Title :
State Estimation of a Nonlinear System by Neural Extended Kalman Filter
Author :
Rajagopal, Karthikeyan ; Pappa, N.
Author_Institution :
Dept. of Instrum. Eng., Anna Univ., Chennai
Abstract :
Online estimation of state variables that are difficult or expensive to measure has been a widely studied problem. But those measurements are needed in a variety of engineering applications such as condition monitoring, fault diagnosis and process control. An observer can be designed to produce an estimate xcapped(k) of the state x(k) by making use of relevant process inputs, outputs and process knowledge in the form of mathematical model. The design of any good state estimator necessitates the development of a nonlinear model of the plant. In this paper, an approach to design a neural network based extended Kalman filter (NNEKF) with a recurrent neural model to estimate the state of a noisy dynamic system has been attempted. The effectiveness of the proposed state estimator has been demonstrated on a three tank benchmark system
Keywords :
Kalman filters; nonlinear control systems; recurrent neural nets; state estimation; NNEKF; extended Kalman filter; neural network; noisy dynamic system; nonlinear system; recurrent neural model; state estimation; tank benchmark system; Condition monitoring; Fault diagnosis; Mathematical model; Neural networks; Nonlinear dynamical systems; Nonlinear systems; Observers; Process control; Recurrent neural networks; State estimation; MIMO Systems; Neural Models; Neural Network based Extended Kalman Filter; Three tank system;
Conference_Titel :
India Conference, 2006 Annual IEEE
Conference_Location :
New Delhi
Print_ISBN :
1-4244-0369-3
Electronic_ISBN :
1-4244-0370-7
DOI :
10.1109/INDCON.2006.302752
