Date of Award
8-2010
Degree Type
Thesis
Degree Name
M.S.
Degree Program
Engineering
Department
Electrical Engineering
Major Professor
Jilkov, V.P.
Second Advisor
Li, X. Rong
Third Advisor
Chen, H.
Abstract
The polytopic model (PM) structure is often used in the areas of automatic control and fault detection and isolation (FDI). It is an alternative to the multiple model approach which explicitly allows for interpolation among local models. This thesis proposes a novel approach to PM estimation by modeling the set of PM weights as a random vector with Dirichlet Distribution (DD). A new approximate (adaptive) PM estimator, referred to as a Quasi-Bayesian Adaptive Kalman Filter (QBAKF) is derived and implemented. The model weights and state estimation in the QBAKF is performed adaptively by a simple QB weights' estimator and a single KF on the PM with the estimated weights. Since PM estimation problem is nonlinear and non-Gaussian, a DD marginalized particle filter (DDMPF) is also developed and implemented similar to MPF. The simulation results show that the newly proposed algorithms have better estimation accuracy, design simplicity, and computational requirements for PM estimation.
Recommended Citation
Katkuri, Jaipal, "Application of Dirichlet Distribution for Polytopic Model Estimation" (2010). University of New Orleans Theses and Dissertations. 1210.
https://scholarworks.uno.edu/td/1210
Rights
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