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Numerical and Evolutionary Optimization

NEO 2017: Numerical and Evolutionary Optimization – NEO 2017 pp 138–157Cite as

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

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 785))

Abstract

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.

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Author information

Authors and Affiliations

  1. Tecnológico Nacional de México, Instituto Tecnológico de Hermosillo, Av. Tecnológico y Periférico Poniente S/N, Hermosillo, Mexico

    R. C. Gutiérrez-Urquídez, G. Valencia-Palomo & O. M. Rodríguez-Elías

  2. Tecnológico Nacional de México, Instituto Tecnológico de Tuxtla Gutiérrez, Carretera Panamericana Km. 1080, C.P. 29050, 599, Tuxtla Gutiérrez, Mexico

    F. R. López-Estrada

  3. CONACYT-Tecnológico Nacional de México, Instituto Tecnológico de Hermosillo, Av. Tecnológico y Periférico Poniente S/N, Hermosillo, Mexico

    J. A. Orrante-Sakanassi

Authors
  1. R. C. Gutiérrez-Urquídez
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  2. G. Valencia-Palomo
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  3. O. M. Rodríguez-Elías
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  4. F. R. López-Estrada
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  5. J. A. Orrante-Sakanassi
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Corresponding author

Correspondence to G. Valencia-Palomo .

Editor information

Editors and Affiliations

  1. Posgrado en Ciencias de la Ingeniería, Tecnológico Nacional de México - Instituto Tecnológico de Tijuana, Tijuana, Baja California, Mexico

    Leonardo Trujillo

  2. Computer Science Department, Cinvestav-IPN, Mexico City, Mexico

    Oliver Schütze

  3. Posgrado en Ciencias de la Ingeniería, Tecnológico Nacional de México - Instituto Tecnológico de Tijuana, Tijuana, Baja California, Mexico

    Yazmin Maldonado

  4. Posgrado en Ciencias de la Ingeniería, Tecnológico Nacional de México - Instituto Tecnológico de Tijuana, Tijuana, Baja California, Mexico

    Paul Valle

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Gutiérrez-Urquídez, R.C., Valencia-Palomo, G., Rodríguez-Elías, O.M., López-Estrada, F.R., Orrante-Sakanassi, J.A. (2019). On the Selection of Tuning Parameters in Predictive Controllers Based on NSGA-II. In: Trujillo, L., Schütze, O., Maldonado, Y., Valle, P. (eds) Numerical and Evolutionary Optimization – NEO 2017. NEO 2017. Studies in Computational Intelligence, vol 785. Springer, Cham. https://doi.org/10.1007/978-3-319-96104-0_7

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