Particle Swarm Optimization Based Fuzzy-Neural Like PID Controller for TCP/AQM Router
Affiliation(s)
Computer Engineering Department, College of Engineering, Nahrain University, Baghdad, Iraq.
Computer Engineering Department, College of Engineering, Nahrain University, Baghdad, Iraq.
ABSTRACT
In this paper a PID Fuzzy-Neural controller (FNC) is designed as an Active Queue Management (AQM) in internet routers to improve the performance of Fuzzy Proportional Integral (FPI) controller for congestion avoidance in computer networks. A combination of fuzzy logic and neural network can generate a fuzzy neural controller which in association with a neural network emulator can improve the output response of the controlled system. This combination uses the neural network training ability to adjust the membership functions of a PID like fuzzy neural controller. The goal of the controller is to force the controlled system to follow a reference model with required transient specifications of minimum overshoot, minimum rise time and minimum steady state error. The fuzzy membership functions were tuned using the propagated error between the plant outputs and the desired ones. To propagate the error from the plant outputs to the controller, a neural network is used as a channel to the error. This neural network uses the back propagation algorithm as a learning technique. Firstly the parameters of PID of Fuzzy-Neural controller are selected by trial and error method, but to get the best controller parameters the Particle Swarm Optimization (PSO) is used as an optimization method for tuning the PID parameters. From the obtained results, it is noted that the PID Fuzzy-Neural controller provides good tracking performance under different circumstances for congestion avoidance in computer networks.
In this paper a PID Fuzzy-Neural controller (FNC) is designed as an Active Queue Management (AQM) in internet routers to improve the performance of Fuzzy Proportional Integral (FPI) controller for congestion avoidance in computer networks. A combination of fuzzy logic and neural network can generate a fuzzy neural controller which in association with a neural network emulator can improve the output response of the controlled system. This combination uses the neural network training ability to adjust the membership functions of a PID like fuzzy neural controller. The goal of the controller is to force the controlled system to follow a reference model with required transient specifications of minimum overshoot, minimum rise time and minimum steady state error. The fuzzy membership functions were tuned using the propagated error between the plant outputs and the desired ones. To propagate the error from the plant outputs to the controller, a neural network is used as a channel to the error. This neural network uses the back propagation algorithm as a learning technique. Firstly the parameters of PID of Fuzzy-Neural controller are selected by trial and error method, but to get the best controller parameters the Particle Swarm Optimization (PSO) is used as an optimization method for tuning the PID parameters. From the obtained results, it is noted that the PID Fuzzy-Neural controller provides good tracking performance under different circumstances for congestion avoidance in computer networks.
Cite this paper
M. Al-Faiz and S. Sadeq, "Particle Swarm Optimization Based Fuzzy-Neural Like PID Controller for TCP/AQM Router," Intelligent Control and Automation, Vol. 3 No. 1, 2012, pp. 71-77. doi: 10.4236/ica.2012.31009.
M. Al-Faiz and S. Sadeq, "Particle Swarm Optimization Based Fuzzy-Neural Like PID Controller for TCP/AQM Router," Intelligent Control and Automation, Vol. 3 No. 1, 2012, pp. 71-77. doi: 10.4236/ica.2012.31009.
