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 work presents a general framework taking 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) 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.

Mots clés: reliability; model predictive control; dynamic Bayesian net-work

Domaines: Automatique

Jean Carlo Salazar, Philippe Weber, Fatiha Nejjari, Didier Theilliol, Ramon Sarrate. MPC Framework for System Reliability Optimization. Editor E Kowalczuk, Zdzisław. Advances in Intelligent Systems and Computing, 386, Springer International Publishing, pp.161-177, 2015, Advanced and Intelligent Computations in Diagnosis and Control, ⟨10.1007/978-3-319-23180-8_12⟩. ⟨hal-01254658⟩