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

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Part of the book series: Lecture Notes in Electrical Engineering ((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.

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Authors and Affiliations

  1. INESC TEC—INESC Technology and Science Department of Engineering, School of Sciences and Technology, Universidade de Trás-os-Montes e Alto Douro, UTAD, 5001-801, Vila Real, Portugal

    Paulo Moura Oliveira

  2. Department of Systems and Control, Jožef Stefan Institute, Jamova cesta 39, Ljubljana, Slovenia

    Damir Vrančić

Authors
  1. Paulo Moura Oliveira
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  2. Damir Vrančić
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Correspondence to Paulo Moura Oliveira .

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Editors and Affiliations

  1. School of Engineering,Campus de Couros, University of Minho School of Engineering,Campus de Couros, Guimarães, Portugal

    Paulo Garrido

  2. School of Engineering,Campus de Couros, University of Minho School of Engineering,Campus de Couros, Guimarães, Portugal

    Filomena Soares

  3. Faculty of Engineering, University of Porto Faculty of Engineering, Porto, Portugal

    António Paulo Moreira

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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

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  • DOI: https://doi.org/10.1007/978-3-319-43671-5_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-43670-8

  • Online ISBN: 978-3-319-43671-5

  • eBook Packages: EngineeringEngineering (R0)

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