Analysis of Fuzzy PI and PD Type Controllers Using Subtractive Clustering

A common way of developing Fuzzy Controller is by determining the rule base and some appropriate fuzzy sets over the controller's input and output ranges. A simple and efficient approach, namely, Fuzzy Subtractive Clustering is used here, which minimizes the number of rules of Fuzzy Logic Controllers. This technique provides a mechanism to obtain the reduced rule set covering the whole input/ output space as well as membership functions for each input variable. In this paper, Fuzzy subtractive clustering approach is shown to reduce 49 rules to 8 rules. Further more, it is also used to the analysis of Fuzzy PI and PD type of controllers with reduced rule set. Simulation of a wide range of linear and nonlinear processes is carried out and results are compared with existing Fuzzy Logic Controller with 49 rules. Copyright c

[1]  Rajani K. Mudi,et al.  A new scheme for fuzzy rule-based system identification and its application to self-tuning fuzzy controllers , 2002, IEEE Trans. Syst. Man Cybern. Part B.

[2]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[3]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[4]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[5]  M. Gopal,et al.  Control Systems: Principles and Design , 2006 .

[6]  Guanrong Chen,et al.  Fuzzy PID controller: Design, performance evaluation, and stability analysis , 2000, Inf. Sci..

[7]  Katsuhiko Ogata,et al.  Modern Control Engineering , 1970 .

[8]  Seema Chopra,et al.  Identification of rules using subtractive clustering with application to fuzzy controllers , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[9]  Rajani K. Mudi,et al.  A robust self-tuning scheme for PI- and PD-type fuzzy controllers , 1999, IEEE Trans. Fuzzy Syst..

[10]  Dr. Hans Hellendoorn,et al.  An Introduction to Fuzzy Control , 1996, Springer Berlin Heidelberg.

[11]  Magne Setnes,et al.  Supervised fuzzy clustering for rule extraction , 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).

[12]  Dimitar Filev,et al.  Generation of Fuzzy Rules by Mountain Clustering , 1994, J. Intell. Fuzzy Syst..

[13]  V. Kumar,et al.  Identification of self-tuning fuzzy PI type controllers with reduced rule set , 2005, Proceedings. 2005 IEEE Networking, Sensing and Control, 2005..