Disturbance modeling and state estimation for offset-free predictive control with state-space process models

• Autorzy: Tatjewski P.

Identyfikatory

DOI

10.2478/amcs-2014-0023

• Języki publikacji: EN

Abstrakty

EN

Disturbance modeling and design of state estimators for offset-free Model Predictive Control (MPC) with linear state-space process models is considered in the paper for deterministic constant-type external and internal disturbances (modeling errors). The application and importance of constant state disturbance prediction in the state-space MPC controller design is presented. In the case with a measured state, this leads to the control structure without disturbance state observers. In the case with an unmeasured state, a new, simpler MPC controller-observer structure is proposed, with observation of a pure process state only. The structure is not only simpler, but also with less restrictive applicability conditions than the conventional approach with extended process-and-disturbances state estimation. Theoretical analysis of the proposed structure is provided. The design approach is also applied to the case with an augmented state-space model in complete velocity form. The results are illustrated on a 2 x 2 example process problem.

Słowa kluczowe

EN

model predictive control; state space model; disturbance rejection; state observer; Kalman filter

PL

sterowanie predykcyjne; model przestrzeni stanów; eliminacja zakłóceń; obserwator stanu; filtr Kalmana

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2014
• Tom: Vol. 24, no. 2
• Strony: 313--323
• Opis fizyczny: Bibliogr. 27 poz., wykr.

Twórcy

autor

Tatjewski P.

• Institute of Control and Computation Engineering, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland

Bibliografia

• [1] Anderson, D. and Moore, J. (2005). Optimal Filtering, Dover Publications Inc, New York, NY.
• [2] Astrom, K. and Wittenmark, B. (1997). Computer Controlled Systems, Prentice Hall, Upper Saddle River, NJ.
• [3] Blevins, T.L., McMillan, G.K., Wojsznis, W.K. and Brown, M.W. (2003). Advanced Control Unleashed, The ISA Society, Research Triangle Park, NC.
• [4] Blevins, T.L., Wojsznis, W.K. and Nixon, M. (2013). Advanced Control Foundation, The ISA Society, Research Triangle Park, NC.
• [5] Camacho, E. and Bordons, C. (1999). Model Predictive Control, Springer Verlag, London.
• [6] Doyle III, F., Ogunnaike, B. and Pearson, R. (1996). Nonlinear model predictive control of a simulated multivariable polymerization reactor using second-order Volterra models, Automatica 32(9): 1285–1301.
• [7] Gonzalez, A.H., Adam, E.J. and Marchetti, J.L. (2008). Conditions for offset elimination in state space receding horizon controllers: A tutorial analysis, Chemical Engineering and Processing 47(12): 2184–2194.
• [8] Hesketh, T. (1982). State-space pole-placing self-tuning regulator using input-output values, IEE Proceedings, Part D 129(4): 123–128.
• [9] Ławryńczuk, M. (2009). Efficient nonlinear predictive control based on structured neural models, International Journal of Applied Mathematics and Computer Science 19(2): 233–246, DOI: 10.2478/v10006-009-0019-1.
• [10] Ławryńczuk, M. and Tatjewski, P. (2010). Nonlinear predictive control based on neural multi-models, International Journal of Applied Mathematics and Computer Science 20(1): 7–21, DOI: 10.2478/v10006-010-0001-y.
• [11] Maciejowski, J. (2002). Predictive Control, Prentice Hall, Harlow.
• [12] Maeder, U. and Morari, M. (2010). Offset-free reference tracking with model predictive control, Automatica 46(9): 1469–1476.
• [13] Morari, M. and Maeder, U. (2012). Nonlinear offset-free model predictive control, Automatica 48(9): 2059–2067.
• [14] Muske, K. and Badgwell, T. (2002). Disturbance modeling for offset-free linear model predictive control, Journal of Process Control 12(5): 617–632.
• [15] Pannocchia, G. and Bemporad, A. (2007). Combined design of disturbance model and observer for offset-free model predictive control, IEEE Transactions on Automatic Control 52(6): 1048–1053.
• [16] Pannocchia, G. and Rawlings, J. (2003). Disturbance models for offset-free model predictive control, AIChE Journal 49(2): 426–437.
• [17] Prett, D. and Garcia, C. (1988). Fundamental Process Control, Butterworths, Boston, MA.
• [18] Qin, S. and Badgwell, T. (2003). A survey of industrial model predictive control technology, Control Engineering Practice 11(7): 733–764.
• [19] Rao, V. and Rawlings, J.B. (2009). Model Predictive Control: Theory and Design, Nob Hill Publishing, Madison, WI.
• [20] Rossiter, J. (2003). Model-Based Predictive Control, CRC Press, Boca Raton, FL.
• [21] Tatjewski, P. (2007). Advanced Control of Industrial Processes, Springer Verlag, London.
• [22] Tatjewski, P. (2008). Advanced control and on-line process optimization in multilayer structures, Annual Reviews in Control 32(1): 71–85.
• [23] Tatjewski, P. (2010). Supervisory predictive control and on-line set-point optimization, International Journal of Applied Mathematics and Computer Science 20(3): 483–495, DOI: 10.2478/v10006-010-0035-1.
• [24] Tatjewski, P. (2011). Disturbance modeling and state estimation for predictive control with different state-space process models, Preprints of the 18th IFAC World Congress, Milan, Italy, pp. 5326–5331.
• [25] Tatjewski, P. (2012). Modeling deterministic disturbances and state filtering in model predictive control with state-space models, in M. Busłowicz and K. Malinowski (Eds.), Advances in Control Theory and Automation, OWPB, Białystok, pp. 263–274.
• [26] Tatjewski, P. and Ławryńczuk, M. (2006). Soft computing in model-based predictive control, International Journal of Applied Mathematics and Computer Science 16(1): 7–26.
• [27] Wang, L. (2009). Model Predictive Control System Design and Implementation Using MATLAB, Springer Verlag, London.