This thesis discusses topics relevant to indoor unmanned quadrotor navigation and control. These topics include: quadrotor modeling, sensor modeling, quadrotor parameter estimation, sensor calibration, quadrotor state estimation using onboard sensors, and cooperative GPS navigation. Modeling the quadrotor, sensor modeling, and parameter estimation are essential components for quadrotor navigation and control. This thesis investigates prior work and organizes a wide variety of models and calibration methods that enable indoor unmanned quadrotor flight. Quadrotor parameter estimation using a particle filter is a contribution that extends current research in the area. This contribution is novel in that it applies the particle filter specifically to quadrotor parameter estimation as opposed to quadrotor state estimation. The advantages and disadvantages of such an approach are explained. Quadrotor state estimation using onboard sensors and without the aid of GPS is also discussed, as well as quadrotor pose estimation using the Extended Kalman Filter with an inertial measurement unit and simulated 3D camera updates. This is done using two measurement updates: one from the inertial measurement unit and one from the simulated 3D camera. Finally, we demonstrate that when GPS lock cannot be obtained by an unmanned vehicle individually. A group of cooperative robots with pose estimates to one anther can exploit partial GPS information to improve global position estimates for individuals in the group. This method is advantageous for robots that need to navigate in environments where signals from GPS satellites are partially obscured or jammed.

The motivation for this research is the need to improve performance of the autonomous flight of a tailsitter UAV. Tailsitter aircraft combine the hovering and vertical take-off and landing capability of a rotorcraft with the long endurance flight capability of a fixed-wing aircraft. The particular aircraft used in this research is the V-Bat, a tailsitter UAV with a conventional wing and the propeller and control surfaces located within a ducted-fan tail assembly. This research focuses on identifying the models and parameters of the V-Bat in hover and level flight as a basis for the design of the control systems for hover, level, and transition modes of flight.Models and parameters were identified from experimental data. Wind-tunnel tests, bench tests, and flight tests were performed in a variety of flight conditions. Wind tunnel tests yielded force and moment coefficients over the full flight envelope of the V-Bat. Models and parameters for longitudinal, lateral, and hover flight are presented. Bench tests were conducted to enhance understanding about the ducted-fan propulsion system and the effectiveness of the control surfaces. The thrust characteristics of the ducted fan were measured. Control derivatives were derived from force and moment measurements. Flight tests were completed to obtain dynamic models of the V-Bat in hover flight. Using frequency-domain system identification methods, frequency-response and transfer function models of roll, pitch, and yaw responses to aileron, elevator, and rudder control input were derived.The results obtained from these experimental tests were used to identify models and parameters of the V-Bat aircraft, giving insight into its behavior and enhancing the control analysis and simulation capabilities for this aircraft, thus providing the increased levels of understanding needed for autonomous flight.

[pt] Este trabalho apresenta um dos procedimentos adotados na avaliação do desempenho da estimação de parâmetros. Este procedimento consiste na determinação de limites inferiores no erro médio quadrático da estimação. São examinados os limites de Cramér-Rao e Ziv-Zakai abordando-se especialmente sua aplicação ao problema da estimação de parâmetros de um processo aleatório gaussiano. Em exemplo ilustrativo os resultados obtidos são aplicados a estimação dos parâmetros da densidade espectral de potência de um processo, supondo-se para esta densidade, um modelo racional simples. / [en] This work presents one of the possible approaches of evaluating the parameter estimation performance. This approach is based on the determination of lover bounds for estimate mean square error. The Cramér-Rao and Ziv-Zakai bounds are studied mainly in the case of gaussian random process parameter estimation. The results are applied as an example to the estimation of the power spectral density parameters of a random process. A simple rational model is used to represent this spectral density.

Engineers and infrastructure owners have to manage an aging civil infrastructure in the US. Engineers have the opportunity to analyze structures using finite element models (FEM), and often base their engineering decisions on the outcome of the results. Ultimately, the success of these decisions is directly related to the accuracy of the finite element model in representing the real-life structure. Improper assumptions in the model such as member properties or connections, can lead to inaccurate results. A major source of modeling error in many finite element models of existing structures is due to improper representation of the boundary conditions. In this study, it is aimed to integrate experimental and analytical concepts by means of parameter estimation, whereby the boundary condition parameters of a structure in question are determined. FEM updating is a commonly used method to determine the "as-is" condition of an existing structure. Experimental testing of the structure using static and/or dynamic measurements can be utilized to update the unknown parameters. Optimization programs are used to update the unknown parameters by minimizing the error between the analytical and experimental measurements. Through parameter estimation, unknown parameters of the structure such as stiffness, mass or support conditions can be estimated, or more appropriately, "updated", so that the updated model provides for a better representation of the actual conditions of the system. In this study, a densely instrumented laboratory test beam was used to carry-out both analytical and experimental analysis of multiple boundary condition setups. The test beam was instrumented with an array of displacement transducers, tiltmeters and accelerometers. Linear vertical springs represented the unknown boundary stiffness parameters in the numerical model of the beam. Nine different load cases were performed and static measurements were used to update the spring stiffness, while dynamic measurements and additional load cases were used to verify these updated parameters. Two different optimization programs were used to update the unknown parameters and then the results were compared. One optimization tool was developed by the author, Spreadsheet Parameter Estimation (SPE), which utilized the Solver function found in the widely available Microsoft Excel software. The other one, comprehensive MATLAB-based PARameter Identification System (PARIS) software, was developed at Tufts University. Optimization results from the two programs are presented and discussed for different boundary condition setups in this thesis. For this purpose, finite element models were updated using the static data and then these models were checked against dynamic measurements for model validation. Model parameter updating provides excellent insight into the behavior of different boundary conditions and their effect on the overall structural behavior of the system. Updated FEM using estimated parameters from both optimization software programs generally shows promising results when compared to the experimental data sets. Although the use of SPE is simple and generally straight-forward, we will see the apparent limitations when dealing with complex, non-linear support conditions. Due to the inherent error associated with experimental measurements and FEM modeling assumptions, PARIS serves as a better suited tool to perform parameter estimation. Results from SPE can be used for quick analysis of structures, and can serve as initial inputs for the more in depth PARIS models. A number of different sensor types and spatial resolution were also investigated for the possible minimum instrumentation to have an acceptable model representation in terms of model and experimental data correlation.

This thesis focuses on an adaptive quadratic spiking model of a motoneuron that is both versatile in its ability to represent a range of experimentally observed neuronal firing patterns as well as computationally efficient for large network simulation. The objective of research is to fit membrane voltage data to the model using a parameter estimation approach involving simulated annealing. By manipulating the system dynamics of the model, a realizable model with linear parameterization (LP) can be obtained to simplify the estimation process. With a persistently excited current input applied to the model, simulated annealing is used to efficiently determine the best model parameters that minimize the square error function between the membrane voltage reference data and data generated by the LP model. Results obtained through simulation of this approach show feasibility to predict a range of different neuron firing patterns.

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