Skip to main content
Log in

An intelligent multiple models based predictive control scheme with its application to industrial tubular heat exchanger system

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

The purpose of this paper is to deal with a novel intelligent predictive control scheme using the multiple models strategy with its application to an industrial tubular heat exchanger system. The main idea of the strategy proposed here is to represent the operating environments of the system, which have a wide range of variation in the span of time by several local explicit linear models. In line with this strategy, the well-known linear generalized predictive control (LGPC) schemes are initially designed corresponding to each one of the linear models of the system. After that, the best model of the system and the LGPC control action are precisely identified, at each instant of time, by an intelligent decision maker scheme (IDMS), which is playing the so important role in realizing the finalized control action for the system. In such a case, as soon as each model could be identified as the best model, the adaptive algorithm is implemented on the both chosen model and the corresponding predictive control schemes. In conclusion, for having a good tracking performance, the predictive control action is instantly updated and is also applied to the system, at each instant of time. In order to demonstrate the effectiveness of the proposed approach, simulations are carried out and the results are compared with those obtained using a nonlinear GPC (NLGPC) scheme as a benchmark approach realized based on the Wiener model of the system. In agreement with these results, the validity of the proposed control scheme can tangibly be verified.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Mazinan AH, Sadati N (2009) Fuzzy predictive control based multiple models strategy for a tubular heat exchanger. Appl Intell. doi:10.1007/s10489-009-0163-1

    Google Scholar 

  2. Mazinan AH, Sadati N (2008) Fuzzy multiple models predictive control of tubular heat exchanger. In: Proc of IEEE world congress on computational intelligence, pp 1845–1852

  3. Mazinan AH, Sadati N (2008) Multiple modeling and fuzzy predictive control of a tubular heat exchanger system. Trans Syst Control 3:249–258

    Google Scholar 

  4. Mazinan AH, Sadati N (2008) Fuzzy multiple modeling and fuzzy predictive control of a tubular heat exchanger system. In: International conference on application of electrical engineering, pp 77–81

  5. Mazinan AH, Sadati N (2008) Fuzzy multiple modeling and fuzzy predictive control of a tubular heat exchanger system. In: International conference on robotics, control and manufacturing technology, pp 93–97

  6. Hong X, Harris CJ (2002) A mixture of experts network structure construction algorithm for modelling and control. Appl Intell 16:59–69

    Article  MATH  Google Scholar 

  7. Flores A, Saez D, Araya J, Berenguel M, Cipriano A (2005) Fuzzy predictive control of a solar power plant. IEEE Trans Fuzzy Syst 1:58–68

    Article  Google Scholar 

  8. Yager RR (1992) A general approach to rule aggregation in fuzzy logic control. Appl Intell 2:333–351

    Article  Google Scholar 

  9. Sousa JMDC, Kaymak U (2001) Model prediction control using fuzzy decision functions. IEEE Trans Syst Man Cybern, Part B, Cybern 1:54–65

    Article  Google Scholar 

  10. Rashidi F, Mazinan AH (2004) Modeling and control of three phase boost rectifiers via wavelet based neural network. Trans Syst 3:494–497

    Google Scholar 

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

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

    Article  MATH  Google Scholar 

  13. Thiaw L, Malti R, Madani K (2003) A multiple models approach for nonlinear systems identification: Comparison between ANN based and conventional implementation. In: Proceeding Book of International Conference on Neural Networks and Artificial Intelligence (ICNNAI 2003), Minsk, Byelorussia, pp 210–214. ISBN 985-444-571-2

  14. Madani K, Chebira A, Rybnik M (2003) Data driven multiple neural network models generator based on a tree-like scheduler. In: Mira J, Alvarez JR (eds) Computational methods in neural modeling. Lecture notes in computer science, vol 2686. Springer, Berlin, pp 382–389. ISBN3-540-40210-1

    Chapter  Google Scholar 

  15. Chebira A, Madani K, Rybnik M (2003) Non linear process identification using a neural network based multiple models generator. In: Mira J, Alvarez JR (eds) Artificial neural nets problem solving methods. Lecture notes in computer science, vol 2687. Springer, Berlin, pp 647–654. ISBN 3-540-40211-X

