Cesare Donati

Dottorando in Ingegneria Elettrica, Elettronica E Delle Comunicazioni , 38o ciclo (2022-2025)
Dipartimento di Elettronica e Telecomunicazioni (DET)

Docente esterno e/o collaboratore didattico
Dipartimento di Automatica e Informatica (DAUIN)

Docente esterno e/o collaboratore didattico
Dipartimento di Elettronica e Telecomunicazioni (DET)


Dottorato di ricerca

Argomento di ricerca

A structured approach to multi-step and physics-based system identification


Interessi di ricerca

Big Data, Machine Learning, Neural Networks and Data Science
Mechatronics and robotics
Systems, Automation and Control


Cesare Donati received the B.Sc. degree and M.Sc. degree in Computer Engineering from Politecnico di Torino in 2019 and 2021, respectively. During his studies, he specialized in the field of Automation and Cyber-Physical Systems, gaining theoretical fundamentals in the main areas of Automation and Control Systems.
After his graduation, he started his activity as a researcher at CNR-IEIIT, focusing on navigation strategies for autonomous vehicles in precision agriculture scenarios. Then, as a pre-doctoral researcher at Politecnico di Torino, his studies continued in the area of filtering, model-based control, and system identification.
Currently, he is a PhD researcher in Electrical, Electronics, and Communications Engineering at Politecnico di Torino, collaborating with CNR-IEIIT. He is a member of the IEEE TC on System Identification and Adaptive Control. His research interests include nonlinear, multi-step, physics-based system identification, filtering and estimation, prediction, optimization, and model-based control.

Research topic:
His research focuses on the study and development of an innovative multi-step system identification approach that relies both on physical knowledge and experimental data. Such a method takes into account the presence of uncertainties, nonlinear effects, physical constraints, and all those gaps that hindered the development of accurate multi-step identification solutions due to model inaccuracies and complexity. This solution is validated on complex and structured systems to verify its capability to provide accurate predictions while achieving physical constraint satisfaction in the considered applications, e.g., in the aerospace field and, in particular, in the emerging sector of space economy and safety-critical space missions, where a strong system identification approach is necessary to get reliable physical parameter estimation. Other possible applications may include the study of epidemiological and biological models, as well as identification in the automotive and agricultural fields.



Corso di laurea magistrale

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Corso di laurea di 1° livello

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