Study on Hybrid Fuzzy Controller Design by Model Reduction Method

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    Background/Objectives: In this paper, we proposed an improved model shrinking method and a hybrid-smith prediction fuzzy control design using a reduced model.

    Methods/Statistical analysis: The method of model reduction is based on Nyquist curve of frequency response, and the reduced model is obtained by considering the response of the transient state and the response of the steady state to the method. The proposed hybrid-smith prediction fuzzy controller tuning method was able to obtain the parameter value by utilizing a reduced model and using a genetic algorithm.

    Findings: The optimum PID controller design method using the reduced model applied a reduced model and a Smith prediction structure that compensates for the delay time in order to improve the control performance, and as a result, in order to minimize the performance index ITAE value I was able to design a controller. Here, the value of the control parameter was used by combining a method of directly obtaining the reduced model and a method of using the genetic algorithm by numerical analysis. In conclusion, the design method of the hybrid-smith Fuzzy control is a method of controlling by combining the PID controller, the Smith prediction compensating the delay time and the fuzzy controller in parallel, the value of the PID control parameter is optimized using the reduced model The value of the PID parameter was determined. The conversion coefficients (GE, GD, GH, GC) of Fuzzy control were obtained by applying genetic algorithm.

    Improvements/Applications: In the proposed method, it is possible to obtain directly the value of the parameter of the optimum PID controller and the value of the Smith prediction by using the reduced model, and the part of the Fuzzy conversion coefficient can be obtained by using the genetic algorithm , The ITAE performance index improved more than the conventional method.

     

     


  • Keywords


    Model reduction, PID controller, smith-predictor, fuzzy controller, ITAE

  • References


      [1] K.J.Astrom and T.Hagglund. (1984). Automatic tuningof simple regulators with specifications on phase and amplitude margins, 20(5) 645-651.

      [2] W.K.Ho, C.C.Hang, W.Wojsznis, and Q.H.Tao. (1996). Frequency domain approach to self-tuning PID control.Contr.Eng. Practice,4(6).807-813

      [3] W.K.Ho, O.P.Gan, E.B.Tay, and E.L.Ang. (1996). Performance and gain and phase margins of well-known PID tuning formulas.IEEE Trans. Contr. Syst. Technol. 4, 473-477.

      [4] M.Zhuang and D.P.Atherton. (1993). Automatic tuning of optimum PID controllers. Proc. Inst. Elect. Eng.140(3). 216-224.

      [5] K.J.Astrom. (1998).Automatic tuning of PID regulators. Instrument Soc. Amer.

      [6] J.Malers and Y.S.Sherif. (1985). Application of fuzzy set theory.IEEE Trans. on System, Man and Cybernetics. 15(1)

      [7] Kevin M. Passino and Stephen yurhovich. (1998).fuzzy control.Addison Wesley Longman, Inc.

      [8] K.Y.Kong, S.C.Goh, C.Y.Ng, H.K.Loo, K.L.Ng, W.L. Cheong, and S.E.Ng. (1995). Feasibility report on frequency domain adaptive controller, Dept. Elect. Eng., Nat. Univ. Singapore, Internal Rep.

      [9] Q.G.Wang, T.H.Lee, H.W.Fung, Q.Bi and Y. Zhang. (1999). PID tuning for improved performance, IEEE Trans. Contro. Syst. Technol. 7(4). 457-465.

      [10] Y.Shamash. (1975). Model reduction using the Routh stability criterion and the Padeapproximation technique. Int. J. Control, 21(3). 475-484.

      [11] David E. Goldberg. (1989).Genetic Algorithms in Search, Optimization, and Machine Learning. Wesley Publishing Company.

      [12] Qing-Guo Wang, Chang-Chieh Hang, and Qiang Bi. (1999). A Technique for Frequency Response Identification from Relay Feedback.IEEE Trans. Contro. Syst. Technol.7(1). 122-128.


 

View

Download

Article ID: 19381
 
DOI: 10.14419/ijet.v7i3.34.19381




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.