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Distributed tree-based model predictive control on a drainage water system.

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Title: Distributed tree-based model predictive control on a drainage water system.
Authors: Maestre, J. M.1 pepemaestre@us.es, Raso, L.2, Van Overloop, P. J.2, De Schutter, B.3
Source: Journal of Hydroinformatics. Apr2013, Vol. 15 Issue 2, p335-347. 13p.
Subjects: Drainage, Predictive control systems, Stochastic programming, Weather forecasting, Mathematical optimization, Prediction models, Performance evaluation
Abstract: Open water systems are one of the most externally influenced systems due to their size and continuous exposure to uncertain meteorological forces. The control of systems under uncertainty is, in general, a challenging problem. In this paper, we use a stochastic programming approach to control a drainage system in which the weather forecast is modeled as a disturbance tree. Each branch of the tree corresponds to a possible disturbance realization and has a certain probability associated to it. A model predictive controller is used to optimize the expected value of the system variables taking into account the disturbance tree. This technique, tree-based model predictive control (TBMPC), is solved in a distributed fashion. In particular, we apply dual decomposition to get an optimization problem that can be solved by different agents in parallel. In addition, different possibilities are considered in order to reduce the communicational burden of the distributed algorithm without reducing the performance of the controller significantly. Finally, the performance of this technique is compared with others such as minmax or multiple MPC. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Hydroinformatics is the property of IWA Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 87402825
AccessLevel: 6
PubType: Periodical
PubTypeId: serialPeriodical
PreciseRelevancyScore: 0
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  Data: Distributed tree-based model predictive control on a drainage water system.
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  Data: <searchLink fieldCode="DE" term="%22Drainage%22">Drainage</searchLink><br /><searchLink fieldCode="DE" term="%22Predictive+control+systems%22">Predictive control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+programming%22">Stochastic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Weather+forecasting%22">Weather forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Performance+evaluation%22">Performance evaluation</searchLink>
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  Data: Open water systems are one of the most externally influenced systems due to their size and continuous exposure to uncertain meteorological forces. The control of systems under uncertainty is, in general, a challenging problem. In this paper, we use a stochastic programming approach to control a drainage system in which the weather forecast is modeled as a disturbance tree. Each branch of the tree corresponds to a possible disturbance realization and has a certain probability associated to it. A model predictive controller is used to optimize the expected value of the system variables taking into account the disturbance tree. This technique, tree-based model predictive control (TBMPC), is solved in a distributed fashion. In particular, we apply dual decomposition to get an optimization problem that can be solved by different agents in parallel. In addition, different possibilities are considered in order to reduce the communicational burden of the distributed algorithm without reducing the performance of the controller significantly. Finally, the performance of this technique is compared with others such as minmax or multiple MPC. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Journal of Hydroinformatics is the property of IWA Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=87402825
RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.2166/hydro.2012.125
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 13
        StartPage: 335
    Subjects:
      – SubjectFull: Drainage
        Type: general
      – SubjectFull: Predictive control systems
        Type: general
      – SubjectFull: Stochastic programming
        Type: general
      – SubjectFull: Weather forecasting
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Performance evaluation
        Type: general
    Titles:
      – TitleFull: Distributed tree-based model predictive control on a drainage water system.
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            NameFull: Maestre, J. M.
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            NameFull: Raso, L.
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            NameFull: Van Overloop, P. J.
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            NameFull: De Schutter, B.
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              M: 04
              Text: Apr2013
              Type: published
              Y: 2013
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