Degree Type

Dissertation

Date of Award

2002

Degree Name

Doctor of Philosophy

Department

Electrical and Computer Engineering

First Advisor

Mustafa Khammash

Second Advisor

Murti Salapaka

Abstract

Nowadays, performance requirements imposed on system control designs have become more and more complicated. For many problems, it is very hard or even impossible to obtain analytic solutions. In recent years, powerful numerical computational tools for solving mathematical programming/optimization problems have been developed. This makes it possible to formulate control design problems as mathematical programming problems and then solve them using numerical optimization techniques. In this thesis, we show that two classes of important robust control design problems can be tackled by employing some newly-emerged mathematical optimization techniques.;In the first part of the thesis, we present a methodology to address the general multiobjective (GMO) control problem involving the ℓ 1 norm, H2 norm, Hinfinity norm and time-domain constraint (TDC). We show that the auxiliary problem resulting after imposing a regularizing condition always admits an optimal solution. Suboptimal solutions with performance arbitrarily close to the optimal cost can be obtained by constructing two sequences of finite dimensional convex optimization problems whose objective values converge to the optimum from below and above. Numerical implementation of the proposed methodology is discussed and several numerical examples are presented to illustrate the effectiveness of the proposed methodology.;In the second part, we consider the integrated parameter and control (IPC) design problem where the system structure parameters enter the state-space representation of the system in a rational manner. Converging finite-dimensional sub-optimal problems are constructed and solved via a linear relaxation technique, whereby a global optimal solution to the IPC problem is computed within any given performance tolerance. Two numerical examples are provided.

DOI

https://doi.org/10.31274/rtd-180813-11317

Publisher

Digital Repository @ Iowa State University, http://lib.dr.iastate.edu

Copyright Owner

Xin Qi

Language

en

Proquest ID

AAI3073474

File Format

application/pdf

File Size

117 pages

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