This work presents a Model Predictive Control (MPC) framework taking into account the usage of the actuators to preserve system reliability while maximizing control performance. Two approaches are proposed to preserve system reliability: a global approach that integrates in the control algorithm a representation of system reliability, and a local approach that integrates a representation of component reliability. The trade-off between the system reliability and the control performance should be taken into account. A methodology for MPC tuning is proposed to handle this trade-off. System and component reliability are computed based on Dynamic Bayesian Network. The effectiveness and benefits of the proposed control framework are discussed through its application to an over-actuated system.

Abstract. 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).

Abstract-A major goal in modern flight control systems is the need of improving the reliability. This work presents a reliable control approach of an octorotor UAV that allows distributing the control effort among the actuators using health actuator information. The octorotor is an over-actuated system where the redundancy of the actuators allows the redistribution of the control effort among the existing actuators according to a given control strategy. The priority is given to each actuator according to the capabilities and reliability of this actuator.

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