in the 1940's and 1950's when piloted aircraft became prevalent in the United States military. It is then argued that UAVs today are in the same context historically as piloted aircraft when flying qualities criteria were first developed. To aid in development of a flying qualities criterion for UAVs, a relevant classification system for UAVs. Two longitudinal flying qualities criteria are developed for application to autonomous UAVs. These criteria center on mission performance of the integrated aircraft and sensor system. The first criterion is based on a sensor platform's ability to reject aircraft disturbances in pitch attitude. The second criterion makes use of energy methods to create a metric to quantify the transmission of turbulence to the sensor platform. These criteria are evaluated with airframe models of different classes of air vehicles using the CASTLE 6 DOF simulation. Another topic in flying qualities is the evaluation of nonlinear control systems in piloted aircraft. A L1 adaptive controller was implemented and tested in a motion based, piloted flight simulator. This is the first time that the L1 controller has been evaluated for piloted handling qualities. Results showed that the adaptive controller was able to recover good flying qualities from a degraded aircraft. The final topic addresses a less direct, but extremely important challenge for flying qualities research and education: a capstone course in flight mechanics teaching flight test techniques and featuring a motion based flight simulator was implemented and evaluated. The course used a mixture of problem based learning and role based learning to create an environment where students could explore key flight mechanics concepts. Evaluation of the course's effectiveness to promote the understanding of key flight mechanics concepts is presented.
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    Robust State Estimation, Uncertainty Quantification, and Uncertainty Reduction with Applications to Wind Estimation
    Gahan, Kenneth Christopher (Virginia Tech, 2024-07-17)
    Indirect wind estimation onboard unmanned aerial systems (UASs) can be accomplished using existing air vehicle sensors along with a dynamic model of the UAS augmented with additional wind-related states. It is often desired to extract a mean component of the wind the from frequency fluctuations (i.e. turbulence). Commonly, a variation of the KALMAN filter is used, with explicit or implicit assumptions about the nature of the random wind velocity. This dissertation presents an H-infinity (H∞) filtering approach to wind estimation which requires no assumptions about the statistics of the process or measurement noise. To specify the wind frequency content of interest a low-pass filter is incorporated. We develop the augmented UAS model in continuous-time, derive the H∞ filter, and introduce a KALMAN-BUCY filter for comparison. The filters are applied to data gathered during UAS flight tests and validated using a vaned air data unit onboard the aircraft. The H∞ filter provides quantitatively better estimates of the wind than the KALMAN-BUCY filter, with approximately 10-40% less root-mean-square (RMS) error in the majority of cases. It is also shown that incorporating DRYDEN turbulence does not improve the KALMAN-BUCY results. Additionally, this dissertation describes the theory and process for using generalized polynomial chaos (gPC) to re-cast the dynamics of a system with non-deterministic parameters as a deterministic system. The concepts are applied to the problem of wind estimation and characterizing the precision of wind estimates over time due to known parametric uncertainties. A novel truncation method, known as Sensitivity-Informed Variable Reduction (SIVR) was developed. In the multivariate case presented here, gPC and the SIVR-derived reduced gPC (gPCr) exhibit a computational advantage over Monte Carlo sampling-based methods for uncertainty quantification (UQ) and sensitivity analysis (SA), with time reductions of 38% and 98%, respectively. Lastly, while many estimation approaches achieve desirable accuracy under the assumption of known system parameters, reducing the effect of parametric uncertainty on wind estimate precision is desirable and has not been thoroughly investigated. This dissertation describes the theory and process for combining gPC and H-infinity (H∞) filtering. In the multivariate case presented, the gPC H∞ filter shows superiority over a nominal H∞ filter in terms of variance in estimates due to model parametric uncertainty. The error due to parametric uncertainty, as characterized by the variance in estimates from the mean, is reduced by as much as 63%.