The week's pick
Reseach Article
A New Weighted Graph-Based Partitioning Algorithm for Decentralized Nonlinear Model Predictive Control of Large-Scale Systems
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 40 - Number 14 |
| Year of Publication: 2012 |
| Authors: Karim Salahshoor, Saeed Kamelian |
10.5120/5046-6915
|
Karim Salahshoor, Saeed Kamelian . A New Weighted Graph-Based Partitioning Algorithm for Decentralized Nonlinear Model Predictive Control of Large-Scale Systems. International Journal of Computer Applications. 40, 14 ( February 2012), 7-14. DOI=10.5120/5046-6915
Abstract
This paper proposes a grouping algorithm for partitioning large-scale nonlinear dynamical systems based on graph theory. The algorithm incorporates a novel scheme to quantify the strengths of graph edges, representing the degree of couplings among the system variables via sensitivity functions. This leads to a weighted graph topology with different weights on the obtained graph edges. An algorithm is then developed to partition systems into some sub-graphs based on the weighted graph. A decentralized nonlinear model predictive control (NMPC) methodology is then formulated for the sub-systems. The overall NMPC design methodology is finally evaluated on a process plant benchmark, consisting of two continuous stirred tank reactors (CSTRs) and a flash separator with a recycle path. A set of tracking and regulatory tests is comparatively conducted exploring the successful performance of the proposed algorithm in the context of the decentralized NMPC methodology with respect to an alternative centralized NMPC control scheme.
References
- C. Ocampo-Martinez, S. Bovo, V. Puig, “Partitioning approach oriented to the decentralised predictive control of large-scale systems”, Journal of process control, 2011.
- Y. Guo, D. Hill, and Y. Wang, ”Nonlinear Decentralized Control of Large-Scale Power Systems”, TECHNICAL REPORT: EE-98020, Electrical and Information Engineering School The University of Sydney, NSW 2006, Australia.
- A. N. Venkat. “Distributed Model Predictive Control: Theory and Applications”, PhD thesis, University of Wisconsin Madison, October 2006. URL http://jbrwww.che.wisc.edu/theses/venkat.pdf.
- S. Oschs, S. Engell, and A. Draeger, ”Decentralized vs. Model Predictive Control of an Industrial Glass Tube Manufacturing Process”, Proc. 1998 IEEE Int. Conf. Control Applications, Trieste, Italy, pp. 16-20.
- R. Scattolini, “Architectures for distributed and hierarchical Model Predictive Control – A review”, Journal of Process Control, 2009, pp. 723–731.
- S. Xu J. Bao, “Distributed control of plantwide chemical processes”, Journal of Process Control, 2009, pp. 1671–1687.
- M.B., Jamoom, E., Feron, and M.W. McConley, ”Optimal Distributed Actuator Control Grouping Schemes”, Proc. 37th IEEE Conf. on Decision and Control, Dec. 1998, pp. 1900-1905.
- N. Motee and B. Sayyar-Rodsari. “Optimal partitioning in distributed model predictive control”, In Proceedings of the American Control Conference, Denver,Colorado, June 2003, pp. 5300–5305.
- C. Ocampo-Martinez, V. Fambrini, D. Barcelli, V. Puig, “Model predictive control of drinking water networks: A hierarchical and decentralized approach”, in: Proceedings of the American Control Conference, Baltimore (USA), 2010.
- P. Fjallstrom, “Algorithms for graph partitioning: A survey”, Linkoping Electronic Articles in Computer and Information Science 3 (10).
- J. Bondy, U. Murty, Graph Theory, Vol. 244 of Graduate Series in Mathematics, Springer, 2008.
- D. ?Siljak, Decentralized control of complex systems, Academic Press, 1991.
- L.Grüne, J. Pannek, Nonlinear Model Predictive control, Springer,2011.
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