Thesis (Ph. D.)--University of Washington, 2001. / Vita. Includes bibliographical references (p. 126-129).

Highway agencies usually face budget uncertainty for pavement maintenance and rehabilitation activities due to limitation in resources and changes in government policies. Highway agencies perform maintenance planning for the pavement network commonly based on the nominal available budget without taking the variability of budget into consideration. The maintenance program based on deterministic budget consideration results in suboptimal maintenance decisions that impact the overall network conditions, if the budget falls short in some future year in the planning horizon. As a result, it is important for highway agencies to adopt maintenance and rehabilitation policies that are protected against the uncertainty in maintenance and rehabilitation budget. In this study a multi-period linear integer programming model is proposed with its robust counterpart considering uncertain maintenance and rehabilitation budget. The proposed model is able to provide a maintenance and rehabilitation program for the pavement network that results in minimal impact of budget variability on the network conditions. A case study was carried out for a network of ten pavement sections. The solution of the robust optimization model was compared to those with deterministic model. The results show that the robust optimization model is an attractive method that can minimize the effect of budget uncertainty on pavement conditions at the network level. / text

published_or_final_version / Statistics and Actuarial Science / Doctoral / Doctor of Philosophy

In longitudinal and spatio-temporal data analysis, repeated measurements from a subject can be either regional- or temporal-dependent. The correct specification of the within-subject covariance matrix cultivates an efficient estimation for mean regression coefficients. In this thesis, robust estimation for the mean and covariance jointly for the regression model of longitudinal data within the framework of generalized estimating equations (GEE) is developed. The proposed approach integrates the robust method and joint mean-covariance regression modeling. Robust generalized estimating equations using bounded scores and leverage-based weights are employed for the mean and covariance to achieve robustness against outliers. The resulting estimators are shown to be consistent and asymptotically normally distributed. Robust variable selection method in a joint mean and covariance model is considered, by proposing a set of penalized robust generalized estimating equations to estimate simultaneously the mean regression coefficients, the generalized autoregressive coefficients and innovation variances introduced by the modified Cholesky decomposition. The set of estimating equations select important covariate variables in both mean and covariance models together with the estimating procedure. Under some regularity conditions, the oracle property of the proposed robust variable selection method is developed. For these two robust joint mean and covariance models, simulation studies and a hormone data set analysis are carried out to assess and illustrate the small sample performance, which show that the proposed methods perform favorably by combining the robustifying and penalized estimating techniques together in the joint mean and covariance model. Capturing dynamic change of time-varying correlation structure is both interesting and scientifically important in spatio-temporal data analysis. The time-varying empirical estimator of the spatial correlation matrix is approximated by groups of selected basis matrices which represent substructures of the correlation matrix. After projecting the correlation structure matrix onto the space spanned by basis matrices, varying-coefficient model selection and estimation for signals associated with relevant basis matrices are incorporated. The unique feature of the proposed model and estimation is that time-dependent local region signals can be detected by the proposed penalized objective function. In theory, model selection consistency on detecting local signals is provided. The proposed method is illustrated through simulation studies and a functional magnetic resonance imaging (fMRI) data set from an attention deficit hyperactivity disorder (ADHD) study. / published_or_final_version / Statistics and Actuarial Science / Doctoral / Doctor of Philosophy

Power generation using wind turbines is a highly researched control field. Many control designs have been proposed based on continuous-time models like PI-control, or state observers with state feedback but without special regard to robustness to model uncertainties. The aim of this thesis was to design a robust digital controller for a wind turbine. The design was based on a discrete-time model in the polynomial framework that was derived from a continuous-time state-space model based on data from a real plant. A digital controller was then designed by interactive pole placement to satisfy bounds on sensitivity functions. As a result the controller eliminates steady state errors after a step response, gives sufficient damping by using dynamical feedback, tolerates changes in the dynamics to account for non linear effects, and avoids feedback of high frequency un modeled dynamics.

Master-Slave teleoperation systems are designed to extend a human's manipulation capability to remote tasks. Recent applications of these systems are in robotic therapy, telesurgery, and medical simulators. In practice, due to the uncertainties in the operator and environment dynamics, and time delay, stability and performance are compromised. Stability-based and performance-based controllers are introduced for these systems. A major class of the former controllers are based on the passivity theory and suffer from the assumed unbounded range of dynamics which is rather unrealistic. The latter class of controllers are mostly adaptive methods that are based on performance optimization. The theme of this thesis is on the development of new stability analysis methods, control strategies, and implementation techniques for enhanced trade-off between stability and performance. I have developed a less conservative passivity-based robust stability method and introduced, for the first time, the notion of Bounded Impedance Absolute Stability. The method provides mathematical and visual aids to incorporate bounds of the passive environment impedance for less conservative guaranteed stability conditions, promising a better compromise between stability and performance. I have extended the new method to include the dynamic range of the human operator for increased stability margins. I have also used the new method to develop a bilateral controller robust to time delays. Furthermore, I have investigated the effect of sampling position versus velocity for various sampling models to obtain less conservative coupled stability conditions for haptic simulation systems. Estimates of the environment dynamics are required to include their variations. Therefore, I have proposed two new real-time parameter estimation methods for linear and nonlinear contacts and experimentally evaluated and compared them with the available techniques. Finally, I have introduced needle insertion as a task in telerobotic systems to combine the expertise of the surgeon with robotic control. Here, the very first few steps needed to effectively control the targeting needles have been taken. I have developed a mechanics-based dynamic model for bevel-tip flexible needles inserted into soft tissues. Finite element models are used to estimate soft tissue deformation, while the mechanics-based model is used to predict needle deflections due to bevel-tip asymmetry. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2011-12-23 01:19:47.535

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