Abstract not available

This thesis solves the stabilizing control of an autonomous motorcycle. The control of an autonomous motorcycle is a challenging and interesting problem in the field because the plant is under-actuated, unstable and nonlinear. Two major problems that have not been considered in the literature are explicitly solved in our work: (i) the robust control problem of the plant subject to uncertainty and exogenous disturbance; (ii) the non-local stabilization of the nonlinear plant. To achieve the first goal, we propose a robust H_infty controller based on the linearized system, which provides a significant improvement in dealing model uncertainty and disturbance attenuation in comparison with those controllers given by classical linear design tools. To achieve the second goal, we propose a nonlinear controller based on the combination of a nonlinear forwarding method with several other methods for the nonlinear plant through identifying an appropriate upper triangular s tructure of the nonlinear system. This yields a stability region, the whole upper space above the level ground, such that the trajectory starting from any position in the upper hemi-sphere with arbitrary initial velocities converges to the upright position. Both results are novel and first results of their kinds in control of an autonomous motorcycle. Computer simulations verify the effectiveness of the proposed controllers.

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

Today, mass customization has emerged as a manufacturing paradigm for a number of enterprises to efficiently and effectively satisfy customers requirements for product variety. The competitive nature of todays market makes it necessary for designers to have a methodology for designing customized products in such a dynamic environment. The Product Platform Constructal Theory Method (PPCTM), developed by Dr. Gabriel Hernandez, provides designers a methodical approach for synthesizing multiple modes of managing variety in the development of product platforms for customized products. The use of the PPCTM results in a hierarchical organization of the modes of managing customization, as well as the specification of their range of application across the product platform. The focus in this thesis is to augment the PPCTM in order to develop an effective product platform design method that alleviates three of its major limitations: inability to deal with uncertain distributions of demand, changing design parameters and changing extents of marketplaces. The infusion of concepts of robustness helps to address the first two limitations making the product platforms unaffected by large variations in demand and design parameters. The compromise Decision Support Problem is proposed to address the third limitation of changing extents of marketplaces by making tradeoffs between objectives of the initial market extent and future probable extensions. The result of this work is an augmented PPCTM that facilitates the synthesis of multiple modes for managing product variety in the presence of a dynamic environment. The augmented method is used to design a line of customizable pressure vessels and hand exercisers.

Thesis (Ph. D.)--Rutgers University, 2008. / "Graduate Program in Statistics and Biostatistics." Includes bibliographical references (p. 93-95).

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.

Cluster analysis is the study of how to partition data into homogeneous subsets so that the partitioned data share some common characteristic. In one to three dimensions, the human eye can distinguish well between clusters of data if clearly separated. However, when there are more than three dimensions and/or the data is not clearly separated, an algorithm is required which needs a metric of similarity that quantitatively measures the characteristic of interest. Linear Grouping Analysis (LGA, Van Aelst et al. 2006) is an algorithm for clustering data around hyperplanes, and is most appropriate when: 1) the variables are related/correlated, which results in clusters with an approximately linear structure; and 2) it is not natural to assume that one variable is a “response”, and the remainder the “explanatories”. LGA measures the compactness within each cluster via the sum of squared orthogonal distances to hyperplanes formed from the data. In this dissertation, we extend the scope of problems to which LGA can be applied. The first extension relates to the linearity requirement inherent within LGA, and proposes a new method of non-linearly transforming the data into a Feature Space, using the Kernel Trick, such that in this space the data might then form linear clusters. A possible side effect of this transformation is that the dimension of the transformed space is significantly larger than the number of observations in a given cluster, which causes problems with orthogonal regression. Therefore, we also introduce a new method for calculating the distance of an observation to a cluster when its covariance matrix is rank deficient. The second extension concerns the combinatorial problem for optimizing a LGA objective function, and adapts an existing algorithm, called BIRCH, for use in providing fast, approximate solutions, particularly for the case when data does not fit in memory. We also provide solutions based on BIRCH for two other challenging optimization problems in the field of robust statistics, and demonstrate, via simulation study as well as application on actual data sets, that the BIRCH solution compares favourably to the existing state-of-the-art alternatives, and in many cases finds a more optimal solution.

