S
Stefano Di Cairano
Researcher at Mitsubishi Electric Research Laboratories
Publications - 242
Citations - 3856
Stefano Di Cairano is an academic researcher from Mitsubishi Electric Research Laboratories. The author has contributed to research in topics: Model predictive control & Control theory. The author has an hindex of 27, co-authored 207 publications receiving 2933 citations. Previous affiliations of Stefano Di Cairano include Ford Motor Company & University of Siena.
Papers
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Journal ArticleDOI
Stochastic MPC With Learning for Driver-Predictive Vehicle Control and its Application to HEV Energy Management
TL;DR: The proposed SMPCL approach outperforms conventional model predictive control and shows performance close to MPC with full knowledge of future driver power request in standard and real-world driving cycles.
Journal ArticleDOI
Reference and command governors for systems with constraints
TL;DR: Reference and command governors are add-on control schemes which enforce state and control constraints on pre-stabilized systems by modifying, whenever necessary, the reference as mentioned in this paper, and have been extensively studied in the literature.
Journal ArticleDOI
Cloud-Based Velocity Profile Optimization for Everyday Driving: A Dynamic-Programming-Based Solution
Engin Ozatay,Simona Onori,James Wollaeger,Umit Ozguner,Giorgio Rizzoni,Dimitar Petrov Filev,John Ottavio Michelini,Stefano Di Cairano +7 more
TL;DR: The optimization of the speed trajectory to minimize fuel consumption and communicate it to the driver is discussed and the solution is generic, and it is applicable to any kind of powertrain structure.
Proceedings ArticleDOI
Reference and command governors: A tutorial on their theory and automotive applications
TL;DR: This paper provides a tutorial overview of reference governors and command governors, which are add-on control schemes for reference supervision and constraint enforcement in closed-loop feedback control systems.
Journal ArticleDOI
Power Smoothing Energy Management and Its Application to a Series Hybrid Powertrain
TL;DR: Simulations of the urban dynamometer driving schedule (UDDS) and US06 cycles using a complete vehicle system model and experimental tests of the UDDS cycle show improved fuel economy with respect to baseline strategies.