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About

Tobias Raff has made significant contributions to the control of nonlinear and sampled-data systems, with a particular focus on observer design and nonlinear model predictive control (NMPC). His most influential work, the 2008 paper on observers with sample-and-hold updating for Lipschitz nonlinear systems (89 citations), introduced novel linear matrix inequality (LMI) conditions to guarantee global stability under nonuniformly sampled measurements. This was complemented by his 2007 study on observers with impulsive dynamical behavior (76 citations), which provided groundbreaking conditions for state estimation in both linear and nonlinear continuous-time systems. Raff also advanced the practical implementation of NMPC by developing a structured LMI-based approach to calculate stabilizing terminal regions for Lure systems, addressing a notoriously difficult challenge in the field. His work on certainty-equivalence feedback design further identified a new class of nonlinear feedbacks ensuring input-to-state stability (ISS) with respect to measurement errors. Through these contributions, Raff has established himself as a key figure in bridging the gap between theoretical control design and practical implementation challenges, with his observer design methods remaining foundational references for researchers working on sampled-data and hybrid systems.