Notes on intelligence based model predictive control scheme: A case study

This paper describes an intelligence based model predictive control scheme in dealing with a complicated system. In the control strategy proposed here, the system has to be first represented through a multi-Takagi-Sugeno-Kang (TSK) fuzzy-based model approach and subsequently a multi-generalized predictive control (GPC) scheme is realized in line with the investigated model outcomes, at a number of operating points of the system. In this control strategy, the proposed multi-GPC scheme is instantly updated to derive the system by activating the best control scheme through a new GPC identifier. To demonstrate the effectiveness of the proposed control scheme, the simulations are carried out and the results are compared with those obtained using the traditional GPC scheme. The results verify the validity of the proposed control scheme.

[1]  D I Soloway,et al.  Neural Generalized Predictive Control: A Newton-Raphson Implementation , 1997 .

[2]  A. H. Mazinan,et al.  Application of intelligence-based predictive scheme to load-frequency control in a two-area interconnected power system , 2011, Applied Intelligence.

[3]  Ming He,et al.  Multiple fuzzy model-based temperature predictive control for HVAC systems , 2005, Inf. Sci..

[4]  Nasser Sadati,et al.  Fuzzy predictive control based multiple models strategy for a tubular heat exchanger system , 2010, Applied Intelligence.

[5]  Shaoyuan Li,et al.  Multi-model predictive control based on the Takagi-Sugeno fuzzy models: a case study , 2004, Inf. Sci..

[6]  Nasser Sadati,et al.  Fuzzy multiple models predictive control of tubular heat exchanger , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

[7]  Ching-Chih Tsai,et al.  Generalized predictive control using recurrent fuzzy neural networks for industrial processes , 2007 .

[8]  K. Najim,et al.  Generalized predictive control based on neural networks , 1996, Neural Processing Letters.

[9]  Balasaheb M. Patre,et al.  Implementation of Neural Network for Generalized Predictive Control: A Comparison between a Newton Raphson and Levenberg Marquardt Implementation , 2009, 2009 WRI World Congress on Computer Science and Information Engineering.

[10]  Chen Zengqiang,et al.  Constrained predictive control based on T-S fuzzy model for nonlinear systems , 2007 .

[11]  V. Wertz,et al.  Generalized predictive control using Takagi-Sugeno fuzzy models , 1999, Proceedings of the 1999 IEEE International Symposium on Intelligent Control Intelligent Systems and Semiotics (Cat. No.99CH37014).

[12]  Nasser Sadati,et al.  On the application of fuzzy predictive control based on multiple models strategy to a tubular heat exchanger system , 2010 .

[13]  Ning Wang,et al.  A fuzzy PID controller for multi-model plants , 2002, Proceedings. International Conference on Machine Learning and Cybernetics.

[14]  Nasser Sadati,et al.  Fuzzy multiple modeling and fuzzy predictive control of a tubular heat exchanger system , 2008 .

[15]  A. H. Mazinan,et al.  An efficient solution to load-frequency control using fuzzy-based predictive scheme in a two-area interconnected power system , 2010, 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE).

[16]  Haralambos Sarimveis,et al.  Fuzzy model predictive control of non-linear processes using genetic algorithms , 2003, Fuzzy Sets Syst..

[17]  Zhiqiang Ge,et al.  Study of Fuzzy Generalized Predictive Control Algorithm On Nonlinear Systems , 2006, First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06).

[18]  N. Sadati,et al.  A case study for fuzzy adaptive multiple models predictive control strategy , 2009, 2009 IEEE International Symposium on Industrial Electronics.

[19]  Nasser Sadati,et al.  An intelligent multiple models based predictive control scheme with its application to industrial tubular heat exchanger system , 2011, Applied Intelligence.

[20]  A. H. Mazinan,et al.  A New Approach to Intelligent Model Based Predictive Control Scheme , 2010, Intell. Inf. Manag..