MIMO Frequency Sampling Filters for Modelling of MIMO System Applications
DOI:
https://doi.org/10.5614/j.eng.technol.sci.2013.45.1.6Abstract
In the modelling of a system based on a system identification approach, data acquisition is the first procedure that must be carried out. The data acquisition process from a real system typically yields large amounts of data. This may lead to unacceptable computational time during the identification process. Raw data may also suffer severe noise disturbance, especially in the high frequency region. In addition, bias estimation will occur if one only considers direct identification from a closed-loop system. To overcome this problem, in this paper a multivariable frequency sampling filter approach is introduced. Multi-input-multi-output (MIMO) raw data are analyzed in order to obtain only relevant and meaningful parameters that describe the empirical model of the analyzed data. By performing this procedure, compressed, cleaned and unbiased data are produced. The efficacy of the MIMO frequency sampling filters was demonstrated by compressing two sets of data: pH neutralization process data and steam generator plant data. The results show that the amount of raw data was successfully compressed and that the output was ready for identification purposes with less computational time, i.e. they could be further used to develop a model of the system, to conduct time and frequency response analysis, and also for developing a new control system design.Downloads
References
Mohd-Mokhtar, R., Aziz, M.H.R.A., Arshad, M.R. & Hussain, N.A.A., Data Compression for Underwater Glider System Using Frequency Sampling Filters, Indian Journal of Geo-Marine Sciences, 40(2), pp. 227-235, 2011.
Mohd-Mokhtar, R., Aziz, M.H.R.A., Arshad, M.R. & Hussain, N.A.A., Data Compression for Underwater Glider System Using Frequency Sampling Filters, in 3rd International Conference on Underwater System Technology: Theory and Applications 2010 (USYS'10), Cyberjaya, Malaysia, pp. 18-23, 2010.
Aziz, M.H.R.A. & Mohd-Mokhtar, R., Multi Input Multi Output Frequency Sampling Filters for Real System Applications, Int. Conf. on Electrical Engineering and Informatics (ICEEI 2011), Bandung, Indonesia, 2011.
Aziz, M.H.R.A. & Mohd-Mokhtar, R., Identification of MIMO Magnetic Bearing System Using Continuous Subspace Method with Frequency Sampling Filters Approach, 37th Annual Conf. of the IEEE Industrial Electronics Society (IECON 2011), Melbourne, Australia, pp. 546-551, 2011.
Mohd-Mokhtar, R., Aziz, M.H.R.A., Arshad, M.R. & Hussain, N.A.A., Model Identification for Underwater Glider System Using Two Stage Identification Approach, ASEAN Symposium on Automatic Control (ASAC2011), Ho Chi Minh, Vietnam, pp. 92-97, 2011.
Mohd-Mokhtar, R., Continuous Time State-space Model Identification with Application to Magnetic Bearing Systems, PhD thesis, School of Electrical and Computer Engineering, RMIT, Melbourne, Australia, 2008.
Mohd-Mokhtar, R. & Wang, L., 2-Stage Identification Based on Frequency Sampling Filters and Subspace Frequency Response, Elektrika: Journal of Electrical Eng., 11(2), pp. 27-33, 2009.
Mohd-Mokhtar, R. & Wang, L., 2-Stage Approach for Continuous Time Identification Using Step Response Estimates, in Proc. Of IEEE Int. Conf. on Systems, Man & Cybernetics (SMC2008), Singapore, pp. 3183-3188, 2008.
Bitmead, R.R. & Anderson, B.D.O., Adaptive Frequency Sampling Filters, IEEE Trans. Circuits Syst., 28, pp. 524-533, 1981.
Parker, P.J. & Bitmead, R.R., Adaptive Frequency Response Identification, Proc. 26th IEEE Conf on Decision and Control, pp. 348-353, 1987.
Stubberud, P.A. & Leondes, C.T., A Frequency Sampling Filter Design Method which Accounts for Finite Word Length Effects, IEEE Trans. Sig. Process. pp. 189-193, 1984.
Goberdhansingh, E., Wang, L. & Cluett, W.R., Robust Frequency Domain Identification, Chem. Eng. Sci. 47, 1989-1999, 1992.
Cluett, W.R., Wang, L. & Zivkovic, A., Development of Quality Bounds for Time and Frequency Domain Models: Application to The Shell Distillation Column, J. Process Control, 7, pp. 75-80, 1997.
Wang, L. & Cluett, W.R., From Plant Data to Process Control: Ideas for Process Identification and PID Design, London: Francis & Taylor, 2000.
Arifin, N., Wang, L., Goberdhansingh, E. & Cluett, W.R., Identification of The Shell Distillation Column Using The Frequency Sampling Filter Model, Journal of Process Control, 5(2), pp.71-76, 1995.
Gawthrop, P.J. & Wang, L., Data Compression for Estimation of Physical Parameters of Stable and Unstable Systems, Automatica, 41, pp. 1313-1321, 2005.
Wang, L., Gawthrop, P.J. & Chessari, C., Indirect Approach to Continuous Time System Identification of Food Extruder, Journal of Process Control, 14, pp. 603-615, 2004.
Wang, L. & Cluett, W.R., Frequency-sampling Filters: An Improved Model Structure for Step-response Identification, Automatica, 33(5), pp. 939-944, 1997.
Wang, L. & Cluett, W.R., Use of PRESS Residuals in Dynamic System Identification, Automatica, 32(5), pp. 781-784, 1996.
Moor, B.L.R.D. DaISy: Database for the Identification of Systems [Online]. Available: http://homes.esat.kuleuven.be/~smc/daisy/,2011 (Information accessed on 11th Feb 2011).
Pellegrinetti, G. & Bentsman, J., Nonlinear Control Oriented Boiler Modeling - A Benchmark Problem for Controller Design, IEEE Trans. On Control Systems Technology, 4(1), pp. 57-64, 1996.
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