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.