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http://dx.doi.org/10.5302/J.ICROS.2007.13.4.286

An Intelligent PID Controller based on Dynamic Bayesian Networks for Traffic Control of TCP  

Cho, Hyun-Choel (동아대학교 전기공학과)
Lee, Young-Jin (한국폴리텍 항공대학 항공전기과)
Lee, Jin-Woo (동아대학교 전기공학과)
Lee, Kwon-Soon (동아대학교 전기공학과)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.13, no.4, 2007 , pp. 286-295 More about this Journal
Abstract
This paper presents an intelligent PID control for stochastic systems with nonstationary nature. We optimally determine parameters of a PID controller through learning algorithm and propose an online PID control to compensate system errors possibly occurred in realtime implementations. A dynamic Bayesian network (DBN) model for system errors is additionally explored for making decision about whether an online control is carried out or not in practice. We apply our control approach to traffic control of Transmission Control Protocol (TCP) networks and demonstrate its superior performance comparing to a fixed PID from computer simulations.
Keywords
intelligent PID; online learning; DBN model; TCP traffic;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 R. A. Krohling and .J. P. Rey, 'Design of optimal disturbance rejection PID controllers using Genetic algorithm,' IEEE Trans. on Evolutionary Computation, vol. 5, no. 1, pp. 78-82, 2001   DOI   ScienceOn
2 D. S. Pereira and J. O. Pinto, 'Genetic algorithm based system identification and PID tuning for optimum adaptive control,' Proc. of IEEE/ASME Int. Conf. on Advanced Intelligent Mechatronics, Monterey, CA, pp. 801-806, 2005   DOI
3 M. Trusca and G. Lazea, 'An adaptive PID learning controller for periodic robot motion,' Proc. ol IEEE Conf. on Control Applications, Istanbul, Turkey, pp. 686-689, 2003
4 M. Faradadi, A. S. Ghafari, and S. K. Hannani, 'PID neural network control of SUT building energy management system,' Proc. of IEEE/ASME Int. Conf .on Advanced Intelligent Mechatronics, Monterey, CA, pp. 682-686, 2005   DOI
5 G. Zhenhai and Z. So, 'Vehicle lane keeping of adaptive PID control with BP neural network self-tuning,' Proc. of IEEE Intelligent Vehicle Symposium, Las Vegas, NV, pp. 84-87, 2005   DOI
6 Y. H. Aoul, A Nafaa, D. Negru, and A. Mchaoua, 'FAFC: fast adaptive fuzzy AQM controller for TCP/IP networks,' IEEE Global Telecommunications Conf., pp. 1319-1323, 2004   DOI
7 R. Fengyuan, L. Chuang, Y. Xunhe, S. Xiuming, and W. Fubao, 'A robust active queue management algorithm based on sliding mode variable structure control,' Proc. of lEEE INFOCOM, pp. 13-20, 2002   DOI
8 H. C. Cho, M. S. Fadali, and H. Lee, 'Neural network control for TCP network congestion,' Proc. ol American Control Conference, pp, 3480-3485, 2005   DOI
9 R. A. DeCarlo, S. H. Zak, and G. P. Mattews, 'Variable structure control of nonlinear multivariable systems: A tutorial,' Proc. of the IEEE, vol. 76, no. 3, pp. 212-232, 1998   DOI   ScienceOn
10 S. Floyd and V. Jacobson, 'Random early detection gateways for congestion avoidance,' IEEE/ACM Trans. on Networking, vol. 1, no. 4, pp. 397-413, 1993   DOI
11 C. V. Hollot, V. Misra, D. Towsley, and W. Gong, 'Analysis and design of controllers for AQM routers supporting TCP flows,' IEEE Trans. on Automatic Control, vol. 47, no. 6, pp. 945-959, 2002   DOI   ScienceOn
12 K. B. Kim and S. H. Low, 'Analysis and design of AQM based on state-space models for stabilizing TCP,' Proc. oj' American Control Conference, pp. 260-265, 2003   DOI
13 T. R. Rangaswamy, J. Shanmugam, and K. P. Mohammed, 'Adaptive fuzzy tuned PID controller for combustion of utility boiler,' Control & Intelligent Systems, vol. 33, no. 1, pp. 63-71, 2005   DOI
14 C. Riverol and V. Napolitano, 'Use of neural networks as a tuning method for an adaptive PID application in a heat exchanger,' Institution of Chemical Engineers, vol. 78, Part A, pp. 1115-1119, 2000   DOI   ScienceOn
15 H. Shu and Y. Pi, 'PID neural networks for time-delay systems,' Computer & Chemical Engineering, vol. 24, pp. 859-862, 2000   DOI   ScienceOn
