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Tytuł artykułu

Application of the simple additive modeling of the first principle model inaccuracies for the offset–free process control

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, the method for simple additive modeling of the first principle model inaccuracies for the offset-free process control is presented. Starting from transformation of the general nonlinear state model into the input affine dynamical equation describing directly the controlled variable, it is shown how to compensate for the potential modeling inaccuracies by lumping them into a single additive parameter. Its on-line estimation procedure based only on the measurement data collected from the process is very simple and effective and the estimate converges without any additional excitation of the process. The discussion on how to apply the suggested model as a basis for the chosen model-based control techniques is presented, and for the processes of the higher relative order, the practical simplification of this approach is shown. The experimental results show the practical applicability of the considered approach for the synthesis of the open loop InternalModel Controller (IMC) and of the Balance-Based Adaptive Controller (B-BAC).
Rocznik
Strony
261--277
Opis fizyczny
Bibliogr. 39 poz., rys.
Twórcy
autor
  • Silesian University of Technology Faculty of Automatic Control, Electronics and Computer Science Institute of Automatic Control ul. Akademicka 16, 44-100 Gliwice, Poland
autor
  • Silesian University of Technology Faculty of Automatic Control, Electronics and Computer Science Institute of Automatic Control ul. Akademicka 16, 44-100 Gliwice, Poland
Bibliografia
  • 1. ASTROM, K.J. and WITTENMARK, B. (1989) Adaptive Control. Addison– Wesley Publishing Company, Reading.
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  • 3. BEQUETTE, B.W. (1989) A one-step-ahead approach to nonlinear process control. In: Proc. of ISA/89 International Conference. ISA – International Society for Measurment and Control, Philadelphia, 711–717.
  • 4. BROSILOW, C. and JOSEPH, B. (2002) Techniques of Model-Based Control. Prentice Hall.
  • 5. CUTLER, C.R. and RAMAKER, B.C. (1980) Dynamic Matrix Control – A Computer Control Algorithm. American Control Conference. American Institute of Chemical Engineers, San Francisco, USA, paper WP5– BJACC.
  • 6. CZECZOT, J. (1998) Model-based adaptive control of fed-batch fermentation process with the substrate consumption rate application. In: R.R. Bitmead, M.A. Johnson and M.J. Grimble, eds., Adaptive Systems in Control and Signal Processing 1998: Proc. of the IFAC Workshop, Glasgow, UK. IFAC Proceedings Volumes, 357–362.
  • 7. CZECZOT, J. (2001) Balance-Based Adaptive Control of the Heat Exchange Process. In: Proc. of 7th IEEE International Conference on Methods and Models in Automation and Robotics MMAR, Międzyzdroje. Wydawnictwo Uczelniane Politechniki Szczecińskiej, 853–858.
  • 8. CZECZOT, J. (2006) Balance-Based Adaptive Control Methodology and its Application to the Nonlinear CSTR. Chemical Eng. and Processing 45(4), 359–371.
  • 9. CZECZOT, J. (2006a) Balance-Based Adaptive Control of a Neutralization Process. International Journal of Control 79(12), 1581–1600.
  • 10. CZECZOT, J. (2007) On the minimum form of the balance-based adaptive controller. In: Proc. of 13th IEEE International Conference on Methods and Models in Automation and Robotics MMAR. Przedsiębiorstwo Produkcyjno–Handlowe ZAPOL, Szczecin, Poland, 445–450.
  • 11. DASGUPTA, S., SHRIVASTAVA, Y. and KRENZER G. (1991) Persistent excitation in bilinear systems. IEEE Trans. on Automatic Control 36, 305–313.
  • 12. DONIDA, F., CASELLA, F. and FERRETTI, G. (2010)Model order reduction for object-oriented models: a control system perspective, Math. Computer Model. Dyn. Syst. 16(3), 269–284.
  • 13. ECONOMOU, C.G., MORARI,M. and PALSSON, B.O. (1986) InternalModel Control. Extension to Nonlinear Systems. Ind. Eng. Chem. Process Des. Dev., 25, 403-411.
  • 14. FALK, E. (2010) A note on POD model reduction methods for DAEs. Math. Computer Model. Dyn. Syst. 16(2), 115–131.
  • 15. GARCIA, C.E. and MORARI, M. (1982) Internal model control 1. A unifying review and some new results. Ind. Engng. Chem. Process. Des. Dev. 21, 308.
  • 16. HENSON, M.A. and SEBORG, D.E. (1991) An internal model control strategy for nonlinear systems. AIChE Journal 37(7), 1065-1081.
  • 17. HENSON, M.A. and SEBORG, D.E. (1997) Nonlinear Process Control. Prentice Hall.
  • 18. ISAACS, S.H., SOEBERG, H. and KUMMEL, M. (1992) Monitoring and Control of a Biological Nutrient Removal Processes: Rate Data as a Source of Information. In: Proc. of IFAC Modelling and Control of Biotechnological Processes. IFAC Publications, Colorado, USA, 239–242.
  • 19. ISIDORI, A. (1989) Nonlinear Control Systems: An Introduction. 2nd edition. Springer Verlag.
  • 20. KLATT, K.U. and MARQUARDT,W. (2009) Perspectives for process systems engineering - Personal views from academia and industry. Computers and Chemical Engineering 33, 563–550.
  • 21. KLOPOT, T., CZECZOT, J. and KLOPOT,W. (2012) Flexible function block for PLC-based implementation of the Balance-Based Adaptive Controller. In: Proc. American Control Conference ACC 2012, Montreal, Canada. Institute of Electrical and Electronics Engineers, Piscataway, 6467–6472.
  • 22. KOKOTOVIC, P., KHALIL, H.K. and O’REILLY, J. (1986) Singular Perturbation Methods in Control: Analysis and Design. Academic Press, London.
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  • 25. MACIEJOWSKI, J.M. (2002) Predictive Control with Constraints. Prentice Hall.
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  • 27. MEADOWS, E.S. and RAWLING, J.B. (1997) Model Predictive Control. Non- linear Process Control. Prentice Hall, Upper Saddle River, New Jersey.
  • 28. MURRAY-SMITH, R. and JOHANSEN, T.A. (1997)Multiple Model Approaches to Modeling and Control. Taylor and Francis, New York.
  • 29. NELLES, O. (2001) Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models. Springer Verlag, Berlin – Heidelberg - New York.
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  • 32. RHINEHART, R.R. and RIGGS, J.B. (1991) Two simple methods for on-line incremental model parameterization. Comp. Chem. Engng 15(3), 181– 189.
  • 33. RHINEHART, R.R., DARBY, M.L. andWADE, H.L. (2011) Editorial – Choosing advanced control. ISA Transactions 50, 2-10.
  • 34. RICHALET, J. (1993) Pratique de la commande predictive. Hermes, Paris.
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  • 36. SEBORG, D.E. (1999) A perspective on advanced strategies for process control. ATP 41(11), 13–31.
  • 37. STEBEL, K., CZECZOT, J. and LASZCZYK, P. (2014) General tuning procedure for the nonlinear balance based adaptive controller. International Journal of Control 87(1), 76–89.
  • 38. TATJEWSKI, P. (2007) Advanced Control of Industrial Processes. Structures and Algorithms. Springer Verlag, London.
  • 39. VAN LITH, P.F., WITTEVEEN, H., BETLEM, B.H.L. and ROFFEL, B. (2001) Multiple nonlinear parameter estimation using PI feedback control. Control Engineering Practice 9, 517–531.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-205e3d7f-0bb3-4b2c-bb38-41f55f3728a7
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