State estimation of a non-linear stochastic distributed parameter system by discrete-time models

Abstract

Techniques of state estimator design for a class of non-linear distributed parameter systems by discrete-time models are presented. Two construction methods of discrete-time models for non-linear stochastic distributed Darameter svstems are presented. One is a method by weighted-average finite-difference approximation and the other is a discrelization method by partial inversion using Green's function. For these discrete-time models, me stability preservation is examined. Based on these sample data discrete-time models, a state filtering algorithm for a non-linear stochastic distributed parameter system is derived by means of the bayesian approach combined with local linearization. The study shows that the discrete-time models of non-linear distributed parameter systems can better be treated in the form of non-linear integral equations, and the state filtering algorithm can be derived from these integral models.

Additional information

Notes on contributors

K. S. LEE

Department of Chemical Engineering, Sogang University. CPO Box 1142, Seoul. Korea.

K. S. CHANG

Department of Chemical Engineering, Pohang Institute of Science and Technology, Pohang, Korea.