On the Selection of Tuning Parameters in Predictive Controllers Based on NSGA-II

  • R. C. Gutiérrez-Urquídez
  • G. Valencia-PalomoEmail author
  • O. M. Rodríguez-Elías
  • F. R. López-Estrada
  • J. A. Orrante-Sakanassi
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 785)


In the design of linear (model) predictive controllers (MPC), tuning plays a very important role. However, there is a problem not yet fully resolved: how to determine the best strategy for the selection of the optimal tuning parameters in order to obtain good performance with a large feasibility region, but maintaining a low computational load of the control algorithm? Because these objectives determine the proper functioning of the controller and are committed to each other, adjusting the controller parameters becomes a difficult task. The main contribution of this paper is to revise a method that uses the Nondominated Sorting Genetic Algorithm II (NSGA-II) for the parameter selection of a predictive control algorithm that has been parameterized with Laguerre functions (LOMPC) in order to explore the efficiency and provide statistical significance of the algorithm. Numerical simulations show that NSGA-II is a useful tool to obtain consistently good solutions for the selection of MPC tuning parameters.


Predictive control NSGA-II Tuning Multi-objective optimization 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • R. C. Gutiérrez-Urquídez
    • 1
  • G. Valencia-Palomo
    • 1
  • O. M. Rodríguez-Elías
    • 1
  • F. R. López-Estrada
    • 2
  • J. A. Orrante-Sakanassi
    • 3
  1. 1.Tecnológico Nacional de MéxicoInstituto Tecnológico de HermosilloHermosilloMexico
  2. 2.Tecnológico Nacional de MéxicoInstituto Tecnológico de Tuxtla GutiérrezTuxtla GutiérrezMexico
  3. 3.CONACYT-Tecnológico Nacional de MéxicoInstituto Tecnológico de HermosilloHermosilloMexico

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