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The Self-tuning PID Control Based on Real-time Adaptive Learning Evolutionary Algorithm

실시간 적응 학습 진화 알고리듬을 이용한 자기 동조 PID 제어

  • Published : 2003.09.01

Abstract

This paper presented the real-time self-tuning learning control based on evolutionary computation, which proves its superiority in finding of the optimal solution at the off-line learning method. The individuals of the populations are reduced in order to learn the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations is proposed. It is possible to control the control object slightly varied as time changes. As the state value of the control object is generated, evolutionary strategy is applied each sampling time because the learning process of an estimation, selection, mutation is done in real-time. These algorithms can be applied; the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes.

Keywords

Evolutionary Algorithm;Real-Time;Adaptive Learning Control;Mutation;Search Region

References

  1. Kim N. I., Lee J. W., 2000,'Design the Sliding Mode Controller on the Estimator against Disturbance,' Journal of KSME., Vol. 24, No.4, pp. 866-873
  2. Hong J. S., Park S. H., Kim H. S., Oh J. U., Jung J., 2000, 'Active Vibration Control on the closed plate acting sound vibrated based on Multi Channel Control Algorithms,' Journal of KSME, Vol. 24, No.6, pp. 1390-1397
  3. Chang S. O., Lee J. K., 2000, 'Development of a Hydraulic Servo System Real-Time Simulator Using a One-Board Microprocessor and Personal Computer,' Proceedings of the 4th Asia-Pacific Conference on Control and Measurement, pp. 128-132, 9-12 July
  4. Chang S. O., Lee J. K., 2000, 'Development of a Hydraulic Servo System Real-Time Simulator Using a One-Board Microprocessor and Personal Computer,' Journal of KSPE, Vol. 17, No.8, pp. 94-99
  5. Chang S. O., Lee J. K., 2000, 'A Study on the Application of the Real-Time Simulator,' Proceddings of the is' Korea Automatic Control Conference. October 19-21
  6. Han J. O. et al, 1999, 'Development of Self-tuning Fuzzy-PID controller for the power plant control of steam Generator for Nuclear Power Plant,' Proceeding of the 14th KACC, pp. 140-143
  7. KIM B. M. et al, 1999, 'GA based PID gain tuning of AWC system in Hot strip mills,' Proceeding of the 14th KACC, pp. 225 -228
  8. Vega P., Prada C., and Aleixandre V., 1991, 'Self-Tuning predictive PID controller,' IEE Proceeding-D, Vol. 138, NO.3 https://doi.org/10.1049/ip-d.1991.0041
  9. Fogel D. B., 1995, Evolutionary Computation. Toward a New Philosophy of Machine Intelligence, Pisoataway, NJ : IEEE Press
  10. Vonk E., Jain L. C., Johnson R. P., 1997, Automatic Generation of Neural Network Architecture Using Evolutionary Computation, World Scientific Publishing Co.
  11. Fogel D. B., Atmar J. W., 1990, 'Comparing Genetic Operators with Gaussian Mutations in Simulated Evolutionary Processes Using Linear Systems,' Biological Cybernetics, Vol. 63:2, pp. 111-114 https://doi.org/10.1007/BF00203032
  12. Back T., 1996, Evolutionary Algorithms in Theory and Practice, Oxford, NY
  13. Schwefel H. P., 1995, Evolution and Optimum Seeking, John Wiley, NJ
  14. Michalewicz Z., Fogel D. B., 2000, How to Solve It: Modern Heuristic, Springer-Verlag, Berlin, pp. 161-184/335-341
  15. Sanchez E., Tomassini M., eds., 1996, Toward Evolvable Hardware: The Evolutionary Engineering Approach, Springer-Verlag, Berlin, pp. 19-47/221-249
  16. Chang S. O., Lee J. K.., 2001, 'New approach to real time adaptive learning control of neural networks based on an evolutionary algorithm(I),' 2001 IEEE International Symposium on Industrial Electronics, 12-16, June https://doi.org/10.1109/ISIE.2001.931996
  17. Chang S. O., Lee J. K., 2001, 'New approach to real time adaptive learning control of neural networks based on an evolutionary algorithm(II),' 2001 IEEE International Symposium on Industrial Electronics, 12-16, June https://doi.org/10.1109/ISIE.2001.931997
  18. Gene F., Franklin et al, 1997, Digital Control of Dynamic systems, Third Ed., Addison Wesley
  19. Astrom K., Hagglund T., 1994, PID Controller : Threory, Design, and Tuning, Second Ed.
  20. Haykin S., 1999, Neural Networks : A comprehensive foundation, second Ed., Prentice-Hall Inc.
  21. J.S.Jang, C.S.Sun, and E.Mizutani, 1997, Neuro-Fuzzy and soft Computing A Computational Approach to Learning and Machine Intelligence, Prentice-Hall Inc.
  22. Chang S. O., Lee J. K., 2001, 'Real-time Adaptive Learning Control Based on Evolutionary algorithm (I),' Proceeding of KSME, Jeju University, pp. 724-729
  23. Chang S. O., Lee J. K., 2001, 'Real-time Adaptive Learning Control Based on Evolutionary algorithm (II),' Proceeding of KSME, Jeju University, pp. 730-733
  24. Chang S. O., Lee J. K., 'A Consideration on Load Disturbance Characteristics of Real-Time Adaptive Learning Controller Based on an Evolutionary Algorithm-Application to an Electro-Hydraulic Servo System,' International Conference on Contorl, Automation and System (ICCAS2001), Je-Ju, KOREA., October 19-21., 2001
  25. Chang S. O., Park Y. H., and Lee J. K., ' A New Method of the Evolutionary Algorithm for Adaptive Learning Control,' IFAC 2002 World Congress (b02), Barcelona, SPAIN., July 21-26., 2002
  26. Chang S. O., Lee J. K., 2002, 'Adaptive Learning Control for Real Time Evolutionary Algorithms,' Journal of KSME, Vol. 26, No.6, pp. 1092 - 1098 https://doi.org/10.3795/KSME-A.2002.26.6.1092
  27. Chang S. O., Lee J. K., 2002, Adaptive Learning Control for Electro-Hydrauic Servo System based on Real Time Evolutionary Algorithm,' Vol. 8, No.7, pp. 584-588
  28. Chang S. O., A Real Time Evolutionary Algorithm for Adaptive Learning Control, Ph.D. Dissertation, Pusan Nationa University, August 2002