论文信息 - Comparative performance analysis of extended Kalman filter and neural observer for state estimation of continuous stirred tank reactor

Comparative performance analysis of extended Kalman filter and neural observer for state estimation of continuous stirred tank reactor

In this paper, a systematic approach to design a non-linear observer to estimate the states of a non-linear system is proposed. The neural network based state filtering algorithm proposed by A.G. Parlos et al. has been used to estimate the state variables, concentration and temperature in the Continuous Stirred Tank Reactor (CSTR) process. CSTR is a typical chemical reactor system with complex nonlinear dynamics characteristics. The variables which characterize the quality of the final product in CSTR are often difficult to measure in realtime and cannot be directly measured using the feedback configuration. In this work, the authors compare the performance of an Extended Kalman Filter (EKF) with respect to Neural Network (NN) based state filter for CSTR that rely solely on concentration estimation of CSTR via measured reactor temperature. The performance of these two filters is analyzed in simulation with Gaussian noise source under various operating conditions and model uncertainties.

M. Geetha | Jovitha Jerome | P. Arun Kumar | Karthik Anadhan

[1] Amir F. Atiya,et al. An algorithmic approach to adaptive state filtering using recurrent neural networks , 2001, IEEE Trans. Neural Networks.

[2] Warren E. Dixon,et al. Dynamic neural network-based robust observers for second-order uncertain nonlinear systems , 2011, IEEE Conference on Decision and Control and European Control Conference.

[3] Martin Pottman,et al. Identification of non-linear processes using reciprocal multiquadric functions , 1992 .

[4] T. Başar,et al. A New Approach to Linear Filtering and Prediction Problems , 2001 .

[5] R. E. Kalman,et al. A New Approach to Linear Filtering and Prediction Problems , 2002 .

[6] K. Srinivasan,et al. Non-linear State Estimation for Continuous Stirred Tank Reactor using Neural Network State Filter , 2006, 2006 Annual IEEE India Conference.

[7] S. Kumar,et al. A critical evaluation and experimental verification of Extended Kalman Filter, Unscented Kalman Filter and Neural State Filter for state estimation of three phase induction motor , 2011, Appl. Soft Comput..