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PSO algorithm-based robust design of PID controller for variable time-delay systems: AQM application
Keywords:
PSO, frequency response, PID, robust control, QFT, AQM, heuristic optimizationAbstract
This paper formulates a robust control for variable time-delay system models. An automatic tuning method for PID-type controller is proposed. The adopted method integrates robust control design using Quantitative Feedback Theory (QFT) with Particle Swan Optimization heuristic algorithms (PSO) to systematize the loop-shaping stage. The objective of the design method is to reach a good compromise among robust stability, robust tracking and disturbance rejection with minimal control effort. The resulting algorithm has attractive features, such as easy implementation, stable convergence characteristic and good computational efficiency. In particular, the results of the control design for active queue management (AQM) systems are presented. Simulations show improved congestion control and quality of service in TCP communication networks.
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References
[1] M. Wu, Y. He and J-H. She, Stability Analysis and Robust Control of Time-Delay Systems. Science Press Beijing, Springer, 2010.
[2]K. Saadaoui, S. Testouri and M. Benrejeb, “Robust stabilizing first-order controllers for a class of time delay systems”, Transaction on Instrumentation, Systems and Automation (ISA), Vol. 49, 2010, pp. 277-282.
[3] H. Houpis, S. Rasmussen and M. García Sanz, Quantitative Feedback Theory: Fundamentals and Applications. 2da. Ed, CRC Press, Florida, 2006.
[4] R. Eberhart and J. Kennedy, “New Optimizer Using Particle Swarm Theory” , Sixth Int. Symposium on micro machine and human science, Nagoya, Japan, 1995, pp.39-43.
[5] X. Hu and R. Eberhart, “Multiobjetive Optimization using Dynamic Neighborhood PSO”, In: Proceedings Evolutionary Computation, (CEC'02). IEEE Press, Honolulu, Vol.2, 2002, pp. 1677-1681.
[6] M. García Sanz, “Quantitative Robust Control Engineering: Theory and Applications”, RTO En-SCI- Vol.166, 2006, pp. 1-44.
[7] W.H. Chen, D.J. Ballance, W.F. Li, “Genetic Algorithm Enabled Computer-Automated Design of QFT Control Systems” in: , Proc. IEEE International Symposium on Computer Aided Control System Design, (ISCACD), 1999, pp.492-497.
[8] R. Muñoz-Mansilla, J. Aranda, J.M. Díaz, D. Chaos and A. Reinoso, “ Design of a Dynamic Positioning
System for a Moored Floating Platform using QFT Robust Control”. In: Proceedings of the 7th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2012, pp. 763-768.
[9] G.D. Halikias, A.C. Zolotas, R. Nandakumar, “Design of optimal robust fixed-structure controllers
using the quantitative feedback theory approach”. In: Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, Vol. 221, No 4, 2007, pp. 697-716.
[10] B. Satpatia, C. Koleyb and S. Dattac, “Robust PID Controller Design using Particle Swarm
Optimization-enabled Automated Quantitative Feedback Theory Approach for a first-order lag
system with minimal dead time”. Systems Science & Control Engineering: An Open Access Journal, Vol.
2, 2014, pp. 502-511.
[11] S. Tavakoli and A. Banookh, “ Robust PI Control Design Using Particle Swarm Optimization”, Journal of Computer Science and Engineering, Vol. 1, No.1, 2010, pp. 36-41.
[12] T. Hussein, “A Genetic Algorithm for Optimum Design of PID Controller in Load Frequency
Control”, World Academy of Science, Engineering and Technology, Vol. 70, 2012, pp. 956-960.
[13] S. Alcántara, R. Vilanova and C. Pedret, “PID control in terms of robustness/performance and
servo/regulator trade-offs: A unifying approach to balanced autotuning”. Journal of Process Control,
Vol. 23, 2013, pp. 527-542.
[14] K. Khandani, A. Jalali and M. Alipoor, “Particle Swarm Optimization Based Design of Disturbance
Rejection PID Controllers for Time Delay Systems”, In: Conf. Intelligent Computing and Intelligent
Systems (ICIS 2009), IEEE Press, Vol. 1, 2009, pp.862-866.
[15] K. Ämtrom and T. Hágglund, Control PID Avanzado, Prentice Hall, Madrid , 2009.
[16] D. Gu and W. Zhang, “Design of an H∞ Based PI Controller for AQM Routers Supporting TCP Flows”, In: 48 th Conference on Decision and Control and 28 th , Chinese Control Conference, IEEE Press, Shanghai, P.R. China, 2009, pp. 603-608.
