Fuzzy Predictive Control for a Tubular Heat Exchanger System

Article Preview

Abstract:

The paper deals with the problem of controlling the outlet temperature of a tubular heat exchanger system by means of flow pressure. The usual industrial system behavior must precisely be modeled and appropriate control action needs to be obtained based on novel techniques. A new multiple models control strategy using the well-known linear generalized predictive control (LGPC) scheme has been proposed in this paper. Then the best model of the system is accurately identified by an intelligent decision mechanism (IDM) which is organized based on both new recursive weight generator and fuzzy adaptive Kalman filter approaches. Simulations are all done and the results are also compared with those obtained using a nonlinear GPC (NLGPC) approach that is realized based on the Wiener model of the system. The results can verify the validity of the proposed control scheme.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 694-697)

Pages:

2195-2199

Citation:

Online since:

May 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Xia L, DeAbreu-Garcia JA, Hartley TT: Modeling and simulation of a heat exchanger. Proc of the IEEE international conference on system engineering, (1991), p.453–456.

Google Scholar

[2] Ho TB, Nguyen TD, Shimodaira H, Kimura M, in: A knowledge discovery system with support for model selection and visualization. Appl Intell 19(2003), 125–141.

Google Scholar

[3] Bakhshandeh R, in: Multiple inputs-multiple outputs adaptive predictive control of a tubular heat exchanger system. MSc Thesis, Electrical Engineering Department, Sharif University of Technology (in Persian) (1994).

Google Scholar

[4] Skrjanc I, Matko D, in: Predictive functional control based on fuzzy model for heat-exchanger pilot plant. IEEE Trans Fuzzy System 8(2000), p.705–711.

DOI: 10.1109/91.890329

Google Scholar

[5] Sadati N, Talasaz A, in: Robust fuzzy multi-model control using variable structure system. Proc of IEEE conference on cybernetics and intelligent systems, vol. 1 (2004), p.497–502.

DOI: 10.1109/iccis.2004.1460465

Google Scholar

[6] Sadati N, Ghadami R, Bagherpour M, in: Adaptive neural net-work multiple models sliding mode control of robotic manipulators using soft switching. Proc of the 17th IEEE international conference on tools with artificial intelligence, (2005), p.431–438.

DOI: 10.1109/ictai.2005.25

Google Scholar

[7] Ding Z, Leung H, Chan K, in: Model-set adaptation using a fuzzy Kalman filter. Proc of the third international IEEE conference on information fusion, vol. 1, (2000), p.2–9.

DOI: 10.1109/ific.2000.862546

Google Scholar

[8] Shiu SCK, Li Y, Zhang F, in: A fuzzy integral based query dispatching model in collaborative case-based reasoning. Appl Intell 21(2004), p.301–310.

DOI: 10.1023/b:apin.0000043562.93194.e9

Google Scholar

[9] Sun S, Zhuge F, Rosenberg J, Steiner RM, Rubin GD, Napel S, in: Learning-enhanced simulated annealing: method, evaluation, and application to lung nodule registration. Appl Intell 28(2007), p.83–99

DOI: 10.1007/s10489-007-0043-5

Google Scholar

[10] Lee KK, Yoon WC, Baek DH, in: A classification method using a hybrid genetic algorithm combined with an adaptive procedure for the pool of ellipsoids. Appl Intell 25(2006), p.293–304.

DOI: 10.1007/s10489-006-0108-x

Google Scholar