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PID Controller Tuning Parameters Using Meta-heuristics Algorithms: Comparative Analysis

  • Mohamed Issa
  • Ahmed Abd Elbaset
  • Aboul Ella Hassanien
  • Ibrahim Ziedan
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 801)

Abstract

Proportional-Integral-Derivative (PID) Controller is a primary component in industrial control systems nowadays. Gain parameters of it have a powerful effect on transient response’s criteria such as integral squared error (ISE), settling time, rise time and overshooting. The power control systems that have the minimum of these criteria. Tuning the parameters to deliver the active case of the transient response of control systems is a hard problem. The traditional method is Ziegler–Nicolas (ZN) method that initially computes the values of the parameters. Meta-heuristics are used to tune these initial parameters’ values to produce more stable transient response has minimum criteria. In this chapter Particle Swarm Optimization algorithm, Genetic algorithm and Sine-Cosine Optimization algorithm are used to tune the parameters of PID controller by minimizing the ISE function and compared the result with that produced by Ziegler–Nicolas method.

Keywords

PID controller Bio-inspired algorithms Genetic algorithm Particle swarm optimization and Sine-Cosine optimization algorithm 

Notes

Acknowledgements

The first author would like to thank Yasmina Fakhry for her collaboration for provide facilities for providing this work.

