Closed loop identification and effect of multiple datasets on parameter estimation : A laboratory scale industrial process

Abstract

This paper describes the application of traditional closed loop system identification techniques and development of dynamic model of a laboratory scale level process. Present work is concerned with the identification of parametric model from real time closed loop experimental data. In this paper the main emphasis is given on the effect of different disturbances and the resultant data sets on model estimation using LabVIEW system Identification toolkit. Comparison of coefficients obtained from different models and effect of multiple datasets on estimation is discussed.