Control of an indoor autonomous mobile communications relay via antenna diversity

Presented in this thesis is a motion planning scheme for enabling a quadrotor unmanned aerial vehicle (UAV) to serve as an autonomous communications relay in indoor or GPS-denied environments. The goal of the algorithm is to maximize the throughput of the end-to-end communications channel. An extremum-seeking controller steers the quadrotor while collision avoidance is provided by artificial potential fields. Extremum-seeking is model-free adaptive control method; it\u27s applicable in situations where there is a nonlinearity in the control problem and the nonlinearity has a local minimum or maximum. The extremum-seeking controller presented here is driven by antenna diversity and attempts to optimize the inputs to an unknown, time-varying cost function characterized by the RF environment. Each of the multiple antennas onboard the quadrotor receives the same incoming packets and provides associated signal strength measurements. The extremum-seeking controller then uses these measurements to autonomously fly the quadrotor communications relay to an optimal location so as to maximize throughput, all without positioning data. This work is motivated by the need to extend the operating ranges of robots in complex urban and indoor environments. The algorithm and necessary technical background are presented in detail. Simulations results verify the validity of the proposed extremum-seeking approach. Experiments demonstrate the feasability of implementing the extremum-seeking controller with tangible hardware

Improving the Subgrid-Scale Representation of Hydrometeors and Microphysical Feedback Effects Using a Multivariate Pdf

The subgrid-scale representation of hydrometeor fields is important for calculating microphysical process rates. In order to represent subgrid-scale variability, the Cloud Layers Unified By Binormals (CLUBB) parameterization uses a multivariate Probability Density Function (PDF). In addition to vertical velocity, temperature, and moisture fields, the PDF includes hydrometeor fields. Previously, each hydrometeor field was assumed to follow a multivariate single lognormal distribution. Now, in order to better represent the distribution of hydrometeors, two new multivariate PDFs are formulated and introduced in part one of this two-part project. The new PDFs represent hydrometeors using either a delta-lognormal or a delta-double-lognormal shape. The two new PDF distributions, plus the previous single lognormal shape, are compared to histograms of data taken from Large-Eddy Simulations (LES) of a precipitating cumulus case, a drizzling stratocumulus case, and a deep convective case. Finally, the warm microphysical process rates produced by the different hydrometeor PDFs are compared to the same process rates produced by the LES. Microphysics processes have feedback effects on moisture and heat content. Not only do these processes influence mean values, but also variability and fluxes of moisture and heat content. For example, evaporation of rain below cloud base may produce cold pools. This evaporative cooling may increase the variability in temperature in the below-cloud layer. Likewise, rain production in the moistest part of cloud tends to decrease variability in cloud water. These effects are usually not included in most coarse-resolution weather and climate models, or else are crudely parameterized. In part two of this two-part project, the microphysical effects on moisture and heat content are parameterized using the PDF method. This approach is based on predictive, horizontally-averaged equations for the variances, covariances, and fluxes of moisture and heat content. These higher-order moment equations contain microphysical source terms. Using a simple warm-rain microphysics scheme, the microphysics terms can be calculated by integrating analytically over the multivariate PDF. A LES of a precipitating cumulus case indicates that microphysical terms are dominant in some budgets. The analytic integrals for the microphysics terms are implemented in the CLUBB model. Interactive single-column simulations agree qualitatively with the LES

Improvements to an analytical multiple-shooting approach for optimal burn-coast-burn ascent guidance

