System reliability aware model predictive control framework
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...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/111780 |
| Acceso en línea: | https://hdl.handle.net/2117/111780 https://dx.doi.org/10.1016/j.ress.2017.04.012 |
| Access Level: | acceso abierto |
| Palabra clave: | Reliability Bayesian statistical decision theory Automatic control Dynamic Bayesian networks Model Predictive Control Reliability Importance Measures Health-Aware Control Fidelitat Estadística bayesiana Control automàtic Àrees temàtiques de la UPC::Informàtica::Automàtica i control |
| Sumario: | 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. |
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