Advertisement

CONTROLO 2016 pp 239-249 | Cite as

Grey Wolf, Gravitational Search and Particle Swarm Optimizers: A Comparison for PID Controller Design

  • Paulo Moura Oliveira
  • Damir Vrančić
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 402)

Abstract

Nature and biologically inspired metaheuristics can be powerful tools to design PID controllers. The grey wolf optimization is one of these promising and interesting metaheuristics, recently introduced. In this study the grey wolf optimization algorithm is proposed to design PID controllers, and the results obtained compared with the ones obtained with gravitational search and particle swarm optimization algorithms. Simulation results obtained with these three bio-inspired metaheuristics applied to a set of benchmark linear plants are presented, considering the design objective of set-point tracking. The results are also compared with two non-iterative PID tuning techniques.

References

  1. 1.
    Kirkpatrick, S., Gellet, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial Intelligence through Simulated Evolution. Wiley, New York (1966)zbMATHGoogle Scholar
  3. 3.
    Rechenberg, I., Eigen, M.: Evolutionsstrategie: Optimierung Technischer Systeme nach Prinzipien der Biologischen Evolution. Frommann-Holzboog Stuttgart (1973)Google Scholar
  4. 4.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)Google Scholar
  5. 5.
    Baluja, S.: Population-based incremental learning: a method for integrating genetic search based function optimization and competitive learning. Technical Report CMU-CS-94-163. Carnegie Mellon University (1994)Google Scholar
  6. 6.
    Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization theory: a survey. Theoret. Comput. Sci. 344, 243–278 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Koza, J.R.: Genetic programming: a paradigm for breeding populations of computers pro-grams to solve problems. Technical report STAN-CS-90-1314. Stanford University (1990)Google Scholar
  8. 8.
    Storn, R., Price, K.V.: Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012, ICSI (1995)Google Scholar
  9. 9.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks IV, Piscataway, pp. 1942–1948 (1995)Google Scholar
  10. 10.
    Yang, X.-S., Deb, S.: Cuckoo search via Lévy flights. In: Proceedings of World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), pp. 210–214. IEEE Publications, India (2009)Google Scholar
  11. 11.
    Yang, X.-S.: Firefly algorithm, Lévy flights and global optimization. In: Bramer, M. et al. (eds.) Research and Development in Intelligent Systems XXVI. Springer, London (2010). doi: 10.1007/978-1-84882-983-1-15
  12. 12.
    Krishnanand, K.N., Ghose, D.: Glowworm swarm based optimization algorithm for multimodal functions with collective robotics applications. Multiagent Grid Syst Int J 209–222 (2006) (IOS Press)Google Scholar
  13. 13.
    Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179, 2232–2248 (2009)CrossRefzbMATHGoogle Scholar
  14. 14.
    Seyedali, M., Mohammad, S.M., Lewis, A.: Grey Wolf optimizer. Adv. Eng. Softw. 46–61 (2014)Google Scholar
  15. 15.
    Ziegler, J.G., Nichols, N.B.: Optimum settings for automatic controllers. Trans. ASME 759–768 (1942)Google Scholar
  16. 16.
    Jones, A.H., Moura Oliveira, P.B.: Genetic auto-tuning of PID controllers. Genetic algorithms in engineering systems: innovations and applications, GALESIA. In: Fifth IEEE Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, No. 414, pp. 141–145 (1995)Google Scholar
  17. 17.
    Moura Oliveira, P.B.: Modern heuristics review for PID control systems optimization: a teaching experiment. In: IEEE-International Conference on Control and Automation (ICCA 2005), pp. 828–833 (2005)Google Scholar
  18. 18.
    Moura Oliveira, P.B., Solteiro Pires, E.J., Novais, P.: Design of Posicast PID control systems using a gravitational search algorithm. Neurocomputing. Available online 9 May 2015. doi: 10.1016/j.neucom.2014.12.101. Elsevier
  19. 19.
    Zhao, S.-Z., Iruthayarajan, M.W., Baskar, S., Suganthan, P.N.: Multi-objective robust PID controller tuning using two lbests multi-objective particle swarm optimization. Inf. Sci. (Elsevier) 181(16), 3323–3335 (2011)CrossRefGoogle Scholar
  20. 20.
    Freire, H.F., Moura Oliveira, P.B., Solteiro Pires, E.J., Bessa, M.: Many-objective PSO PID controller tuning. In: CONTROLO’2014—Proceedings of the 11th Portuguese Conference on Automatic Control Lecture Notes in Electrical Engineering, vol. 321, pp. 183–192. Springer (2014)Google Scholar
  21. 21.
    Sharma, Y., Saikia, L.C.: Automatic generation control of a multi-area ST—thermal power system using Grey Wolf optimizer algorithm based classical controllers. Electr. Power Energy Syst. 73, 853–862 (2015)CrossRefGoogle Scholar
  22. 22.
    Madadi, A., Motlagh, M.M.: Optimal control of DC motor using Grey Wolf optimizer algorithm. Tech. J. Eng. Appl. Sci. (2014). ISSN 2051-0853Google Scholar
  23. 23.
    Korayem, L., Khorsid, M., Kassem, S.S.: Using Grey Wolf algorithm to solve the capacitated vehicle routing problem. In: IOP Conference Series: Mathematical Science and Engineering 83 (2015)Google Scholar
  24. 24.
    Åström, K.J., Hågglund, T.: Benchmark systems for PID control. In: IFAC Workshop on Digital Control: Past, Present and Future, Spain, pp. 181–183 (2000)Google Scholar
  25. 25.
    Vrančić, D., Strmčnik, S., Juričić, Đ.: A magnitude optimum multiple integration method for filtered PID controller. Automatica 37, 1473–1479 (2001)CrossRefzbMATHGoogle Scholar
  26. 26.
    Vinoth-Ray, A.: Stepwise method for tuning PI controllers using ITAE criteria (2012). http://www.embedded.com/design/prototyping-and-development/4391181/A-stepwise-method-for-tuning-PI-controllers-using-ITAE-criteria. Accessed 13 Jan 2016

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.INESC TEC—INESC Technology and Science Department of Engineering, School of Sciences and TechnologyUniversidade de Trás-os-Montes e Alto Douro, UTADVila RealPortugal
  2. 2.Department of Systems and ControlJožef Stefan InstituteLjubljanaSlovenia

Personalised recommendations