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

- Journal title : Journal of Institute of Control, Robotics and Systems
- Volume 13, Issue 4, 2007, pp.286-295
- Publisher : Institute of Control, Robotics and Systems
- DOI : 10.5302/J.ICROS.2007.13.4.286

Title & Authors

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

Cho, Hyun-Choel; Lee, Young-Jin; Lee, Jin-Woo; Lee, Kwon-Soon;

Cho, Hyun-Choel; Lee, Young-Jin; Lee, Jin-Woo; Lee, Kwon-Soon;

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;

Language

Korean

References

1.

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

2.

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

3.

S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall, Upper Saddle River, NJ, 1999

4.

B. Kosko, Fuzzy Engineering, Prentice Hall, Upper Saddle River, NJ, 1997

5.

D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Professional. 1989

6.

D, Dasgupta, Artificial Immune Systems and Their Applications. Springer. 1998

7.

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

8.

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

9.

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

10.

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

11.

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

12.

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

13.

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

14.

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

15.

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

16.

H. Shu and Y. Pi, 'PID neural networks for time-delay systems,' Computer & Chemical Engineering, vol. 24, pp. 859-862, 2000

17.

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

18.

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

19.

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

20.

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

21.

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

22.

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

23.

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

24.

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

25.

G. F. Franklin, J. D. Powell, and A. Emami-Nacini, Feedback Control of Dynamic Systems, Prentice Hall, Upper Saddle River, NJ, 2006

26.

M. Saerens and A. Soquet, 'Neural controller based on back-propagation algorithm,' lEE Proceedings - F, vol. 138, no. 1, pp. 55-62, 1991

27.

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

28.

T. M. Mitchell, Machine Learning, McGraw-Hill International Editions, 1997

29.

H. C. Cho, 'Dynamic Bayesian networks for online stochastic modeling,' Ph.D. Dissertation, University of Nevada-Reno, 2006

30.

K. Murphy, 'Dynamic Bayesian networks: Representation, Inference and Learning.' Ph. D. Dissertation, University of California-Berkeley, 2002

31.

T. K. Moon and W. C. Stirling, Mathematical Methods and Algorithms for Signal Processing, Prentice Hall, Upper Saddle River, NJ, 2000

32.

P. Baldi and Y. Chauvin, 'Smooth on-line learning algorithm for hidden Markov models,' Neural Computation, vol. 6, no. 2, pp. 307-318, 1994

33.

V . Jacobson and M. Karels, 'Congestion avoidance and control,' Proc. of ACM SIGCOMM, pp. 314-329, 1988

34.

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

35.

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

36.

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

37.

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

38.

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