State-Space Fuzzy-Neural Predictive Control

Part of the Studies in Computational Intelligence book series (SCI, volume 657)


The purpose of this work is to give an idea about the available potentials of state-space predictive control methodology based on fuzzy-neural modeling technique and different optimization procedures for process control. The proposed controller methodologies are based on Fuzzy-Neural State-Space Hammerstein model and variants of Quadratic Programming optimization algorithms. The effects of the proposed approaches are studied by simulation experiments to control a primary drying cycle in small-scale freeze-drying plant. The obtained results show a well-driven drying process without violation of the system constraints and accurate minimum error model prediction of the considered system states and output.


Model Predictive Control Nonlinear Model Predictive Control Optimal Control Policy Model Predictive Control Algorithm Sublimation Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Eng, Pract. 11, 733–764 (2003)CrossRefGoogle Scholar
  2. 2.
    Holkar, K.S., Waghmare L.M.: An overview of model predictive control. Int. J. Control Autom. 3(4) (2010)Google Scholar
  3. 3.
    Pearson, R.K.: Selecting nonlinear model structures for computer control. J. Process Control 13 (2003)Google Scholar
  4. 4.
    Passino, K., Yourkovic, S.: Fuzzy Control. Adisson-Wesley (1998)Google Scholar
  5. 5.
    Dalhoumi, L.: Fuzzy predictive control based on Takagi-Sugeno model for nonlinear systems. In: Proceeding of 7th International Multi-Conference on Systems Signals and Devices, pp. 1–7 (2010)Google Scholar
  6. 6.
    Hadjili, M.: Modelling and control using Takagi-Sugeno fuzzy models. In: Proceeding of Electronics, Communications and Photonics Conference, pp. 1–6 (2011)Google Scholar
  7. 7.
    Mendes, J.: Adaptive fuzzy generalized predictive control based on Discrete-Time T-S fuzzy model. In: Proceeding of IEEE Conference of Emerging Technologies and Factory Automation, pp. 1–8 (2010)Google Scholar
  8. 8.
    Chadl,i M., Borne, P.: Multiple Models Approach in Automation: Takagi-Sugeno Fuzzy Systems, Wiley, (2012)Google Scholar
  9. 9.
    Bai, E.: An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems. Automatica 4(3), 333–338 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Janczak, A.: Neural network approach for identification of Hammerstein systems. Int. J. Control 76(17), 1749–1766 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Westwick, D., Kearney, R.: Identification of a Hammerstein model of the stretch reflex EMG using separable least squares. In: Proceedings of World Congress on Medical Physics and Biomedical Engineering. (2000)Google Scholar
  12. 12.
    Rizvi, S., Al-Duwaish, H.: A PSO-subspace algorithm for identification of Hammerstein models. In: Proceeding of IFAC Conference on Control Applications of Optimization (2009)Google Scholar
  13. 13.
    Ashooria, A., Moshiria, B., Khaki-Sedighb, A., Bakhtiari, M.: Optimal control of a nonlinear fed-batch fermentation process using model predictive approach. J. Process Control 19(7), 1162–1173 (2009)CrossRefGoogle Scholar
  14. 14.
    Li, Y., Kashiwa, H.: High-order volterra model predictive control and its application to a nonlinear polymerization process. Int. J. Autom. Comput. 2, 208–214 (2006)CrossRefGoogle Scholar
  15. 15.
    Franks, F.: Freeze-Drying of Pharmaceuticals and Biopharmaceuticals. The Royal Society of Chemistry (2007)Google Scholar
  16. 16.
    Oetjen, G., Hasley, P.: Freeze-Drying 2nd edn.. Willey (2004)Google Scholar
  17. 17.
    Prasad, K.: Downstream process technology: a new horizon in biotechnology. PHI Learning (2010)Google Scholar
  18. 18.
    Aghbashlo, M., Kianmehr, M.H., Nazghelichi, T., Rafiee, S.: Optimization of an artificial neural network topology for predicting drying kinetics of carrot cubes using combined response surface and genetic algorithm. Drying Technol. 29(7), 770–779 (2011)CrossRefGoogle Scholar
  19. 19.
    Trelea, I.C., Passot, S., Fonseca, F., Marin, M.: An interactive tool for the optimization of freeze-drying cycles based on quality criteria. Drying Technol. 25(5), 741–751 (2010)CrossRefGoogle Scholar
  20. 20.
    Fissore, D., Pisano, R., Barresi, A.: a model-based framework to optimize pharmaceuticals freeze drying. Drying Technol. 30(9), 946–9589 (2012)CrossRefGoogle Scholar
