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Decompositions for MPC of Linear Dynamic Systems with Activation Constraints

Author

Listed:
  • Pedro Henrique Valderrama Bento da Silva

    (Department of Automation and Systems Engineering, Federal University of Santa Catarina, Florianópolis 88040-900, Brazil)

  • Eduardo Camponogara

    (Department of Automation and Systems Engineering, Federal University of Santa Catarina, Florianópolis 88040-900, Brazil)

  • Laio Oriel Seman

    (Department of Automation and Systems Engineering, Federal University of Santa Catarina, Florianópolis 88040-900, Brazil
    Graduate Program in Applied Computer Science, University of Vale do Itajaí, Itajaí 88302-901, Brazil)

  • Gabriel Villarrubia González

    (Expert Systems and Applications Lab, Faculty of Science, University of Salamanca, Plaza de los Caídos s/n, 37008 Salamanca, Spain)

  • Valderi Reis Quietinho Leithardt

    (COPELABS, Universidade Lusófona de Humanidades e Tecnologias, 1749-024 Lisboa, Portugal
    Departamento de Informática da Universidade da Beira Interior, 6200-001 Covilhã, Portugal
    VALORIZA, Research Center for Endogenous Resources Valorization, Instituto Politécnico de Portalegre, 7300-555 Portalegre, Portugal)

Abstract

The interconnection of dynamic subsystems that share limited resources are found in many applications, and the control of such systems of subsystems has fueled significant attention from scientists and engineers. For the operation of such systems, model predictive control (MPC) has become a popular technique, arguably for its ability to deal with complex dynamics and system constraints. The MPC algorithms found in the literature are mostly centralized, with a single controller receiving the signals and performing the computations of output signals. However, the distributed structure of such interconnected subsystems is not necessarily explored by standard MPC. To this end, this work proposes hierarchical decomposition to split the computations between a master problem (centralized component) and a set of decoupled subproblems (distributed components) with activation constraints, which brings about organizational flexibility and distributed computation. Two general methods are considered for hierarchical control and optimization, namely Benders decomposition and outer approximation. Results are reported from a numerical analysis of the decompositions and a simulated application to energy management, in which a limited source of energy is distributed among batteries of electric vehicles.

Suggested Citation

  • Pedro Henrique Valderrama Bento da Silva & Eduardo Camponogara & Laio Oriel Seman & Gabriel Villarrubia González & Valderi Reis Quietinho Leithardt, 2020. "Decompositions for MPC of Linear Dynamic Systems with Activation Constraints," Energies, MDPI, vol. 13(21), pages 1-26, November.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:21:p:5744-:d:439021
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    References listed on IDEAS

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    4. Rahmaniani, Ragheb & Crainic, Teodor Gabriel & Gendreau, Michel & Rei, Walter, 2017. "The Benders decomposition algorithm: A literature review," European Journal of Operational Research, Elsevier, vol. 259(3), pages 801-817.
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    Cited by:

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