Thesis (Ph. D. in Mechanical Engineering)--Naval Postgraduate School, December 2004. / Thesis advisor(s): Brij N. Agrawal, Young S. Shin. Includes bibliographical references (p. 163-165). Also available online.

Thesis (Ph. D.)--University of Wisconsin--Madison, 1966. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Bibliographical references: leaves 158-183.

Thesis (M.S.)--University of Wisconsin--Madison, 1970. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

Thesis (Ph. D.)--Mechanical Engineering, Georgia Institute of Technology, 2006. / Zinn, Ben, Committee Chair ; Glezer, Ari, Committee Member ; Shelton, Samuel, Committee Member ; Lieuwen, Tim, Committee Member ; Neumeier, Yedidia, Committee Member.

It is assumed that processes to be identified or controlled can be described by linear or non-linear differential equations with unknown coefficients aᵢ. For on-line identifications a model is constructed to have the same form of differential equation as the process, but with adjustable parameters αᵢ replacing the aᵢ. The parameters αᵢ are adjusted in a steepest descent fashion; they are shown to converge to the aᵢ as long as an adjustment gain K(t) is non-negative, and not identically zero. An approximate analysis is carried out to determine the best constant K which gives the fastest identification. Optimal control theory is introduced to find the best piecewise continuous K(t) in the interval 0 ≤ K(t) ≤ Kmax . From the exact solution in a special case, a switched suboptimal K which always gives fast identification is determined. Identification schemes are developed for processes which include an unknown non-linearity that, can be assumed to be piecewise linear. An adaptive control, system is developed to control processes with unknown time-varying coefficients. The system is shown to be stable as long as the process inverse is stable; the process need not be stable. Systems to control linear and non-linear unstable processes are designed and simulated. The limitations of the adaptive system are determined, and compared with the limitations of conventional feedback systems. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

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