A Modified T-S Model Fuzzy Adaptive Control System Based on Genetic Algorithm

Full Text (PDF, 264KB), PP.8-14

Views: 0 Downloads: 0


Xiaofeng. Lian 1,* Zaiwen. Liu 1 Zhanguo. Wang 1

1. Beijing Technology and Business University/College of Computer and Information Engineering, Beijing, China

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2011.03.02

Received: 16 Aug. 2010 / Revised: 8 Dec. 2010 / Accepted: 13 Feb. 2011 / Published: 8 Jun. 2011

Index Terms

T-S Model, Fuzzy Control, Fuzzy Prediction, Genetic Algorithm, Wastewater


According to the characteristics of the nonlinear,long time-delays and time-variation in the MSG wastewater treatment system based on three-phase fluidized bed bioreactor(FBBR),amodified T-S model fuzzy adaptive control system based on genetic algorithm(GA)is presented in this paper.In the system,firstly using GA to optimize the membership functions,then reducing the dimension of fuzzy controller and simplifying the rules by an integral unit. Moreover, adopt a prediction method to compensate the time-delay of system, which based on the theory of fuzzy. Finally, the method is verified by experiments.Simulation experimental results show that the method is feasible and effective, which provides an effective approach to solve the problem of process control with long time-delays,large inertia and time-variation.

Cite This Paper

Xiaofeng. Lian, Zaiwen. Liu, Zhanguo. Wang, "A Modified T-S Model Fuzzy Adaptive Control System Based on Genetic Algorithm", International Journal of Information Technology and Computer Science(IJITCS), vol.3, no.3, pp.8-14, 2011. DOI:10.5815/ijitcs.2011.03.02


[1]A.Arumugam, P.L.Sabarethinam.“Performance of a Three-Phase Fluidized Bed Reactor with Different Support Particles in Treatment of Dairy Wastewater,” ARPN Journal of Engineering and Applied Sciences, vol.3, no.5, pp:42-44, 2008.

[2]Rajasimman,M,Karthikeyan,C.“Starch Wastewater Treatment in a Three Phase Fluidized Bed Bioreactor with Low Density Biomass Support,”J.Appl.Sci.Environ. Manage, vol.11, no.3,pp:97-102, 2007.

[3]S.Carlos-Hernandez, J.F.Beteau, E.N.Sanchez.“Intelligent Control Strategy for An Anaerobic Fluidized Bed Reactor,”10th International IFAC Symposium on Computer Applications in Biotechnology, vol.1, pp:69-74 ,2007. 

[4]Ma Jin-xia,Wang Shi-he,Shen Qian-yu.“Pollutants Removal Efficiency of Membrane-Fluidized Bed Bioreactor”,China Water & Wastewater,vol.23,No.15,pp:73-74,2007.

[5]ChiGa Atsushi, Mizutani Hiroshi.“A Study of Fuzzy Adaptive Control on Supercritical Coalfired Thermal Power Plant”, Transactions of the Japan Society of Mechanical Engineers,pp:765-770,2000.

[6]Tong Shao-cheng,Xu Wei-min,Chai Tian-you.“Adaptive Fuzzy Control for MIMO Nonlinear Systems”,Acta Automatica Sinica,vol.24,No.6,1998.

[7]E.Araujo Filho,Sandra A.Sandri,Elbert E.N.Macau.A.“New Class of Adaptive Fuzzy Control Systems applied in an Industrial Thermal Vacuum Process”,The 8th IEEE International Conference on Emerging Technologies and Factory Automation,pp:426-431,2001.

[8]Zaiwen Liu,Wandong Li,Xiaoyi Wang etal.“A Control Method of Dissolved Oxygen in Sewage Treatment Based on Fuzzy-Simth,”2009 International Conference on Artificial Intelligence and Computational Intelligence, pp:569-572, 2009.

[9]Zaiwen Liu.“The Method of DO Optimal Control for SBR Wastewater Treatment Process,”Computers and Applied Chemistry, vol.24, no.2,pp:231-234,2007.

[10]Xie Wei,Eisaka Toshio.“Design of Takagi-Sugeno Fuzzy Control Systems Based on Youla Parameterization”,Faji Shisutemu Shinpojiumu Koen Ronbunshu,vol.22,pp:8E3-4,2006.

[11]Chen Li-yun.“An improved design method of fuzzy PID controller of T-S model”,Journal of TianJin University of Technology and Education,vol.15,No.2,pp:33-34,2005.

[12]Schlegel, M.,Večerek,O.“Robust design of Smith predictive controller for moment model set”,Proceedings of the 16th IFAC World Congress,pp:427-432,2006.

[13]Pan Che,Chen Xiao-nan,Yang Pei-lin.“The fuzzy-prediction control of time-varying and delay system”, Control & Automation, vol.19,No.12,pp:27-28,2003