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Recent Advances in Computational Optimization pp 155–168Cite as

A New Hybrid GA-FA Tuning of PID Controller for Glucose Concentration Control

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 470))

Abstract

In this paper a hybrid scheme using Firefly Algorithm (FA) - Genetic Algorithm (GA) is introduced. The novel hybrid meta-heuristics algorithm is realized and applied to PID controller parameter tuning in Smith Predictor for a nonlinear control system. The controller is used to control feed rate and to maintain glucose concentration at the desired set point for an E. coli MC4110 fed-batch cultivation process. The hybrid FA-GA adjustments are done based on several pre-tests. Simulation results indicate that the applied hybrid algorithm is effective. Good closed-loop system performance is achieved on the basis of the considered PID controllers tuning procedures. Moreover, the observed results are compared to the ones obtained by applying the pure FA and pure GA. The comparison shows that the proposed hybrid algorithm is highly competitive with standard FA and GA for considered here optimization problem.

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References

  1. Akpinar, S., Bayhan, G.M.: A Hybrid Genetic Aalgorithm for Mixed Model Assembly Line Balancing Problem with Parallel Workstations and Zoning Constraints. Engineering Applications of Artificial Intelligence 24(3), 449–457 (2011)

    Article  Google Scholar 

  2. Al-Duwaish, H.N.: A Genetic Approach to the Identification of Linear Dynamical Systems with Static Nonlinearities. International Journal of Systems Science 31(3), 307–313 (2000)

    Article  Google Scholar 

  3. Arndt, M., Hitzmann, B.: Feed Forward/feedback Control of Glucose Concentration during Cultivation of Escherichia coli. In: 8th IFAC Int. Conf. on Comp. Appl. in Biotechn, pp. 425–429 (2001)

    Google Scholar 

  4. Aström, K., Hagglund, T.: Advanced PID Control. Instrument Society of America (2006)

    Google Scholar 

  5. Benjamin, K.K., Ammanuel, A.N., David, A., Benjamin, Y.K.: Genetic Algorithm using for a Batch Fermentation Process Identification. Journal of Applied Sciences 8(12), 2272–2278 (2008)

    Article  Google Scholar 

  6. Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)

    MATH  Google Scholar 

  7. Brownlee, J.: Clever Algorithms. Nature-Inspired Programming Recipes, LuLu (2011)

    Google Scholar 

  8. da Silva, M.F.J., Perez, J.M.S., Pulido, J.A.G., Rodriguez, M.A.V.: AlineaGA - A Genetic Algorithm with Local Search Optimization for Multiple Sequence Alignment. Appl. Intell. 32, 164–172 (2010)

    Article  Google Scholar 

  9. Galvez-Carrillo, M., De Keyser, R., Ionescu, C.: Application of a Smith Predictor based Nonlinear Predictive Controller to a Solar Power Plant. In: 7th IFAC Symposium on Nonlinear Control Systems, Pretoria, South Africa, August 21-24, pp. 188–193 (2007)

    Google Scholar 

  10. Garipov, E.: PID Controllers. Automatics and Informatics, vol. 3 (2006) (in Bulgarian)

    Google Scholar 

  11. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley Longman, London (2006)

    Google Scholar 

  12. Gundogdu, O.: Optimal-tuning of PID Controller Gains using Genetic Algorithms. Journal of Engineering Sciences 11(1), 131–135 (2005)

    Google Scholar 

  13. Holland, J.H.: Adaptation in Natural and Artificial Systems, 2nd edn. MIT Press, Cambridge (1992)

    Google Scholar 

  14. Kim, J.S., Kim, J.-H., Park, J.-M., Park, S.-M., Choe, W.-Y., Heo, H.: Auto Tuning PID Controller based on Improved Genetic Algorithm for Reverse Osmosis Plant. World Academy of Science, Engineering and Technology 47, 384–389 (2008)

    Google Scholar 

  15. Kumar, S.M.G., Rakesh, B., Anantharaman, N.: Design of Controller using Simulated Annealing for a Real Time Process. International Journal of Computer Applications 2, 1053–1368 (2010)

    Google Scholar 

  16. Paplinski, J.P.: The Genetic Algorithm with Simplex Crossover for Identification of Time Delays. Intelligent Information Systems, pp. 337–346 (2010)

    Google Scholar 

  17. Puangdownreong, D., Kulworawanichpong, T., Sujitjorn, S.: Input weighting optimization for PID controllers based on the adaptive tabu search. IEEE TENCON 4, 451–454 (2004)

