Andrea Alessandretti
Universidade do Porto, École Polytechnique Fédérale de Lausanne, Hilti (Liechtenstein)
Papers
7
Total Citations
73
H-Index
4
About
Andrea Alessandretti is a leading researcher in control theory and robotics, whose work bridges the gap between theoretical optimization and real-world autonomous systems. His primary contributions lie in Model Predictive Control (MPC) and cooperative multi-agent systems, with a strong focus on economic optimization and stability. His most cited work (36 citations) introduces an Input-to-State-Stability approach to economic MPC, a framework that allows for the minimization of economic performance indices while guaranteeing convergence—a critical advance for industrial efficiency. In the domain of field robotics, he has developed novel strategies for UAVs in maritime operations, addressing communication constraints by enabling autonomous relay nodes. His work on Cooperative Moving Path Following (CMPF) introduces event-based control to ensure multiple robotic vehicles maintain formation relative to a moving reference frame, a key enabler for coordinated missions. More recently, Alessandretti has applied factor graph-based frameworks to enhance robotic precision in construction, compensating for deflection and backlash using high-accuracy accelerometers. With a publication record spanning from foundational stability theory to applied cloud-based control, his research demonstrates a consistent ability to solve complex, real-world coordination and optimization problems.
Research Focus
Key Achievements
Top Papers
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