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Evolutionary Tuning of Optimal PID Controllers for Second Order Systems Plus Time Delay

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Computational Intelligence (IJCCI 2014)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 620))

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Abstract

PID stands for ‘‘proportional, integral, derivative’’ The PID controller is the most widely used industrial device for monitoring and controlling processes. Those three elements are the basics of a PID Controller. Each one performs a different task and has a different effect on the functioning of a system. The expected behavior of a system depends of the setting of those parameters. There are alternatives to the traditional rules of PID tuning, but there is not yet a study showing that the use of heuristic algorithms it is indeed better than using classic methods of optimal tuning. This is developed in this paper. An evolutionary algorithm MAGO (Multidynamics Algorithm for Global Optimization) is used to optimize the controller parameters minimizing the ITAE performance index. The procedure is applied to a set of benchmark problems modeled as Second Order Systems Plus Time Delay (SOSPD) plants. The evolutionary approach gets a better overall performance comparing to traditional methods (Bohl and McAvoy, ITAE-Hassan, ITAE-Sung), regardless the plant used and its operating mode (servo or regulator), covering all restrictions of the traditional methods and extending the maximum and minimum boundaries between them.

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References

  1. Niculescu, S.I., Verriest, E.I., Dugard, L., Dion, J.D.: Stability and robust stability of time-delay systems: a guided tour. Lecture notes in control and information sciences. In: Stability and Control of Time-Delay Systems, vol. 228, pp. 1–71. Springer, London (1998)

    Google Scholar 

  2. Desanti, J.: Robustness of tuning methods of based on models of first-order plus dead time PI and PID controllers (in Spanish). Universidad de Costa Rica, Escuela de Ingeniería Eléctrica (2004)

    Google Scholar 

  3. O’Dwyer, A.: Handbook of PI and PID controller tuning rules, vol. 2. Imperial College Press, London (2009)

    Google Scholar 

  4. Mora, J.: Performance and robustness of the methods based on second order models plus dead time tuning PID controllers. (in Spanish). Escuela de Ingeniería Eléctrica, Universidad de Costa Rica. (2004)

    Google Scholar 

  5. Solera Saborío, E.: PI/PID controller tuning with IAE and ITAE criteria for double pole plants. (in Spanish). Escuela de Ingeniería Eléctrica, Universidad de Costa Rica. (2005)

    Google Scholar 

  6. Liu, G.P., Daley, S.: Optimal-tuning PID control for industrial systems. Control Eng. Pract. 9(11), 1185–1194 (2001)

    Article  Google Scholar 

  7. Tavakoli, S., Griffin, I., Fleming, P.J.: Multi-objective optimization approach to the PI tuning problem. IEEE C. Evol. Computat. 3165–3171 (2007)

    Google Scholar 

  8. Chang, W.D., Yan, J.J.: Optimum setting of PID controllers based on using evolutionary programming algorithm. J. Chin. Inst. Eng. 27(3), 439–442 (2004)

    Article  Google Scholar 

  9. Junli, L., Jianlin, M., Guanghui, Z.: Evolutionary algorithms based parameters tuning of PID controller. Chinese Control and Decision Conference (CCDC), pp. 416–420 (2011)

    Google Scholar 

  10. Saad, M.S., Jamaluddin, H., Darus, I.Z.M.: PID controller tuning using evolutionary algorithms. WSEAS Trans. Syst. Contr. 4 (2012)

    Google Scholar 

  11. Saad, M.S., Jamaluddin, H., Darus, I.Z.M.: Implementation of PID controller tuning using differential evolution and genetic algorithms. Int. J. Innov. Comput. I. Control. 8(11), 7761–7779 (2012)

    Google Scholar 

  12. Hernández-Riveros, J., Arboleda-Gómez, A.: Multi-criteria decision and multi-objective optimization for constructing and selecting models for systems identification. WIT Trans. Model Sim. 55 (2013)

    Google Scholar 

  13. Michail, K., Zolotas, A.C., Goodall, R.M., Whidborne, J.F.: Optimised configuration of sensors for fault tolerant control of an electro-magnetic suspension system. Int. J. Syst. Sci. 43(10), 1785–1804 (2012)

    Article  Google Scholar 

  14. Whitley, D.: An overview of evolutionary algorithms: practical issues and common pitfalls. Inform. Softw. Tech. 43(14) (2001)

    Article  Google Scholar 

  15. Lozano, J.A., Larrañaga, P., Inza, I., Bengoetxea, E. (eds.): Towards a new evolutionary computation: advances on estimation of distribution algorithms. In: Studies in Fuzziness and Soft Computing, vol. 192. Springer, Heidelberg (2006)

    Google Scholar 

  16. Hernández, J.A., Ospina, J.D.: A multi dynamics algorithm for global optimization. Math. Comput. Model. 52(7), 1271–1278 (2010)

    Article  MathSciNet  Google Scholar 

  17. Hernández-Riveros, J.-A., Villada-Cano, D.: Sensitivity analysis of an autonomous evolutionary algorithm. Lecture Notes in Computer Science. In: Advances in Artificial Intelligence, vol. 763, pp. 271–280. Springer, Heidelberg (2012)

