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Local-global correction of simplified EMA models using machine learning tools (SOM + ANN or response surfaces)

keywords ELECTROMECHANICAL SERVOMECHANISM, EMA, MACHINE LEARNING, NUMERICAL MODELLING, NUMERICAL SIMULATION, SIMPLIFIED NUMERICAL MODELS

Reference persons MATTEO DAVIDE LORENZO DALLA VEDOVA, PAOLO MAGGIORE

External reference persons BERRI Pier Carlo
QUATTROCCHI Gaetano

Research Groups 16-ASTRA: Additive manufacturing for Systems and sTRuctures in Aerospace

Thesis type NUMERICAL-EXPERIMENTAL

Description Research objective: design and implement possible local-global correction techniques of simplified numerical models of onboard electromechanical actuators (EHAs) using machine learning tools (SOM + ANN or response surfaces).

See also  https://www.mdpi.com/2076-0825/9/3/50?utm_source=releaseissue&utm_medium=email&utm_campaign=releaseissue_actuators&utm_term=titlelink37#

Required skills Basics of numerical modeling in Matlab-Simulink; basic notions on on-board systems (electromechanical actuators aka EMA) and post-processing of data in the Matlab environment. Basic knowledge of the main machine learning tools (SOM + ANN or response surfaces) is preferable.

Deadline 10/07/2024      PROPONI LA TUA CANDIDATURA