    Chapter  Google Scholar 

  16. Thiaw L, Rybnik M, Malti R, Chebira A, Madani K (2004) A comparative study between a multi-models based approach and an artificial neural network based technique for nonlinear systems identification. Comput Int Sci J 3(1):66–74. ISSN 1727-6209

    Google Scholar 

  17. Bouyoucef E, Chebira A, Rybnik M, Madani K (2005) Multiple neural network model generator with complexity estimation and self-organization abilities. Int Sci J Comput 4(3):20–29. ISSN 1727-6209

    Google Scholar 

  18. Madani K, Thiaw L (2005) Multi-model based identification: application to nonlinear dynamic behavior prediction. In: Saeed, K., Mosdorf, R., Pejas, J., Hilmola, O.-P., Sosnowski, Z., (ed) Image analysis, computer graphics, security systems and artificial intelligence applications, pp 365–375. ISBN 83-87256-86-2

  19. Thiaw L, Madani K (2006) Self-organizing multi-model based identification: Application to nonlinear dynamic systems’ behavior prediction. Image Process Commun J 10(2):63–74. ISSN 1425-140X

    Google Scholar 

  20. Madani K, Thiaw L (2007) Self-organizing multi-modeling: A different way to design intelligent predictors. Neuro Comput 70(16–18):2836–2852. ISSN 0925-2312

    Google Scholar 

  21. Murray-Smith R, Johansen TA (1997) Multiple model approaches to modeling and control. Taylor & Francis, London. ISBN 0-7484-0595-X

    Google Scholar 

  22. Guerci J, Feria E (1991) Multi-model predictive transform estimation. In: Proc of aerospace and electronics conference, pp 119–125

  23. Ning L, Shao-Yuan L, Yu-Geng X (2004) Multi-model predictive control based on the Takagi-Sugeno fuzzy models: a case study. In: Proc of IEEE conference on information science, pp 247–263.

  24. Wang N (2002) A fuzzy PID controller for multi-model plant. In: Proc of IEEE conference on machine learning and cybernetics, pp 1401–1406

  25. Qi-Gang G, Dong-Feng W, Pu H, Bi-Hua L (2003) Multi-model GPC for steam temperature system of circulating fluidized bed boiler. In: Proc of IEEE international conference on machine learning and cybernetics, vol 2, pp 906–911

  26. Sadati N, Bagherpour M, Ghadami R (2005) Adaptive multi-model CMAC-based supervisory control for uncertain MIMO systems. In: Proc of the 17th IEEE international conference on tools with artificial intelligence, Hong Kong, China, Nov 2005, pp 457–461

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

  28. Skrjanc I, Matko D (2000) Predictive functional control based on fuzzy model for heat-exchanger pilot plant. IEEE Trans Fuzzy Syst 8:705–711

    Article  Google Scholar 

  29. Matko D, Kavsek-Biasizzo K, Skrjanc I, Music G (2000) Generalized predictive control of a thermal plant using fuzzy model. In: Proc of the American control conference, vol 3, pp 2053–2057

  30. Abe N, Seki K, Kanoh H (1996) Two degree of freedom internal model control for single tubular heat exchanger system. In: Proc of the IEEE international symposium on industrial electronics, vol 1, pp 260–265

  31. Fazlur Rahman MHR, Devanathan R (1994) Feedback linearisation of a heat exchanger. In: Proc of the 33rd IEEE international conference on decision and control, vol 3, pp 2936–2937

  32. Fazlur Rahman MHR, Devanathan R (1994) Modeling and dynamic feedback linearization of a heat exchanger. In: Proc of the third IEEE international conference on control applications, vol 3, pp 1801–1806

  33. Sadati N, Talasaz A (2004) Robust fuzzy multimodel control using variable structure system. In: Proc of IEEE conference on cybernetics and intelligent systems, vol 1, pp 497–502

  34. Sadati N, Ghadami R, Bagherpour M (2005) Adaptive neural network multiple models sliding mode control of robotic manipulators using soft switching. In: Proc of the 17th IEEE international conference on tools with artificial intelligence, pp 431–438

