International Journal of Fuzzy Logic and Intelligent Systems

Korean Institute of Intelligent Systems (한국지능시스템학회)
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High-speed Integer Fuzzy Controller without Multiplications

  • Lee Sang-Gu (Department of Computer Engineering, Hannam University)
  • Published : 2006.09.01
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Abstract

In high-speed fuzzy control systems applied to intelligent systems such as robot control, one of the most important problems is the improvement of the execution speed of the fuzzy inference. In particular, it is more important to have high-speed operations in the consequent part and the defuzzification stage. To improve the speedup of fuzzy controllers for intelligent systems, this paper presents an integer line mapping algorithm to convert [0, 1] real values of the fuzzy membership functions in the consequent part to a $400{\times}30$ grid of integer values. In addition, this paper presents a method of eliminating the unnecessary operations of the zero items in the defuzzification stage. With this representation, a center of gravity method can be implemented with only integer additions and one integer division. The proposed system is analyzed in the air conditioner control system for execution speed and COG, and applied to the truck backer-upper control system. The proposed system shows a significant increase in speed as compared with conventional methods with minimal error; simulations indicate a speedup of an order of magnitude. This system can be applied to real-time high-speed intelligent systems such as robot arm control.

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References

  1. E. S. Angel, Interactive Computer Graphics, 3rd ed., Addison-Wesley, Boston, MA, USA, 2002
  2. G. Aranguren, et. al., 'Hardware implementation of a pipeline fuzzy controller,' Fuzzy Sets and Systems 128 (2002) 61-79 https://doi.org/10.1016/S0165-0114(01)00133-6
  3. J. E. Bresenham, 'Incremental line compaction,' The Computer Journal (1982) 116-120
  4. J. D. Foley, A. van Dam, S. Feiner, and J. Hughes, Computer Graphics: Principles and Practice, 2nd ed., Addison-Wesley, Boston, MA, USA, 1990
  5. A. M. Ibrahim, Fuzzy Logic for Embedded Systems Applications, Elsevier, 2004
  6. D. Kim and I.H. Cho, 'An accurate and cost-effective COG defuzzifier without the multiplier and the divider,' Fuzzy Sets and Systems 104 (1999) 229-244 https://doi.org/10.1016/S0165-0114(97)00199-1
  7. Y. D. Kim, 'High speed flexible fuzzy hardware for fuzzy information processing,' IEEE Trans. Systems, Man, and Cybernetics - Part A 27 (1) (1997) 45-56 https://doi.org/10.1109/3468.553224
  8. S. G. Lee and J. D. Carpinelli, 'VHDL Implementation of Very High-speed Integer Fuzzy Controller,' Proc. 2005 IEEE Int. Conf. on Systems, Man and Cybernetics, pp. 588-593, Honolulu, HI, USA, Oct. 2005
  9. T-H S. Li and S-J Chang, 'Autonomous Fuzzy Parking Control of a Car-Like Mobile Robot,' IEEE Trans. on Systems, Man and Cybernetics - Part A, Vol. 33, No.4, pp. 451-465, Jul. 2003 https://doi.org/10.1109/TSMCA.2003.811766
  10. C-T Lin and C. S. G. Lee, Neural Fuzzy Systems, Prentice-Hall, 1996
  11. E. H. Mamdani, 'Application of fuzzy algorithms for control of a simple dynamic plant,' Proc. IEE 121 (12) (1974) 1585-1588
  12. F. Marcelloni and M. Aksit, 'Fuzzy logic-based object-oriented methods to reduce quantization error and contextual bias problems in software development,' Fuzzy Sets and Systems 145 (2004) 57-80 https://doi.org/10.1016/j.fss.2003.10.005
  13. J. J. Saade and H. B. Diab, 'Defuzzification Techniques for Fuzzy Controllers,' IEEE Trans. Systems, Man, and Cybernetics - Part B, 30 (1) (2000) 223-229 https://doi.org/10.1109/3477.826965
  14. Z. Salcic, 'High-speed customizable fuzzy-logic processor: architecture and implementation,' IEEE Trans. Systems, Man, and Cybernetics - Part A 31 (6) (2001) 731-737 https://doi.org/10.1109/3468.983430
  15. L. Xin and J. M. Mendel, 'Generating fuzzy rules by learning from examples,' IEEE Trans. System, Man, and Cybernetics 22 (6) (1992) 1414-1427 https://doi.org/10.1109/21.199466
  16. J. Yen and R. Langari, Fuzzy Logic: Intelligence, Prentice Hall, Englewood Cliffs, NJ, USA, 1999
  17. N. Yubazaki, M. Otani, et. al., 'Fuzzy inference chip FZP-0401A based on interpolation algorithm,' Fuzzy Sets and Systems 98 (1998) 299-310 https://doi.org/10.1016/S0165-0114(96)00371-5
  18. L. A. Zadeh, 'Fuzzy Sets,' Information and Control 8 (1965) 338-353 https://doi.org/10.1016/S0019-9958(65)90241-X
  19. Z. Zhang and J. Chang, 'A fuzzy control algorithm with high controlling precision,' Fuzzy Sets and Systems, (140), pp. 375-385, 2003