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MPC Framework for System Reliability Optimization

  • Jean C. SalazarEmail author
  • Philippe Weber
  • Fatiha Nejjari
  • Didier Theilliol
  • Ramon Sarrate
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 386)

Abstract

This work presents a general framework taking into account system and components reliability in a Model Predictive Control (MPC) algorithm. The objective is to deal with a closed-loop system combining a deterministic part related to the system dynamics and a stochastic part related to the system reliability from an availability point of view. The main contribution of this work consists in integrating the reliability assessment computed on-line using a Dynamic Bayesian Network (DBN) through the weights of the multiobjective cost function of the MPC algorithm. A comparison between a method based on the components reliability (local approach) and a method focused on the system reliability sensitivity analysis (global approach) is considered. The effectiveness and benefits of the proposed control framework are presented through a Drinking Water Network (DWN) simulation.

Keywords

Reliability Model predictive control Dynamic Bayesian network 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jean C. Salazar
    • 1
    Email author
  • Philippe Weber
    • 2
  • Fatiha Nejjari
    • 1
  • Didier Theilliol
    • 2
  • Ramon Sarrate
    • 1
  1. 1.Universitat Politècnica de Catalunya, Research Center for Supervision Safety and Automatic Control (CS2AC)TerrassaSpain
  2. 2.Université de Lorraine, Centre de Recherche En Automatique de Nancy (CRAN)Vandoeuvre-lès-NancyFrance

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