In recent years, combat aircraft with external stores have experienced a decrease in their mission capabilities due to lack of robustness of the current passive wing/store flutter suppression system to both structured as well as unstructured uncertainties. The research program proposed here is to investigate the feasibility of using a piezoceramic wafer actuator for active control of store flutter with the goal of producing a robust feedback system that demonstrates increased performance as well as robustness to modeling errors. This approach treats the actuator as an active soft-decoupling tie between the wing and store, thus isolating the wing from store pitch inertia effects. Advanced control techniques are used to assess the nominal performance and robustness of wing/store system to flutter critical uncertainties. NOTE: (10/2009) An updated copy of this ETD was added after there were patron reports of problems with the file. / Ph. D.

Many industrial statisticians employ the techniques of Response Surface Methodology (RSM) to study and optimize products and processes. A second-order Taylor series approximation is commonly utilized to model the data; however, parametric models are not always adequate. In these situations, any degree of model misspecification may result in serious bias of the estimated response. Nonparametric methods have been suggested as an alternative as they can capture structure in the data that a misspecified parametric model cannot. Yet nonparametric fits may be highly variable especially in small sample settings which are common in RSM. Therefore, semiparametric regression techniques are proposed for use in the RSM setting. These methods will be applied to an elementary RSM problem as well as the robust parameter design problem. / Ph. D.

Proton therapy, a promising alternative to conventional photon therapy, has gained widespread acceptance in clinical practice. This is attributed to its superior depth-dose curve that has a negligible dose beyond the maximum range of the proton. A proton treatment planning requires a multitude of parameters and are either manually selected or optimized using mathematical formulation. However, a proton treatment plan is also subject to various systematic and random uncertainties that must be taken into account during optimization. Robust optimization is a commonly used method for integrating the setup and range uncertainties in proton therapy. In addition to the uncertainties accounted for during the treatment planning phase, others can arise during the course of treatment and are often hard to predict. Changes in the patient's anatomy represent uncertainties that can significantly affect planned dose delivery. Therefore, adaptive planning is typically performed intermittently or regularly, depending on the changes in anatomy. Paper II included in this thesis proposed a method of adaptive planning that takes into account the impact of the patient's respiratory motion at the treatment site, such as the lungs and abdomen for 4D robust optimization. This method uses dose mimicking to reproduce the results as initially planned.   This additional stage of adaptive planning can introduce new complexities and uncertainties into the treatment process. One such uncertainty arise from daily cone beam computed tomography (CBCT) images which are required for treatment plan adaptation. Several strategies have been proposed in the past to improve the quality of these images, but each strategy has its advantages and disadvantages, depending on the site of treatment. In Paper I, a method was proposed that combined the advantages of other frequently used methods to create an improved method for generating daily images with CT-like image quality. This can contribute towards the goal of online adaptive in the near future with reduced uncertainties. This thesis will provide a brief introduction and an in-depth chapter to elucidate the background, better understand the physics of proton therapy, the process of treatment planning, and the need for adaptive planning. / European Union’s Horizon 2020 Marie Skłodowska-Curie Actions under Grant Agreement No. 955956

