PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2014 | 43 | nr 2 | 261--277
Tytuł artykułu

Application of the Simple Additive Modeling of the First Principle Model Inaccuracies for the Offset-Free Process Control

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
An optimal control problem with a state constraint of inequality type and with dynamics described by a semilinear hyperbolic equation in divergence form with the non-homogeneous boundary condition of the third kind is considered. The state constraint contains a functional parameter that belongs to the class of continuous functions and occurs as an additive term. We study the properties of solutions of linear hyperbolic equations in divergence form with measures in the original data and compute the first variations of functionals on the basis of a so-called two-parameter needle variation of controls. We consider the necessary conditions for minimizing sequences in an optimal control problem with a pointwise in time state constraint of inequality type and with dynamics described by a semilinear hyperbolic equation in divergence form with the non-homogeneous boundary condition of the third kind. For the parametric optimization problem, we also consider regularity and normality conditions stipulated by the differential properties of its value function. (original abstract)
Rocznik
Tom
43
Numer
Strony
261--277
Opis fizyczny
Twórcy
  • Silesian University of Technology
  • Silesian University of Technology
Bibliografia
  • ASTROM, K.J., WITTENMARK, B. (1989), Adaptive Control. Addison-Wesley Publishing Company, Reading.
  • BASTIN, G., DOCHAIN, D. (1990), On-line Estimation and Adaptive Control of Bioreactors. Elsevier Science Publishers, Amsterdam.
  • 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.
  • BROSILOW, C., JOSEPH, B. (2002), Techniques of Model-Based Control. Prentice Hall.
  • CUTLER, C.R., 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.
  • 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.
  • 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.
  • CZECZOT, J. (2006), Balance-Based Adaptive Control Methodology and its Application to the Nonlinear CSTR. Chemical Eng. and Processing 45(4), 359-371.
  • CZECZOT, J. (2006), Balance-Based Adaptive Control of a Neutralization Process. International Journal of Control 79(12), 1581-1600.
  • 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.
  • DASGUPTA, S., SHRIVASTAVA, Y., KRENZER G. (1991), Persistent excitation in bilinear systems. IEEE Trans. on Automatic Control 36, 305-313.
  • DONIDA, F., CASELLA, F., FERRETTI, G. (2010), Model order reduction for object-oriented models: a control system perspective, Math. Computer Model. Dyn. Syst. 16(3), 269-284.
  • ECONOMOU, C.G., MORARI, M., PALSSON, B.O. (1986), InternalModel Control. Extension to Nonlinear Systems. Ind. Eng. Chem. Process Des. Dev., 25, 403-411.
  • FALK, E. (2010), A note on POD model reduction methods for DAEs. Math. Computer Model. Dyn. Syst. 16(2), 115-131.
  • GARCIA, C.E., MORARI, M. (1982), Internal model control 1. A unifying review and some new results. Ind. Engng. Chem. Process. Des. Dev. 21, 308.
  • HENSON, M.A., SEBORG, D.E. (1991), An internal model control strategy for nonlinear systems. AIChE Journal 37(7), 1065-1081.
  • HENSON, M.A., SEBORG, D.E. (1997), Nonlinear Process Control. Prentice Hall.
  • ISAACS, S.H., SOEBERG, H., 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.
  • ISIDORI, A. (1989), Nonlinear Control Systems: An Introduction. 2nd edition. Springer Verlag.
  • KLATT, K.U., MARQUARDT, W. (2009), Perspectives for process systems engineering - Personal views from academia and industry. Computers and Chemical Engineering 33, 563-550.
  • KLOPOT, T., CZECZOT, J., 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.
  • KOKOTOVIC, P., KHALIL, H.K., O'REILLY, J. (1986), Singular Perturbation Methods in Control: Analysis and Design. Academic Press, London.
  • KRAVARIS, C., HAHN, J., CHU, Y. (2012), Advances and selected recent developments in state and parameter estimation. Comp. Chem. Engng. 51, 111-123.
  • LEE, P.L., SULLIVAN, G.R. (1988), Generic model control (GMC). Comp. Chem. Engng. 12(6), 573-580.
  • MACIEJOWSKI, J. M. (2002), Predictive Control with Constraints. Prentice Hall.
  • METZGER, M. (2001), Easy programmable MAPI controller based on simplified process model. In: Proc. of the IFAC Workshop on Programmable Devices and Systems, Gliwice, Elsevier, 166-170.
  • MEADOWS, E.S., RAWLING, J.B. (1997), Model Predictive Control. Non- linear Process Control. Prentice Hall, Upper Saddle River, New Jersey.
  • MURRAY-SMITH, R., JOHANSEN, T.A. (1997), Multiple Model Approaches to Modeling and Control. Taylor and Francis, New York.
  • NELLES, O. (2001), Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models. Springer Verlag, Berlin - Heidelberg - New York.
  • PARRISH, J.R., BROSILOW, C.B. (1988), Nonlinear inferential control. AIChE Journal 34, 633-644.
  • RHINEHART, R.R., RIGGS, J.B. (1990), Process Control through Nonlinear Modeling. Control 3(7), 86.
  • RHINEHART, R.R., RIGGS, J.B. (1991), Two simple methods for on-line incremental model parameterization. Comp. Chem. Engng 15(3), 181- 189.
  • RHINEHART, R.R., DARBY, M.L., WADE, H.L. (2011), Editorial - Choosing advanced control. ISA Transactions 50, 2-10.
  • RICHALET, J. (1993), Pratique de la commande predictive. Hermes, Paris.
  • RICHALET, J., RAULT, A., TESTUD, J.L., PAPON, J. (1978), Model Predictive Heuristic Control: Application to Industrial Processes. Automatica 14(2), 413-428.
  • SEBORG, D.E. (1999), A perspective on advanced strategies for process control. ATP 41(11), 13-31.
  • STEBEL, K., CZECZOT, J., LASZCZYK, P. (2014), General tuning procedure for the nonlinear balance based adaptive controller. International Journal of Control 87(1), 76-89.
  • TATJEWSKI, P. (2007), Advanced Control of Industrial Processes. Structures and Algorithms. Springer Verlag, London.
  • VAN LITH, P.F., WITTEVEEN, H., BETLEM, B.H.L., ROFFEL, B. (2001), Multiple nonlinear parameter estimation using PI feedback control. Control Engineering Practice 9, 517-531.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171483282

Zgłoszenie zostało wysłane

Zgłoszenie zostało wysłane

Musisz być zalogowany aby pisać komentarze.
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.