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A Caltech Library Repository Feedhttp://www.rssboard.org/rss-specificationpython-feedgenenWed, 31 Jan 2024 19:14:42 +0000Estimation and Control of Stochastic Chemical Systems
https://resolver.caltech.edu/CaltechTHESIS:05252018-085149423
Authors: {'items': [{'id': 'Hwang-Myung-Kyu', 'name': {'family': 'Hwang', 'given': 'Myung Kyu'}}]}
Year: 1971
DOI: 10.7907/BG3X-F787
<p>Chapter II</p>
<p>The control of nonlinear lumped-parameter systems is
considered with unknown random inputs and measurement noise.
A scheme is developed whereby a nonlinear filter is included
in the control loop to improve system performance. Pure
time delays in the control loop are also examined. A computational
example is presented for the proportional control on temperature
of a CSTR subject to random disturbances, applying a nonlinear least
square filter.</p>
<p>Chapter III</p>
<p>Least square filtering and interpolation algorithms are derived
for states and parameters in nonlinear distributed systems with unknown
additive volume, boundary and observation noises, and with volume and
boundary dynamical inputs governed by stochastic ordinary differential
equations. Observations are assumed to be made continuously in time at
continuous or discrete spatial locations. Two methods are presented
for derivation of the filter. One is the limiting procedure of the
finite dimensional description of partial differential equation systems
along the spatial axis, applying known filter equations in ordinary
differential equation systems. The other is to define a least square
estimation criterion and convert the estimation problem into an optimal
control problem, using extended invariant imbedding technique in
partial differential equations. As an example, the derived filter is
used to estimate the state and parameter in a nonlinear hyperbolic system
describing a tubular plug flow chemical reactor. Also a heat
conduction problem is studied with the filtering and interpolation
algorithms.</p>
<p>Chapter IV</p>
<p>New necessary and sufficient conditions are presented for the
observability of systems described by nonlinear ordinary differential
equations with nonlinear observations. The conditions are based on
extension of the necessary and sufficient conditions for observability
of time-varying linear systems to the linearized trajectory of the
nonlinear system. The result is that the local observability of
any initial condition can be readily determined, and the observability
of the entire initial domain can be computed. The observability of
conĀstant parameters appearing in the differential equations is also
considered.</p>https://thesis.library.caltech.edu/id/eprint/10935