Profile monitoring is a well-known approach used in statistical process control where the quality of the product or process is characterized by a profile or a relationship between a response variable and one or more explanatory variables. Profile monitoring is conducted over two phases, labeled as Phase I and Phase II. In Phase I profile monitoring, regression methods are used to model each profile and to detect the possible presence of out-of-control profiles in the historical data set (HDS). The out-of-control profiles can be detected by using the statis-tic. However, previous methods of calculating the statistic are based on using all the data in the HDS including the data from the out-of-control process. Consequently, the ability of using this method can be distorted if the HDS contains data from the out-of-control process. This work provides a new profile monitoring methodology for Phase I analysis. The proposed method, referred to as the cluster-based profile monitoring method, incorporates a cluster analysis phase before calculating the statistic. Before introducing our proposed cluster-based method in profile monitoring, this cluster-based method is demonstrated to work efficiently in robust regression, referred to as cluster-based bounded influence regression or CBI. It will be demonstrated that the CBI method provides a robust, efficient and high breakdown regression parameter estimator. The CBI method first represents the data space via a special set of points, referred to as anchor points. Then a collection of single-point-added ordinary least squares regression estimators forms the basis of a metric used in defining the similarity between any two observations. Cluster analysis then yields a main cluster containing at least half the observations, with the remaining observations comprising one or more minor clusters. An initial regression estimator arises from the main cluster, with a group-additive DFFITS argument used to carefully activate the minor clusters through a bounded influence regression frame work. CBI achieves a 50% breakdown point, is regression equivariant, scale and affine equivariant and distributionally is asymptotically normal. Case studies and Monte Carlo results demonstrate the performance advantage of CBI over other popular robust regression procedures regarding coefficient stabil-ity, scale estimation and standard errors. The cluster-based method in Phase I profile monitoring first replaces the data from each sampled unit with an estimated profile, using some appropriate regression method. The estimated parameters for the parametric profiles are obtained from parametric models while the estimated parameters for the nonparametric profiles are obtained from the p-spline model. The cluster phase clusters the profiles based on their estimated parameters and this yields an initial main cluster which contains at least half the profiles. The initial estimated parameters for the population average (PA) profile are obtained by fitting a mixed model (parametric or nonparametric) to those profiles in the main cluster. Profiles that are not contained in the initial main cluster are iteratively added to the main cluster provided their statistics are "small" and the mixed model (parametric or nonparametric) is used to update the estimated parameters for the PA profile. Those profiles contained in the final main cluster are considered as resulting from the in-control process while those not included are considered as resulting from an out-of-control process. This cluster-based method has been applied to monitor both parametric and nonparametric profiles. A simulated example, a Monte Carlo study and an application to a real data set demonstrates the detail of the algorithm and the performance advantage of this proposed method over a non-cluster-based method is demonstrated with respect to more accurate estimates of the PA parameters and improved classification performance criteria. When the profiles can be represented by vectors, the profile monitoring process is equivalent to the detection of multivariate outliers. For this reason, we also compared our proposed method to a popular method used to identify outliers when dealing with a multivariate response. Our study demonstrated that when the out-of-control process corresponds to a sustained shift, the cluster-based method using the successive difference estimator is clearly the superior method, among those methods we considered, based on all performance criteria. In addition, the influence of accurate Phase I estimates on the performance of Phase II control charts is presented to show the further advantage of the proposed method. A simple example and Monte Carlo results show that more accurate estimates from Phase I would provide more efficient Phase II control charts. / Ph. D.

It is common to assume that the noise disturbing measuring devices is of a Gaussian nature. But this assumption is not always fulfilled. A few examples are the cases where the measurement device fails periodically, the data transmission from device to microprocessor fails or the A/D conversion fails. In these cases the noise will no longer be Gaussian distributed, but rather the noise will be a mixture of Gaussian noise and data not related to the physical process. This posses a problem for estimators derived under the Gaussian assumption, in the sense L that these estimators are likely to produce highly biased estimates in a non Gaussian environment. This thesis devises a way to robustify the Pseudo Linear Identification algorithm (PLID) which is a joint parameter and state estimator of a Kalman filter type. The PLID algorithm is originally derived under a Gaussian noise assumption. The PLID algorithm is made robust by filtering the measurements through a nonlinear odd symmetric function, called the mb function, and let the covariance updating depend on how far away the measurement is from the prediction. In the original PLID the measurements are used unfiltered in the covariance calculation. / Master of Science

