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.
This work has been funded by the Spanish Ministry of Economy and Competitiveness through the CICYT project SHERECS (ref. DPI2011-26243), and by the European Commission through contract EFFINET (ref. FP7-ICT2011-8-318556).
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Salazar, J.C., Weber, P., Nejjari, F., Theilliol, D., Sarrate, R. (2016). MPC Framework for System Reliability Optimization. In: Kowalczuk, Z. (eds) Advanced and Intelligent Computations in Diagnosis and Control. Advances in Intelligent Systems and Computing, vol 386. Springer, Cham. https://doi.org/10.1007/978-3-319-23180-8_12
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