Launch mission planning and ascent guidance is one of the most notable engineering fields where optimization tools and optimal control theory have found routine applications. Optimality is critical to achieve the full performance of a launch vehicle. In the case of a multi-stage launch, allowing for optimized coast arcs between burns can significantly reduce propellant consumption and enhance mission capability. These coast arcs, however, render the optimal control problem more sensitive and increase algorithm convergence difficulties. This work presents detailed improvements to an analytical multiple-shooting (AMS) method for reliable generation of the optimal exo-atmospheric ascent trajectory. The trajectory consists of two burns separated by an optimized coast arc. The problem is in closed-form and quadratures. A strong effort is made in increasing the robustness, reliability, and flexibility of the algorithm. The improvements include an introduction of a more sophisticated numerical method, replacement of the current coast arc solution with a completely general, compact, and easily implementable method capable of determining the solution to machine precision, and a direct treatment of the orbital insertion conditions and resulting unknown multipliers. An aerospace industry standard trajectory optimization software, Optimal Trajectories by Implicit Simulation (OTIS), is employed to compare the results and verify the improved AMS algorithm. A wide range of mission scenarios are tested using the algorithm in open-loop solution and closed-loop simulation

The Presidency and Political Equality

When black Americans and white Americans want the president to do different things, who wins? When low-income earners prefer different government action than do middle and high-income earners, whose preferences are reflected in presidential behavior? Recent studies show that congressional behavior often most closely follows the preferences of the white and the wealthy, but we know relatively little about presidential behavior. Since the president and Congress make policy together, it is important to understand the extent of political equality in presidential behavior. We examine the degree to which presidents have provided equal representation to these groups over the past four decades. We compare the preferences of these groups for federal spending in various budget domains to presidents’ subsequent budget proposals in those domains from 1974 to 2010. Over this period, presidents’ proposals aligned more with the preferences of whites and high-income earners. However, Republican presidents are driving this overall pattern. Democratic presidents represent racial and income groups equally, but Republicans’ proposals are much more consistent with the spending preferences of whites and high-income earners. This pattern of representation reflects the composition of the president\u27s party coalition and the spending preferences of groups within the party coalition. Who gets what they want from government? That is, whose preferences for government policy are best reflected in the policies government creates? Sidney Verba (2003, 663) argued “the equal consideration of the preferences and interests of all citizens” is “one of the bedrock principles in a democracy.” However, several recent studies of U.S. politics find that the wealthy and whites are more likely than the poor and racial/ethnic minorities to see their preferences reflected in government behavior and policy (e.g., Bartels 2008; Ellis 2012; Flavin 2012; Gilens 2012; Gilens and Page 2014; Griffin and Newman 2008; Jacobs and Page 2005;). Additional works qualify, critique, and complicate these studies (e.g., Bhatti and Erikson 2011; Soroka and Wlezien 2008;), finding that the degree of inequality in political outcomes varies across political contexts (e.g., Brunner, Ross, and Washington 2013; Ellis 2013;; Rigby and Wright 2011, 2013;), though few have argued that the American political system reflects the preferences of various income and racial groups equally. The majority of this literature examines the content of public policy or congressional behavior. We know much less about presidential representation of income and racial/ethnic groups. Although presidency scholars have made major strides in understanding when and how much presidential behavior mirrors public preferences (e.g., Canes-Wrone and Shotts 2004; Cohen 1997; Druckman and Jacobs 2011; Erikson, MacKuen, and Stimson 2002, Rottinghaus 2006;), according to Druckman and Jacobs (2009), we have just begun to appreciate which groups’ preferences presidents represent best. In this study, we seek a more complete understanding of whose preferences are best represented in presidential behavior. In doing so, we build on and contribute to a growing literature that examines inequality in political representation more broadly (e.g., Enns and Wlezien 2011). It is not a foregone conclusion that the patterns of inequality seen elsewhere in the American political system would also characterize the presidency. The president serves as a national leader, rather a representative of a smaller, sometimes more homogeneous, constituency like members of Congress (Baker 2008), which can generate different incentives to represent specific groups. Moreover, presidential and congressional representation may differ given institutional differences in method of election, term length, term limits, and citizens’ different expectations of these elected officials. For example, legislators who are retiring, thus free from electoral pressures to represent their constituents’ preferences, behave differently than legislators running for reelection (e.g., Rothenberg and Sanders 2007). Since second-term presidents spend half their tenure in office without the possibility of reelection, unlike the majority of members of Congress, presidential behavior may differ from congressional behavior. If so, minority representation may vary significantly across the branches of government. In the particular policy arena we study here, presidents’ proposals for federal government spending, the president and members of Congress may often have different incentives for representation. Presumably, the public holds the president more accountable than members of Congress for the composition of the budget simply because the president proposes an entire budget. Members of Congress can request additional spending on areas of particular ideological or economic interest to their constituents, but members do not propose entire budgets, meaning they can often make requests without the hard choices the president must make: a dollar increase in one program means a dollar decrease in another or the president must bear the political cost of a bloated budget. Moreover, it is important to examine equality of representation in the context of the presidency because the president plays the strongest and most direct role in representing citizens’ preferences of any single actor in the American political system. A Member of Congress may represent her constituents well or poorly, but in the end, she is but one of 435 or 100 members in a single chamber of a bicameral institution that comprises one of three branches of government. A constituent may be especially well represented by her member of the House, but that member has limited influence on the outputs of the House, much less the ultimate output of the policymaking process involving the House, Senate, and president. Thus, a connection between public preferences and policy outputs is important (Gilens 2012), but does not tell us much about the behavior of any specific individuals. In contrast, the president is a single actor who can often take direct action (Howell 2003). Of course, the president often relies heavily on others in the administration, but within the executive branch, the president\u27s opinion is decisive, unlike individual lawmakers’. Thus, within the American system, the president has the most power to expand, shrink, or even reverse the patterns of unequal congressional representation. Consequently, the presidency should be of great significance to representation scholars. We examine the degree to which presidents have provided equal representation to racial groups (blacks and whites—unfortunately our data source did not identify Latinos for most of our period of study) and to income groups (low-, middle-, and high-income earners) over the past four decades. We observe whether these groups prefer government spending to increase, decrease, or remain about the same. We then compare group preferences to presidents’ subsequent budget proposals to see whether presidential behavior matches group preferences. Doing so enables us to see, for example, how often presidents propose a spending increase in a domain when a group prefers more spending in that policy area. Analyzing data from 1974 to 2010, we find that presidents’ proposals match whites’ and high-income earners’ preferences significantly more often than the preferences of African Americans and low-income earners. In particular, Republican presidents’ proposals are more often congruent with the spending preferences of whites and the wealthy. Democrats, on the other hand, tend to match the groups’ preferences equally. This pattern of presidential representation reflects the composition of current party coalitions. That is, presidents act most consistently with the preferences of the largest groups in their party coalitions, leading to different patterns of representation for Democrats and Republicans