Vertical Takeoff and Landing (VTOL) Unmanned Aerial Vehicles (UAVs) possess several beneficial attributes, including requiring minimal space to takeoff, hover, and land. The tailsitter is a type of VTOL airframe that combines the benefits of VTOL capability with the ability to achieve efficient horizontal flight. One type of tailsitter, the Quadrotor Biplane (QRBP), can transition the vehicle from hover as a quadrotor to horizontal flight as a biplane. The vehicle used in this thesis is a QRBP designed with special considerations for fully autonomous operation in an outdoor environment in the presence of model uncertainties. QRBPs undergo a rotation of 90° about its pitch axis during transition from vertical to horizontal flight that induces strong aerodynamic forces that are difficult to model, thus necessitating the use of a robust control method to overcome the resulting uncertainties in the model. A feedback-linearizing controller augmented with an H-Infinity robust control is developed to regulate the altitude and pitch angle of the vehicle for the whole flight regime, including the ascent, transition forward, and landing. The performance of the proposed control design is demonstrated through numerical simulations in MATLAB and outdoor flight tests. The H-Infinity controller successfully tracks the prescribed trajectory, demonstrating its value as a computationally inexpensive, robust control technique for QRBP tailsitter UAVs. / Master of Science / Vertical Takeoff and Landing (VTOL) Unmanned Aerial Vehicles (UAVs) are a special type of UAV that can takeoff, hover, and land vertically, which lends several benefits. VTOL aircraft have recently gained popularity due to their potential to serve as fast and efficient payload delivery vehicles for e-commerce. One type of VTOL aircraft, the Quadrotor Biplane (QRBP) combines the ability of a quadrotor aircraft to hover, with the efficient horizontal flight of a biplane. Such a vehicle is able to takeoff and land in confined spaces, and also travel large distances on a single battery. However, the takeoff maneuver of a QRBP involves pitching from vertical to horizontal flight, which causes the vehicle to experience strong aerodynamic effects that are difficult to accurately model. Thus, to autonomously perform this unique maneuver, a robust control technique is necessary. A robust UAV controller is one that functions even when there is a degree of uncertainty in the predicted behavior of the vehicle, such as differences between estimated and actual vehicle parameters, or the presence of external disturbances such as wind. Therefore, a robust controller known as H-Infinity is developed to regulate the altitude and pitch angle of the QRBP as it takes off, transitions to forward flight, flies as a biplane, transitions back to vertical flight, and lands. The performance of the proposed control design is validated using numerical simulations performed in MATLAB, and flight tests. The H-Infinity controller successfully tracks the prescribed trajectory, demonstrating its value as a reliable, computationally inexpensive, robust control technique for QRBP UAVs.

Land cover classification using Synthetic Aperture Radar (SAR) data has been a topic of great interest in recent literature. Food commodities output prediction through crop identification, environmental monitoring, and forest regrowth tracking are some of the many problems that can be aided by land cover classification methods. The need for fast and automated classification methods is apparent in a variety of applications involving vast amounts of SAR data. One fundamental step in any supervised learning classification algorithm is the selection and/or extraction of features present in the dataset to be used for class discrimination. A popular method that has been proposed for feature extraction from polarimetric data is to decompose the data into the underlying scattering mechanisms. In this research, the Freeman and Durden scattering model is applied to ALOS PALSAR fully polarimetric data for feature extraction. Efficient methods for solving the complex system of equations present in the scattering model are developed and compared. Using the features from the Freeman and Durden work, the classification capability of the model is assessed on amazon rainforest land cover types using a supervised Support Vector Machine (SVM) classification algorithm. The quantity of land cover types that can be discriminated using the model is also determined. Additionally, the performance of the median as a robust estimator in noisy environments for multi-pixel windowing is also characterized. / Master of Science / Land type classification using Radar data has been a topic of great interest in recent literature. Food commodities output prediction through crop identification, environmental monitoring, and forest regrowth tracking are some of the many problems that can be aided by land cover classification methods. The need for fast and automated classification methods is apparent in a variety of applications involving vast amounts of Radar data. One fundamental step in any classification algorithm is the selection and/or extraction of discriminating features present in the dataset to be used for class discrimination. A popular method that has been proposed for feature extraction from polarized Radar data is to decompose the data into the underlying scatter components. In this research, a scattering model is applied to real world data for feature extraction. Efficient methods for solving the complex system of equations present in the scattering model are developed and compared. Using the features from the scattering model, the classification capability of the model is assessed on amazon rainforest land types using a Support Vector Machine (SVM) classification algorithm. The quantity of land cover types that can be discriminated using the model is also determined and compared using different estimators.

Cyclostationarity feature-based (C-FB) detection and classification is a large field of research that has promising applications to intelligent receiver design. Cyclostationarity FB classification and detection algorithms have been applied to a breadth of wireless communication signals — analog and digital alike. This thesis reports on an investigation of existing methods of extracting cyclostationarity features and then presents a novel robust solution that reduces SNR requirements, removes the pre-processing task of estimating occupied signal bandwidth, and can achieve classification rates comparable to those achieved by the traditional method while based on only 1/10 of the observation time. Additionally, this thesis documents the development of a novel low order consideration of the cyclostationarity present in Continuous Phase Modulation (CPM) signals, which is more practical than using higher order cyclostationarity. Results are presented — through MATLAB simulation — that demonstrate the improvements enjoyed by FB classifiers and detectors when using robust methods of estimating cyclostationarity. Additionally, a MATLAB simulation of a CPM C-FB detector confirms that low order C-FB detection of CPM signals is possible. Finally, suggestions for further research and contribution are made at the conclusion of the thesis. / Master of Science