[17] J. Sun and M. Zukerman, “RaQ: A Robust Active Queue Management Scheme based on rate and
queue length”, Computer Communications, Vol. 30, 2007, pp. 1731–1741.
[18] M. Rouhani, M.R. Tanhatalab and A. Shokohi-Rostami, “Nonlinear Neural Network Congestion
Control Based on Genetic Algorithm for TCP/IP Networks”, In: 2 nd Int. Conf. Computational Intelligence, Comm. Systems and Networks (CICSyN), 2010, pp. 1-6.
[19] M. Voicu, “Robust Controller Including a Modified Smith Predictor for AQM Supporting TCP Flow”,
Journal of Control Engineering and Applied Informatics, Vol. 14, No. 3, 2012, pp. 3-8.
[20] R.K. Sundaram and P.K. Padhy, “GA-Based PI-PD Controller for TCP Routers”. International Journal
of Machine Learning and Computing, Vol. 3, No. 4, 2013, pp. 361-364.
[21] 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”, in: Proceedings of the Conference on Applications, Technology, Architectures, and Protocols for Computer Communication, (ACM SIGCOMM), 2000, pp.151-160.
[22] C.V. Hollot, V. Misra, D. Towsley and W. Gong, “Analysis and Design of Controllers for AQM Routers Supporting TCP Flows”, IEEE Transactions on Automatic Control, Vol. 47, No. 6, 2002, pp. 945-959.
[23] C.V. Hollot, V. Misra, D. Towsley and W. Gong, “A Control Theoretic Analysis of RED”, in:
Proceedings of the Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies. INFOCOM vol. 3, 2001, pp.1510-1519.
[24] D.P. Tian, “A Review of Convergence Analysis of Particle Swarm Optimization”, Int. Journal of Grid
and Distributed Computing,Vol.6, No.6, 2013, pp.117-128.
[25] H. Ye, W. Luo and Z. Li, “Convergence Analysis of Particle Swarm Optimizer and Its Improved
Algorithm Based on Velocity Differential Evolution”. Computational Intelligence and Neuroscience, 2013.
http://www.hindawi.com/journals/cin/2013/384125/. (Accessed June 2015).
[26] P.F Quet and H. Ozbay, “On the Design of AQM Supporting TCP Flow Using Robust Control
Theory”, In: 42 nd Conf. on Decision and Control, IEEE Press, USA, 2003, pp. 4220-4224.
[27] M.J. Hayes, S.M. Mahdi Alavi and P. Van de Ven, “Robust Active Queue Management using a
Quantitative Feedback Theory Based Loop-Shaping Framework”, In: American Control Conference, New York , USA, 2007, pp. 3077-3082.
[2]K. Saadaoui, S. Testouri and M. Benrejeb, “Robust stabilizing first-order controllers for a class of time delay systems”, Transaction on Instrumentation, Systems and Automation (ISA), Vol. 49, 2010, pp. 277-282.
[3] H. Houpis, S. Rasmussen and M. García Sanz, Quantitative Feedback Theory: Fundamentals and Applications. 2da. Ed, CRC Press, Florida, 2006.
[4] R. Eberhart and J. Kennedy, “New Optimizer Using Particle Swarm Theory” , Sixth Int. Symposium on micro machine and human science, Nagoya, Japan, 1995, pp.39-43.
[5] X. Hu and R. Eberhart, “Multiobjetive Optimization using Dynamic Neighborhood PSO”, In: Proceedings Evolutionary Computation, (CEC'02). IEEE Press, Honolulu, Vol.2, 2002, pp. 1677-1681.
[6] M. García Sanz, “Quantitative Robust Control Engineering: Theory and Applications”, RTO En-SCI- Vol.166, 2006, pp. 1-44.
[7] W.H. Chen, D.J. Ballance, W.F. Li, “Genetic Algorithm Enabled Computer-Automated Design of QFT Control Systems” in: , Proc. IEEE International Symposium on Computer Aided Control System Design, (ISCACD), 1999, pp.492-497.
[8] R. Muñoz-Mansilla, J. Aranda, J.M. Díaz, D. Chaos and A. Reinoso, “ Design of a Dynamic Positioning
System for a Moored Floating Platform using QFT Robust Control”. In: Proceedings of the 7th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2012, pp. 763-768.
[9] G.D. Halikias, A.C. Zolotas, R. Nandakumar, “Design of optimal robust fixed-structure controllers
using the quantitative feedback theory approach”. In: Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, Vol. 221, No 4, 2007, pp. 697-716.
