Riccardo Scattolini
Papers
4
Total Citations
85
H-Index
4
About
Riccardo Scattolini is a leading figure in advanced process control and systems engineering, with his most influential work centering on distributed and predictive control strategies. He is best known for pioneering a hierarchical, distributed model predictive control (MPC) algorithm for tracking problems in dynamically coupled systems, a framework that guarantees stability and constraint satisfaction while enabling scalable coordination. This foundational approach, detailed in his highly cited 2014 paper, has been extended to multi-agent robotics, including formation control and collision avoidance for unicycle autonomous robots, demonstrating real-world applicability. His recent research pushes into data-driven methods, notably the development of Control Affine Neural NARX models for nonlinear system identification and model-based control design, achieving over 9 citations since 2024. With over 34 citations on his seminal tracking paper alone, Scattolini’s work bridges theoretical rigor and practical implementation, influencing both industrial process control and autonomous multi-robot systems. His contributions are essential reading for researchers in distributed optimization, nonlinear control, and intelligent automation.
Research Focus
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Top Papers
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