EarthWeb   
HomeAccount InfoLoginSearchMy ITKnowledgeFAQSitemapContact Us
     

   
  All ITKnowledge
  Source Code

  Search Tips
  Advanced Search
   
  

  

[an error occurred while processing this directive]
Previous Table of Contents Next


16.7 Review/Preview

In this chapter we addressed the concern that fuzzy controllers are capable of providing only approximate solutions. A chaotic system requiring precise control actions has been effectively manipulated using a genetic algorithm-designed fuzzy controller. Although the particular application presented in this chapter required a large number of linguistic descriptive terms (29 for each of the condition variables), it is clear that fuzzy systems can provide precise control.

In the next chapter we turn our attention to another issue that should be considered when employing a fuzzy controller. The large number of linguistic terms used in the current chapter to describe the condition variables presented only a small problem. However, a bigger problem occurs when a large number of condition variables must be considered. In such a case, the size of the rule set can rapidly become unmanageable. Such a case occurred for the authors when we were asked by the U.S. Army to investigate helicopter flight-control problems. In the next chapter we will consider ways of exploiting domain knowledge to ease the development of fuzzy controllers; we will discuss the development of a flight controller for a UH-1 helicopter.

References

Berge, P., Pomeau, Y., & Vidal, C. (1984). Order within chaos. New York, NY: John Wiley & Sons.

Borman, S. (1991). Researchers find order, beauty in chaotic chemical systems. Chemical and Engineering News, January, 18–29.

Brady, B. T. (1978). Prediction of failures in mines — An overview. Bureau of Mines Report of Investigations (number 8285).

Cao, Z., Kandel, A., & Li, L. (1991). A new model of fuzzy reasoning. In Fuzzy Sets and Systems, 311–325.

Coughlin, J., & Kranz, R. (1991). New approaches to studying rock burst associated seismicity in mines. Proceedings of the 32nd U.S. Rock Mechanics Symposium, 105–109.

Karr, C. L., Stanley, D. A., & Scheiner, B. J. (1991). A genetic algorithm applied to least squares curve fitting. Bureau of Mines Report of Investigations (number 9339).

Moon, F. C. (1987). Chaotic vibrations. New York, NY: John Wiley & Sons.

Ochs, T. L., & Hartman, A. D. (1988). Improved arc stability in electric arc furnace steel making. In The U.S. Bureau of Mines’ (Eds): New Steel-Making Technology from the Bureau of Mines, Bureau of Mines Information Circular number 9195, 2–11.

Ott, E., Grebogi, C., & Yorke, J. A. (1990). Controlling chaos. Physical Review Letters, 64 (11), 1196–1199.

Poincaré, H. (1921). The foundation of science: Science and method. English Translation, New York, NY: The Science Press.

Romeiras, F. J., Ott, E. Grebogi, C., & Dayawansa, W. P. (1991). Controlling chaotic dynamical systems. Proceedings of the American Control Conference, 121–130.

Tufillaro, N. B., & Albano, A. M. (1986). Chaotic dynamics of a bouncing ball. American Journal of Physics, 54(10), 939–944.

Van der Pol, B., & Van der Mark, J. (1927). Frequency demultiplication. Nature 120, 363–364.


Previous Table of Contents Next

Copyright © CRC Press LLC

HomeAccount InfoSubscribeLoginSearchMy ITKnowledgeFAQSitemapContact Us
Products |  Contact Us |  About Us |  Privacy  |  Ad Info  |  Home

Use of this site is subject to certain Terms & Conditions, Copyright © 1996-2000 EarthWeb Inc. All rights reserved. Reproduction in whole or in part in any form or medium without express written permission of EarthWeb is prohibited. Read EarthWeb's privacy statement.