Multi-Hierarchical Model Predictive Control Based on K-Means Clustering Algorithms

Abstract:

Article Preview

Multi-model predictive control has become an effective method for nonlinear system. But the traditional multi-model has large tracking error compared with desired output when it is used to solve the operating condition with large scale transition. To solve this problem, this paper presents a new structure of multi-model called multi-hierarchical model. The new structure consists of many layers that each layer is constituted of different number of multiple models. In each layer, multi-model is obtained by partial least squares method after k-means clustering algorithm divides the global working spaces into desired parts. Because of this special structure, the models chose from different layers can deal with the operating condition changed with large scale. At the end of this paper, experiments are carried on the pH neutralization process which is a MIMO nonlinear system and the simulation results demonstrate that the multi-hierarchical model is superior to single-hierarchical model with smaller model tracking error faster convergence speed and better stability.

Info:

Periodical:

Advanced Materials Research (Volumes 211-212)

Edited by:

Ran Chen

Pages:

147-151

DOI:

10.4028/www.scientific.net/AMR.211-212.147

Citation:

L. L. Liu and L. F. Zhou, "Multi-Hierarchical Model Predictive Control Based on K-Means Clustering Algorithms", Advanced Materials Research, Vols. 211-212, pp. 147-151, 2011

Online since:

February 2011

Export:

Price:

$35.00

[1] Anass Boukhris, Gilles Mourot, Jose Ragot. Nonlinear dynamic system identification: a multi-model approach. International Journal of Control. Vol. 72 (1999), pp.591-604.

DOI: 10.1080/002071799220795

[2] E. Mazor, A. Averbuch, Y. Bar-Shalom, J. Dayan. Interacting multiple model methods in target tracking: a survey. IEEE Transactions on Aerospace and Electronic Systems. Vol. 34 (1998).

DOI: 10.1109/7.640267

[3] David Arthur, Bodo Manthey, Heiko Roglin. k-means has polynomial smoothed complexity. 2009 50th Annual IEEE Symposium on Foundations of Computer Science: Focs 2009, Proceedings, pp.405-414.

DOI: 10.1109/focs.2009.14

[4] S. Joe Qin. Recursive PLS algorithms for adaptive data modeling. Computers chem. Engng Vol. 22 (1998), pp.503-514.

DOI: 10.1016/s0098-1354(97)00262-7

[5] Ning Li, Shao-Yuan Lia, Yu-Geng Xi. Multi-model predictive control based on the Takagi-Sugeno fuzzy models: a case study. Information Sciences. Vol. 165 (2004), p.247–263.

DOI: 10.1016/j.ins.2003.10.011

[6] Zhou Luwen, Zhou Lifang. Multiple modeling method based on advanced k-means clustering. Journals of university of science and technology. Vol. 35 (2005).

[7] Linlin liu, zhou lifang, shenggang xie. A novel supervised multi-model modeling method based on k-means clustering. The 22nd Chinese control and decision conference, (2010).

DOI: 10.1109/ccdc.2010.5498925

[8] Chengshan Qian, Chengzhong Hu, Changsheng Jiang, Yanqing Wang. Non-fragile Multiple model Switching Control for Nonlinear Systems. Proceedings of the IEEE International Conference on Automation and Logistics, (2007).

DOI: 10.1109/ical.2007.4338525

[9] Elisa Franco, Thomas Parisini, Simona Sacone. Stable multi-model switching control of a class of nonlinear systems. Automation Lab Technical Report, (2004).

[10] Carlos Bordons, Eduardo F. Camacho. A Generalized Predictive Controller for a Wide Class of Industrial Processes. IEEE Transactions on control systems technology. Vol. 6 (1998), p.372–387, (1998).

DOI: 10.1109/87.668038

In order to see related information, you need to Login.