References

  1. 1.
    Bennett, S.: A History of Control Engineering, 1930–1955, p. 48. IET (1993). ISBN 978-0-86341-299-8Google Scholar
  2. 2.
    Hamid, N.H.A., Md Mahanijah, K., Faieza, H.Y.: Application of PID controller in controlling refrigerator temperature. In: 5th International Colloquium on Signal Processing & Its Applications CSPA 2009. IEEE (2009)Google Scholar
  3. 3.
    Chen, D., Seborg, D.E.: PI/PID controller design based on direct synthesis and disturbance rejection. Ind. Eng. Chem. Res. 41(19), 4807–4822 (2002)CrossRefGoogle Scholar
  4. 4.
    Åström, K.J., et al.: Automatic tuning and adaptation for PID controllers-a survey. Control Eng. Pract. 1(4), 699–714 (1993)Google Scholar
  5. 5.
    Vinagre, B.M., et al.: Fractional PID controllers for industry application. A brief introduction. J. Vibr. Control 13(9–10), 1419–1429 (2007)Google Scholar
  6. 6.
    Larsen, P.M.: Industrial applications of fuzzy logic control. Int. J. Man-Mach. Stud. 12(1), 3–10 (1980)Google Scholar
  7. 7.
    Neuhaus, R.: Diode Laser Locking and Linewidth Narrowing (PDF). Retrieved 8 June 2015Google Scholar
  8. 8.
    Ziegler, J.G., Nichols, N.B.: Optimum settings for automatic controllers. Trans. ASME 64(11) (1942)Google Scholar
  9. 9.
    Araki, M.: PID control. In: Unbehauen, H. (ed.) Control Systems, Robotics, and Automation: System Analysis and Control: Classical Approaches II, pp. 58–79. EOLSS Publishers Co. Ltd., Oxford, UK (2009). ISBN-13: 9781848265912Google Scholar
  10. 10.
    Tim, W.: PID without a Ph.D. (PDF). EE Times-India (2000)Google Scholar
  11. 11.
    Li, Y., Ang, K.H., Chong, G.C.Y.: Patents, software, and hardware for PID control: an overview and analysis of the current art. IEEE Control Syst. 26(1), 42–54 (2006)Google Scholar
  12. 12.
    Hoos, H.H., Stützle, T.: Stochastic Local Search: Foundations and Applications. Elsevier, San Francisco (2004)Google Scholar
  13. 13.
    Govan, A.: Introduction to optimization. North Carolina State University, SAMSI NDHS, Undergraduate Workshop (2006)Google Scholar
  14. 14.
    Spall, J.C.: Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control, vol. 65. Wiley, New York (2005)Google Scholar
  15. 15.
    Kaveh, A., Mahdavi, V.R.: Colliding bodies optimization method for optimum discrete design of truss structures. Comput. Struct. 139, 43–53 (2014)CrossRefGoogle Scholar
  16. 16.
    Hatamlou, A.: Black hole: a new heuristic optimization approach for data clustering. Inf. Sci. 222, 175–184 (2013)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179(13), 2232–2248 (2009)CrossRefGoogle Scholar
  18. 18.
    Holland, J.H.: Genetic algorithms. Sci. Am. 267(1), 66–72 (1992)CrossRefGoogle Scholar
  19. 19.
    Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Dorigo, M., Birattari, M.: Ant colony optimization. In: Encyclopedia of Machine Learning, pp. 36–39. Springer, Boston (2010)Google Scholar
  21. 21.
    Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global Optim. 39(3), 459–471 (2007)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: 1995 Proceedings of the Sixth International Symposium on Micro Machine and Human Science, MHS’95, pp. 39–43. IEEE (1995)Google Scholar
  23. 23.
    Calvini, M., et al.: PSO-based self-commissioning of electrical motor drives. IEEE Trans. Ind. Electr. 62(2), 768–776 (2015)Google Scholar
  24. 24.
    Khanna, V., et al.: A three diode model for industrial solar cells and estimation of solar cell parameters using PSO algorithm. Renew. Energy 78, 105–113 (2015)Google Scholar
  25. 25.
    Tuvayanond, W., Parnichkun, M.: Position control of a pneumatic surgical robot using PSO based 2-DOF H∞ loop shaping structured controller. Mechatronics 43, 40–55 (2017)CrossRefGoogle Scholar
  26. 26.
    Sridhar, R., et al.: Optimization of heterogeneous bin packing using adaptive genetic algorithm. In: IOP Conference Series. Materials Science and Engineering, vol. 183. no. 1. IOP Publishing (2017)Google Scholar
  27. 27.
    Lai, C., et al.: Genetic algorithm based current optimization for torque ripple reduction of interior PMSMs. IEEE Trans. Ind. Appl. (2017)Google Scholar
  28. 28.
    Saljoughi, E.: Application of genetic programming as a powerful tool for modeling of cellulose acetate membrane preparation. Chem. Ind. 1, 4 (2017)Google Scholar
  29. 29.
    Barley, M.H., Turner, N.J., Goodacre, R.: Recommendations on the implementation of genetic algorithms for the directed evolution of enzymes for industrial purposes. ChemBioChem (2017)Google Scholar
  30. 30.
    Eiben, A.E., et al.: Genetic algorithms with multi-parent recombination. In: PPSN III: Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature, pp. 78–87Google Scholar
  31. 31.
    Akbari, Z.: A multilevel evolutionary algorithm for optimizing numerical functions. IJIEC 2(2011), 419–430 (2010)Google Scholar
  32. 32.
    Mirjalili, S.: SCA: a sine-cosine algorithm for solving optimization problems. Knowl. Based Syst. 96, 120–133 (2016)CrossRefGoogle Scholar
  33. 33.
    Kumar, N., et al.: Single Sensor based MPPT of partially shaded PV system for battery charging by using Cauchy and Gaussian Sine Cosine optimization. IEEE Trans. Energy Convers. (2017)Google Scholar
  34. 34.
    Kumar, V., Kumar, D.: Data clustering using Sine Cosine algorithm: data clustering using SCA. In: Handbook of Research on Machine Learning Innovations and Trends, pp. 715–726. IGI Global (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mohamed Issa
    • 1
  • Ahmed Abd Elbaset
    • 1
  • Aboul Ella Hassanien
    • 2
  • Ibrahim Ziedan
    • 1
  1. 1.Computer and Systems Engineering Department, Faculty of EngineeringZagazig UniversityZagazigEgypt
  2. 2.Faculty of Computer and InformationCairo UniversityGizaEgypt

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