Application of Dirichlet Distribution for Polytopic Model Estimation

<p>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.</p>

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical Engineering

Year

2010

Author dc:creator

• Katkuri, Jaipal

Contributors dc:contributor

• Jilkov, V.P.
• Li, X. Rong
• Chen, H.

• Model interpolation
• Quasi-Bayes procedure for mixtures
• Dirichlet distribution
• Jump-Markov linear systems
• Polytopic model

Repository record dc:identifier

https://scholarworks.uno.edu/td/1210

OAI identifier oai:identifier

oai:scholarworks.uno.edu:td-2193