  35. Chang BR, Tsai H (2007) Composite of adaptive support vector regression and nonlinear conditional heteroscedasticity tuned by quantum minimization for forecasts. Appl Intell 27:277–289

    Article  Google Scholar 

  36. Liang K, Yao X, Newton CS (2001) Adapting self-adaptive parameters in evolutionary algorithms. Appl Intell 15:171–180

    Article  MATH  Google Scholar 

  37. Neri F, Toivanen J, Makinen RAE (2007) An adaptive evolutionary algorithm with intelligent mutation local searchers for designing multidrug therapies for HIV. Appl Intell 27:219–235

    Article  Google Scholar 

  38. Saez D, Cipriano A (1997) Design of fuzzy model based predictive controller and its application to an inverted pendulum. In: Proc of the sixth IEEE international conference on fuzzy systems, vol 2, pp 915–919

  39. Hadjili ML, Wertz V, Scorletti G (1998) Fuzzy model-based predictive control. In: Proc of IEEE decision and control, vol 3, pp 2927–2929

  40. Huang S, Tan KK, Lee TH (2002) Applied predictive control. Springer, London

    Google Scholar 

  41. Clarke DW (1988) Application of generalized predictive control to industrial processes. IEEE Control Syst Mag 49–55

  42. Sousa JM (2000) Optimization issues in predictive control with fuzzy objective functions. Int J Intell Syst 15:879–899

    Article  MATH  Google Scholar 

  43. Zamarreno JM, Vega P (1999) Neural predictive control application to a highly non-linear system. Eng Appl Artif Intell 12:149–158

    Article  Google Scholar 

  44. Gadkar KG, Doyle FJ III, Crowley TJ, Varner JD (2003) Cybernetic model predictive control of a continuous bioreactor with cell recycle. Biotechnol Prog 19:1487–1497

    Article  Google Scholar 

  45. Saha P, Krishnan SH, Rao VSR, Patwardhan SC (2004) Modeling and predictive control of MIMO nonlinear systems using Wiener-Laguerre models. Chem Eng Commun 8:1083–1120

    Google Scholar 

  46. Franco E, Sacone S, Parisini T (2004) Practically stable nonlinear receding-horizon control of multi-model systems. In: Proc of IEEE Conference on Decision and Control, 3, pp 3241–3246

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

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

    Article  MATH  Google Scholar 

  49. Zhang Y, Chi Z, Liu X, Wang X (2007) A novel fuzzy compensation multi-class support vector machine. Appl Intell 27:21–28

    Article  Google Scholar 

  50. Chen S, Chen S (2005) A prioritized information fusion method for handling fuzzy decision-making problems. Appl Intell 22:219–232

    Article  MATH  Google Scholar 

  51. Li JH, Lim MH, Cao Q (2005) A qos-tunable scheme for ATM cell scheduling using evolutionary fuzzy system. Appl Intell 23:207–218

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  54. Karr CL, Wilson E (2003) A self-tuning evolutionary algorithm applied to an inverse partial differential equation. Appl Intell 19:147–155

    Article  MATH  Google Scholar 

  55. Lee Z (2008) A robust learning algorithm based on support vector regression and robust fuzzy cerebellar model articulation controller. Appl Intell 29:47–55

    Article  Google Scholar 

  56. Arefi MM, Montazeri A, Poshtan J, Jahed-Motlagh MR (2006) Nonlinear model predictive control of chemical processes with a Wiener identification approach. In: Proc of IEEE conference on industrial technology, pp 1735–1740

  57. Rueda A, Cristea S, Prada CD, Keyser RD (2005) Non-linear predictive control for a distillation column. In: Proc of 44th IEEE conference on decision and control, pp 5156–5161

  58. Cengel YA, Turner RH (2004) Fundamentals of thermal fluid sciences, 2nd edn. McGraw-Hill, New York

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. H. Mazinan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mazinan, A.H., Sadati, N. An intelligent multiple models based predictive control scheme with its application to industrial tubular heat exchanger system. Appl Intell 34, 127–140 (2011). https://doi.org/10.1007/s10489-009-0185-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-009-0185-8

Keywords

Navigation