Multifactor Optimization of a Fuzzy-PID Controller Using Genetic Algorithm

Abstract:

The design of a Fuzzy-PID controller involves setting the fuzzy rules, membership functions and its associated scaling factors. How to obtain a better control result and how these scaling factors affect the controller’s performance are still a challenge. In this paper, the automatic position control system of a Hille 100 experimental rolling mill was used as a research testbed. Based on the mathematical control model of the rolling mill, a Fuzzy-PID controller was developed, and the process of implementing global optimization considering all these factors simultaneously by using genetic algorithm is introduced in detail. Through simulation, the performance of the control system with multifactor optimized Fuzzy-PID controller is given, and compared with that with only the fuzzy rules optimized in the controller. By simulation tests, it is found that these factors will influence the control performance of the controller, and that they are highly coupled with each other. The more factors for a Fuzzy-PID controller are optimized, the better the solution will be. It can also be inferred from the study that asymmetrical membership functions have more potential in improving a fuzzy controller’s performance than symmetrical ones. The multifactor optimization method presented in this paper can in principle also be used to solve other complicated optimization issues.

• Periodical: Advanced Materials Research (Volume 422)
• Pages: 268-275
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.422.268
• Online since: December 2011
• Authors: Xiao Feng He, Wei Chen, Bi Hai Zhu, Zheng Yi Jiang, Christopher Cook
• Keywords: Fuzzy-PID Controller, Genetic Algorithm (GA), Multifactor Optimization, Rolling Mill

References

[1] Y.X. Xing, W.B. Zhan, L.C. Yuan, and Y.X. Xiao, "Fuzzy Logic based Thickness Control System and Its Simulation," The 1st International Conference on Information Science and Engineering (ICISE2009), Nanjing, China, pp.3792-3795, (2009)

[2] P. Tandra, and R. P. Nikhil, "SOGARG: A Self-Organized Genetic Algorithm-Based Rule Generation Scheme for Fuzzy Controllers," IEEE Transactions on Evolutionary Computation, Calcutta, India, pp.397-415, (2003)

DOI: 10.1109/tevc.2003.815377

[3] F. Janabi-Sharifi: Control Engineering Practice, Vol.13 (2005), pp.1-13

[4] A. Homaifar, and E. McCormick, "Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms," IEEE Trans. Fuzzy Syst., Greensboro, North Carolina, USA, pp.129-139, (1995)

DOI: 10.1109/91.388168

[5] M. S. Alam, and M. O. Tokhi: Engineering Applications of Artificial Intelligence, Vol. 21 (2008), pp.858-873

[6] X.F. He, L. Luo, B.H. Zhu, Z.Y. Jiang, and C. Cook, "Optimization of the Fuzzy-PID Controller for a Hille 100 Experimental Rolling Mill based on a Genetic Algorithm," 2011 International Conference on Computers, Communications, Control and Automation (CCCA 2011), Hong Kong, China, pp.258-263, (2011)

[7] F. Janabi-Sharifi, and J. Liu, "Design of a Self-Adaptive Fuzzy Tension Controller for Tandem Rolling," IEEE Transactions On Industrial Electronics, Toronto, Ontario, Canada, pp.1428-1438, (2005)

DOI: 10.1109/tie.2005.855653

[8] H.X. Zhang, B. Zhang, and F. Wang, "Automatic Fuzzy Rules Generation Using Fuzzy Genetic Algorithm," Proceedings of the Sixth International Conference on Fuzzy Systems and Knowledge Discovery, Tianjin, China, pp.107-112, (2009)

DOI: 10.1109/fskd.2009.420