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Evolutionary Game-Based Dynamical Tuning for Multi-objective Model Predictive Control

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Book cover Developments in Model-Based Optimization and Control

Part of the book series: Lecture Notes in Control and Information Sciences ((LNCIS,volume 464))

Abstract

Model predictive control (MPC) is one of the most used optimization-based control strategies for large-scale systems, since this strategy allows to consider a large number of states and multi-objective cost functions in a straightforward way. One of the main issues in the design of multi-objective MPC controllers, which is the tuning of the weights associated to each objective in the cost function, is treated in this work. All the possible combinations of weights within the cost function affect the optimal result in a given Pareto front. Furthermore, when the system has time-varying parameters, e.g., periodic disturbances, the appropriate weight tuning might also vary over time. Moreover, taking into account the computational burden and the selected sampling time in the MPC controller design, the computation time to find a suitable tuning is limited. In this regard, the development of strategies to perform a dynamical tuning in function of the system conditions potentially improves the closed-loop performance. In order to adapt in a dynamical way the weights in the MPC multi-objective cost function, an evolutionary game approach is proposed. This approach allows to vary the prioritization weights in the proper direction taking as a reference a desired region within the Pareto front. The proper direction for the prioritization is computed by only using the current system values, i.e., the current optimal control action and the measurement of the current states, which establish the system cost function over a certain point in the Pareto front. Finally, some simulations of a multi-objective MPC for a real multivariable case study show a comparison between the system performance obtained with static and dynamical tuning .

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Acknowledgments

Authors would like to thank COLCIENCIAS (grant 6172) and Agència de Gestió d’Ajust Universitaris i de Recerca AGAUR for supporting J. Barreiro-Gómez. This work has been partially supported by the projects “Drenaje urbano y cambio climático: Hacia los sistemas de alcantarillados del futuro, Fase II. COLCIENCIAS”, and ECOCIS (Ref. DPI2013-48243-C2-1).

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Authors and Affiliations

  1. Automatic Control Department, Institut de Robòtica i Informàtica Industrial (CSIC-UPC), Universitat Politècnica de Catalunya, Llorens i Artigas, 4-6, 08028, Barcelona, Spain

    Julián Barreiro-Gomez & Carlos Ocampo-Martinez

  2. Departamento de Ingeniería Eléctrica y Electrónica, Universidad de Los Andes, Carrera 1 No 18A-10, Bogotá, Colombia

    Julián Barreiro-Gomez & Nicanor Quijano

Authors
  1. Julián Barreiro-Gomez
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  2. Carlos Ocampo-Martinez
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  3. Nicanor Quijano
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Corresponding author

Correspondence to Julián Barreiro-Gomez .

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Editors and Affiliations

  1. Laboratory of Signals and Systems, CentraleSupélec-CNRS-Université Paris-Sud, Université Paris-Saclay, Gif sur Yvette, France

    Sorin Olaru

  2. Department of Industrial Automation, Univ. of Chem.Tech. and Metallurgy Department of Industrial Automation, Sofia, Bulgaria

    Alexandra Grancharova

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

    Fernando Lobo Pereira

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Barreiro-Gomez, J., Ocampo-Martinez, C., Quijano, N. (2015). Evolutionary Game-Based Dynamical Tuning for Multi-objective Model Predictive Control. In: Olaru, S., Grancharova, A., Lobo Pereira, F. (eds) Developments in Model-Based Optimization and Control. Lecture Notes in Control and Information Sciences, vol 464. Springer, Cham. https://doi.org/10.1007/978-3-319-26687-9_6

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  • DOI: https://doi.org/10.1007/978-3-319-26687-9_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26685-5

  • Online ISBN: 978-3-319-26687-9

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