×

Offset-free strategy by double-layered linear model predictive control. (English) Zbl 1251.93052

Summary: In the real applications, the Model Predictive Control (MPC) technology is separated into two layers, that is, a layer of conventional dynamic controller, based on which is an added layer of steady-state target calculation. In the literature, conditions for offset-free linear model predictive control are given for combined estimators (for both the artificial disturbance and system state), steady-state target calculation, and dynamic controller. Usually, the offset-free property of the double-layered MPC is obtained under the assumption that the system is asymptotically stable. This paper considers the dynamic stability property of the double-layered MPC.

MSC:

93B40 Computational methods in systems theory (MSC2010)
93D20 Asymptotic stability in control theory
PDF BibTeX XML Cite
Full Text: DOI

References:

[1] S. J. Qin and T. A. Badgwell, “A survey of industrial model predictive control technology,” Control Engineering Practice, vol. 11, no. 7, pp. 733-764, 2003.
[2] D. Q. Mayne, J. B. Rawlings, C. V. Rao, and P. O. M. Scokaert, “Constrained model predictive control: stability and optimality,” Automatica, vol. 36, no. 6, pp. 789-814, 2000. · Zbl 0949.93003
[3] A. Bemporad, F. Borrelli, and M. Morari, “Min-max control of constrained uncertain discrete-time linear systems,” Institute of Electrical and Electronics Engineers, vol. 48, no. 9, pp. 1600-1606, 2003. · Zbl 1364.93181
[4] L. Chisci, J. A. Rossiter, and G. Zappa, “Systems with persistent disturbances: predictive control with restricted constraints,” Automatica, vol. 37, no. 7, pp. 1019-1028, 2001. · Zbl 0984.93037
[5] B. Ding, “Properties of parameter-dependent open-loop MPC for uncertain systems with polytopic description,” Asian Journal of Control, vol. 12, no. 1, pp. 58-70, 2010.
[6] B. Kouvaritakis, J. A. Rossiter, and J. Schuurmans, “Efficient robust predictive control,” Institute of Electrical and Electronics Engineers, vol. 45, no. 8, pp. 1545-1549, 2000. · Zbl 0988.93022
[7] D. Li and Y. Xi, “The feedback robust MPC for LPV systems with bounded rates of parameter changes,” Institute of Electrical and Electronics Engineers, vol. 55, no. 2, pp. 503-507, 2010. · Zbl 1368.93174
[8] H. Huang, D. Li, Z. Lin, and Y. Xi, “An improved robust model predictive control design in the presence of actuator saturation,” Automatica, vol. 47, no. 4, pp. 861-864, 2011. · Zbl 1215.93049
[9] Y. I. Lee and B. Kouvaritakis, “Receding horizon output feedback control for linear systems with input saturation,” IEE Proceedings of Control Theory and Application, vol. 148, pp. 109-115, 2001.
[10] C. Løvaas, M. M. Seron, and G. C. Goodwin, “Robust output-feedback model predictive control for systems with unstructured uncertainty,” Automatica, vol. 44, no. 8, pp. 1933-1943, 2008. · Zbl 1283.93113
[11] D. Q. Mayne, S. V. Raković, R. Findeisen, and F. Allgöwer, “Robust output feedback model predictive control of constrained linear systems: time varying case,” Automatica, vol. 45, pp. 2082-2087, 2009. · Zbl 1175.93072
[12] B. Ding, “Constrained robust model predictive control via parameter-dependent dynamic output feedback,” Automatica, vol. 46, no. 9, pp. 1517-1523, 2010. · Zbl 1201.93039
[13] B. Ding, Y. Xi, M. T. Cychowski, and T. O’Mahony, “A synthesis approach for output feedback robust constrained model predictive control,” Automatica, vol. 44, no. 1, pp. 258-264, 2008. · Zbl 1138.93340
[14] B. Ding, B. Huang, and F. Xu, “Dynamic output feedback robust model predictive control,” International Journal of Systems Science, vol. 42, no. 10, pp. 1669-1682, 2011. · Zbl 1260.93047
[15] B. Ding, “Dynamic output feedback predictive control for nonlinear systems represented by a Takagi-Sugeno model,” IEEE Transactions on Fuzzy Systems, vol. 19, no. 5, pp. 831-843, 2011.
[16] B. C. Ding, “Output feedback robust MPC based-on direct input-output model,” in Proceedings of the Chinese Control and Decision Conference, Taiyuan, China, 2012.
[17] D. E. Kassmann, T. A. Badgwell, and R. B. Hawkins, “Robust steady-state target calculation for model predictive control,” AIChE Journal, vol. 46, no. 5, pp. 1007-1024, 2000.
[18] T. Zou, H. Q. Li, X. X. Zhang, Y. Gu, and H. Y. Su, “Feasibility and soft constraint of steady state target calculation layer in LP-MPC and QP-MPC cascade control systems,” in Proceedings of the 4th International Symposium on Advanced Control of Industrial Processes (ADCONIP ’11), pp. 524-529, Hangzhou, China, 2011.
[19] K. R. Muske and T. A. Badgwell, “Disturbance modeling for offset-free linear model predictive control,” Journal of Process Control, vol. 12, no. 5, pp. 617-632, 2002.
[20] G. Pannocchia and J. B. Rawlings, “Disturbance models for offset-free model-predictive control,” AIChE Journal, vol. 49, no. 2, pp. 426-437, 2003.
[21] B. C. Ding, T. Zou, and H. G. Pan, “A discussion on stability of offset-free linear model predictivecontrol,” in Proceedings of the Chinese Control and Decision Conference, Taiyuan, China, 2012.
[22] T. Zhang, G. Feng, and X. J. Zeng, “Output tracking of constrained nonlinear processes with offset-free input-to-state stable fuzzy predictive control,” Automatica, vol. 45, no. 4, pp. 900-909, 2009. · Zbl 1162.93370
[23] M. V. Kothare, V. Balakrishnan, and M. Morari, “Robust constrained model predictive control using linear matrix inequalities,” Automatica, vol. 32, no. 10, pp. 1361-1379, 1996. · Zbl 0897.93023
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.