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Multivariable CRHPC (constrained receding-horizon predictive control) algorithm with improved numerical properties

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Języki publikacji
EN
Abstrakty
EN
This paper is concerned with the stabilising constrained receding-horizon predictive control algorithm (CRHPC) for multivariable processes. The optimal inputprofile is calculated by means of a new method the purpose of witch is to avoid inverting usually ill-conditioned matrices. additionally, ralatively simple formulae for calculating free and forced output predictions for the ARX process model, as well as the analytical stabilising control law in the unconstained case are derived, without the necessity of solving a matrix Diophantine equation.
Rocznik
Strony
59--79
Opis fizyczny
Bibliogr. 24 poz., rys., tab.
Twórcy
  • Warsaw University of Technology, Institute of Control and Computation Engineering ul. Nowowiejska 15/19, 00-665 Warszawa, Poland, lawrynczuk@ia.pw.edu.pl
autor
  • Warsaw University of Technology, Institute of Control and Computation Engineering ul. Nowowiejska 15/19, 00-665 Warszawa, Poland
Bibliografia
  • [1] A. Bemporad, L. Chisci and L. Mosca: On the stabilizing property of SIORHC. Automatica, 30(12), (1994), 2013-2015.
  • [2] R. R. Bitmead, M. Gevhrs and V. Wertz: Adaptive optimal control – The thinking man's GPC. Prentice Hall. Englewood Cliffs, 1990.
  • [3] E. F. Camacho and C. Bordons: Model predictive control. Springer. London. 1999.
  • [4] C. C. Chen and L. Shaw: On receding horizon feedback control. Automatica, 18(3), (1982), 349-352.
  • [5] L. Chisci and L. Mosca: Stabilizing l-O receding horizon control of CARMA plants. IEEE Trans. on Automatic Control, 39(3), (1994), 614-618
  • [6] D. W. Clarke and R. Scattolini: Constrained receding-horizon predictive control. Proc. IEE. Part D. 138(4), (1991), 347-354.
  • [7] D. W. Clarke and C. Mohtadi: Properties of generalized predictive control. Automatica, 25(6), (1989), 859-875.
  • [8] D. W. Clarke, C. Mohtadi and P. S. Tuffs: Generalized predictive control - 1. The basic algorithm. Automatica 23(2), (1987), 137-148, II. Extensions and interpretations, 149-160.
  • [9] C. R. Cutler and B. L. Ramaker: Dynamic matrix control - a computer control algorithm. AIChE National Meeting. Houston. Texas. (1979). Proc. Joint. Aut. Control Conf., San Fransisco, (1980).
  • [10] G. H. Golub and C. F. Van Loan: Matrix compulations. The Johns Hopkins University Press. Baltimoare and London, 1989.
  • [11] M. A. Henson: Nonlinear model predictive control: current status and future directions. Computers and Chemical Engineering. 23(2), (1998), 187-202.
  • [12] S. S. Keerthi and E. G. Gilbert: Optimal infinite-horizon feedback laws for a general class of constrained discrete-time systems: stability and moving-horizon approximations. J. Optimization Theory and Applications. 57(2), (1988), 265-293.
  • [13] J. M. Maciejowski: Predictive control with constraints. Prentice Hall, 2002.
  • [14] D. Q. Mayne: Control of constrained dynamic systems. European J. of Control, 7(2-3), (2001), 87-99.
  • [15] D. Q. Mayne, J. B. Rawlingcs. C. V. Rao and P. O. M. Scokaert: Constrained model predictive control: Stability and optimality. Automatica, 36(6), (2000). 789-814.
  • [16] D. Q. Mayne and H. Michalska: Receding horizon control of nonlinear systems. IEEE Trans. on Automatic Control, 35(7), (1990), 814-824.
  • [17] E. S. Meadows, M. A. Henson, J. W. Eaton and J. B. Rawlings: Receding horizon control and discontinuous suite feedback stabilization. Int. J. of Control. 62(5), (1995), 1217-1229.
  • [18] M. Morari and J. H. Lee: Model predictive control: past, present and future. Computers and Chemical Engineering. 23(4-5), (1999), 667-682.
  • [19] E. Mosca and J. Zhang: Stable redesign of predictive control. Automatica, 2816), (1992), 1229-1233.
  • [20] R. Rouhani and R. K. Mehra: Model algoritmic control (MAC): basic theoretical properties. Automatica, 18(4), (1982), 401-114.
  • [21] R. Scattouni and S. Bittantl: On the choice of the horizon in long-range predictive control - some simple criteria. Automatica, 26(5), (1990), 915-917.
  • [22] P. O. M. Scokaert, D. Q. Mayne and J. B. Rawlings: Suboptimal model predictive control (feasibility implies stability). IEEE Trans. on Automatic Control 44(3), (1999), 648-654.
  • [23] P. O. M. Scokaert and D. W. Clarke: Stabilising properties of constrained predictive control. Proc. IEE. Part D,. 141(5), (1994), 295-304.
  • [24] R Tatjewski: Advanced control of industrial processes. Structures and algorithms. Akademicka Oficyna Wydawnicza Exit, Warszawa, 2002, (in Polish).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSW3-0007-0005
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