MPC framework for system reliability optimization

Jean Carlo Salazar (1) , Philippe Weber (2) , Fatiha Nejjari (1) , Didier Theilliol (2) , Ramon Sarrate (1)

1 Automatic Control Department
2 CRAN - Centre de Recherche en Automatique de Nancy

This paper presents a general framework that takes into account system and components reliability in a Model Predictive Control (MPC) algorithm. The objective is to deal from an availability point of view with a closed-loop system combining a deterministic part related to the system dynamics and a stochastic part related to the system reliability. The main contribution of this work consists in integrating the reliability assessment computed on-line using a Dynamic Bayesian Network (DBN) to compute the weights of the multiobjective cost function into the MPC algorithm. Also, 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 shown by its application on a Drinking Water Network (DWN).

Mots clés: reliability; dynamic Bayesian Network; model predictive control

Domaines: Automatique

Jean Carlo Salazar, Philippe Weber, Fatiha Nejjari, Didier Theilliol, Ramon Sarrate. MPC framework for system reliability optimization. 12th International Conference on Diagnostics of processes and systems, Sep 2015, Ustka, Poland. pp.161-177, ⟨10.1007/978-3-319-23180-8_12⟩. ⟨hal-01254683⟩