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Substractive Clustering Based Fuzzy Hybrid Reference Control to Improve Transient Response of PID Controller

Endra Joelianto, P. Sitanggang

Abstract


PID controller has inherent limitations in fulfilling the control design objectives. Tuning parameters should trade off the requirement of tracking set-point performance, disturbance rejection and stability robustness. Combination of hybrid reference control (HRC) with PID controller results in the transient response performance can be independently achieved without deteriorating the stability robustness requirement. This paper proposes a fuzzy based HRC where the membership functions are obtained by using substractive clustering technique. The proposed method guarantees transient response performances satisfaction and takes less time in developing the fuzzy system.

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References


Joelianto E, Williamson D. Discrete event reference control. Proc 36th IEEE Conf Dec Contr, 1997, 1: 692―697

Joelianto E, Williamson D. Optimal full state hybrid reference control. Math Theory Network & Syst, 1998, 941―944

Joelianto E. Linear Hybrid Reference Control Systems. PhD Thesis, The Australian Nat Univ, Canberra, 2000

Zadeh L A. Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans on Syst Man Cybern, 1973, 3(1): 28―44

Wang L X. Adaptive Fuzzy Systems and Control: Design and Stability Analysis. New Jersey: Prentice Hall Int, 1994

Misir D, Malki H A. Liapunov stability for a fuzzy PID controlled flexible-joint manipulator. Int J of Comp Appl in Tech, 2006, 27(2-3): 97―106

Mohan B M, Sinha A. Analytical structure and stability analysis of a fuzzy PID controller. Appl Soft Computing, 2008, 8(1): 749―758

Joelianto E, Tansri O. Fuzzy logic based hybrid reference control for improving transient response performance of PID controller. ITB Journal, 2007, 39A(1&2): 124―145 (in Indonesia)

Takagi T, Sugeno M. Fuzzy identification of systems and its application

to modeling and control, IEEE Trans on Syst Man Cybern, 1985, 15(1)

Coughanowr D R. Process Systems Analysis and Control 2 Ed. Singapore: McGraw-Hill Int Edition, 1991

Smith C A, Corripio A B. Principles an Practice of Automatic Process

Control. Canada: John Wiley and Sons, 1985

Chiu S L. Fuzzy model identification based on cluster estimation. J. of

Intell and Fuzzy Systems, 1994, 2: 267―278

Chiu S L. Extracting fuzzy rules from data for function approximation

and pattern classification. In Dubois D, Prade H, Yager R. Fuzzy Information Engineering: A Guided Tour of Applications. John Wiley & Sons, 1997

Hammouda K. A comparative study of data clustering techniques. SYDE 625: Tools of Intelligent Systems Design, Course project, http://pami.uwaterloo.ca/~hammouda/publications.php, 2000

Fuzzy Logic Toolbox For Use with MATLAB. Natick: The Math-Works Inc, 2002




DOI: http://dx.doi.org/10.21535%2FProICIUS.2008.v4.844

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