16 D. Garg and N. Gulati, 'Neural network based intelligent control and PID control of a magnetic levitation system,' Proc. of ASME Dynamic Systems and Control Division, New Orleans, LA, pp. 1013-1020, 2002
17 Y. Yu, H. Ying, and Z. Bi, 'The dynamic fuzzy method to tune the weight factors of neural fuzzy PID controller,' Proc. of IEEE lnt. Joint Conf. on Neural Networks, Budapest, Hungary, pp. 2397-2402, 2004
18 G. M. Khoury, M. Saad, H. Y. Kanaan, and C. Asmar, 'Fuzzy PID control of a five DOF robot arm,' J. of Intelligent & Rohotic Systems, vol. 40, pp. 299-320, 2004   DOI
19 M. Guzelkaya, I. Eksin, and E. Yesil, 'Self-tuning of PID fuzzy logic controller coefficients via relative rate observer,' Engineering Application of Artificial Intelligence, vol. 16, pp. 227-236, 2003   DOI   ScienceOn
20 D. D. Kukolj, S. B. Kuzmanovic, and E. Levi, 'Design of a PlD-like compound fuzzy logic controller,' Engineering Application of Artificial Intelligence, vol. 14, pp. 785-803, 2001   DOI   ScienceOn
21 F. Karray, W. Gueaieb, and S. Al-Sharhan, 'The hierarchical expert tuning of PID controllers using tools of soft computing,' IEEE Trans. on Systems. Man. and Cybernetics-Part B: Cybernetics, vol. 32, no. 1, pp. 77-90, 2002   DOI   ScienceOn
22 S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall, Upper Saddle River, NJ, 1999
23 B. Kosko, Fuzzy Engineering, Prentice Hall, Upper Saddle River, NJ, 1997
24 D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Professional. 1989
25 D, Dasgupta, Artificial Immune Systems and Their Applications. Springer. 1998
26 V . Jacobson and M. Karels, 'Congestion avoidance and control,' Proc. of ACM SIGCOMM, pp. 314-329, 1988   DOI
27 K. Murphy, 'Dynamic Bayesian networks: Representation, Inference and Learning.' Ph. D. Dissertation, University of California-Berkeley, 2002
28 T. K. Moon and W. C. Stirling, Mathematical Methods and Algorithms for Signal Processing, Prentice Hall, Upper Saddle River, NJ, 2000
29 P. Baldi and Y. Chauvin, 'Smooth on-line learning algorithm for hidden Markov models,' Neural Computation, vol. 6, no. 2, pp. 307-318, 1994   DOI   ScienceOn
30 S. Ablameyko, M. Gori, L. Goras, and V. Piuri, editors, Impact of Neural Networks on Signal Processing and Communications, of Limitations and Future Trends in Neural Computation, NATO Science Series, 2003
31 T. M. Mitchell, Machine Learning, McGraw-Hill International Editions, 1997
32 H. C. Cho, 'Dynamic Bayesian networks for online stochastic modeling,' Ph.D. Dissertation, University of Nevada-Reno, 2006
33 M. Saerens and A. Soquet, 'Neural controller based on back-propagation algorithm,' lEE Proceedings - F, vol. 138, no. 1, pp. 55-62, 1991
34 G. F. Franklin, J. D. Powell, and A. Emami-Nacini, Feedback Control of Dynamic Systems, Prentice Hall, Upper Saddle River, NJ, 2006
35 Y. J. Lee, H. C. Cho, and K. S. Lee, 'Immune algorithm based active PID control for structure systems,' J. of Mechanical Science & Technology, vol. 20, no. 11, pp. 1823-1833, 2006   과학기술학회마을   DOI   ScienceOn
36 L. Tian, 'Intelligent self-tuning of PID control for the robotic testing system for human musculoskeletal joints test,' Annals of Biomedical Engineering, vol. 32, no. 6, pp. 899-909, 2004   DOI   ScienceOn
37 A. S. Zayed, A. Hussain, and M. .J. Grimble, 'A nonlinear PID-based multiple controller incorporating a multilayered neural network learning submodel,' Control & Intelligent Systems, vol. 34, no. 3, pp. 177-184, 2006   DOI
38 G. Tan, H. Xiao, and Y. Wang, 'Optimal fuzzy PID controller with adjustable factors and its application to intelligent artificial legs,' High Technology Letters, vol. 10, no. 2, pp. 73-77, 2004
39 L. Reznik, O. Ghanayem, and A. Sounnistrov, 'PID plus fuzzy controller structures as design base for industrial applications,' Engineering Application of Artificial Intelligence, vol. 13, pp. 419-430, 2000   DOI   ScienceOn
40 Y.-C. Chenh, L.-Q. Ye, F. Chuang, and W.-Y. Cai, 'Anthropomorphic intelligent PID control and its application in the hydro turbine governor,' Proc. of Int. Conf. on Machine Learning & Cybernetics, Beijing, China, pp. 391-395, 2002   DOI