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A comparative study on applications of artificial intelligence-based multiple models predictive control schemes to a class of industrial complicated systems

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Abstract

The purpose of the present research is to demonstrate a comparative study on applications of artificial intelligence-based multiple models predictive control schemes, which are considered in a number of referenced materials. These control schemes are to implement on a class of industrial complicated systems, as long as the traditional related cases are not to guarantee the desired tracking performance, efficiently. In reality, the research proposed here, in its present form, outlines the achieved results of the control schemes, which are all organized based on both the multiple models strategy and the linear model based predictive control approach, as well. In one such case, the outcomes are focused on an industrial tubular heat exchanger system, which has so many applicabilities in real and academic environments. The traditional schemes are almost implemented on the system to control the outlet temperature of the inner tube by either the temperature or the flow of the fluid flowing, concurrently, through the shell tube. In some situations, the appropriate control scheme realization is not possible, due to the fact that the whole of system coefficients variation cannot quite be covered by the control action. In case of the matter presented, the techniques need to be organized, which the tracking performance both in the system coefficients and also in the desired set point variations could acceptably be guaranteed. Hereinafter, the performance mentioned and also the weight of realization of each one of the proposed control schemes have been surveyed, while all of them are presented in shortened version and therefore their details are not thoroughly given here. In such a case, some schemes are now available in the corresponding research, that are fully referenced, in the present investigation. In agreement with the acquired results, the validity of the control schemes are tangibly verified and also compared with respect to each other. Consequently, the finalized control schemes are suggested, where the advantages and its disadvantages of each one of them over the system are accurately investigated in line with the related reasons.

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References

  1. Nagy, Z.K., Mahn, B., Franke, R., Allgower, F.: Evaluation study of an efficient output feedback nonlinear model predictive control for temperature tracking in an industrial batch reactor. Control Eng. Pract., 15, 839–850 (2007)

  2. Chen, Q., Gao, L., Dougal, R.A., Quan, S.: Multiple model predictive control for a hybrid proton exchange membrane fuel cell system. J. Power Sources, 191, 473–482 (2009)

  3. Nunez, A., Saez, D., Oblak, S., Skrjanc, I.: Fuzzy-model-based hybrid predictive control. ISA Trans., 48, 24–31 (2009)

  4. Ning, L., Shao-Yuan, L., Yu-Geng, X.: Multi-model predictive control based on the Takagi-Sugeno fuzzy models: a case study. In: Proceedings of IEEE Conference on Information Science, pp. 247–263 (2004)

  5. Kassem, A.M.: Modeling and control design of a stand alone wind energy conversion system based on functional model predictive control. Energy Syst. 3(3), 303–323 (2012)

    Article  Google Scholar 

  6. Prakash, J., Srinivasan, K.: Design of nonlinear PID controller and nonlinear model predictive controller for a continuous stirred tank reactor. ISA Trans., 48, 273–282 (2009)

  7. Mazinan, A.H., Sadati, N.: Fuzzy multiple models predictive control of tubular heat exchanger. In: Proceedings of IEEE World Congress on Computational Intelligence, Hong-Kong, pp. 1845–1852 (2008)

  8. Mazinan, A.H., Sadati, N.: Fuzzy multiple modeling and fuzzy predictive control of a tubular heat exchanger system. In: International Conference on Application of Electrical Engineering, pp. 77–81 (2008)

  9. Mazinan, A.H., Sadati, N.: 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 (2008)

  10. Mazinan, A.H., Sadati, N.: On the Application of Fuzzy Predictive Control Based on Multiple Models Strategy to a Tubular Heat Exchanger System. Transactions of the Institute of Measurement and Control. SAGE Publisher, Beverly Hills (2009). doi:10.1177/0142331209345153. (in press)

    Google Scholar 

  11. Mazinan, A.H., Sadati, N.: An Intelligent Multiple Models Based Predictive Control Scheme. Applied Intelligence, Springer Publisher, New York (2009). doi:10.1007/s10489-009-0185-8. (in press)

    Google Scholar 

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

    Google Scholar 

  13. Mazinan, A.H., Sadati, N., Ahmadi-Noubari, H.: A case study for fuzzy adaptive multiple models predictive control strategy. In: Proceedings of IEEE International Symposium on Industrial Electronics, Korea, pp. 1172–1177 (2009)

  14. Mazinan, A.H., Sadati, N.: Fuzzy Predictive Control Based Multiple Models Strategy for a Tubular Heat Exchanger System. Applied Intelligence, Springer Publisher, New York (2009). doi:10.1007/s10489-009-0163-1. (in press)

    Google Scholar 

  15. Habbi, H., Kinnaert, M., Zelmat, M.: A complete procedure for leak detection and diagnosis in a complex heat exchanger using data-driven fuzzy models. ISA Trans., 48, 354–361 (2009)

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

  17. Huang, S., Tan, K.K., Lee, T.H.: Applied Predictive Control. Springer-Verlag, London (2002)

    Google Scholar 

  18. Sadati, N., Ghadami, R., Bagherpour, M.: Adaptive neural network multiple models sliding mode control of robotic manipulators using soft switching. In: Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence, pp. 431–438 (2005)

