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Nicholas Cartocci

PhD Student
Advanced Robotics
Research center
About

Nicholas Cartocci received a master's degree in Computer Science and Robotics Engineering from the University of Perugia, Italy, in 2019. From October 2018 to March 2019, he was a Visiting Scholar in the Department of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, WV, USA. From April 2020 to May 2022, he was a research assistant at the University of Perugia. Currently, he is a PhD Student at XoLab, Advanced Robotics, Italian Institute of Technology (IIT), Genova, Italy.

Education

Title: PhD in Advanced and Humanoid Robotics
Institute: University of Genoa
Location: Genoa
Country: Italy
From: 2022 To: null

Title: Master's degree in Computer Engineering and Robotics
Institute: University of Perugia
Location: Perugia
Country: Italy
From: 2017 To: 2019

Title: Bachelor's degree in Computer Science and Electronic Engineering
Institute: University of Perugia
Location: Perugia
Country: Italy
From: 2013 To: 2016

All Publications
2022
Cartocci N., Napolitano M.R., Costante G., Valigi P., Fravolini M.L.
Aircraft robust data-driven multiple sensor fault diagnosis based on optimality criteria
Mechanical Systems and Signal Processing, vol. 170
2022
Cartocci N., Napolitano M.R., Crocetti F., Costante G., Valigi P., Fravolini M.L.
Data-Driven Fault Diagnosis Techniques: Non-Linear Directional Residual vs. Machine-Learning-Based Methods
Sensors, vol. 22, (no. 7)
2022
Cartocci N., Monarca A., Costante G., Fravolini M.L., Dogan K.M., Yucelen T.
Linear Control of a Nonlinear Aerospace System via Extended Dynamic Mode Decomposition
AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022
2022
Cartocci N., Crocetti F., Costante G., Valigi P., Fravolini M.L.
Robust Multiple Fault Isolation Based on Partial-orthogonality Criteria
International Journal of Control, Automation and Systems, vol. 20, (no. 7), pp. 2148-2158
Article Journal
2021
Cartocci N., Napolitano M.R., Costante G., Fravolini M.L.
A comprehensive case study of data-driven methods for robust aircraft sensor fault isolation
Sensors, vol. 21, (no. 5), pp. 1-24