Isolation of Interacting Channels in Decentralized Control Systems Using Instrumental Variables Method

Abstract:

This paper describes the use of partial correlation based instrumental variables method for the identification and isolation of weak interaction dynamics between subsystems in decentralized control systems. Unlike the available methods based on the ordinary least square, the proposed method clearly discriminates the interaction channels that have significant contribution to the interconnected subsystem from the ones which do not by reducing the model error that arises due to the process inputs correlation. The efficacy of the proposed method is illustrated through a case study.

• Periodical: Applied Mechanics and Materials (Volume 625)
• Pages: 435-438
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.625.435
• Online since: September 2014
• Authors: Mohamed Rahim, M. Ramasamy*, Lemma Dendena Tufa, A. Faisal
• Keywords: Decentralized Control, Instrumental Variables, Subsystem Interaction

References

[1] R. D. Gudi and J. B. Rawlings, Identification for decentralized model predictive control, AIChE Journal, 52 (2006) 2198-2210.

DOI: 10.1002/aic.10781

[2] P. D. Christofides, R. Scattolini, D. Muñoz de la Peña, and J. Liu, Distributed model predictive control: A tutorial review and future research directions, Computers & Chemical Engineering, 51 (2013) 21-41.

DOI: 10.1016/j.compchemeng.2012.05.011

[3] R. Tóth, V. Laurain, M. Gilson, and H. Garnier, Instrumental variable scheme for closed-loop LPV model identification, Automatica, 48 (2012) 2314-2320.

DOI: 10.1016/j.automatica.2012.06.037

[4] L. Ljung, System identification, 2nd Ed., Prentice Hall PTR, New Jersey, (1999).

[5] L. Mišković, A. Karimi, and M. Gevers, Closed-loop identification of multivariable systems: With or without excitation of all references?, Automatica, 44 (2008) 2048-(2056).

DOI: 10.1016/j.automatica.2007.11.016

[6] M. Gevers, A. S. Bazanella, and L. Mišković, Identifiability and informative experiments in open and closed-loop identification, in Modeling, Estimation and Control, ed: Springer, (2007) 151-170.

DOI: 10.1007/978-3-540-73570-0_13

[7] A. S. Bazanella, M. Gevers, and L. Miškovic, Closed-loop identification of MIMO systems: a new look at identifiability and experiment design, European Journal of Control, (2010) 228-239.

DOI: 10.3166/ejc.16.228-239

[8] K. A. Clarke, The phantom menace: Omitted variable bias in econometric research, Conflict Management and Peace Science, 22 (2005) 341-352.

DOI: 10.1080/07388940500339183

[9] L. F. Martins and V. J. Gabriel, Linear instrumental variables model averaging estimation, Computational Statistics & Data Analysis, 71 (2014) 709-724.

DOI: 10.1016/j.csda.2013.05.008

[10] J. P. Congalidis, J. R. Richards, and W. H. Ray, Modeling and control of a copolymerization reactor, in American Control Conference, (1986) 1779-1793.

DOI: 10.23919/acc.1986.4789216