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Parametric System Identification using Closed loop Step Response
Objectives: Present work describes the development of a dynamic model of laboratory scale level process using System Identification toolkit from LabVIEW graphical programming language. Methods/Statistical analysis: The dynamic characteristics of industrial processes are affected when they are recognized with different operating conditions. It is always advantageous to have an efficient model to design and implement suitable control configuration to obtain better performance. The identification process has several stages. Findings: The first stage comprises design of experiment, conduction, collection of experimental data. Useful portion of the input output data is selected and preprocessed to eliminate any outliers. In the second stage the experimental data is prepared in to different datasets which are further used to estimate autoregressive exogenous (ARX) models. Application/Improvements: Estimated models are simulated in both direct and indirect methods and the results are compared to draw conclusions.
ARX Model, Closed loop Step Response, Data Driven Modeling, Parametric System Identification, System Identification.
- Eng YH,Teo KM,Chitre M, Ming K.Online system identification of an autonomous underwater vehicle via in-field experiments.IEEE Journal of OceanicEngineering.2016 Jan; 41(1).
- Astrom KJ, Eykhoff P. System identification- a survey.Automatic.1971Mar; 7(2):123–62.
- Lia T,Wang Q-G,Haung H-P. A tutorial review on process identification from step or relay feedback test. Journal of Process Control. 2013Sep; 23:1597–623.
- Perez NAH, Witrant E, Sename O. A time-delay approach for modeling and control of mist in a poiseuille flow. IEEE Control Conference (ECC); 2014 Jun.
- Åström AJ, Hägglund T. PID controller: Theory, design, and tuning. 2nd ed., ISA Society of America, Research Triangle Park, NC; 1995.
- Liu T, Gao F. Closed-loop step response identification of integrating and unstable processes.Chemical Engineering Science; 2010 May. p. 2884–95.
- Ferguson WS. Optical closed loop experiments for accurate step response model identification. National Library of Canada; 2011.
- Eckman DP. Automatic process control. Wiley Eastern Ltd; 1993
- Aljamaan I, Westwick D, Foley M. Prediction error identification of Hammerstein models in the presence of ARIMA disturbance. IEEE Conference, Control Application (CCA); 2014 Dec.
- Gevers M. A personal view of the development of system identification a 30 year journeythrough an exciting field.IEEE Control Systems Magazine;2006 Dec. p.93–105.
- Suganda P, Krishnaswamy PR, Rangaiah GP. On-line process identification from closed loop test under PI control.Chemical Engineering Research and Design.1998 May; 76:451–7.
- Instruction Manual of Level process station Trainer LPS 581. Vi Microsystems.
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