Name
Abstract | ||
|---|---|---|
This paper 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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.ress.2017.04.012 | Reliability Engineering & System Safety |
Keywords | Field | DocType |
Reliability,Dynamic Bayesian networks,Model Predictive Control,Reliability Importance Measures,Health-Aware Control | Control algorithm,Model predictive control,Control engineering,Engineering,Reliability engineering,Actuator,Dynamic Bayesian network | Journal |
Volume | ISSN | Citations |
167 | 0951-8320 | 6 |
PageRank | References | Authors |
0.68 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jean C. Salazar | 1 | 6 | 0.68 |
| Philippe Weber | 2 | 44 | 3.49 |
| Fatiha Nejjari | 3 | 117 | 16.29 |
| ramon sarrate | 4 | 35 | 4.87 |
| D. Theilliol | 5 | 31 | 6.57 |