Michele Taragna

Michele Taragna is a Tenured Associate Professor in the Department of Electronics and Telecommunications (DET) at the Polytechnic University of Turin, actively teaching across degree programs: Experimental Modeling for PhD students in Electrical, Electronic and Communications Engineering (2019-2025), Estimation and System Identification for Mechatronic Engineering Master's program (2019-2026), and Automatic Control for Computer Engineering Bachelor's program (2019-2026) as course holder or collaborator.

His research centers on Systems and Control Engineering, with primary interests in data-driven control for autonomous vehicles and fleets, direct virtual sensors, and machine learning-enhanced system identification. Key areas include Set Membership methods for robustness under bounded noise, computational complexity reduction in Nonlinear Model Predictive Control (NMPC), sensor fusion for robotics, and applications in automotive suspensions. This work aligns with ERC sectors PE7_1 (Control engineering), PE1_20 (Control theory), and PE6_12 (Scientific computing).

Trends in his publications (2024-2004) reveal sustained innovation in applying Set Membership identification to NMPC for autonomous vehicles, achieving real-time feasibility through search domain reduction. Sensor fusion techniques using Kalman filters for mobile manipulators and data-driven filter design for uncertain LTI systems with bounded noise are recurring themes, emphasizing practical implementation and computational efficiency.

Scientific awards: None documented in provided materials.

Advising and research funding:

• Supervised PhD student Mattia Boggio (2020-2024) in Electrical, Electronic and Communications Engineering; thesis on Real-time Nonlinear Model Predictive Control with domain reduction.
• Led the nationally funded PRIN project Controllo ad alte prestazioni a partire dai dati sperimentali (2007-2009) as Scientific Responsible.

He is a core member of the Automatica research group within DET, focusing on system identification, control design, and validation for dynamic systems with applications in automotive and robotics domains.