IIM  Vol.2 No.1 , January 2010
A New Approach to Intelligent Model Based Predictive Control Scheme
ABSTRACT
This paper describes a new approach to intelligent model based predictive control scheme for deriving a complex system. In the control scheme presented, the main problem of the linear model based predictive control theory in dealing with severe nonlinear and time variant systems is thoroughly solved. In fact, this theory could appropriately be improved to a perfect approach for handling all complex systems, provided that they are firstly taken into consideration in line with the outcomes presented. This control scheme is organized based on a multi-fuzzy-based predictive control approach as well as a multi-fuzzy-based predictive model approach, while an intelligent decision mechanism system (IDMS) is used to identify the best fuzzy-based predictive model approach and the corresponding fuzzy-based predictive control approach, at each instant of time. In order to demonstrate the validity of the proposed control scheme, the single linear model based generalized predictive control scheme is used as a benchmark approach. At last, the appropriate tracking performance of the proposed control scheme is easily outperformed in comparison with previous one.

Cite this paper
nullA. MAZINAN and M. KAZEMI, "A New Approach to Intelligent Model Based Predictive Control Scheme," Intelligent Information Management, Vol. 2 No. 1, 2010, pp. 14-20. doi: 10.4236/iim.2010.21002.
References
[1]   A. H. Mazinan and M. F. Kazemi, “An efficient solution to load‐frequency control using fuzzy‐based predictive scheme in a two–area interconnected power system,” The 2nd International Conference on Computer and Automation Engineering, 2010.

[2]   A. H. Mazinan and N. Sadati, “An intelligent multiple models based predictive control scheme with its application to industrial tubular heat exchanger system,” Applied Intelligence, Springer Publisher, DOI 10.1007/s10489- 009-0185-8, in press, 2009.

[3]   A. H. Mazinan and N. Sadati, “Fuzzy predictive control based multiple models strategy to a tubular heat exchanger system,” Applied Intelligence, Springer Publisher, DOI 10.1007/s10489-009-0163-1, in press, 2009.

[4]   A. H. Mazinan and N. Sadati, “On the application of fuzzy predictive control based on multiple models strategy to a tubular heat exchanger system,” Transactions of the Institute of Measurement & Control, SAGE Publisher, DOI 10.1177/0142331209345153, in press, 2009.

[5]   A. H. Mazinan and A. H. Hosseini, ”Application of intelligent based predictive scheme to load-frequency control in a two-area interconnected power system,” Applied Intelligence, in press, 2009.

[6]   A. H. Mazinan and N. Sadati, “A comparative study on applications of artificial intelligence based multiple models predictive scheme to industrial tubular heat exchanger system,” ISA Transactions, Elsevier Publisher, in press, 2009.

[7]   A. H. Mazinan, N. Sadati, and H. Ahmadi-Noubari, “A case study for fuzzy adaptive multiple models predictive control strategy,” in Proc. of IEEE World Symposium on Industral Electronics, pp. 1172–1177, 2009.

[8]   A. H. Mazinan and N. Sadati,”Fuzzy multiple models predictive control of tubular heat exchanger,” in Proc. of IEEE World Congress on Computational Intelligence, pp. 1845–1852, 2008.

[9]   A. H. Mazinan and N. Sadati, “Multiple modeling and fuzzy predictive control of a tubular heat exchanger system,” Transactions on Systems and Control, Vol. 3, pp. 249–258, 2008.

[10]   A. H. Mazinan and N. Sadati, “Fuzzy multiple modeling and fuzzy predictive control of a tubular heat exchanger system,” International Conference on Application of Electrical Engineering, pp. 77–81, 2008.

[11]   A. H. Mazinan and N. Sadati, “Fuzzy multiple modeling and fuzzy predictive control of a tubular heat exchanger system,” International Conference on Robotics, Control and Manufacturing Technology, China, pp. 93–97, April 2008.

 
 
Top