As break downs of production transformers are costly and often result in long outages it is essential for reliability of operation that the transformers are manufactured to be robust. The aim of this M.Sc. thesis is to investigate if deviations should be made from the standard IEC 60076 – Power Transformers in order to describe a robust large power transformer for procurement. The investigation has been conducted in three stages. In the first stage a definition of the term robust was developed in consultation with the client Vattenfall Vattenkraft. In the second stage six parameters, called key parameters, which significantly affect transformer robustness were identified. The identification was based on an analysis of transformer construction, a description of occurring faults and an analysis of statistics. In the third and final stage the standard was evaluated regarding requirements on the six key parameters. The evaluation was primarily conducted by means of interviewing persons with relevant knowledge of the evaluated aspects. The identified key parameters are: Temperature, regarding mainly hot spot temperature rises; Type and properties of the insulation; Quality and performance of the manufacturing facility; Ability to withstand external short circuit; Loss evaluation, since the magnitude of the losses dictates the forces developed during transients, and finally impurities within the insulation. It has been found that, in order to describe a robust transformer, deviations from the standard should be made in the specification for procurement. A total of 22 recommended deviations have been presented including the following: The hot spot temperature at the core surface should be specified and verified by calculation and test. The rated voltage for insulation sizing should be chosen higher than the standard suggests. The ability to withstand external short circuit should be validated by calculation. The loss evaluation should be specified, a value of 11,000 euro/kW for no load losses is recommended. The transformer tank lid should be welded in place in order to ensure a tight seal over time. The investigation was limited to the internal parts of large production power transformers with a rated apparent power of at least 20 MVA and a rated voltage of 40 – 400 kV. OLTCs and transformer bushings are not covered.

Cluster analysis is the study of how to partition data into homogeneous subsets so that the partitioned data share some common characteristic. In one to three dimensions, the human eye can distinguish well between clusters of data if clearly separated. However, when there are more than three dimensions and/or the data is not clearly separated, an algorithm is required which needs a metric of similarity that quantitatively measures the characteristic of interest. Linear Grouping Analysis (LGA, Van Aelst et al. 2006) is an algorithm for clustering data around hyperplanes, and is most appropriate when: 1) the variables are related/correlated, which results in clusters with an approximately linear structure; and 2) it is not natural to assume that one variable is a “response”, and the remainder the “explanatories”. LGA measures the compactness within each cluster via the sum of squared orthogonal distances to hyperplanes formed from the data. In this dissertation, we extend the scope of problems to which LGA can be applied. The first extension relates to the linearity requirement inherent within LGA, and proposes a new method of non-linearly transforming the data into a Feature Space, using the Kernel Trick, such that in this space the data might then form linear clusters. A possible side effect of this transformation is that the dimension of the transformed space is significantly larger than the number of observations in a given cluster, which causes problems with orthogonal regression. Therefore, we also introduce a new method for calculating the distance of an observation to a cluster when its covariance matrix is rank deficient. The second extension concerns the combinatorial problem for optimizing a LGA objective function, and adapts an existing algorithm, called BIRCH, for use in providing fast, approximate solutions, particularly for the case when data does not fit in memory. We also provide solutions based on BIRCH for two other challenging optimization problems in the field of robust statistics, and demonstrate, via simulation study as well as application on actual data sets, that the BIRCH solution compares favourably to the existing state-of-the-art alternatives, and in many cases finds a more optimal solution.

Dissertation (Ph.D. in Astronautical Engineering)--Naval Postgraduate School, March 2009. / Dissertation supervisor: Agrawal, Brij N. "March 2009." Description based on title screen as viewed on April 23, 2009. Author(s) subject terms: Robust control, adaptive optics, segmented mirrors, Shack-Hartmann wavefront sensor, space telescopes, H[infinity], flexible structures. Includes bibliographical references (p. 95-97). Also available in print.