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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kirkpatrick, S., Gellet, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)
Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial Intelligence through Simulated Evolution. Wiley, New York (1966)
Rechenberg, I., Eigen, M.: Evolutionsstrategie: Optimierung Technischer Systeme nach Prinzipien der Biologischen Evolution. Frommann-Holzboog Stuttgart (1973)
Holland, J.H.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)
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)
Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization theory: a survey. Theoret. Comput. Sci. 344, 243–278 (2005)
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)
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)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks IV, Piscataway, pp. 1942–1948 (1995)
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)
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
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)
Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179, 2232–2248 (2009)
Seyedali, M., Mohammad, S.M., Lewis, A.: Grey Wolf optimizer. Adv. Eng. Softw. 46–61 (2014)
Ziegler, J.G., Nichols, N.B.: Optimum settings for automatic controllers. Trans. ASME 759–768 (1942)
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)
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)
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
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)
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)
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)
Madadi, A., Motlagh, M.M.: Optimal control of DC motor using Grey Wolf optimizer algorithm. Tech. J. Eng. Appl. Sci. (2014). ISSN 2051-0853
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)
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)
Vrančić, D., Strmčnik, S., Juričić, Đ.: A magnitude optimum multiple integration method for filtered PID controller. Automatica 37, 1473–1479 (2001)
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
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing Switzerland
About this paper
Cite this paper
Oliveira, P.M., Vrančić, D. (2017). Grey Wolf, Gravitational Search and Particle Swarm Optimizers: A Comparison for PID Controller Design. In: Garrido, P., Soares, F., Moreira, A. (eds) CONTROLO 2016. Lecture Notes in Electrical Engineering, vol 402. Springer, Cham. https://doi.org/10.1007/978-3-319-43671-5_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-43671-5_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-43670-8
Online ISBN: 978-3-319-43671-5
eBook Packages: EngineeringEngineering (R0)