In this thesis, the plant identification, state estimation based on the identified plant and also the design of a neuro-controller using multi-layer perceptrons (MLPs) for a complex system are presented. The quasi-linear system to be controlled is both unstable and nonlinear. The complete nonlinear feedback control system is designed without a priori information of the plant dynamics, using only measured input/output data. The first design step is to combine a conventional method of multivariable system identification with a dynamic multi-layer perceptron (MLP) to achieve a constructive method of system identification. Based on the identified linear model of the system, states will be estimated and converted to more appropriate state for control in the second design step. The class of quasilinear nonlinear systems is assumed to operate nominally around an equilibrium point in the neighborhood of which a linearized model exists to represent the system, although normal operation is not limited to the linear region. The results presented here provide an accurate discrete-time nonlinear model, which is used in the design of a nonlinear state estimator. The controller design is derived from a switched-linear feedback controller from the estimated states using the identified linearized model of the system around each suitable operating point, as a role model for the neuro-controller in the initial phase. Finally, using the partially trained controller, the neuro-controller can be further trained "on-line" using a selected performance index to guide the learning. A prototype problem, an inverted pendulum system, is simulated as a physical system to be identified and to be controlled. Simulation results indicate that the present design method is very reliable comparing with other methods and hence is suitable for both identifying and controlling critical industrial processes. The prominent feature of this method is that no specific model information is initially required throughout the identification and control of the nonlinear plant. As an application of identifying an unknown plant in power electronics systems, an empirical data modeling approach which aims at generating small-signal equivalent models and also nonlinear models for a general class of converters, including resonant converters, and subsystems in a distributed power system is presented. / Ph. D.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/40199 |
| Date | 26 October 2005 |
| Creators | Choi, Ju-Yeop |
| Contributors | Electrical Engineering, VanLandingham, Hugh F., Cho, Bo H., Baumann, William T., Bay, John S., Rees, Loren P. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Dissertation, Text |
| Format | xii, 140 leaves, BTD, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
| Relation | OCLC# 31459307, LD5655.V856_1994.C565.pdf |
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