Study of Fuzzy Control for Intelligent Cantilever Beam Based on Genetic Algorithm

Abstract:

The design of fuzzy controller is the key of fuzzy control system, while the core of fuzzy controller design lies in fuzzy rules, whose performance determines the control effect of fuzzy system. General fuzzy rules are obtained from expert experience, in which much subjectivity exists. In this paper, a fuzzy controller is designed by taking an intelligent cantilever beam as the research object. And a method using the genetic algorithm to optimize fuzzy rules is proposed and the genetic coding as well as the fitness function is confirmed. Finally, the simulation model of intelligent cantilever beam is built by Matlab/Simulink, and the vibration control effects of fuzzy controller optimized by genetic algorithm are compared with those un-optimized. The simulation results indicate that the vibration amplitude of intelligent cantilever beam has a significant decrease and the vibration decay rate has a significant increase after the fuzzy rules optimized.

• Periodical: Advanced Materials Research (Volumes 204-210)
• Pages: 25-30
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.204-210.25
• Online since: February 2011
• Authors: Jing Jun Zhang, Xiao Pin Guo, Li Li He, Rui Zhen Gao
• Keywords: Fuzzy Controller, Fuzzy Rule, Genetic Algorithm (GA), Intelligent Structure, MATLAB

References

[1] LimMN, Rahardjm S, Gwee B H: A GA paradigm for learning fuzzy rules [J]．Fuzzy Sets and Systems, 82(2): 177-186 (1996).

DOI: 10.1016/0165-0114(95)00254-5

[2] Shi Xinmin, Hao Zhengqing: Fuzzy control and matlab simulation [M]．Tsinghua University Press, Being Jiaotong University Press (2008).

[3] Lin J, LiuWeizheng: Experimental evaluation of a piezoelectric vibration absorber using a simplified fuzzy controller in a cantilever beam [J]. Journal of Sound and Vibration, 296: 567-582 (2006).

DOI: 10.1016/j.jsv.2006.01.066

[4] Teng Tsoliang, Peng Chengping, Chuang Chun: A study on the application of fuzzy theory to structural active control [J]. Comput Methods Appl Mech. Engrg, 189: 439-448 (2000).

[5] Gustavo Luiz C M de Abreu, Jos F. Ribeiro: A self-organizing fuzzy logic controller for the active control of flexible structures using piezoelectric actuators [J]. Applied Soft Computing, 1: 271-283 (2002).

DOI: 10.1016/s1568-4946(02)00020-0

[6] Wei jingjun, Qiu zhicheng, Ye chunde: Experimental study of active vibration control Piezoelectric Flexible Beam Based on Fuzzy Control [J], journal of vibration and shock，27(12): 91-96 (2008).

DOI: 10.1109/wcica.2008.4593207

[7] K. Parsa, T.A. Lasky, and B. Ravani: Design and implementation of a mechatronic, all-accelerometer inertial measurement unit, IEEE/ASME Trans. Mechatronics, vol. 12, no. 6, pp.640-650 (2007).

DOI: 10.1109/tmech.2007.910080

[8] Wang Dezhou, Zhang Chijiang, Chen Jianjun. Hardware-in-the-loop fuzzy vibration control simulation for intelligent structures [J]. Aeronautical Computing Technique, 36(3): 93-96 (2006).

[9] Li dongxu, Large Flexible Space Truss Structures: Dynamic Analyses and Vibration Fuzzy Control [M], Science Press (2008).

[10] Lim M H，Rahardardja S , Gwee B H. A GA paradigm for learning fuzzy rules. Fuzzy sets and system (1996).

DOI: 10.1016/0165-0114(95)00254-5

[11] C. Mares, C. Surace, An application of genetic algorithms to identify damage in elastic structures, Journal of Sound and Vibration 195 195-215 (1996).

DOI: 10.1006/jsvi.1996.0416

[12] Zhang Jingjun, Cao Liya, Fuzzy control and simulation of piezoelectric intelligent structure vibration [J], engineering mechanics, 10(26): 228-232 (2009).