    Google Scholar 

  18. Ranganath, M., Renganathan, S., Gokulnath, C.: Identification of Bioprocesses using Genetic Algorithm. Bioprocess Engineering 21, 123–127 (1999)

    Article  Google Scholar 

  19. Roeva, O.: Improvement of Genetic Algorithm Performance for Identification of Cultivation Process Models. In: Advances Topics on Evolutionary Computing, Book Series: Artificial Intelligence Series-WSEAS, pp. 34–39 (2008)

    Google Scholar 

  20. Roeva, O., Slavov, T.: Firefly Algorithm Tuning of PID Controller for Glucose Concentration Control during E. coli Fed-batch Cultivation Process. In: Federated Conference on Computer Science and Information Systems, WCO 2012, Poland, pp. 455–462 (2012)

    Google Scholar 

  21. Roeva, O., Slavov, T.: Fed-Batch Cultivation Control Based on Genetic Algorithm PID Controller Tuning. In: Dimov, I., Dimova, S., Kolkovska, N. (eds.) NMA 2010. LNCS, vol. 6046, pp. 289–296. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  22. Slavov, T., Roeva, O.: Genetic Algorithm Tuning of PID Controller in Smith Predictor for Glucose Concentration Control. Int. J. Bioautomation 15(2), 101–114 (2011)

    Google Scholar 

  23. Smith, O.J.M.: A Controller to Overcome Dead Time. ISA Journal 6, 28–33 (1959)

    Google Scholar 

  24. Syam, W.P., Al-Harkan, I.M.: Comparison of Three Meta Heuristics to Optimize Hybrid Flow Shop Scheduling Problem with Parallel Machines. World Academy of Science, Engineering and Technology 62, 271–278 (2010)

    Google Scholar 

  25. Tahouni, N., Smith, R., Panjeshahi, M.H.: Comparison of Stochastic Methods with Respect to Performance and Reliability of Low-temperature Gas Separation Processes. The Canadian Journal of Chemical Engineering 88(2), 256–267 (2010)

    Google Scholar 

  26. Tseng, L.-Y., Lin, Y.-T.: A Hybrid Genetic Local Search Algorithm for the Permutation Flowshop Scheduling Problem. Europen J. of Operational Res. 198(1), 84–92 (2009)

    Article  MATH  Google Scholar 

  27. Wang, Q., Spronck, P., Tracht, R.: An Overview of Genetic Algorithms Applied to Control Engineering Problems. Machine Learning and Cybernetics 3, 1651–1656 (2003)

    Google Scholar 

  28. Yang, X.S.: Nature-inspired Meta-heuristic Algorithms. Luniver Press, Beckington (2008)

    Google Scholar 

  29. Yang, X.-S.: Firefly Algorithms for Multimodal Optimization. In: Watanabe, O., Zeugmann, T. (eds.) SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  30. Yang, X.S.: Firefly Algorithm, Stochastic Test Functions and Design Optimisation. International Journal of Bio-inspired Computation 2(2), 78–84 (2010)

    Article  Google Scholar 

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Authors and Affiliations

  1. Institute of Biophysics and Biomedical Engineering, BAS 105 Acad. G. Bonchev Str., 1113, Sofia, Bulgaria

    Olympia Roeva

  2. Technical University of Sofia, 8 Kliment Ohridski Bulv., 1000, Sofia, Bulgaria

    Tsonyo Slavov

Authors
  1. Olympia Roeva
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  2. Tsonyo Slavov
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Corresponding author

Correspondence to Olympia Roeva .

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Editors and Affiliations

  1. , Institute of Information and, Bulgarian Academy of Sciences, Acad. G. Bonchev str. Bl. 25A, Sofia, 1113, Bulgaria

    Stefka Fidanova

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Roeva, O., Slavov, T. (2013). A New Hybrid GA-FA Tuning of PID Controller for Glucose Concentration Control. In: Fidanova, S. (eds) Recent Advances in Computational Optimization. Studies in Computational Intelligence, vol 470. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00410-5_9

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  • DOI: https://doi.org/10.1007/978-3-319-00410-5_9

  • Publisher Name: Springer, Heidelberg

  • Print ISBN: 978-3-319-00409-9

  • Online ISBN: 978-3-319-00410-5

  • eBook Packages: EngineeringEngineering (R0)

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