The optimal GNN-PID control system using particle swarm optimization algorithm

International Journal of Innovative Computing, Information and Control

Volumn 5, Issue 10, 2009, Pages 3457-3469

  Li, Guo Dong ^a   Masuda, Shiro ^a   Yamaguchi, Daisuke ^b   Nagai, Masatake ^b  

^a TOKYO METROPOLITAN UNIVERSITY   (Japan)

^b KANAGAWA UNIVERSITY   (Japan)

Author keywords

Grey model (GM); Neural network (NN); Particle swarm optimization (PSO); PID control

Indexed keywords

CONTROL ACCURACY; CONTROL PERFORMANCE; CONTROL PROBLEMS; DELAY CONTROL; GREY MODEL; GREY MODEL (GM); GREY PID CONTROL; GREY SYSTEM THEORY; MANUFACTURING PROCESS; NEURAL NETWORK (NN); PARTICLE SWARM OPTIMIZATION ALGORITHM; PID CONTROL; PID CONTROL ALGORITHM;

ALGORITHMS; COMPUTER CONTROL SYSTEMS; COMPUTER SIMULATION; CONTROL THEORY; GRANULAR MATERIALS; NEURAL NETWORKS; PROPORTIONAL CONTROL SYSTEMS; SYSTEM THEORY; THREE TERM CONTROL SYSTEMS; TWO TERM CONTROL SYSTEMS;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 70350490549     PISSN: 13494198     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (28)

1. S. F. Liu and Y. Lin, Grey Information Theory and Practical Applications, Springer-Verlag Publishers, USA, 2006.
2. C. C. Lee, Fuzzy logic in control systems: Fuzzy logic. Parts I and II, IEEE Trans. Syst. Man Cybern, vol. 20, pp. 404-435, 1990.
3. S. Z. He, S. Tan, F. L. Xu, Fuzzy self-tuning of PID controllers, Fuzzy Set Syst, vol. 56, pp. 37-46, 1993.
4. P. P. Wang and C. Y. Tyan, Fuzzy dynamic system and fuzzy linguistic controllers classification, Automatica, vol. 30, no. 11, pp. 1769-1774, 1994.
5. H. Han, Fuzzy controller design with input saturation, International Journal of Innovative Computing, Information and Control, vol. 4, no. 10, pp. 2507-2521, 2008.
6. C. H. Tsai and H. C. Lu, Design and implementation of a DSP-based grey-fuzzy controller for induction motor drive, Electric Machines and Power Systems, vol. 28, pp. 373-384, 2000.
7. J. L. Deng, Control problem of grey systems, Syst. Control Lett., vol. 1, no. 5, pp. 288-294, 1982.
8. J. L. Deng, Introduction to Grey System Theory, Journal of Grey System, vol. 1, pp. 1-24, 1989.
9. S. J. Huand and C. L. Huang, Control of an inverted pendulum using grey prediction model, IEEE Trans. on Industry Application, vol. 36, no. 2, pp. 452-458, 2000.
10. X. H. Zhi, X. P. Zhu, Y. S. Liu and Z. P. Gu, The method of gray model control for the vibration of rotor system, Kybernetes, vol. 33, no. 2, pp. 464-469, 2004.
11. C. C. Kung and K. H. Su, Supervisory grey-based tracking control for non-linear dynamic systems, Journal of Systems and Control Engineering, vol. 220, pp. 41-52, 2006.
12. S. L. Su, Y. C. Su and J. F. Huang, Grey-based power control for DS-CDMA cellular mobile systems, IEEE Trans. on Vehicular Technology, vol. 49, no. 6, pp. 2081-2088, 2000.
13. G. D. Li, D. Yamaguchi and M. Nagai, Application hybrid grey dynamic model to forecasting compensatory control, Engineering Computations, vol. 24, no. 7, pp. 699-711, 2007.
14. H. B Zhu, H. Kai, K. Eguchi, Z. Y. Guo and J. Wang, Application of BPNN in classification of time intervals for intelligent intrusion detection decision response system, International Journal of Innovative Computing, Information and Control, vol. 4, no. 10, pp. 2483-2491, 2008.
15. T. J. Su, J. W. Jhang and C. C. Hou, A hybrid artificial neural networks and particle swarm optimization for function approximation, International Journal of Innovative Computing, Information and Control, vol. 4, no. 9, pp. 2363-2374, 2008.
16. M. Y. Chen, Z. Li, L. Zhou, H. Xiong and X. An, SCGM-model and grey control of "poor" information systems, Kybernetes, vol. 33, no. 2, pp. 231-237, 2004.
17. G. E. Box and G. M. Jenkins, Times Series Analysis: Fforecasting and Control, Holda-Day, 1976.
18. H. L. Mu, Y. Kondou, Y. Tonooka, W. S. Zhou, Y. D. Ning and K. Sakamoto, Analyses on accuracy and sensitivity of grey energy model, J. Japan Institute of Energy, vol. 83, no. 11, pp. 908-915, 2004.
19. C. B. Lin, S. F. Su and Y. T. Hsu, High-precision forecast using grey models. International Journal of Systems Science, vol. 32, no. 5, pp. 609-619, 2001.
20. S. F. Su, C. B. Lin and Y. T. Hsu, A high precision global prediction approach based on local prediction approaches, IEEE Trans Syst Man Cybern, vol. 32, no. 4, pp. 416-425, 2002.
21. L. C. Hsu, Applying the grey prediction model to the global integrated circuit industry, Technological Forecasting & Social Change, vol. 70, no. 5, pp. 563-574, 2003.
22. G. D. Li, D. Yamaguchi and M. Nagai, A GM (1, 1) -Markov chain combined model with an application to predict the number of Chinese international airlines, Technological Forecasting & Social Change, Elsevier, vol. 74, no. 8, pp. 1465-1481, 2007.
23. C. C. Hsu and C. Y. Chen, Applications of improved grey prediction model for power demand forecasting, Energy Convers Manag, vol. 44, pp. 2241-2249, 2003.
24. G. D. Li, D. Yamaguchi and M. Nagai, Application GM (1, 1) -Markov chain combined model to China automobile industry, International Journal of Industrial and Systems Engineering, vol. 2, no. 3, pp. 327-347, 2007.
25. C. Rodriguea, A modular neural network approach to fault diagnosis, IEEE Trans. on Neural Networks, vol. 7, no. 2, pp. 326-340, 1996.
26. J. Kennedy and R. Eberhart, Particle swarm optimization, Proc. of the IEEE Int. Conf. Neural Networks, vol. 4, pp. 1942-1948, 1995.
27. D. F. Wang, P. Han, W. Han and H. J. Liu, Typical grey prediction control methods and simulation studies, IEEE Second Int. Conf. Machine Learning and Cybernetics, vol. 2, no. 5, pp. 513-518, 2003.
28. G. D. Li, D. Yamaguchi, K. Mizutani and M. Nagai, New proposal and accuracy evaluation of grey prediction GM, IEICE Trans. Fundamentals, vol. E90-A (6), pp. 1188-1197, 2007.