  21. 21.
    Pisano, R., Barresi, A., Fissore, D.: Innovation in monitoring food freeze drying. drying technology. Selected Papers Presented at the 17th International Drying Symposium, Part 2, vol. 29, issue (16), pp. 1920–1931 (2011)Google Scholar
  22. 22.
    Barresi, A., Pisano, R., Rasetto, V., Fissore, D., Marchisio, D.: Model-based monitoring and control of industrial freeze-drying processes: effect of batch non-uniformity. Drying Technol. 28(5), 577–590 (2010)CrossRefGoogle Scholar
  23. 23.
    Velardi, S., Hammouri, H., Barresi, A.: In-line monitoring of the primary drying phase of the freeze-drying process in vial by means of a Kalman filter based observer. Chem. Eng. Res. Des. 87(10), 1409–1419 (2009)CrossRefGoogle Scholar
  24. 24.
    Pisano, R., Fissore, D., Barresi, A.: Freeze-drying cycle optimization using model predictive control techniques. Ind. Eng. Chem. Res. 50, 7363–7379 (2011)CrossRefGoogle Scholar
  25. 25.
    Daraoui, N., Dufour, P., Hammouri, H., Hottot, A.: Model predictive control during the primary drying stage of lyophilisation. Control Eng. Pract. 18(5), 483–494 (2010)CrossRefGoogle Scholar
  26. 26.
    Cubillos, F., Vyhmeister, E., Acuña, G., Alvarez, P.: Rotary dryer control using a grey-box neural model scheme. Drying Technol. 29(15), 1820–1827 (2011)CrossRefGoogle Scholar
  27. 27.
    Polat, K., Kirmaci, V.: A novel data preprocessing method for the modeling and prediction of freeze-drying behavior of apples: multiple output-dependent data scaling. Drying Technol. 30(2), 185–196 (2012)CrossRefGoogle Scholar
  28. 28.
    Jumah, R., Mujumdar, A.: Modeling intermittent drying using an adaptive neuro-fuzzy inference system. Drying Technol. 23(5), 1075–1092 (2005)CrossRefGoogle Scholar
  29. 29.
    Terzyiska, M., Todorov, Y., Petrov, M.: Nonlinear model predictive controller using a fuzzy-neural Hammerstein model. In: Proceedings of international conference “Modern Trends in Control”, pp. 299–308 (2006)Google Scholar
  30. 30.
    Todorov, Y., Petrov, M.: Model Predictive Control of a Lyophilization plant: a simplified approach using Wiener and Hammerstein systems. J. Control Intell. Syst. 39(1), 23–32 (2011). Acta PressMathSciNetzbMATHGoogle Scholar
  31. 31.
    Todorov, Y., Ahmed, S., Petrov, M.: Model predictive control of a Lyophilization plant: a newton method approach. J. Inform. Technol. Control IX(4), 9–15 (2011)Google Scholar
  32. 32.
    Todorov Y., Ahmed, S., Petrov M.: State-Space Predictive Control of a Lyophilization plant: A fuzzy-neural Hammerstein model approach. In: Proceedings of the 1st IFAC Workshop Dynamics and Control in Agriculture and Food Processing, pp. 181–186 (2012)Google Scholar
  33. 33.
    Todorov Y., Ahmed, S., Petrov, M., Chitanov, V.: implementations of a Hammerstein fuzzy-neural model for predictive control of a Lyophilization plant. In: Proceedings of the 6th IEEE Conference on “Intelligent Systems”, vol. 2, pp. 315–319 (2012)Google Scholar
  34. 34.
    Osowski, S.: Sieci neuronowe do przetwarzania infromacji. Oficyna Wydawnycza Policehniki Warzawsikej (2000)Google Scholar
  35. 35.
    Maciejowski, J.: Predictive Control with Constraints. Prentiss Hall (2002)Google Scholar
  36. 36.
    Rossiter, A. Model Based Predictive Control: A Practical Approach. CRC Press (2003)Google Scholar
  37. 37.
    Camacho, E., Bordons, C.: Model Predictive Control, 2nd edn. (2004)Google Scholar
  38. 38.
    Wang, L.: Model Predictive Control System Design and Implementation Using MATLAB. Springer (2009)Google Scholar
  39. 39.
    Fletcher, R.: Practical Methods for Optimization, 2nd edn. Wiley (2006)Google Scholar
  40. 40.
    Schoen, M., Jefferis, R.: Simulation of a controlled freeze drying process. In: Proceedings of IASTED International Conference, pp. 65–68 (1993)Google Scholar
  41. 41.
    Schoen, M.: A Simulation model for primary drying phase of Freeze-drying. Int. J. Pharm. 114, 159–170 (1995)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Department of “Intelligent Systems”Institute of Information and Communication Technologies, Bulgarian Academy of SciencesSofiaBulgaria
  2. 2.Department of “Informatics and Statistics”University of Food TechnologiesPlovdivBulgaria
  3. 3.Department of “Control Systems”Technical Univeristy- Sofia, branch PlovdivPlovdivBulgaria

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