Robust estimation of statistical parameters is traditionally believed to exist in a trade space between robustness and efficiency. This thesis examines the Minimum Hellinger Distance Estimator (MHDE), which is known to have desirable robustness properties as well as desirable efficiency properties. This thesis confirms that the MHDE is simultaneously robust against outliers and asymptotically efficient in the univariate location case. Robustness results are then extended to the case of simple linear regression, where the MHDE is shown empirically to have a breakdown point of 50%. A geometric algorithm for solution of the MHDE is developed and implemented. The algorithm utilizes the Riemannian manifold properties of the statistical model to achieve an algorithmic speedup. The MHDE is then applied to an illustrative problem in power system state estimation. The power system is modeled as a structured linear regression problem via a linearized direct current model; robustness results in this context have been investigated and future research areas have been identified from both a statistical perspective as well as an algorithm design standpoint. / Master of Science

Why, despite the territorial fragmentation and unresolved conflicts in both countries, does NATO maintain a public commitment to a 2008 decision promising the future membership of Ukraine and Georgia? It can be argued that the "Bucharest decision" has prompted the very attack that NATO membership was meant to prevent. Russia has invaded both states to, among other things, prevent their likely incorporation in NATO. What causes publicly articulated military alliance policy aspirations to endure when they induce such geopolitical conflict, and geopolitical transformation, that it undermines their purpose? This dissertation takes these puzzles as its object of inquiry. The focus of the study is Ukraine and Georgia's partial integration into NATO from 2007 to 2020. This research uses the concepts of robust action and rhetorical action to examine the two countries' growing partnerships with the alliance during this period. It defines robust action as a series of ambiguous moves to achieve tactical goals while maintaining long term flexibility. Rhetorical action is defined as the strategic use of arguments to serve an agent's interests. By using a narrative analysis method, the study draws from a body of NATO official texts and speeches and a set of original interviews to illustrate the public and private narratives used by political and military officials to help them make sense of NATO's engagement with Ukraine and Georgia. Existing literature on NATO expansion has not addressed how the alliance has adapted the process of integrating aspirant countries short of membership. Moreover, the literature on robust action has not focused on how international security organizations like NATO can use ambiguous actions to tackle complex challenges and maintain flexibility. The study argues that NATO's engagement with Ukraine and Georgia since Bucharest constitutes a robust action strategy. Through a combination of rhetorical and material support, NATO has simultaneously been able to maintain the appearance of a commitment to the two countries, show Western resolve and solidarity in opposing Russia and sustaining the United States' preferred vision of Europe's security order, all while denying Ukraine and Georgia "full membership" in the alliance. Ukraine, Georgia, and their allies in NATO have used rhetorical action, arguments based on the self-defined liberal values and norms of the Euro-Atlantic community that NATO represents on the one hand, and the historical precedent of an open door policy toward membership, on the other, to rhetorically entrap NATO into staying committed. The study shows how multilateral commitments are more layered than the traditional membership/no membership choice and how NATO has been able to successfully maintain such a commitment through both rhetoric and action while avoiding a direct war with Russia. It concludes however that NATO's commitment is untenable for a military alliance based on defense and deterrence. This has implications for the future of NATO expansion and the overall trajectory of the alliance. / Doctor of Philosophy / The possibility of further expanding NATO to Ukraine and Georgia has been among the alliance's greatest challenges since the 2008 Bucharest summit decision, which promised the future membership of the two countries. Many accounts tend to focus on the original motivation behind the decision rather than NATO's practice of maintaining a commitment to such a decision in the light of the unresolved conflicts and territorial fragmentation of both states. This study, by contrast, examines the rhetoric and action in the making of the two countries' deepening partnerships with NATO since Bucharest. This research examines how through a set of ambiguous rhetoric and action NATO has been able to maintain the appearance of a commitment to Ukraine and Georgia, project Western resolve against Russian opposition, and sustain the United States' preferred vision of the European security order, all while denying the two countries membership in the alliance. Moreover, the advocates for Ukraine and Georgia use arguments based on NATO's identity, values, and the precedent of prior expansions to convince the alliance into staying committed to their eventual membership. The study shows how NATO has devised a formula for integrating aspirant members, short of "full membership." It is useful because it shows how, in practice, multilateral commitments are more layered than they are traditionally understood. While NATO has been able to successfully maintain this commitment through both rhetoric and action, such a commitment clashes with important qualities of adaptability and flexibility to changing strategic realities, crucial to the endurance of a military alliance over the long term.