Certain investigations on performance of particle filters for state estimation and fault diagnosis in stochastic nonlinear system

Abstract

State estimation is largely applied to many engineering problems for estimating the states of a system using a sequence of noisy measurements The knowledge of such states can be used for monitoring fault diagnosis and for enhancing the control performance. Many Bayesian state estimation methods have been developed so far for providing solution to the nonlinear state estimation problem Recursive implementations of Monte Carlo based statistical signal processing are known as particle filters which are potentially suited for estimating the states of highly nonlinear and non Gaussian system The Particle Filter PF requires the design of proposal density and the suitable choice of proposal distribution is the key design issue as it can significantly affect the performance of the filter The primary objective of this research work is to investigate on the estimation performance of the particle filtering method with different proposal densities and develop a novel fault diagnosis approach based on a multi model method for stochastic nonlinear systems The system considered in this work for carrying out the simulation study is Continuous Stirred Tank Reactor CSTR The CSTR is a typical chemical reactor system with complex nonlinear dynamic characteristics It comprises of two state variables the product concentration and reactor temperature Simulations of the system and its state estimation have been carried out using MATLAB program in an open loop condition The Sequential Importance Resampling Particle Filter SIR PF is designed and implemented for estimating the states of a stochastic nonlinear CSTR system This filter uses a set of weighted particles to approximate the posterior distribution of the state vector and evolve the state estimates in parallel. It ensures that the particles of equal weight are formed through resampling It assumes that the transition prior is the proposal density and the importance weights are obtained from the likelihood function newline