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Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 195-199).

Thesis (M.S.)--Rochester Institute of Technology, 1994. / Typescript. Includes bibliographical references (leaf 123).

A new approach to the identification problem of linear continuous-time time-invariant systems from input-output measurements is presented. Both parametric and nonparametric system models are considered. The new approach is based on the use of continuous-time functions, the modal functions, defined in terms of the system output, the output derivatives and the state variables under the assumption that the order n of the observable system is known a priori. The modal functions are obtained by linear filtering operations of the system output, the output derivatives and the state variables so that the modal functions are independent of the system instantaneous state. In this case, the modal functions are linear functions of the input exponential modes, and they contain none of the system exponential modes unlike the system general response which contains modes from both the system and the input. The filters parameters, the modal parameters are estimated using linear regression techniques. The modal functions and the modal parameters of the output and its derivatives are used to identify parametric input-output and state models of the system. The coefficients of the system characteristic polynomial are obtained by solving n algebraic equations formed from the estimates of the modal parameters. Estimates of the parameters associated with the system zeros are obtained by solving another set of linear algebraic equation. The system frequency response and step response are estimated using the output modal function. The impulse response is obtained by filtering the estimated step response using the output first derivative modal parameters. A new method is presented to obtain the system poles as the eigenvalues of a data matrix formed from the system free response. The coefficients of the system characteristic polynomial are obtained from the data matrix through a simple recursive equation. This method has some important advantages over the well known Prony's method. The state modal functions are used to obtain a minimum-time observer that gives the continuous-time system state as a direct function of input-output samples in n sampling intervals. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

Batch reactors are often used to produce high quality products because any batch that does not meet quality speci cations can be easily discarded. However, for high-value products, even a few wasted batches constitute substantial economic loss. Fortunately, databases of historical data that can be exploited to improve operation are often readily available. Motivated by these considerations, this thesis addresses the problem of direct, data-based quality control for batch processes. Speci cally, two novel datadriven modeling and control strategies are proposed. The rst approach addresses the quality modeling problem in two steps. To begin, a partial least squares (PLS) model is developed to relate complete batch trajectories to resulting batch qualities. Next, the so called missing-data problem, encountered when using PLS models partway through a batch, is addressed using a data-driven, multiple-model dynamic modeling approach relating candidate input trajectories to future output behavior. The resulting overall model provides a causal link between inputs and quality and is used in a model predictive control scheme for direct quality control. Simulation results for two di erent polymerization reactors are presented that demonstrate the e cacy of the approach. The second strategy presented in this thesis is a state-space motivated, timeinvariant quality modeling and control approach. In this work, subspace identi cation methods are adapted for use with transient batch data allowing state-space dynamic models to be identifi ed from historical data. Next, the identifi ed states are related through an additional model to batch quality. The result is a causal, time-independent model that relates inputs to product quality. This model is applied in a shrinking horizon model predictive control scheme. Signi cantly, inclusion of batch duration as a control decision variable is permitted because of the time-invariant model. Simulation results for a polymerization reactor demonstrate the superior capability and performance of the proposed approach. / Thesis / Doctor of Philosophy (PhD) / High-end chemical products, ranging from pharmaceuticals to specialty plastics, are key to improving quality of life. For these products, production quality is more important than quantity. To produce high quality products, industries use a piece of equipment called a batch reactor. These reactors are favorable over alternatives because if any single batch fails to meet a quality specifi cation, it can be easily discarded. However, given the high-value nature of these products, even a small number of discarded batches is costly. This motivates the current work which addresses the complex topic of batch quality control. This task is achieved in two steps: first methods are developed to model prior reactor behavior. These models can be applied to predict how the reactor will behave under future operating policies. Next, these models are used to make informed decisions that drive the reaction to the desired end product, eliminating o -spec batches.

A systematic optimization of the Cohen class time-frequency transformation for detecting the parameters change is developed. The local moments approach to change detection is proposed and a general formula for the local moments is derived. The optimal kernel functions of the time-frequency transformation are determined based on the combined criteria of maximum sensitivity with respect to parameters change and minimum distortion of physical interpretation of the local moments. The sensitivity of the local moment with respect to a certain kind of inputs is analyzed and a most "convenient" and a "worst" input are identified. The results are presented in the form of the case studies for detecting parameters change in simple linear systems. / Graduation date: 1992

Thesis (M.S.)--Rochester Institute of Technology, 1990. / Spine title: Model reduction of complex dynamic systems. Includes bibliographical references.

Thesis (M.S.)--Rochester Institute of Technology, 1994. / Typescript. Includes bibliographical references (leaves 107-108).

Xi-Cheng Lou ... [et al.]. / Bibliography: leaf 14. / "October, 1983." / Air Force Office of Scientific Research Contract AFOSR-82-0258 Natural Sciences and Engineering Research Council of Canada Grant A-1240

by John L. Wyatt, Jr., William M. Siebert, Han-Ngee Tan. / "October, 1982." / Bibliography: p. 16-17. / National Science Foundation Grant No. ECS 800 6878