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Low-order Control Relevant Reduction of Distributed Parameter
Systems
Authors:
Daguang
Zheng and Karlene A. Hoo
Department of Chemical Engineering, Texas Tech University, Lubbock, TX
Abstract
Accurate solutions of distributed parameter systems may be represented as the
sum of an infinite series. Control however, requires low order models primarily
for implementation. As such, developing low order models of high fidelity is
important in the control of true distributed parameter systems . This work
addresses this issue by employing and comparing methods that arrive at low order
models either from input-output data or from exact descriptions of the process.
Using these approximate low order models, linear and nonlinear feedback
controllers are synthesized to address disturbance compensation and model
parameter uncertainty. Two candidate processes are introduced and used to
demonstrate these concepts.
Publication Information: Download Introduction -PDF
format
Chemical Engineering Science, 56, pp 6683--6710, 2001
Corresponding Author: Karlene A. Hoo
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