Intelligent Tuning Of a PID Controller Using Immune Algorithm

면역 알고리즘을 이용한 PID 제어기의 지능 튜닝

  • 김동화 (한밭대학교 제어계측공학과)
  • Published : 2002.01.01

Abstract

This paper suggests that the immune algorithm can effectively be used in tuning of a PID controller. The artificial immune network always has a new parallel decentralized processing mechanism for various situations, since antibodies communicate to each other among different species of antibodies/B-cells through the stimulation and suppression chains among antibodies that form a large-scaled network. In addition to that, the structure of the network is not fixed, but varies continuously. That is, the artificial immune network flexibly self-organizes according to dynamic changes of external environment (meta-dynamics function). However, up to the present time, models based on the conventional crisp approach have been used to describe dynamic model relationship between antibody and antigen. Therefore, there are some problems with a less flexible result to the external behavior. On the other hand, a number of tuning technologies have been considered for the tuning of a PID controller. As a less common method, the fuzzy and neural network or its combined techniques are applied. However, in the case of the latter, yet, it is not applied in the practical field, in the former, a higher experience and technology is required during tuning procedure. In addition to that, tuning performance cannot be guaranteed with regards to a plant with non-linear characteristics or many kinds of disturbances. Along with these, this paper used immune algorithm in order that a PID controller can be more adaptable controlled against the external condition, including moise or disturbance of plant. Parameters P, I, D encoded in antibody randomly are allocated during selection processes to obtain an optimal gain required for plant. The result of study shows the artificial immune can effectively be used to tune, since it can more fit modes or parameters of the PID controller than that of the conventional tuning methods.

Keywords

References

  1. R. Brooks: A Robust Layered Control System for a Mobile Robot, IEEE Journal R&A, Vol.2, No. 7, pp.14-23, 1986
  2. R. Brooks, 'Intelligence without reason,' Proc. of the IJCAI-91, pp.569-595, 1991
  3. A. Ishiguro, T. Kondo, Y. Watanabe and Y. Uchikawa, Dynamic behavior arbitration of autonomous mobile robots using immune networks, In Proc. of ICEC 95, Vol.2, pp.722-727, 1995 https://doi.org/10.1109/ICEC.1995.487474
  4. N. K. Jerne, The immune system, Scientific American, Vol.229, No.1, pp.52-60, 1973
  5. J. D .Farmer, N. H. Packard and A. S. Perelson, The immune system, adaptation, and machine learning, Fig. 12. Response to minimum values on parameter learning of immune network.(P=variation; I, D=20) Physica. D, pp.187-204, 1986 https://doi.org/10.1016/0167-2789(86)90240-X
  6. F. J. Valera, A. Coutinho, B. Dupire and N. N. Vaz., Cognitive networks: Immune, neural, and Otherwise', Theoretical Immunology, Vol.2, pp.359-375, 1988
  7. J. Stewart, 'the Immune System: Emergent self-assertion in an autonomous network' In Proceedings of ECAL-93, pp.1012-1018, 1993
  8. H. Bersini and F. J. Valera, 'the Immune learning mechanisms: Reinforcement, recruitment and their applications: Reinforcement, recruitment and their applications' Computing with biological metaphors, Ed. R. Paton, Chapman & Hall, pp. 166-192, 1994
  9. Kazuyuki Mori and Makoto Tsukiyama, 'Immune algorithm with searching diversity and its application to resource allocation problem' Trans. JIEE, Vol.113-C, no.10, 3
  10. John E. Hunt, 'an adaptive, distributed learning system based on the immune system, conference https://doi.org/10.1109/ICSMC.1995.538156
  11. Alessio Gaspar, 'Artificial immune system, ' 1999 IEE conference
  12. Dong Hwa Kim, 'tuning of a PID controller using immune network model and fuzzy Set June 15, ISIE2001, Pusan
  13. Dong Hwa Kim, 'tuning of a PID controller using immune network model and fuzzy Set July 28, IFSA 2001, Vancouver