Design and Analysis of Experiments in Networks: Reducing Bias from Interference

Estimating the effects of interventions in networks is complicated due to interference, such that the outcomes for one experimental unit may depend on the treatment assignments of other units. Familiar statistical formalism, experimental designs, and analysis methods assume the absence of this interference, and result in biased estimates of causal effects when it exists. While some assumptions can lead to unbiased estimates, these assumptions are generally unrealistic in the context of a network and often amount to assuming away the interference. In this work, we evaluate methods for designing and analyzing randomized experiments under minimal, realistic assumptions compatible with broad interference, where the aim is to reduce bias and possibly overall error in estimates of average effects of a global treatment. In design, we consider the ability to perform random assignment to treatments that is correlated in the network, such as through graph cluster randomization. In analysis, we consider incorporating information about the treatment assignment of network neighbors. We prove sufficient conditions for bias reduction through both design and analysis in the presence of potentially global interference; these conditions also give lower bounds on treatment effects. Through simulations of the entire process of experimentation in networks, we measure the performance of these methods under varied network structure and varied social behaviors, finding substantial bias reductions and, despite a bias–variance tradeoff, error reductions. These improvements are largest for networks with more clustering and data generating processes with both stronger direct effects of the treatment and stronger interactions between units. Keywords: causal inference; field experiments; peer effects; spillovers; social contagion; social network analysis; graph partitionin