[10] B. Satpatia, C. Koleyb and S. Dattac, “Robust PID Controller Design using Particle Swarm
Optimization-enabled Automated Quantitative Feedback Theory Approach for a first-order lag
system with minimal dead time”. Systems Science & Control Engineering: An Open Access Journal, Vol.
2, 2014, pp. 502-511.
[11] S. Tavakoli and A. Banookh, “ Robust PI Control Design Using Particle Swarm Optimization”, Journal of Computer Science and Engineering, Vol. 1, No.1, 2010, pp. 36-41.
[12] T. Hussein, “A Genetic Algorithm for Optimum Design of PID Controller in Load Frequency
Control”, World Academy of Science, Engineering and Technology, Vol. 70, 2012, pp. 956-960.
[13] S. Alcántara, R. Vilanova and C. Pedret, “PID control in terms of robustness/performance and
servo/regulator trade-offs: A unifying approach to balanced autotuning”. Journal of Process Control,
Vol. 23, 2013, pp. 527-542.
[14] K. Khandani, A. Jalali and M. Alipoor, “Particle Swarm Optimization Based Design of Disturbance
Rejection PID Controllers for Time Delay Systems”, In: Conf. Intelligent Computing and Intelligent
Systems (ICIS 2009), IEEE Press, Vol. 1, 2009, pp.862-866.
[15] K. Ämtrom and T. Hágglund, Control PID Avanzado, Prentice Hall, Madrid , 2009.
[16] D. Gu and W. Zhang, “Design of an H∞ Based PI Controller for AQM Routers Supporting TCP Flows”, In: 48 th Conference on Decision and Control and 28 th , Chinese Control Conference, IEEE Press, Shanghai, P.R. China, 2009, pp. 603-608.
[17] J. Sun and M. Zukerman, “RaQ: A Robust Active Queue Management Scheme based on rate and
queue length”, Computer Communications, Vol. 30, 2007, pp. 1731–1741.
[18] M. Rouhani, M.R. Tanhatalab and A. Shokohi-Rostami, “Nonlinear Neural Network Congestion
Control Based on Genetic Algorithm for TCP/IP Networks”, In: 2 nd Int. Conf. Computational Intelligence, Comm. Systems and Networks (CICSyN), 2010, pp. 1-6.
[19] M. Voicu, “Robust Controller Including a Modified Smith Predictor for AQM Supporting TCP Flow”,
Journal of Control Engineering and Applied Informatics, Vol. 14, No. 3, 2012, pp. 3-8.
[20] R.K. Sundaram and P.K. Padhy, “GA-Based PI-PD Controller for TCP Routers”. International Journal
of Machine Learning and Computing, Vol. 3, No. 4, 2013, pp. 361-364.
[21] 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”, in: Proceedings of the Conference on Applications, Technology, Architectures, and Protocols for Computer Communication, (ACM SIGCOMM), 2000, pp.151-160.
[22] C.V. Hollot, V. Misra, D. Towsley and W. Gong, “Analysis and Design of Controllers for AQM Routers Supporting TCP Flows”, IEEE Transactions on Automatic Control, Vol. 47, No. 6, 2002, pp. 945-959.
[23] C.V. Hollot, V. Misra, D. Towsley and W. Gong, “A Control Theoretic Analysis of RED”, in:
Proceedings of the Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies. INFOCOM vol. 3, 2001, pp.1510-1519.
[24] D.P. Tian, “A Review of Convergence Analysis of Particle Swarm Optimization”, Int. Journal of Grid
and Distributed Computing,Vol.6, No.6, 2013, pp.117-128.
[25] H. Ye, W. Luo and Z. Li, “Convergence Analysis of Particle Swarm Optimizer and Its Improved
Algorithm Based on Velocity Differential Evolution”. Computational Intelligence and Neuroscience, 2013.
http://www.hindawi.com/journals/cin/2013/384125/. (Accessed June 2015).
[26] P.F Quet and H. Ozbay, “On the Design of AQM Supporting TCP Flow Using Robust Control
Theory”, In: 42 nd Conf. on Decision and Control, IEEE Press, USA, 2003, pp. 4220-4224.
[27] M.J. Hayes, S.M. Mahdi Alavi and P. Van de Ven, “Robust Active Queue Management using a
Quantitative Feedback Theory Based Loop-Shaping Framework”, In: American Control Conference, New York , USA, 2007, pp. 3077-3082.
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Published
2015-11-01
How to Cite
Baldini, P. N., Calandrini, G., Doñate, P., & Bambill, H. (2015). PSO algorithm-based robust design of PID controller for variable time-delay systems: AQM application. Journal of Computer Science and Technology, 15(02), p. 100–106. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/547
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