References
[1] L. Peterson and B. Davie, “Computer Network a Systems Approach,” 3rd Edition, Morgan Kaufmann, Waltham, 2003. [2] B. Braden, D. Clark and J. Crowcroft, “Recommendations on Queue Management and Congestion Avoidance in the Internet,” Network Working Group, Internet Society, Reston, 1998. [3] S. Floyd and V. Jacobson, “Random Early Detection Gateway for Congestion Avoidance,” IEEE Transactions on Networking, Vol. 1, No. 4, 1993, pp. 397-413. doi:10.1109/90.251892 [4] C. V. Hollot, V. Misra, D. Towsley and W. B. Gong, “On Designing Improved Controllers for AQM Routers Supporting TCP Flows,” Proceedings of IEEE INFOCOM, Anchorage, 24-26 April 2001, pp. 1726-1734. [5] V. Misra, W.-B. Gong and D. Towsley, “Fluid-Based Analysis of a Network of AQM Routers Supporting TCP Flows with an Application to RED,” Proceedings of the Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, Stockholm, 28 August-1 September 2000, pp. 151-160. [6] C. V. Hollot, V. Misra, D. Towsley and W. B. Gong, “A Control Theoretic Analysis of RED,” Proceedings of IEEE INFOCOM, Anchorage, 24-26 April 2001, pp. 15101519. [7] M. Jalili, F. Roudsari, A. Dehestani and H. Fesharaki “Adaptive Fuzzy Active Queue Management,” Proceedings of FORTE Workshops, Islamic Azad University, Tehran, 2004, pp. 196-208. [8] M. Y. Waskasi, M. J. Yazdanpanah and N. Yazdani, “A New Active Queue Management Algorithm Based on Neural Networks PI,” University of Tehran, Tehran, 2005. [9] M. Z. AL-Faiz and A. M. Mahmood, “Fuzzy Genetic Controller for Congestion Avoidance in Computer Networks,” Proceeding of Engineering Conference on Control, Computer and Mechatronics, Baghdad, 30-31 January 2011, pp. 206-212. [10] B. Kosko, “Neural Networks and Fuzzy Systems,” Prentice Hall, Upper Saddle River, 1992. [11] D. T. Pham, “Neural Networks for Identification, Prediction and Control,” Springer, Berlin, 1995. doi:10.1007/978-1-4471-3244-8 [12] M. Brown, and C. Harris, “Neuro-Fuzzy Adaptive Modeling and Control,” Prentice-Hall Inc., Englewood Cliffs, 1994. [13] L. Reznik, “Fuzzy Controllers,” Biddles Ltd., Norfolk, 1997.
[1] L. Peterson and B. Davie, “Computer Network a Systems Approach,” 3rd Edition, Morgan Kaufmann, Waltham, 2003. [2] B. Braden, D. Clark and J. Crowcroft, “Recommendations on Queue Management and Congestion Avoidance in the Internet,” Network Working Group, Internet Society, Reston, 1998. [3] S. Floyd and V. Jacobson, “Random Early Detection Gateway for Congestion Avoidance,” IEEE Transactions on Networking, Vol. 1, No. 4, 1993, pp. 397-413. doi:10.1109/90.251892 [4] C. V. Hollot, V. Misra, D. Towsley and W. B. Gong, “On Designing Improved Controllers for AQM Routers Supporting TCP Flows,” Proceedings of IEEE INFOCOM, Anchorage, 24-26 April 2001, pp. 1726-1734. [5] V. Misra, W.-B. Gong and D. Towsley, “Fluid-Based Analysis of a Network of AQM Routers Supporting TCP Flows with an Application to RED,” Proceedings of the Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, Stockholm, 28 August-1 September 2000, pp. 151-160. [6] C. V. Hollot, V. Misra, D. Towsley and W. B. Gong, “A Control Theoretic Analysis of RED,” Proceedings of IEEE INFOCOM, Anchorage, 24-26 April 2001, pp. 15101519. [7] M. Jalili, F. Roudsari, A. Dehestani and H. Fesharaki “Adaptive Fuzzy Active Queue Management,” Proceedings of FORTE Workshops, Islamic Azad University, Tehran, 2004, pp. 196-208. [8] M. Y. Waskasi, M. J. Yazdanpanah and N. Yazdani, “A New Active Queue Management Algorithm Based on Neural Networks PI,” University of Tehran, Tehran, 2005. [9] M. Z. AL-Faiz and A. M. Mahmood, “Fuzzy Genetic Controller for Congestion Avoidance in Computer Networks,” Proceeding of Engineering Conference on Control, Computer and Mechatronics, Baghdad, 30-31 January 2011, pp. 206-212. [10] B. Kosko, “Neural Networks and Fuzzy Systems,” Prentice Hall, Upper Saddle River, 1992. [11] D. T. Pham, “Neural Networks for Identification, Prediction and Control,” Springer, Berlin, 1995. doi:10.1007/978-1-4471-3244-8 [12] M. Brown, and C. Harris, “Neuro-Fuzzy Adaptive Modeling and Control,” Prentice-Hall Inc., Englewood Cliffs, 1994. [13] L. Reznik, “Fuzzy Controllers,” Biddles Ltd., Norfolk, 1997.