Recursive least squares for online dynamic identification on gas turbine engines

dc.contributor.author	Zhou, Li
dc.contributor.author	Nikolaidis, Theoklis
dc.contributor.author	Nalianda, Devaiah
dc.date.accessioned	2016-10-06T13:47:32Z
dc.date.available	2016-10-06T13:47:32Z
dc.date.issued	2016-07-19
dc.identifier.citation	Li Z, Nikolaidis T, Nalianda D, Recursive least squares for online dynamic identification on gas turbine engines, Journal of Guidance, Control, and Dynamics, Vol. 39, Issue 1, 2016, pp. 2594-2601	en_UK
dc.identifier.issn	0731-5090
dc.identifier.uri	https://dspace.lib.cranfield.ac.uk/handle/1826/10679
dc.identifier.uri	http://dx.doi.org/10.2514/1.G000408
dc.description.abstract	Online identification for a gas turbine engine is vital for health monitoring and control decisions because the engine electronic control system uses the identified model to analyze the performance for optimization of fuel consumption, a response to the pilot command, as well as engine life protection. Since a gas turbine engine is a complex system and operating at variant working conditions, it behaves nonlinearly through different power transition levels and at different operating points. An adaptive approach is required to capture the dynamics of its performance.	en_UK
dc.language.iso	en	en_UK
dc.publisher	American Institute of Aeronautics and Astronautics	en_UK
dc.rights	Attribution-NonCommercial 4.0 International
dc.rights.uri	http://creativecommons.org/licenses/by-nc/4.0/
dc.title	Recursive least squares for online dynamic identification on gas turbine engines	en_UK
dc.type	Article	en_UK