<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Essential ingredients for fault-tolerant control are the ability to represent system behaviour following the occurrence of a fault, and the ability to exploit this representation for deciding control actions. Gaussian processes seem to be very promising candidates for the first of these, and model predictive control has a proven capability for the second. We therefore propose to use the two together to obtain fault-tolerant control functionality. Our proposal is illustrated by several reasonably realistic examples drawn from flight control.</p></abstract>

Essential ingredients for fault-tolerant control are the ability to represent system behaviour following the occurrence of a fault, and the ability to exploit this representation for deciding control actions. Gaussian processes seem to be very promising candidates for the first of these, and model predictive control has a proven capability for the second. We therefore propose to use the two together to obtain fault-tolerant control functionality. Our proposal is illustrated by several reasonably realistic examples drawn from flight control.

Fault Tolerant Control Using Gaussian Processes and Model Predictive Control

Department of Engineering University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, UK

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Fault Tolerant Control Using Gaussian Processes and Model Predictive Control"}

Essential ingredients for fault-tolerant control are the ability to represent system behaviour following the occurrence of a fault, and the ability to exploit...

Fault Tolerant Control Using Gaussian Processes and Model Predictive Control

Online veröffentlicht: 25. März 2015

Seitenbereich: 133 - 148

Eingereicht: 31. Jan. 2014

DOI: https://doi.org/10.1515/amcs-2015-0010

© by Xiaoke Yang

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.