    Google Scholar 

  18. Yu, X., Gen, M.: Introduction to evolutionary algorithms. Springer, London (2010)

    Book  Google Scholar 

  19. Åström, K.J., Hägglund, T.: PID controllers: theory, design and tuning. Instrument Society of America (1995)

    Google Scholar 

  20. Bohl, A.H., McAvoy, T.J.: Linear feedback vs. time optima control. II. The regulator problem. Ind. Eng. Chem. Proc. Dd. 15(1), 30–33 (1976)

    Article  Google Scholar 

  21. Hassan, G.A.: Computer-aided tuning of analog and digital controllers. Control Comput. 21(1), 1–6 (1993)

    MathSciNet  Google Scholar 

  22. Sung, S.W.: Lee, O.J., Lee, I.B., Yi, S.H.: Automatic tuning of PID controller using second-order plus time delay model. J. Chem. Eng. Jpn. 29(6), 990–999 (1996)

    Article  Google Scholar 

  23. Lagunas, J.: Tuning of PID controllers using a multi-objective genetic algorithm, (NSGA-II). (in Spanish) (Doctoral dissertation) Departamento de Control Automático. Centro de Investigación y de Estudios Avanzados, México (2004)

    Google Scholar 

  24. Åström, K.J., Hägglund, T.: Benchmark systems for PID control. IFAC Workshop on Digital Control. Spain. 181–182 (2000)

    Google Scholar 

  25. Fleming, P.J., Purshouse, R.C.: Evolutionary algorithms in control systems engineering: a survey. Control Eng. Pract. 10(11), 1223–1241 (2002)

    Article  Google Scholar 

  26. Li, Y., Ang, K.H., Chong, G.C.: PID control system analysis and design. IEEE Contr. Syst. Mag. 26(1), 32–41 (2006)

    Article  Google Scholar 

  27. Vinter, R.: Optimal Control. Systems & Control: Foundations & Applications. Springer, London (2000)

    Google Scholar 

  28. Montgomery, D.C.: Introduction to statistical quality control. Wiley, New York (2008)

    Google Scholar 

  29. Skolicki, Z., De Jong, K.: The influence of migration sizes and intervals on island models. In: Proceedings of the conference on Genetic and evolutionary computation ACM, pp. 1295–1302 (2005)

    Google Scholar 

  30. Hansen, N.: Towards a new evolutionary computation: the CMA evolution strategy: a comparing review. In: Studies in Fuzziness and Soft Computing, vol. 192. Springer, Heidelberg (2006)

    Google Scholar 

  31. Herreros, A., Baeyens, E., Perán, J.R.: Design of PID-type controllers using multiobjective genetic algorithms. ISA T. 41(4), 457–472 (2002)

    Article  Google Scholar 

  32. Iruthayarajan, M.W., Baskar, S.: Evolutionary algorithms based design of multivariable PID controller. Expert Syst. Appl. 36(5) (2009)

    Article  Google Scholar 

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Authors and Affiliations

  1. Facultad De Minas, Universidad Nacional de Colombia – Sede Medellín, Medellín, Colombia

    Jesús-Antonio Hernández-Riveros, Jorge-Humberto Urrea-Quintero & Cindy-Vanessa Carmona-Cadavid

Authors
  1. Jesús-Antonio Hernández-Riveros
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  2. Jorge-Humberto Urrea-Quintero
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  3. Cindy-Vanessa Carmona-Cadavid
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Corresponding author

Correspondence to Jesús-Antonio Hernández-Riveros .

Editor information

Editors and Affiliations

  1. Ingenería Informática, Escuela Técnica Superior de, Granada, Spain

    Juan Julian Merelo

  2. aSEEB-ISR-IST, Technical University of Lisbon (IST), Lisbon, Portugal

    Agostinho Rosa

  3. Facultad de Informática, University of Murcia, Murcia, Spain

    José M. Cadenas

  4. University of Coimbra, Coimbra, Portugal

    António Dourado

  5. Images, Signals and Intelligence, University PARIS-EST Créteil (UPEC), Créteil, France

    Kurosh Madani

  6. Instituto Politécnico de Setúbal (IPS), Setúbal, Portugal

    Joaquim Filipe

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Hernández-Riveros, JA., Urrea-Quintero, JH., Carmona-Cadavid, CV. (2016). Evolutionary Tuning of Optimal PID Controllers for Second Order Systems Plus Time Delay. In: Merelo, J.J., Rosa, A., Cadenas, J.M., Dourado, A., Madani, K., Filipe, J. (eds) Computational Intelligence. IJCCI 2014. Studies in Computational Intelligence, vol 620. Springer, Cham. https://doi.org/10.1007/978-3-319-26393-9_1

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  • DOI: https://doi.org/10.1007/978-3-319-26393-9_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26391-5

  • Online ISBN: 978-3-319-26393-9

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