  19. Sadati, N., Bagherpour, M., Ghadami, R.: Adaptive multi-model CMAC-based supervisory control for uncertain MIMO systems. In: Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence, Hong Kong, China, pp. 457–461 (2005)

  20. Sadati, N., Talasaz, A.: Robust fuzzy multimodel control using variable structure system. In: Proceedings of IEEE Conference on Cybernetics and Intelligent Systems, vol. 1, pp. 497–502 (2004)

  21. Chang, W., Ku, C., Huang, P., Chang, W.: Fuzzy controller design for passive continuous-time affine TS fuzzy models with relaxed stability conditions. ISA Trans., 48, 295–303 (2009)

  22. Palm, R.: Multiple-step-ahead prediction in control systems with Gaussian process models and TS-fuzzy models. Eng. Appl. Artif. Intell., 20, 1023–1035 (2007)

  23. Su, B., Chen, Z., Yuan, Z.: Constrained predictive control based on T-S fuzzy model for nonlinear systems. J. Syst. Eng. Electron. 18, 95–100 (2007)

  24. Saha, P., Krishnan, S.H., Rao, V.S.R., Patwardhan, S.C.: Modeling and predictive control of MIMO nonlinear systems using Wiener-Laguerre models. Chem. Eng. Commun. 8, 1083–1120 (2004)

    Article  Google Scholar 

  25. Mollov, S., Babuska, R., Abonyi, J., Verbruggen, H.B.: Effective optimization for fuzzy model predictive control. IEEE Trans. Fuzzy Syst., 12, 661–676 (2004)

  26. Li, N., Li, S., Xia, Y.: Multi-model predictive control based on the TakagiSugeno fuzzy models: a case study. Inform. Sci., 247–263 (2004)

  27. Mansour, S.E.: Tuning Proportional-Integral controllers to approximate simplified predictive control performance. ISA Trans. 4, 417–422 (2009)

  28. He, M., Cai, W., Lib, S.: Multiple fuzzy model-based temperature predictive control for HVAC systems. Inform. Sci., 169, 155–174 (2005)

  29. Zhang, R., Wang, S., Xue, A., Ren, Z., Li, P.: Adaptive extended state space predictive control for a kind of nonlinear systems. ISA Trans. 48, 491–496 (2009)

  30. Arefi, M.M., Montazeri, A., Poshtan, J., Jahed-Motlagh, M.R.: Nonlinear model predictive control of chemical processes with a Wiener identification approach. In: Proceedings of IEEE Conference on Industrial Technology, pp. 1735–1740 (2006)

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

    Google Scholar 

  32. Loebis, D., Sutton, R., Chudley, J., Naeem, W.: Adaptive tuning of a Kalman filter via fuzzy logic for an intelligent AUV navigation system. Control Eng. Pract., 12, 1531–1539 (2004)

  33. Ahn, K.K., Truong, D.Q.: Online tuning fuzzy PID controller using robust extended Kalman filter. J. Process Control, 1011–1023 (2009)

  34. Nandola, N., Bhartiya, S.: A multiple model approach for predictive control of nonlinear hybrid systems. J. Process Control, 18, 131–148 (2008)

  35. Cengel, Y.A., Turner, R.H.: Fundamentals of Thermal Fluid Sciences, 2nd edn. McGraw-Hill, New York (2004)

    Google Scholar 

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Acknowledgments

The corresponding author would like to express all the best and the warmest regards to the respected Editors of ‘Energy System (ENSY)’, Springer Publisher, as well as the whole of respected potential anonymous reviewers, for suggesting their impressive, desirable, constructive and technical comments on the present investigation to be improved. Afterwards, Dr. Mazinan is highly grateful to the Islamic Azad University (IAU), South Tehran Branch, Tehran, Iran in support of the present research, which is carried out under contract with the Research Department of the IAU, South Tehran Branch. And also he appreciates Mrs. Maryam Aghaei Sarchali, Miss Mohadeseh Mazinan and finally Mr. Mohammad Mazinan for their sufficient supports in the process of paper investigation and organization.

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Authors and Affiliations

  1. Department of Control Engineering, Faculty of Electrical Engineering, South Tehran Branch, Islamic Azad University (IAU), No. 209, North Iranshahr St, P.O. Box 11365/4435, Tehran, Iran

    A. H. Mazinan

  2. Department of Control Engineering, South Tehran Branch, Islamic Azad University (IAU), No. 209, North Iranshahr St, P.O. Box 11365/4435, Tehran, Iran

    A. R. Khalaji

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Correspondence to A. H. Mazinan.

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Mazinan, A.H., Khalaji, A.R. A comparative study on applications of artificial intelligence-based multiple models predictive control schemes to a class of industrial complicated systems. Energy Syst 7, 237–269 (2016). https://doi.org/10.1007/s12667-015-0155-7

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